SD-EAR: Energy Aware Routing in Software Defined Wireless Sensor Networks

Anuradha Banerjee; D. M. Akbar Hussain

doi:10.3390/app8071013

Banerjee, Anuradha;Hussain, D. M. Akbar

2018-06-21 00:00:00

applied sciences Article SD-EAR: Energy Aware Routing in Software Deﬁned Wireless Sensor Networks 1 , 2 Anuradha Banerjee * and D. M. Akbar Hussain Department of Computer Applications, Kalyani Government Engineering College, Nadia 741325, West Bengal, India Department of Energy Technology, Aalborg University, 9100 Esbjerg, Denmark; akh@et.aau.dk * Correspondence: anuradha79bn@gmail.com Received: 10 May 2018; Accepted: 29 May 2018; Published: 21 June 2018 Abstract: In today’s internet-of-things (IoT) environment, wireless sensor networks (WSNs) have many advantages, with broad applications in different areas including environmental monitoring, maintaining security, etc. However, high energy depletion may lead to node failures in WSNs. In most WSNs, nodes deplete energy mainly because of the ﬂooding and broadcasting of route-request (RREQ) packets, which is essential for route discovery in WSNs. The present article models wireless sensor networks as software-deﬁned wireless sensor networks (SD-WSNs) where the network is divided into multiple clusters or zones, and each zone is controlled by a software-deﬁned network (SDN) controller. The SDN controller is aware of the topology of each zone, and ﬁnds out the optimum energy efﬁcient path from any source to any destination inside the zone. For destinations outside of the zone, the SDN controller of the source zone instructs the source to send a message to all of the peripheral nodes in that zone, so that they can forward the message to the peripheral nodes in other zones, and the process goes on until a destination is found. As far as energy-efﬁcient path selection is concerned, the SDN controller of a zone is aware of the connectivity and residual energy of each node. Therefore, it is capable of discovering an optimum energy efﬁcient path from any source to any destination inside as well as outside of the zone of the source. Accordingly, ﬂow tables in different routers are updated dynamically. The task of route discovery is shifted from individual nodes to controllers, and as a result, the ﬂooding of route-requests is completely eliminated. Software-deﬁned energy aware routing (SD-EAR)also proposes an innovative sleeping strategy where exhausted nodes are allowed to go to sleep through a sleep request—sleep grant mechanism. All of these result in huge energy savings in SD-WSN, as shown in the simulation results. Keywords: energy; ﬂow table; internet of things; peripheral nodes; routing; software deﬁned network (sdn); wireless sensor networks (wsns); zone 1. Introduction The internet of things (IoT) has gained considerable attention recently from industry and academia. An important component of IoT is wireless sensor networks (WSN). Energy efﬁciency in the routing of WSNs is extremely necessary to preserve energy and increase the lifetime of nodes [1–43]. Nodes in wireless sensor networks deplete quickly if a huge number of route discoveries are initiated. One route discovery means the broadcasting of route-request to all of the nodes in the network. This requires an excessive number of unnecessary forwarding of route-requests, especially if the source or the routers do not know of a recent destination or location. In order to save energy, nodes try to go to sleep, but there is no central authority who can judge whether the sleep request is valid or not. Therefore, nodes take decisions on their own, and this information is propagated only to its neighbors; it cannot be circulated throughout the network to avoid energy consumption. Therefore, if a source wants to Appl. Sci. 2018, 8, 1013; doi:10.3390/app8071013 www.mdpi.com/journal/applsci Appl. Sci. 2018, 8, x FOR PEER REVIEW 2 of 28 Appl. Sci. 2018, 8, 1013 2 of 28 to its neighbors; it cannot be circulated throughout the network to avoid energy consumption. Therefore, if a source wants to communicate with one such sleeping node, then all of its associated forwarding of route-requests will go in vain. communicate with one such sleeping node, then all of its associated forwarding of route-requests will go in vain. 1.1. What is SDN? 1.1. What is SDN? Software defined networking (SDN) is a recent trend of a network that introduces the idea of eliminating tight coupling between the control and the forwarding plane. The network can be broadly Software deﬁned networking (SDN) is a recent trend of a network that introduces the idea divided into three components: a centralized controller, hosts, and switches that connect a pair of of eliminating tight coupling between the control and the forwarding plane. The network can be hosts, a pair of switches, or a host–switch pair. Northbound and southbound application broadly divided into three components: a centralized controller, hosts, and switches that connect programming interfaces (APIs) are there to establish communication between various network a pair of hosts, a pair of switches, or a host–switch pair. Northbound and southbound application entities. The centralized controller has the responsibility of configuring forwarding planes (popularly programming interfaces (APIs) are there to establish communication between various network entities. termed as flow tables) according to which switches forward the packets during communication. The The centralized controller has the responsibility of conﬁguring forwarding planes (popularly termed basic structure of an SDN appears in Figure 1. as ﬂow tables) according to which switches forward the packets during communication. The basic SDN is deployed in various types of networks, such as LAN, MAN, WAN, data center networks, structure of an SDN appears in Figure 1. etc. The advantages are flexible network control without sacrificing forwarding performance, ease of SDN is deployed in various types of networks, such as LAN, MAN, WAN, data center networks, implementation, administration, and ample opportunities of reducing energy consumption [44,45]. etc. The advantages are ﬂexible network control without sacriﬁcing forwarding performance, ease of SDN has proven to be extremely successful in highly scalable data centers extending from private implementation, administration, and ample opportunities of reducing energy consumption [44,45]. enterprises to public sectors where managing big data is not the only issue [8–10]; thousands of new SDN has proven to be extremely successful in highly scalable data centers extending from private data are being generated every day to satisfy the requirements of various applications initiated by enterprises to public sectors where managing big data is not the only issue [8–10]; thousands of new different levels of users. OpenFlow-based SDN data centers have set a new trend as far as data are being generated every day to satisfy the requirements of various applications initiated by performance efficiency is concerned. Scheduling the flows to various ports is a very important different levels of users. OpenFlow-based SDN data centers have set a new trend as far as performance component of network flow control. Also, the occasional deactivation of switches is required to efﬁciency is concerned. Scheduling the ﬂows to various ports is a very important component of preserve energy. network ﬂow control. Also, the occasional deactivation of switches is required to preserve energy. Figure 1. Structure of a software deﬁned network (SDN). Figure 1. Structure of a software defined network (SDN). Reduction of energy consumption is an important aspect of efficiency in SDN. Reduction of energy consumption is an important aspect of efﬁciency in SDN. 1 1.2. .2. C Contributions ontributions o of f tthe he Pres Present ent P Paper aper In the present paper, software deﬁned energy aware routing (SD-EAR) and SDN divide the In the present paper, software defined energy aware routing (SD-EAR) and SDN divide the c completely ompletely di distributed stributed s str tru uctur cture e of of aa sensor sensornetwork networkinto into cl clusters usters or or zones zonewher s whe ereach e each contr cont oller rolle is r i in s charge of one zone. The controllers are aware of the topologies of the associated zone, and also keep in charge of one zone. The controllers are aware of the topologies of the associated zone, and also k track eep tof rac peripheral k of periph nodes. eral no Peripheral des. Periph nodes eral nin odes a zone in a z ar oe nnodes e are nthat odeshave that some have sportion ome poof rtio radio n of rcir adi cle o outside the zone. SD-EAR particularly aims at proposing an energy-efﬁcient routing protocol along circle outside the zone. SD-EAR particularly aims at proposing an energy-efficient routing protocol a with longa w sleeping ith a sleep strategy ing stra totpr egomote y to pro ener mo gy te en preservation ergy preserv in a the tion network. in the nIts etw novelties ork. Its n ar ov eel as tifollows: es are as follows: Appl. Sci. 2018, 8, 1013 3 of 28 (i) Normally in a WSN, whenever a source needs to establish a route to a sink, it mostly applies blind ﬂooding [27,33,38,39] or gossiping [38,39]. In ﬂooding, each router retransmits the route-request (RREQ) generated by a source to all of its neighbors, whereas the gossiping technique enables the routers to select a subset of neighbors to rebroadcast the route-request packet. Both of these suffer from redundancies that unnecessarily eat up energies in routers. In SD-EAR, since the SDN controller of each zone is aware of the zonal topology, it is capable of ﬁnding all of the possible paths between a given source and a destination through the depth-ﬁrst traversal technique if both the source and destination are within the same zone. Among all of these paths, one optimal path is selected by a fuzzy controller named FUZZ-OPT-ROUTE, which is embedded within each SDN controller. If the destination is not within same zone as the source, then the SDN controller of the source zone instructs the source to send a request for discovering the route to the destination or to all of the peripheral nodes in the zone, through the optimum paths selected by the FUZZ-OPT-ROUTE embedded in the SDN controller of the source. Routers perform similar to the source. SD-EAR gives special weight to the presence of alternative nodes in live communication paths. As a result, the broadcasting of a route-request is eliminated irrespective of intra-zone or inter-zone communication, resulting in huge message saving. (ii) An alternative node n of a node n in a live communication route R, should be such that energy v u efﬁciency as well as the lifetime of R should not decrease after replacing n with n . This provides u v a facility to improve the residual energy and lifetime of a route. It may happen that after replacing the least energy node with an alternative, the new minimum energy becomes higher than the previous minimum. In that case, the new minimum will depend upon other nodes in the route except the one with the previous minimum energy. Assuming all of these nodes to be equally likely, we consider both the minimum and average residual energy to compute the residual energy effect of a route. Please note that the alternatives for a node are searched if its battery is almost exhausted. (iii) A sleeping strategy is also proposed where nodes that request to go to sleep are granted sleep by the SDN controller of the zone, provided the situation demands so. All of these help to greatly reduce the energy consumption in the network, and increase network throughput. Also, the provision of forceful sleep is introduced in which if all of the neighbors of a node are suffering from exhausted battery, then n is directed by the SDN controller to go to sleep. All of these result in great energy saving by avoiding the broadcasting of route-requests during route discovery as well as rediscovery. 1.3. Organization of the Article Related work is presented in Section 2, whereas a detailed network framework appears in Section 3. Section 4 illustrates routing in SD-EAR along with proposed sleep request—sleep grant mechanism. Section 5 presents simulation results, and Section 6 concludes the paper. 2. Related Work Currently, most applications and services in WSN, such as network ﬂexibility, network management, energy conservation, network robustness, and packet loss probability, rely on a data exchange between the sensor nodes and the switch infrastructure, or among sensor nodes. We divide all of the energy-efﬁcient approaches to WSNs in two groups: non-software deﬁned and software deﬁned. These are described below. 2.1. Non-Software Deﬁned Energy-Efﬁcient Approaches LEACH (Low energy adaptive clustering hierarchy), SPIN (Sensor protocol for information via negotiation), threshold-sensitive energy-efﬁcient sensor networks (TEEN), etc. are extremely important from the perspective of non-software deﬁned energy efﬁciency in sensor networks. The idea of LEACH protocol is to organize the sensor nodes into clusters, where each cluster has one cluster head CH that Appl. Sci. 2018, 8, 1013 4 of 28 acts as a router to the base station. However, the LEACH protocol has problems; one of those problems is the random selection of a cluster head or CHs [35,36,43]. This process does not consider the location of sensor nodes in the wireless sensor network, and hence, the sensor nodes may be very far from their CH, causing them to consume more energy to communicate with the CH [35,36,43]. The role of the cluster head is rotated in order to balance the energy consumption among multiple nodes. The optimum route chosen for communication is the shortest one computed using Dijkstra’s shortest path algorithm [36]. However, the shortest route is not always the most energy-efﬁcient option. SPIN [38] is another energy-efﬁcient routing protocol worthy of mention; which is a modiﬁcation of classic ﬂooding. In classical ﬂooding, the information is forwarded on every outgoing link of a node. This drains out the battery of a huge number of nodes in the sensor network. SPIN was developed to overcome this drawback. It is an adaptive routing protocol that transmits the information ﬁrst by negotiating. It proposes the use of metadata of actual data to be sent. Metadata contains a description of the actual message that the node wants to send. Actual data will be transmitted only if the node wishes to receive it, that is, it is similar to being keen to watch a movie after viewing its trailer. In this context, we humbly state that SPIN requires the broadcasting of metadata at least. However, an important aspect of the energy efﬁciency of SD-EAR arises from it not requiring broadcasting to establish routes. TEEN (threshold-sensitive energy-efﬁcient sensor networks) [46] protocol was proposed for time-critical applications. Here, sensor nodes sense the medium continuously, but data transmission is done less frequently. A cluster head sensor sends its members a hard threshold (HT), which is the threshold value of sensed attribute, and a soft threshold (ST), which is a small change in the value of the sensed attribute that triggers the node to switch on its transmitter and transmit. The main drawback of this scheme is that if the thresholds are not received, the nodes will never communicate, and the user will not get any data from the network at all. Also, it has the complexity associated with forming clusters at multiple levels and the method of implementing threshold-based functions [46]. 2.2. Software Deﬁned Energy-Efﬁcient Approaches Figueiredo et al. [12] used multiple adaptive hybrid approaches, which are considered better than a single algorithm. This work (which subsequently will be referred to as software deﬁned wireless sensor network 1, or SD-WSN1, in this article) discusses the usage of policies to establish adaptive routing rules so that the WSN elements of a network can acquire more ﬂexible and accessible development and maintenance tasks. The routing policy is either proactive or reactive. If the number of nodes in the network is less than a pre-deﬁned threshold, then proactive routing is applied to determine the forwarding rules in order to save the cost of route discovery. However, if the number of nodes in the network crosses a pre-deﬁned threshold, then proactive routing becomes impossible due to an abnormal rise in the size of the ﬂow table. Reactive routing follows the behavior of ad hoc distance vector routing, or AODV. This applies ﬂooding for route discovery. Ejaz et al. [20] designed an energy-efﬁcient SD-WSN (which subsequently will be referred to as SD-WSN2 in this article) to minimize the energy consumption of energy transmitters. This article is based on the energy that is transferred to sensor nodes through energy transmitters. It aims at the optimal placement of energy transmitters, which depends on the trade-off between the minimum energy charged in the network and a fair distribution of energy. This does not propose any energy-efﬁcient routing protocol or sleeping strategy of nodes. Xiang et al. [21] studied an energy-efﬁcient routing algorithm for SD-WSN (which subsequently will be referred to as SD-WSN3 in this article) by selecting the control nodes and assigning different tasks dynamically through control nodes. SD-WSN3 selects some control nodes out of all of the nodes in the network using the particle swarm optimization method. These nodes assign tasks to others depending upon their residual energy. However, it is very difﬁcult to determine the exact number of required control nodes, and how many nodes will be under the control of one particular control node. Appl. Sci. 2018, 8, 1013 5 of 28 Luo et al. [10] proposed a software-deﬁned WSN architecture to solve the rigidity of policies and the difﬁculty of management by addressing Sensor OpenFlow. Jacobsson and Orfanidis [11] adopted low-cost off-the-shelf hardware and an individual deployment method to ﬂexibly reconﬁgure WSN networking and in-network processing functionality. Regarding the network management, Gante et al. [13] presented a smart management in WSN by employing the OpenFlow protocol and modifying the forwarding rule in the controller, which can use the uniﬁed standard to communicate with all of the nodes. A networking solution called SDN-WISE was proposed by Galluccio et al. [14] to simplify the management of the network. Olivier et al. [15] proposed a cluster-based architecture with multiple base stations used as hosts for the SDN control functions as well as cluster heads, and adopted a structured and hierarchical management. Modieginyane et al. [16] summarized the application challenges faced by WSNs for monitored environments and the opportunities that can be realized in applications of WSNs using SDN. Jayashree et al. [19] proposed a framework for a software-deﬁned wireless sensor network, where the sensor node only performed the forwarding. The controller was implemented as the base station in this framework, thus reducing the energy consumption. For packet loss probability and robustness, Levendovszky et al. [22] provided the Hopﬁeld neural network to guarantee uniform packet loss probabilities for the nodes. Sachenko et al. [23] presented modiﬁed correction codes based on a residual number system, high correction characteristics, and a simpliﬁed coding procedure to show the characteristics of these codes, which can improve the data transmission robustness in WSN. Jin et al. [24] dealt with the packet loss by designing a hierarchical data transmission framework of a suitable industrial environment. For a real industrial environment, the commonly-used WSN framework discussed above is not well suited to the environment’s unique characteristics, such as a harsh application environment, a strict requirement for data transmission, data diversity, and so on. The traditional SD-WSN framework is a universal network framework that is not speciﬁc to the IoT. In order to resolve these problems and adapt the framework for an industrial environment, we will present an improved SD-WSN framework in the next section. 3. Network Framework in SD-EAR The software deﬁned wireless sensor network framework consists of the following layers, as shown in Figure 2. Appl. Sci. 2018, 8, 1013 6 of 28 Appl. Sci. 2018, 8, x FOR PEER REVIEW 6 of 28 Figure 2. Network framework. Figure 2. Network framework. 3.1. Physical Layer (PL) 3.1. Physical Layer (PL) This layer consists of several sensor nodes that connect with the SDN controller through This Open layer Flowconsists protocolof [4several ], and rec sensor eive rel nodes evant f that lowconnect table infwith ormathe tionSDN from contr the SD oller N cont thrr ough oller iOpenFlow n that zone. Please note that nodes are divided into certain zones, and each zone is under one unique protocol [4], and receive relevant ﬂow table information from the SDN controller in that zone. controller. Please note that nodes are divided into certain zones, and each zone is under one unique controller. 3.2. Virtualization Layer (VL) 3.2. Virtualization Layer (VL) The key nodes of the sensor network in the physical layer map to virtual key nodes, in order to The key nodes of the sensor network in the physical layer map to virtual key nodes, in order to form the virtual node layer (virtual layer). form the virtual node layer (virtual layer). 3.3. Control Layer (CL) 3.3. Control Layer (CL) Controllers in the control layer provide routing, sleep monitoring, and other services to achieve Controllers in the control layer provide routing, sleep monitoring, and other services to achieve the the intelligent management of the WSN. OpenFlow protocol is used to transmit optimum route intelligent management of the WSN. OpenFlow protocol is used to transmit optimum route information information to the various nodes that are required to discover the route to some destination. to the various nodes that are required to discover the route to some destination. Controllers are capable Controllers are capable of generating interrupts to awaken sleepy nodes, and granting sleep requests of generating to worthy interr or ne upts edy nod to awaken es. sleepy nodes, and granting sleep requests to worthy or needy nodes. Depending upon the residual energy of the neighbors of a node, certain nodes are also sent mandatory sleep messages, which are forceful instructions for the node to go to sleep, even though Appl. Sci. 2018, 8, 1013 7 of 28 it did not ask for a sleep session. All of these contribute to the reduced energy consumption of the network. 3.4. Application Layer (AL) The strategy of each application is deﬁned in the application layer. Each application obtains the network status (topology, node status, etc.) from the control layer, and makes decisions according to that strategy. The strategy instructs how services will be provided to the sensor node. 4. SD-EAR in Detail 4.1. Network Model Network is modeled as a graph G = (V, E) where V represents the set of vertices or sensor nodes, and E represents the set of edges between those nodes. The SDN controller of a zone Z (1 p P; P is the number of all of the zones in the network) consists of the two following tables: (i) NET-TOPL (ii) NODE-STAT The network topology of zone Z is stored in the form of the adjacency list in NET-TOPL . p p The attributes of this table are: (i) node-id (ii) neighbor-list node-id speciﬁes the unique identiﬁcation number of a node, whereas neighbor-list is the set of neighbors of the node. NODE-STAT is a table that contains information about all of the nodes within the zone Z . p p The components of this information are: (i) node-id (ii) (latitude, longitude) pair (iii) radio range (iv) maximum energy (v) most recent residual energy (vi) most recent rate of energy depletion (vii) minimum receive power (viii) sleep status (ix) last sleep timestamp (x) peripheral status For a node n , the present geographical position in terms of the (latitude, longitude) pair is speciﬁed as (x , y ), with its radio range being rad . Maximum energy, the last known residual energy, i i i and the rate of battery depletion are denoted as m-en , r-en , and dep . Sleep status slp is 1 if currently i i i i n is sleeping; it is 0 otherwise. -sl is the most recent timestamp when n went to sleep. The peripheral i i i status of n is given by pphr . It is set to 1 if the radio circle of current node n is not completely i i i embedded within the area of the zone, i.e., it may have some neighbor belonging to any neighboring zone of Z . Otherwise, it is set to 0. Whenever a node n starts its operation in the network, it has to ﬁrst register itself by sending a registration request message to the SDN controller. The components of the registration request are: (i) message-type-id (0 for registration) (ii) node-id (iii) (latitude, longitude) pair Appl. Sci. 2018, 8, 1013 8 of 28 Appl. Sci. 2018, 8, x FOR PEER REVIEW 8 of 28 (iv) radio range (v) maximum energy (v) maximum energy Receiving the registration request of n , the corresponding SDN controller checks the NODE-STAT Receiving the registration request ofi ni, the corresponding SDN controller checks the NODEp - table to determine whether any entry with same node identifier n exists within that table. If yes, STATp table to determine whether any entry with same node identiifier ni exists within that table. If then an id-clash message will be sent back to n . The components of an id-clash message are: yes, then an id-clash message will be sent bacik to ni. The components of an id-clash message are: (i) (i) m message-type-id essage-type-id (1 (1) ) (ii) node-id (ii) node-id On the other hand, if no such event of a node identifier clash is detected, then a registration- On the other hand, if no such event of a node identiﬁer clash is detected, then a registration-success success message is sent to ni. The attributes of registration-success are the same as id-clash; only the message is sent to n . The attributes of registration-success are the same as id-clash; only the message-type-id is set to 2. After that, the peripheral status of ni is determined. It finds out whether message-type-id is set to 2. After that, the peripheral status of n is determined. It ﬁnds out whether the the radio circle of ni is completely embedded within a zone. Three types of zone structures are radio circle of n is completely embedded within a zone. Three types of zone structures are considered: considered: polygonal, circular, and elliptic. The polygonal structure is the closest to the real life polygonal, circular, and elliptic. The polygonal structure is the closest to the real life scenario, while the scenario, while the circular structure is the ideal one, where the neighborhood of a node spreads circular structure is the ideal one, where the neighborhood of a node spreads around the node equally around the node equally in all directions. Please note that the circle is a special kind of ellipse, where in all directions. Please note that the circle is a special kind of ellipse, where both foci are at the both foci are at the same point (the center). Therefore, both circular and elliptic structures have been same point (the center). Therefore, both circular and elliptic structures have been considered in our considered in our simulation. Below, we derive the conditions under which a polygonal, circular, or simulation. Below, we derive the conditions under which a polygonal, circular, or elliptic zone embeds elliptic zone embeds a radio circle of ni. If the radio circle of ni is completely embedded in the zone, a radio circle of n . If the radio circle of n is completely embedded in the zone, then pphr is set to 0; i i i then pphri is set to 0; otherwise, it is 1. otherwise, it is 1. Situation 1: The zone under inspection is polygonal, defined by consecutive vertices (xv+q, yv+q) Situation 1: The zone under inspection is polygonal, deﬁned by consecutive vertices (x , y ) v+q v+q s.t. 0 ≤ q ≤ Q; the node to be embedded has center (xi, yi) and a radiorange ofradi. s.t. 0 q Q; the node to be embedded has center (x , y ) and a radiorange ofrad . i i i The situation is expressed in Figure 3. The situation is expressed in Figure 3. F Figure igure 3. 3. P Polygonal olygonal z zone one a and nd ra radio-cir dio-circl cle e e embedding. mbedding. Consider one particular edge E from (xv, yv) to (xv+1, yv+1). The slope m1 of E is mathematically Consider one particular edge E from (x , y ) to (x , y ). The slope m of E is mathematically v v v+1 v+1 1 expressed in Equation (1): expressed in Equation (1): m = (y y )/(x x ) (1) 1m1 = v+1 (yv+1 − y v v)/(xvv+1 +1 − xv) v (1) Let C be the center of the radio-circle of n . The shortest distance from C to edge E is given by the Let C be the center of the radio-circle of ni. The shortest distance from C to edge E is given by length of perpendicular from C to E. Assume that the perpendicular drawn on E from C intersects E at the length of perpendicular from C to E. Assume that the perpendicular drawn on E from C intersects point W. Then, the slope m of CW is related to m1 as in Equation (2): E at point W. Then, the slope m2 of CW is related to m1 as in Equation (2): m2 = (−1)/m1 (2) m = (1)/m (2) 2 1 CW passes through the point (xi, yi). Based on this, the equation of CW appears in Equation (3): CW passes through the point (x , y ). Based on this, the equation of CW appears in Equation (3): i i y = m2 x + b2 (3) y = m x + b (3) 2 2 where: m2 = −(xv+1 − xv)/(yv+1 − yv) (4) and: Appl. Sci. 2018, 8, 1013 9 of 28 where: m = (x x )/(y y ) (4) v v 2 v+1 v+1 Appl. Sci. 2018, 8, x FOR PEER REVIEW 9 of 28 and: b = (y + (x x )/(y y ) x ) (5) v v 2 i v+1 v+1 i b2 = (yi + (xv+1 − xv)/(yv+1 − yv) xi) (5) The equation of the edge E from (x , y ) to (x , y ) is mathematically expressed in Equation (6): v v v+1 v+1 The equation of the edge E from (xv, yv) to (xv+1, yv+1) is mathematically expressed in Equation (6): y = m x + b (6) 1 1 y = m1 x + b1 (6) s.t. b = (x y x y )/(x x ) (7) 1 v+1 v v v+1 v+1 v s.t. b1 = (xv+1yv − xv yv+1)/(xv+1 − xv) (7) The coordinates (w , w ) of the point of intersection of edge E and CW (i.e., the point W) are x y The coordinates (wx, wy) of the point of intersection of edge E and CW (i.e., the point W) are given by: given by: w = (b b )/(m m ) (8) x 2 1 1 2 wx = (b2 − b1)/(m1 − m2) (8) w = (m b m b )/(m m ) (9) y 1 2 2 1 1 2 wy = (m1 b2 − m2 b1)/(m1 − m2) (9) If the Cartesian distance between the two points (x , y ) and (w , w ) is greater than or equal i i x y to rad , then it can be concluded that the radio-circle of n is embedded within the given zone. If the Cartesian distance between the two points (xi, yi) and (wx, wy) is greater than or equal to i i The mathematical version of the condition appears in Equation (10): radi, then it can be concluded that the radio-circle of ni is embedded within the given zone. The mathematical version of the condition appears in Equation (10): 2 2 0.5 [{(x w ) + (y w ) }] rad (10) i x i y i 2 2 0.5 [{(xi − wx) + (yi − wy) }] ≥ radi (10) If such a condition (as in Equation (10)) is satisﬁed by all of the edges of the polygon, then it can If such a condition (as in Equation (10)) is satisfied by all of the edges of the polygon, then it can be concluded that the radio circle of n is completely bounded by the polygonal zone. be concluded that the radio circle of ni is completely bounded by the polygonal zone. Situation 2: The zone Z under inspection is circular, deﬁned by cnter (x(Z ), y(Z )) and radius p p p Situation 2: The zone Zp under inspection is circular, defined by cnter (x(Zp), y(Zp)) and radius rad(Z ); the node to be embedded has center (x , y ) and a radio range rad of n p i i i i rad(Zp); the node to be embedded has center (xi, yi) and a radio range radi of ni The situation is expressed in Figure 4. The situation is expressed in Figure 4. Figure 4. Circular zone and radio-circle embedding. Figure 4. Circular zone and radio-circle embedding. In Figure 4, AQ is the radius of the zone; AM speciﬁes the distance between the centers of the In Figure 4, AQ is the radius of the zone; AM specifies the distance between the centers of the zone and node n . Please note that node n is positioned at point M. MN is the radio range of the node. i i zone and node ni. Please note that node ni is positioned at point M. MN is the radio range of the node. The mathematical expressions for AQ, AM, and MN appear in Equations (11)–(13): The mathematical expressions for AQ, AM, and MN appear in Equations (11–(13): AQ AQ = =rad(Z rad(Zp )) ((11) 11) 2 2 0.5 2 2 0.5 AM = [{(x(Z ) x ) + (y(Z ) y ) }] (12) AM = [{(x(Zp) − xi) + (y(Zp) − yi) }] (12) p i p i MN = rad (13) MN = radi (13) The required conditions for circle embedding appears in Equation (14): (AQ − AM − MN) > 0 (14) Situation 3: The zone under inspection is elliptic, as defined by Equation (15); the node to be embedded has center (xi, yi) and a radio range of radi. Appl. Sci. 2018, 8, 1013 10 of 28 The required conditions for circle embedding appears in Equation (14): (AQ AM MN) > 0 (14) Situation 3: The zone under inspection is elliptic, as deﬁned by Equation (15); the node to be Appl. Sci. 2018, 8, x FOR PEER REVIEW 10 of 28 embedded has center (x , y ) and a radio range of rad . i i i 2 2 2 2 (x h) /a +(y k) /b = 1 (15) 2 2 2 2 (x − h) /a +(y − k) /b = 1 (15) s s.t. .t. a a > > b b. . T The he ssituation ituation is is re repr pres esented ented iin n F Figur iguree 5. 5. Figure 5. Elliptic zone and radio-circle embedding. Figure 5. Elliptic zone and radio-circle embedding. The node to be embedded has a center of O (xi, yi) and a radio range of radi. (h,k) is the center of The node to be embedded has a center of O (x , y ) and a radio range of rad . (h,k) is the center i i i the ellipse. We draw a line AB through O i.e., the center of the radio circle of ni, parallel to the Y axis. of the ellipse. We draw a line AB through O i.e., the center of the radio circle of n , parallel to the The line intersects the ellipse at points A and B. Similarly, we draw a horizontal line through O, i.e., Y axis. The line intersects the ellipse at points A and B. Similarly, we draw a horizontal line through O, the center of the radio circle of ni, parallel to the X axis. The line intersects the ellipse at points C and i.e., the center of the radio circle of n , parallel to the X axis. The line intersects the ellipse at points D. In order to compute the coordination of A and B, we need to put x = xi into the equation of the C and D. In order to compute the coordination of A and B, we need to put x = x into the equation of ellipse, and calculate the corresponding x coordinates. Similarly, to calculate the coordinates of C and the ellipse, and calculate the corresponding x coordinates. Similarly, to calculate the coordinates of D, we need to put y = yi. C and D, we need to put y = y . Putting x = xi in the equation of the ellipse: Putting x = x in the equation of the ellipse: 2 2 2 2 (xi − h) /a +(y − k) /b = 1 (16) 2 2 2 2 (x h) /a + (y k) /b = 1 (16) Without any loss of generality, the y coordinates of A and B are y1 and y2, respectively, s.t.: Without any loss of generality, the y coordinates of A and B are y and y , respectively, s.t.: 2 2 0.5 1 2 y1 = b{k/b + ((1 − (xi− h) /a )) } (17) 2 2 0.5 2 2 0.5 y2 = b{k/b – ((1 − (xi − h) /a )) } (18) y = b{k/b + ((1 (x h) /a )) } (17) 1 i Putting y = yi in the equation of the ellipse: 2 2 0.5 y = b{k/b ((1 (x h) /a )) } (18) 2 i 2 2 2 2 (x − h) /a +(yi − k) /b = 1 Putting y = y in the equation of the ellipse: Therefore, the x coordinates of A and B are either x1 or x2 s.t.: 2 2 2 2 (x h) /a + (y k) /b = 1 i 2 2 0.5 x1 = a{h/a + ((1 − (yi − k) /b )) } (19) 2 2 0.5 x2 = a{h/a –((1 − (yi − k) /b )) } (20) Therefore, the x coordinates of A and B are either x or x s.t.: 1 2 OA = |y1 −yi| 2 2 0.5 x = a{h/a + ((1 (y k) /b )) } (19) 1 i OB = | y2 −yi| 2 2 0.5 x = a{h/a ((1 (y k) /b )) } (20) 2 i OC = |x1 − xi| OA = |y y | 1 i OD = |x2 − xi| If OA < min(b,OB) and OC < min(a,OD) and OC > OA, then the circle is embedded in the zonal ellipse. After determining the peripheral characteristics of the new node, ni, the SDN controller inserts an entry ENTR(i) corresponding to ni in the NODE-STATp table. ENTR(i) = (ni, xi, yi, radi, m-eni, m-eni, 0, 0, −1, pphri) (21) Accordingly, NET-TOPLp is also updated. The information in this table corresponding to ni is (ni, EN(i)) where EN(i) is the set of neighbors of ni in zone Zp itself. A node nj in zone Zp will be the Appl. Sci. 2018, 8, 1013 11 of 28 OB = | y y | 2 i OC = |x x | 1 i OD = |x x | 2 i If OA < min(b,OB) and OC < min(a,OD) and OC > OA, then the circle is embedded in the zonal ellipse. After determining the peripheral characteristics of the new node, n , the SDN controller inserts an entry ENTR(i) corresponding to n in the NODE-STAT table. ENTR(i) = (n , x , y , rad , m-en , m-en , 0, 0, 1, pphr ) (21) i i i i i i i Accordingly, NET-TOPL is also updated. The information in this table corresponding to n is p i (n , EN(i)) where EN(i) is the set of neighbors of n in zone Z itself. A node n in zone Z will be the p p i i j neighbor of n provided the following condition is true. Generally in sensor networks, the links are Appl. Sc i i. 2018, 8, x FOR PEER REVIEW 11 of 28 bi-directional [2,4,10,15,17]. We have kept this property intact for SD-EAR for simplicity purposes. neighbor of ni provided the following condition is true. Generally in sensor networks, the links are 2 2 0.5 bi-directional [2,4,10,15,17]. We have kept this property intact for SD-EAR for simplicity purposes. rad ({(x x ) + (y y ) }) (22) i i j i j 2 2 0.5 radi ≥ ({(xi − xj) + (yi − yj) }) (22) The ﬂowchart for node registration process appears in Figure 6, where node n is about to start its The flowchart for node registration process appears in Figure 6, where node ni is about to start operation at zone Z . Let the SDN controller of Z be denoted as S . its operation p at zone Zp. Let the SDN controller of p Zp be denoted as Sp. p Figure 6. Node registration process. Figure 6. Node registration process. 4.2. Optimum Route Selection 4.2. Optimum Route Selection Whenever a node ni wants to communicate with another node nj, it sends an opt-route-select message to the SDN controller. The attributes of opt-route-select are: Whenever a node n wants to communicate with another node n , it sends an opt-route-select i j (i) message-type-id (3) message to the SDN controller. The attributes of opt-route-select are: (ii) source-id (ni) (i) message-type-id (iii) destination (3) -id (nj) (iv) timestamp (v) traversed-zone-list (initially it is {Zp} only; this field is particularly required for inter-zone communication. Whenever opt-route-select arrives at a new zone, the identification number of the new zone is added to the list) Receiving opt-route-select from ni, the SDN controller of zone Zp checks to see whether destination nj belongs to the same zone or not. This is performed by searching nj in the NODE-STATp table. If nj ∈ extr(NODE-STATp, 1), then an intra-zone route selection is performed; otherwise, an Appl. Sci. 2018, 8, 1013 12 of 28 (ii) source-id (n ) (iii) destination-id (n ) (iv) timestamp (v) traversed-zone-list (initially it is {Z } only; this ﬁeld is particularly required for inter-zone communication. Whenever opt-route-select arrives at a new zone, the identiﬁcation number of the new zone is added to the list) Receiving opt-route-select from n , the SDN controller of zone Z checks to see whether destination i p n belongs to the same zone or not. This is performed by searching n in the NODE-STAT table. j j If n 2 extr(NODE-STAT , 1), then an intra-zone route selection is performed; otherwise, an inter-zone j p Appl. Sci. 2018, 8, x FOR PEER REVIEW 12 of 28 route section is performed. Please note that extr(NODE-STAT , 1) is a function that extracts 1-th ﬁeld from the NODE-STAT table. inter-zone route section is performed. Please note that extr(NODE-STATp, 1) is a function that extracts After the selection of the optimal route, the SDN controller informs about the chosen sequence of 1-th field from the NODE-STATp table. routes to the s Af ou ter rcth e e o f sec le oc m tio m n o un f t ih ca e o tio pn tim in al r an out oe p , tth -re o u SD te N -r cont eply rol m lee rs is na for ge m . s T a h be ou at t t tr he ib ch utos eseo n s f o eq p u te -r no ce u te-reply are: of routes to the source of communication in an opt-route-reply message. The attributes of opt-route- (i) message-type-id (4) reply are: (ii) source-id (n ) (i) message-type-id (4) (ii) source-id (ni) (iii) destination-id (n ) (iii) destination-id (nj) (iv) optimal sequence of routers with locations (iv) optimal sequence of routers with locations The ﬂowchart of route discovery appears in Figure 7. The flowchart of route discovery appears in Figure 7. Figure 7. Node registration process. Figure 7. Node registration process. 4.2.1. Intra-Zone Route Discovery with Example If nj belongs to the same zone Zp, then the SDN controller finds all of the routes from ni to nj through the depth first traversal of the graph starting from ni. Among all of the available paths, the Appl. Sci. 2018, 8, 1013 13 of 28 4.2.1. Intra-Zone Route Discovery with Example Appl. Sci. 2018, 8, x FOR PEER REVIEW 13 of 28 If n belongs to the same zone Z , then the SDN controller ﬁnds all of the routes from n to n j i j through the depth ﬁrst traversal of the graph starting from n . Among all of the available paths, optimum one is selected based on a fuzzy controller FUZZ-OPT-ROUTE, which is embedded in the the optimum one is selected based on a fuzzy controller FUZZ-OPT-ROUTE, which is embedded in controller. This situation is depicted in Figure 8a, while Figure 8b shows the steps with respect to the controller. This situation is depicted in Figure 8a, while Figure 8b shows the steps with respect to Figure 8a. Without any loss of generality, we consider a polygonal zone. Figure 8a. Without any loss of generality, we consider a polygonal zone. (a) (b) Figure 8. (a)Source n and destination n are in same zone Z ; (b)optimum route selection by S in Figure 8. (a)Source n ii and destination n jj are in same zone Z pp; (b)optimum route selection by S ppin Figure 8a. Figure 8a. Intra-zone communication is loop-free Intra-zone communication is loop-free The communication inside a zone is completely controlled by the SDN controller of the zone, The communication inside a zone is completely controlled by the SDN controller of the zone, which is aware of the topology of the entire zone. While discovering the optimum route between two which is aware of the topology of the entire zone. While discovering the optimum route between nodes inside a zone, the controller of that zone applies depth-first traversal, which automatically two nodes inside a zone, the controller of that zone applies depth-ﬁrst traversal, which automatically eliminates loops. Therefore, intra-zone communication is loop-free. eliminates loops. Therefore, intra-zone communication is loop-free. 4.2.2. 4.2.2. Inter Inter-Zone -Zone Route Route Discovery Discovery with with Example Example If destination nj doesn’t belong to the same zone Zp as source ni, then the SDN controller Sp finds If destination n doesn’t belong to the same zone Z as source n , then the SDN controller S ﬁnds j p i p all all of of t the he rro oute utes s f frro om m n n i to to al alll of of t the heperipheral peripheral n nodes odes o off Z Z p thr through ough the the depth-ﬁrst depth-first tr traversal aversal of of th thee i p graph, starting from ni. graph, starting from n . The The s situation ituation ca can n b bee s seen een i inn F Figur iguree 99a, a, while while F Figur iguree 9 9bb shows shows t the he s steps teps o off o optimum ptimum r route oute selection in case of inter-zone route discovery. In this case, opt-route-select has to be propagated to selection in case of inter-zone route discovery. In this case, opt-route-select has to be propagated to neighbors neighbors Z Z p,, t too. oo. IIn n tthat hat ccase, ase, th the e SSDN DN cont contr rol oller ler fiﬁnds nds th the e ro routes utes frfr oom m nn i toto all all ofof the the nod nodes es nqn ∈ p i q extr(NODE-STATp,1) s.t. extr(NODE-STATp,9) = 1. nq forwards the opt-route-select to all of the 2 extr(NODE-STAT ,1) s.t. extr(NODE-STAT ,9) = 1. n forwards the opt-route-select to all of the p p q neighbors neighbors that that do do not not belong belong to to Z Zp.. The neighbors of nq ask the SDN controllers of their respective zones to find out routes to all of The neighbors of n ask the SDN controllers of their respective zones to ﬁnd out routes to all of the the nodes nodesin in its its neighbor neighbor list. list Communication . Communication i in neighbor n neighbzones or zone may s m be ayagain be ag intra-zone ain intra-zor one inter or i -zone. nter- zone. Please note that SDN controllers belonging to multiple zones do not directly communicate with Please note that SDN controllers belonging to multiple zones do not directly communicate with each each other other.. R Rather ather,, th thee peripheral peripheral nod nodes es of of v various arious zones zones com communicate municate w with ith e each ach ot other her .. I Inter nter-zone -zone communication is actually a collection of multiple intra-zone communications. communication is actually a collection of multiple intra-zone communications. In inter-zone communication, if a SDN controller Sq of a zone Zq receives opt-route-select from more than one of its nodes corresponding to the same destination, then it sends opt-route-reply to the one with the highest residual energy (nb), while the others (say nc) are instructed to remain silent. Appl. Sci. 2018, 8, 1013 14 of 28 In inter-zone communication, if a SDN controller S of a zone Z receives opt-route-select from q q more than one of its nodes corresponding to the same destination, then it sends opt-route-reply to the Appl. Sci. 2018, 8, x FOR PEER REVIEW 14 of 28 one with the highest residual energy (n ), while the others (say n ) are instructed to remain silent. (a) (b) Figure 9. F(ig a) urSour e 9. (ace ) Sou n rcand e ni and destination destination nn j arar e n e ot i not n sain me z same one; (b zone; ) optim( u b m ) roptimum oute selection route for inter selection - for i j zone situation. inter-zone situation. The attributes of this silence message are as follows: The attributes of this silence message are as follows: (i) message-type-id (5) (i) message-type-id (5) (ii) source-id (n ) (iii) destination-id (n ) (iv) router-id (n ) c Appl. Sci. 2018, 8, 1013 15 of 28 Inter-zone communication is loop-free The traversed-zone-list ﬁeld contains the sequence of zones that one particular opt-route-select message has traversed. Therefore, one opt-route-select message cannot be propagated within a zone more than once. This avoids loops. Moreover, intra-zone communication from a node to the peripheral nodes of the zone are deﬁnitely loop-free, because they are determined by the SDN controller of that zone by dint of the depth-ﬁrst traversal of the zonal topological graph. This technique is loop-free, as already mentioned, in case of intra-zone communication. INPUT PARAMETERS OF FUZZ-OPT-ROUTE reft(R) or residual energy effect of route R This parameter indicates how the residual energy of nodes belonging to R inﬂuence the performance of the route. It is mathematically modeled in Equation (23): > (f-en (R) f-en (R)) if min r-en > 1 1 2 k n 2 R reft R = (23) ( ) min r-en Otherwise > k n 2 R f-en (R) = 1 {1/(min r-en + 2)} (24) 1 k n 2 R such that k 6= start(R) and k 6= end(R) start(R) and end(R) specify the source and destination nodes, respectively, in route R. If, for one particular route, no router nodes are there, then (minr-en ) among all of the routers n 2R, will be 0. k k f-en (R) = 1 {(R)/( r-en + 2)} 2 k (25) n 2 R From the formulation in Equation (23), it is evident that reft(R) ranges between 0 and 1. It is close to 1 if the minimum and average residual energies of R are high. The high values of this parameter are beneﬁcial for the performance of the route. In Equation (24), 2 is added to the denominator to avoid a zero value when the min r-en among all of the routers n 2 R is 0. Similarly, in Equation (25), k k 2 is added to the denominator to avoid a zero value when r-en of all of then 2 R is 0. If any k k one of the f-en (R) and f-en (R) is 0, then the effect of the other one will be completely eliminated, 1 2 which is undesirable. trim(R) or the transmission energy impact of route R Transmission energy impact depends on the minimum received power of the nodes in route R and the maximum distance between consecutive nodes. With the increase in this distance and the minimum received power of the successors (by successor we mean all of the nodes except the source) in R, the transmission cost increases, while trim(R) decreases. trim(R) = (f-recv (R) f-recv (R)) (26) 1 2 f-recv (R) = (1 mrpw(succ(n , R))/((R) (max mrpw (n ) + 1)) 1 k v n 2 R n 2 Z v p f-recv (R) = max {1, dist(n , succ(n , R))} 2 k k n 2 R succ(n , R) is the successor node of n in R. mrpw(n ) is the minimum received power of node n . v v k k dist(n , n ) speciﬁes the Cartesian distance between nodes n and n . a b a b Appl. Sci. 2018, 8, 1013 16 of 28 From the formulation in Equation (26), we can see that trim lies between 0 and 1. High values of it contribute to improve the performance of R. neff(R) or node efﬁciency in R The node efﬁciency in a path will be high, as most of the nodes are awake, and alternatives are available to sleeping nodes (if any). A node n will be an alternative to another node n w.r.t. route u v provided the following conditions are satisﬁed: n 2 / R if n 2 R (27a) u v EN(u) EN(v) (27b) {r-en /(dep + dep )} (r-en /dep ) (27c) u u v v v Condition (27a) speciﬁes that n should not belong to any live communication route passing through n . Condition (27b) says that n must have all of the neighbors of n as its neighbors. v v u The condition (27c) indicates that the additional forwarding load from neighbors of n should not reduce the lifetime of n below the lifetime of n . If n is an alternative of n , then n 2 alt(v) where v u u v u alt(v) is the set of all of the alternatives of n . The mathematical formulation of neff appears in Equation (28): 0.5 neff(R) = (eff (R) eff (R)) (28) 1 2 eff (R) = (1 (R)/(R)) eff (R) = 1 [1/{( alt(k)/(R)) + 2}] 2 å n 2 R k 6= start(R) and k 6= end(R) From Equation (28), it is evident that the efﬁciency of a route increases with the number of awake nodes and the number of alternatives to sleepy nodes. neff lies between 0 and 1. High values of it indicate the efﬁciency of the associated route. FUZZY RULE BASES OF FUZZ-OPT-ROUTE All of the input parameters of the FUZZ-OPT-ROUTE are divided into four equal ranges between 0 and 1. The ranges and corresponding fuzzy premise variables are as follows: (i) 0–0.25 (LOW or ‘L’ is short) (ii) 0.25–0.50 (SATISFACTORY or ‘S’ in short) (iii) 0.50–0.75 (HIGH or ‘H’ in short) (iv) 0.75–1.00 (VERY HIGH or ‘VH’ in short) The rule bases of this fuzzy controller appear in Tables 1 and 2. Table 1 combines reft and trim, producing temporary output temp1. Both of these parameters are assigned equal weight, since both are equally important in determining the efﬁciency of the route. If a node has high residual energy but suffers from huge energy consumption due to the long distance from its successor in a communication path, then the energy efﬁciency of a path greatly reduces. Therefore, temp1 = min (reft, trim) in Table 1. Examples are: reft = H, trim = S, temp1 = S; reft = S, trim = L, temp1 = L; and reft = VH, trim = S, temp1 = S. The composition of temp1 and neff is presented in Table 2. Then eff of a route increases with an increased number of awake nodes and alternatives to sleepy nodes, if any. temp1 is given more weight than neff, only because a huge number of awake nodes will not do. They need to have high residual energy and a low transmission energy requirement in each hop so that the route can last for a long time. The inﬂuence of neff increases when temp1 is not high. Hence, in Table 2, when temp1 = L Appl. Sci. 2018, 8, 1013 17 of 28 or S, the output opt-performance = min(temp1, neff). On the other hand, when temp1 = H or VH, small values of neff are capable of reducing it to some extent. For example, when temp1 = H and neff = L, opt-performance = S. The optimal path is the one that produces the best opt-performance. In the case of multiple optimal options, any one with the least weight is elected as optimal. Weight(R), that is, the weight of an optimal route R, is mathematically formulated below in Equation (29): Weight(R) = ((R) ( (R) + 1)) (29) (R) speciﬁes the total number of sleepy nodes in R, while (R) is the total number of nodes in R. If still a tie remains, then any one is chosen for communication. Table 1. Composition of reft and trim producing temp1. Reft! L S H VH Trim # L L L L L S L S S S H L S H H VH L S H VH Table 2. Composition of temp1 and neff producing opt-performance. Temp1 ! L S H VH Neff# L L L S H S L S H H H L S H VH VH L S H VH 4.2.3. Example of Optimum Path Selection Table 3 speciﬁes all of the node ids in Figure 10 along with their descriptions. Based on these assumptions, we are about to ﬁnd out the optimal route from n to n at current timestamp 100. b d The signiﬁcance of all of the symbols used in Table 3 have been mentioned earlier in this section. For the purpose of simplicity, we have assumed that all of the links are bi-directional. The latitude and longitudinal positions are given in Table 3. It is seen from Figure 10 that n is directly connected to n only, while n is directly connected to n , n , n , n 0 , and n 0 . Among them, n 0 and n 0 are e e a g u v u v out of Z . So, EN(a) = {n } and pphr = 0, since n 2 Z . On the other hand, EN(a) = {n , n , n , n 0 , A e a e A a b g u n 0 } and pphr = 1, since n 0 , n 0 2 / Z . Similarly, the EN and pphr of other nodes can be computed. v e u v Other attribute values are assumed. The performances of different routes from n to n are calculated b d in the FUZZ-OPT-ROUTE of SDN controller of Z . Table 3. Node identiﬁers and descriptions. Node-id Latitude, Longitude EN m-en r-en dep slp -slp pphr mrpw n (0,0) n 40 25 2 0 1 0 1 a e n (10,10) n , n and n 15 3 2 0 1 0 1 b e f c n (11,11) n , n and n 30 20 4 0 90 0 2 b f b n (11.5,11.5) n , n 80 20 4 0 1 0 1 d c g n (3,2) n , n 80 20 10 0 80 1 3 e a n (12, 12) n , n 40 25 3 1 1 0 1 f b c n (11.75,11.75) n 60 30 2 1 85 1 1 d Appl. Sci. 2018, 8, 1013 18 of 28 Appl. Sci. 2018, 8, x FOR PEER REVIEW 18 of 28 Figure 10. Topology of zones ZA and ZB along with neighbors. Figure 10. Topology of zones Z and Z along with neighbors. A B The different routes from nb to nd obtained through a depth-first traversal of topology of ZA, are The different routes from n to n obtained through a depth-ﬁrst traversal of topology of Z , b d A as follows: are as follows: (i) R1: nb→nc→nd (i) (ii R) : n R2: ! nb→ nnc! →nn d 1 b c d (iii) R3: nb→nc→nf→nd (ii) R : n ! n ! n 2 b d (iv) R4: nb→nf→nc→nd (iii) R : n ! n ! n ! n 3 b c f d (v) R5: nb→ ne→ ng→nd (iv) R : n ! n ! n ! n 4 b f d Performance of R1 (v) R : n ! n ! n ! n 5 b e g d 0.5 reft(R1) = [{1 − 1/(20 + 2)} {1 − 3/(3 + 20 + 20 + 2)}] = 0.9438 Performance of R 0.5 0.5 0.5 trim(R1) = [{1 − (2 + 1)/(3 × 4)}/max{2 , 0.5 }] = 0.7282 0.5 0.5 reft(R ) = [{1 1/(20 + 2)} {1 3/(3 + 20 + 20 + 2)}] = 0.9438 neff(R1) = [{1 − 0/3} (1 − 1/(1 + 2))] = 0.8176 0.5 0.5 0.5 After fuzzification, the fuzzy premise variables corresponding t reft(R1), trim(R1), andneff(R1) trim(R ) = [{1 (2 + 1)/(3 4)}/max{2 , 0.5 }] = 0.7282 become VH, H, and VH, respectively. Their combination, according to Tables 1 and 2, is VH. 0.5 neff(R ) = [{1 0/3} (1 1/(1 + 2))] = 0.8176 Hence, opt-performance = VH After fuzziﬁcation, the fuzzy premise variables corresponding t reft(R ), trim(R ), andneff(R ) 1 1 1 Performance of R2 become VH, H, and VH, respectively. Their combination, according to Tables 1 and 2, is VH. Hence, opt-performance = VH Appl. Sci. 2018, 8, 1013 19 of 28 Performance of R 0.5 reft(R ) = [{1 1/(25 + 2)} {1 3/(3 + 25 + 20 + 2)}] = 0.9509 0.5 0.5 0.5 trim(R ) = [{1 (1 + 1)/(3 4)}/max{8 , 0.5 , 1}] = 0.5426 0.5 neff(R ) = [{1 0/(3)} (1 1/(0 + 2))] = 0.7071 After fuzziﬁcation, fuzzy premise variables corresponding t reft(R ), trim(R ), andneff(R ) become 2 2 2 VH, H, and H. Their combination, according to Tables 1 and 2, is VH. Hence, opt-performance = H Performance of R 0.5 reft(R ) = [{1 1/(20 + 2)} {1 4/(3 + 20 + 25 + 20 + 2)}] i.e., reft(R ) = 0.94845 0.5 0.5 0.5 trim(R ) = [{1 (2 + 1 + 1)/(4 4)}/max{2 , 0.5 }] = 0.866 n is sleeping. We need to search for alternatives of n . Only n has the same set of neighbors as n . However, f f f n belongs to this route R itself. So, n cannot be an alternative to n . So, n has no alternative: c 3 c f f 0.5 neff(R ) = [{1 1/(3 + 1)} (1 1/(0 + 2))] = 0.61237 After fuzziﬁcation, the fuzzy premise variables corresponding t reft(R ), trim(R ), and neff(R ) 3 3 3 become VH, VH, and H. Their combination, according to Tables 1 and 2, is VH. Hence, opt-performance = VH Performance of R 0.5 reft(R ) = [{1 1/(20 + 2)} {1 4/(3 + 20 + 25 + 20 + 2)}] i.e., reft(R ) = 0.94845 0.5 0.5 0.5 0.5 trim(R ) = [{1 (2 + 1 + 1)/(4 4)}/max{8 , 2 , 0.5 }] = 0.5147 n is sleeping. We need to search for alternatives of n . Only n has same set of neighbors as n . f f f However, n belongs to the route R itself. So, n cannot be an alternative to n . So, n has no alternative. c 3 c f f 0.5 neff(R ) = [{1 1/(3 + 1)} (1 1/(0 + 2))] = 0.61237 After fuzziﬁcation, the fuzzy premise variables corresponding t reft(R ), trim(R ), and neff(R ) 4 4 4 become VH, H, and H. Their combination, according to Tables 1 and 2, is H. Hence, opt-performance = H Performance of R 0.5 reft(R ) = [{1 1/(20 + 2)} {1 4/(3 + 20 + 30 + 20 + 2)}] i.e., reft(R ) = 0.9498 0.5 0.5 0.5 0.5 trim(R ) = [{1 (3 + 1 + 1)/(4 4)}/max{105 , 1.25 , 76.5625 }] i.e., trim(R ) = 0.229 5 Appl. Sci. 2018, 8, 1013 20 of 28 In this path, only n is sleeping. We need to search for alternatives of n . However, no node in g g zone Z has the same set of neighbors as n . So, n has no alternative. g g 0.5 neff(R ) = [{1 1/(3 + 1)} (1 1/(0 + 2))] = 0.61237 After fuzziﬁcation, the fuzzy premise variables corresponding t reft(R ), trim(R ), and eff(R ) 5 5 5 become VH, L, and H, respectively. Their combination, according to Tables 1 and 2, is L. Hence, opt-performance = L R and R both have performance VH. Therefore, we compute the weight of these paths. 1 4 Weight(R ) = 3 1 = 3 Weight(R ) = 4 2 = 8 Therefore, R is chosen as optimal. 4.2.4. Sleeping Strategy Whenever a node n wants to go to sleep, it needs to ask for permission from the SDN controller of that zone. It gets a sleep grant provided that the following conditions are true: (i) (r-en /dep ) < TH where TH is a threshold. i i (ii) If n is part of any live communication route, then it must have at least one valid alternative. The strategy of computing an alternative is present in (27a)–(27c). Also, there is one forceful sleep grant mechanism. When all of the neighbors of a node n are sleeping, then n is instructed by the SDN controller in that zone to go to sleep if it does not want to transmit anything. It has practically no reason to listen to the network, because it has no way of receiving any traffic. The attributes of a sleep request are: (i) message-type-id (5) (ii) node-id (n ) Similarly, the attributes of a sleep grant are: (i) message-type-id (6) (ii) node-id (n ) 5. Simulation Experiments 5.1. Simulation Environment This experiment is based on SDN and the IEEE 802.15.4 protocol for wireless sensor networks. The network emulator for the framework is Mininet [30], and the controller is Floodlight [31]. Floodlight runs on a server with an AMD Opteron processor 6348 and 16 GB memory. The server is installed with Linux kernel version 2.6.32. Mininet runs on a separate server, and the servers are connected by a 10 Gbps Ethernet network. Details appear in Table 4. Table 4. Simulation parameters. Network Parameters Values Number of nodes 50, 75, 100, 125, 150, 180 Network area 500 m 500 m Radio range 10 m–40 m Initial energy of nodes 10 j–20 j Size of each packet 512 bytes Appl. Sci. 2018, 8, 1013 21 of 28 The simulation metrics are: (i) Overall Message Cost (OMC)—This is a summation of messages sent by all of the nodes in the network. msg denotes the total number of messages sent by n throughout the simulation period. k k OMC = msg n 2 NW (ii) Overall Energy Consumed (OEC)—This is a summation of energy consumed in all of the nodes Appl. Sci. 2018, 8, x FOR PEER REVIEW 21 of 28 in the network. OEC = (m-en r-en ) k k (ii) Overall Energy Consumed (OEC)—This is a summation of energy consumed in all of the nodes n 2 NW in the network. (iii) Network Throughput (NT)—This is the percentage of data packets that were successfully OEC = ∑ (m-enk − r-enk) delivered to their respective destinations. t-p is the number of packets transmitted throughout nk ∈ NW the communication session, and d-p is the number of packets successfully delivered to their (iii) Network Throughput (NT)—This is the percentage of data packets that were successfully destinations throughout the simulation run. delivered to their respective destinations. t-p is the number of packets transmitted throughout NT = (d-p/t-p) 100 the communication session, and d-p is the number of packets successfully delivered to their destinations throughout the simulation run. (vi) Average Delay (AD)—This is a summation of delay faced by all of the packets in the network, NT = (d-p/t-p) × 100 divided by the number of packets transmitted. PAC is the set of all of the packets transmitted (vthr i) oughout Average D the elsimulation. ay (AD)—Th d-l(pac) is is a sdenotes ummatio the n odelay f delasuf y fa fer ceed d bby y apacket ll of thP eAC: packets in the network, divided by the number of packets transmitted. PAC is the set of all of the packets transmitted AD = å d-l(pac)/|PAC| throughout the simulation. d-l(pac) denotes the delay suffered by packet PAC: pac 2 PAC AD = ∑ d-l(pac)/|PAC| pac ∈ PAC (v) Percentage of alive nodes per established communication session (PALCS)—This isthe summation (v) Percentage of alive nodes per established communication session (PALCS)—This isthe of the number of alive nodes (ALVN(CS)) in session CS multiplied by 100 anddivided (by total summation of the number of alive nodes (ALVN(CS)) in session CS multiplied by 100 number of nodes in optimal route in the same session total number of established sessions). anddivided (by total number of nodes in optimal route in the same session × total number of SES is the set of all of the sessions established throughout the simulation time. route(CS) is total established sessions). SES is the set of all of the sessions established throughout the simulation number of nodes in an optimal route in session CS. time. route(CS) is total number of nodes in an optimal route in session CS. PALCS = (ALVN(CS) 100)/(|SES| route(CS)) PALCS = å∑ (ALVN(CS) × 100)/(|SES| × route(CS)) CS 2 SES CS ∈ SES 5.2.5Simulation .2. Simulati Results on Results Graphical Graphicomparisons cal comparisof onSD-EAR s of SD-with EAR w its state-of-the-art ith its state-of-c th ompetitors e-art comLEACH, petitors LEA SPIN, CH, SD-WSN1, SPIN, SD- SD-WSN2, WSN1, SD and -WS SD-WSN3, N2, and S ar D e-WS presented N3, are p in r Figur esentes ed 11 in– F 20 igu . res 11–20. 1000 LEACH SPIN SD-EAR 50 75 100 125 150 180 Number of nodes Figure 11. Overall Message Cost (OMC) vs. number of nodes. Figure 11. Overall Message Cost (OMC) vs. number of nodes. LEACH SPIN SD-EAR 50 75 100 125 150 180 Number of nodes Figure 12. Overall Energy Consumed (OEC) vs. number of nodes. OEC OMC Appl. Sci. 2018, 8, x FOR PEER REVIEW 21 of 28 (ii) Overall Energy Consumed (OEC)—This is a summation of energy consumed in all of the nodes in the network. OEC = ∑ (m-enk − r-enk) nk ∈ NW (iii) Network Throughput (NT)—This is the percentage of data packets that were successfully delivered to their respective destinations. t-p is the number of packets transmitted throughout the communication session, and d-p is the number of packets successfully delivered to their destinations throughout the simulation run. NT = (d-p/t-p) × 100 (vi) Average Delay (AD)—This is a summation of delay faced by all of the packets in the network, divided by the number of packets transmitted. PAC is the set of all of the packets transmitted throughout the simulation. d-l(pac) denotes the delay suffered by packet PAC: AD = ∑ d-l(pac)/|PAC| pac ∈ PAC (v) Percentage of alive nodes per established communication session (PALCS)—This isthe summation of the number of alive nodes (ALVN(CS)) in session CS multiplied by 100 anddivided (by total number of nodes in optimal route in the same session × total number of established sessions). SES is the set of all of the sessions established throughout the simulation time. route(CS) is total number of nodes in an optimal route in session CS. PALCS = ∑ (ALVN(CS) × 100)/(|SES| × route(CS)) CS ∈ SES 5.2. Simulation Results Graphical comparisons of SD-EAR with its state-of-the-art competitors LEACH, SPIN, SD- WSN1, SD-WSN2, and SD-WSN3, are presented in Figures 11–20. 1000 LEACH SPIN SD-EAR 50 75 100 125 150 180 Number of nodes Appl. Sci. 2018, 8, 1013 22 of 28 Figure 11. Overall Message Cost (OMC) vs. number of nodes. LEACH SPIN SD-EAR 50 75 100 125 150 180 Appl. Sci. 2018, 8, x FOR PEER REVIEW 22 of 28 Number of nodes Figure 12. Overall Energy Consumed (OEC) vs. number of nodes. Figure 12. Overall Energy Consumed (OEC) vs. number of nodes. Appl. Sci. 2018, 8, x FOR PEER REVIEW 22 of 28 Appl. Sci. 2018, 8, x FOR PEER REVIEW 22 of 28 LEACH SPIN LEACH LEACH SD-EAR 70 SPIN SPIN SD-EAR 70 SD-EAR 50 75 100 125 150 180 Number of nodes 50 75 100 125 150 180 50 75 100 125 150 180 Figure 13. Network Throughput (NT) vs. number of nodes. Number of nodes Number of nodes Figure 13. Network Throughput (NT) vs. number of nodes. Figure 13. Network Throughput (NT) vs. number of nodes. Figure 13. Network Throughput (NT) vs. number of nodes. LEACH LEACH LEACH SPIN SPIN SPIN SD-EAR 1050 75 100 125 150 180 SD-EAR SD-EAR 0 Number of nodes 50 75 100 125 150 180 50 75 100 125 150 180 Number of nodes Number of nodes Figure 14. Average Delay (AD) vs. number of nodes. Figure 14. Average Delay (AD) vs. number of nodes. Figure 14. Average Delay (AD) vs. number of nodes. 95 Figure 14. Average Delay (AD) vs. number of nodes. LEACH LEACH SPINLEACH SPIN 75 SD-ESAR PIN 50 75 100 125 150 180 SD-EAR SD-EAR 60 Number of nodes 50 75 100 125 150 180 50 75 100 125 150 180 Figure 15. Percentage of alive nodes per established communication session (PALC) vs. number of nodes. Number of nodes Number of nodes Figure 15. Percentage of alive nodes per established communication session (PALC) vs. number of nodes. Figure 15. Percentage of alive nodes per established communication session (PALC) vs. number of nodes. Figure 15. Percentage o 10 f00 alive nodes per established communication session (PALC) vs. number of nodes. SD- WSN1 SD- SD- SD- 800 WSN1 WSN2 WSN1 SD- SD- SD- WSN2 WSN3 200 WSN2 SD- SD-EAR SD- 200 WSN3 WSN3 50 75 100 125 150 180 SD-EAR SD-EAR 0 Number of nodes 50 75 100 125 150 180 50 75 100 125 150 180 Number of nodes Number of nodes OMC NT PALC OMC NT PALC OMC OEC OMC NT PALC AD AD AD Appl. Sci. 2018, 8, x FOR PEER REVIEW 22 of 28 LEACH SPIN SD-EAR 50 75 100 125 150 180 Number of nodes Figure 13. Network Throughput (NT) vs. number of nodes. LEACH SPIN SD-EAR 50 75 100 125 150 180 Number of nodes Figure 14. Average Delay (AD) vs. number of nodes. LEACH SPIN SD-EAR 50 75 100 125 150 180 Number of nodes Appl. Sci. 2018, 8, 1013 23 of 28 Figure 15. Percentage of alive nodes per established communication session (PALC) vs. number of nodes. SD- WSN1 SD- WSN2 SD- WSN3 SD-EAR Appl. Sci. 2018, 8, x FOR PEER REVIEW 23 of 28 Appl. Sci. 2018, 8, x FOR PEER REVIEW 23 of 28 50 75 100 125 150 180 Appl. Sci. 2018, 8, x FOR PEER REVIEW 23 of 28 Number of nodes Figure 16. OMC vs. number of nodes. Figure 16. OMC vs. number of nodes. Figure 16. OMC vs. number of nodes. Figure 16. OMC vs. number of nodes. SD-WSN1 SD-WSN1 SD-WSN1 SD-WSN2 6000 SD-WSN2 SD-WSN2 SD-WSN3 SD-WSN3 SD-WSN3 SD-EAR 2000 SD-EAR SD-EAR 50 75 100 125 150 180 50 75 100 125 150 180 50 Numb 75 er of 100 nod 125 es 150 180 Number of nodes Number of nodes Figure Figure 17. 17.OEC OEC v vs.s.number numberof ofnodes. nodes. Figure 17. OEC vs. number of nodes. Figure 17. OEC vs. number of nodes. SD-WSN1 SD-WSN1 SD-WSN1 90 SD-WSN2 90 SD-WSN2 SD-WSN2 SD-WSN3 SD-WSN3 SD-WSN3 80 SD- SD- EAR EAR SD-EAR 50 75 100 125 150 180 60 50 75 100 125 150 180 50 75 100 125 150 180 Number of nodes Number of nodes Number of nodes Figure 18. NT vs. number of nodes. Figure 18. NT vs. number of nodes. Figure 18. NT vs. number of nodes. Figure 18. NT vs. number of nodes. SD-WSN1 SD-WSN1 SD-WSN1 SD-WSN2 SD-WSN2 SD-WSN2 SD-WSN3 20 30 SD-WSN3 10 SD-WSN3 SD-EAR SD-EAR SD-EAR 50 75 100 125 150 180 50 75 100 125 150 180 50 Numb 75 er of 100 nod 125 es 150 180 Number of nodes Number of nodes Figure 19. AD vs. number of nodes. Figure 19. AD vs. number of nodes. Figure 19. AD vs. number of nodes. Figure 19. AD vs. number of nodes. OEC NT OEC AD NT OMC OEC AD NT NT PALC AD AD Appl. Sci. 2018, 8, 1013 24 of 28 Appl. Sci. 2018, 8, x FOR PEER REVIEW 24 of 28 SD-WSN1 SD-WSN2 SD-WSN3 SD-EAR 50 75 100 125 150 180 Number of nodes Figure 20. PALC vs. number of nodes. Figure 20. PALC vs. number of nodes. 5.2.1. SD-EAR versus LEACH, SPIN LEACH divides the sensor network into multiple clusters. The technique for electing a cluster-head is 5.2.1. SD-EAR versus LEACH, SPIN the main source of energy efficiency here. If a node has already played the role of a cluster-head in several LEACH divides the sensor network into multiple clusters. The technique for electing a cluster- runs, then its chance of becoming a cluster-head in the next run again reduces greatly. This encourages head is the main source of energy efficiency here. If a node has already played the role of a cluster- the rolling of cluster-heads by the balancing load. However, LEACH does not consider the location of head in several runs, then its chance of becoming a cluster-head in the next run again reduces greatly. sensor nodes [35,36,43] and therefore, certain cluster members may stay very far from the cluster-head. This encourages the rolling of cluster-heads by the balancing load. However, LEACH does not So, the energy consumed by the cluster-head in transmitting a message to those members that are consider the location of sensor nodes [35,36,43] and therefore, certain cluster members may stay very located far, increases substantially. Also, LEACH applies Dijkstra’s shortest path algorithm to select the far from the cluster-head. So, the energy consumed by the cluster-head in transmitting a message to optimum path [43]. The authors would like to state here that shortest path, in most cases, is not the those members that are located far, increases substantially. Also, LEACH applies Dijkstra’s shortest most energy-efficient one. Hence, the performance improvement produced by SD-EAR over LEACH is path algorithm to select the optimum path [43]. The authors would like to state here that shortest noteworthy. This is evident in Figures 11–15. path, in most cases, is not the most energy-efficient one. Hence, the performance improvement SPIN [38] is also a very important energy-efﬁcient protocol that broadcasts metadata, which is data produced by SD-EAR over LEACH is noteworthy. This is evident in Figures 11–15. about data. If a neighbor develops interest about actual data after receiving the metadata, only then SPIN [38] is also a very important energy-efficient protocol that broadcasts metadata, which is is data sent to it. The energy efﬁciency in SPIN arises from the size of the metadata being much less data about data. If a neighbor develops interest about actual data after receiving the metadata, only than the size of the actual data, and the actual data is sent to a subset of neighbors instead of all of the then is data sent to it. The energy efficiency in SPIN arises from the size of the metadata being much neighbors, as in an ordinary broadcast. less than the size of the actual data, and the actual data is sent to a subset of neighbors instead of all On the other hand, in SD-EAR, the broadcasting of an opt-route-select is not performed, of the neighbors, as in an ordinary broadcast. not even in inter-zone communication. Actually, in the inter-zone route discovery, opt-route-select On the other hand, in SD-EAR, the broadcasting of an opt-route-select is not performed, not even is at most multicast to all of the peripheral nodes, as well as also through energy-efﬁcient in inter-zone communication. Actually, in the inter-zone route discovery, opt-route-select is at most paths, as instructed by the SDN controller in that zone. Those routes do not break easily, multicast to all of the peripheral nodes, as well as also through energy-efficient paths, as instructed and the requirement of route-rediscovery arises signiﬁcantly less in our proposed protocol, by the SDN controller in that zone. Those routes do not break easily, and the requirement of route- which reduces the message cost in SD-EAR compared with LEACH and SPIN, as seen from Figure 11. rediscovery arises significantly less in our proposed protocol, which reduces the message cost in SD- EAR compared with LEACH and SPIN, as seen from Figure 11. 5.2.2. SD-EAR versus SD-WSN1, SD-WSN2 and SD-WSN3 SD-WSN1 applies either a proactive or reactive routing policy depending upon the total number 5.2.2. SD-EAR versus SD-WSN1, SD-WSN2 and SD-WSN3 of nodes in the network. If the number of nodes is low, then proactive routing is applied, where the SD-WSN1 applies either a proactive or reactive routing policy depending upon the total number identiﬁers of successors in optimum paths to various destinations are stored in routing tables. This does of nodes in the network. If the number of nodes is low, then proactive routing is applied, where the not require route discovery before each communication session, and saves energy by eliminating the identifiers of successors in optimum paths to various destinations are stored in routing tables. This broadcast operation during route discovery when the number of nodes is low [12]. However, when the does not require route discovery before each communication session, and saves energy by number of nodes becomes high, routing table storage becomes inefﬁcient; also, keeping track of stale eliminating the broadcast operation during route discovery when the number of nodes is low [12]. routes is difﬁcult. In that case, it applies reactive routing and the corresponding protocol is ad hoc on However, when the number of nodes becomes high, routing table storage becomes inefficient; also, demand distance vector routing, or AODV. Among the various paths through which the route-request keeping track of stale routes is difficult. In that case, it applies reactive routing and the corresponding arrives at its destination from the source, the path with the least number of hops is elected as optimal. protocol is ad hoc on demand distance vector routing, or AODV. Among the various paths through No energy efﬁciency is applied during optimum route selection. Also, there is no provision of giving which the route-request arrives at its destination from the source, the path with the least number of rest to exhausted nodes. SD-WSN1 is capable of avoiding the broadcasting of route-request only when hops is elected as optimal. No energy efficiency is applied during optimum route selection. Also, number of nodes is low enough. there is no provision of giving rest to exhausted nodes. SD-WSN1 is capable of avoiding the broadcasting of route-request only when number of nodes is low enough. PALC Appl. Sci. 2018, 8, 1013 25 of 28 SD-WSN2 [20] is concerned with only the optimum placement of energy transmitters. Energy transmitters provide energy to the nodes when they suffer from a shortage of charge. Please note that the recharging of nodes is less frequent than the initiation of new communication sessions. SD-WSN3 [21] selects a few control nodes among all of the nodes in the network. These nodes assign task to others, depending upon the residual energy. However, here, the authors want to humbly state that residual energy is not the only criteria. A node n may have higher residual energy (say for example, 50 mj) than another node n (30 mj), but this does not mean that n will live longer. It depends j i on the energy depletion rate or packet-forwarding load. If we assume that the energy depletion rate of n is 10 mj/s and that of n is 2 mj/s, then the residual lifetime of n and n are 5 s and 15 s, respectively. i j i j SD-EAR considers different aspects of energy efficiency, such as residual energy, the energy depletion rate, the distance between consecutive nodes in a route, the number of sleeping nodes, and their alternatives. All of these criteria enable the establishment of long-lasting routes and the requirement of route rediscovery reduces up to a greater extent than SD-WSN1, SD-WSN2, and SD-WSN3. SD-EAR converts the broadcasting of a route-request to multicasting opt-route-select to only the peripheral node group whose member cardinality is generally much smaller than the total number of nodes in a zone. Therefore, SD-EAR suffers from much less message cost than its state-of-the-art software defined competitors, as seen in Figure 16. Overall Energy Consumed (OEC) The most important aspect of SD-EAR is that it completely eliminates broadcasting during route discovery. This saves a huge number of messages. Also, route selection strategy in SD-EAR is extremely energy-efﬁcient. The eligibility of a route depends upon the pairwise distance between consecutive nodes, the number of alternatives, the residual energy, the rate of energy depletion, etc. A sleeping strategy is proposed too, that examines the need of sleep of a node after receiving a sleep request. A sleep is granted by the SDN controller if the zonal topology has a provision of its alternative, so that the alive communication session does not face any link breakage. The provision of forceful sleep instruction from the SDN controller to different nodes is possible in SD-EAR. Link breakages are repaired proactively. All of these things greatly reduce the overall energy consumption, or OEC, in SD-EAR compared with its competitors. This is seen in Figures 12 and 17. However, as the number of nodes increases, the OEC also increases for all of the protocols, but still, the growth is least steep for SD-EAR. Percentage of alive nodes per established communication session (PALCS) Routing protocol in SD-EAR balances the load between various nodes in the network and greatly reduces the overall energy consumption. The reduction in message cost cuts down on the energy consumption in various nodes. Moreover, balancing the load prohibits overwhelming differences between the residual energy of various nodes. This increases the number of alive nodes in different communication sessions, as shown in Figures 15 and 20. Network Throughput (NT) We all know that if the number of messages in the network increases upto a great extent, then it gives rise to a huge amount of message contention and collision, yielding a high rate of packet loss. Since the competitors of SD-EAR suffer from much more message overhead than our proposed protocol SD-EAR, the network throughput produced by SD-EAR is much higher than others, as shown in Figures 13 and 18. It is seen in the ﬁgures that for all of the protocols, network throughput initially increases with the number of nodes; then, it starts decreasing. The reason is that with the increase in the number of nodes, more links are formed, and more data packets are delivered to their respective destinations. However, when the network becomes saturated with nodes, message contention and collision increases, reducing network throughput. Appl. Sci. 2018, 8, 1013 26 of 28 Average Delay (AD) We have already mentioned that broadcasting for route-request is completely eliminated in SD-EAR. Therefore, the delay in route discovery is reduced by a great extent in our proposed protocol. Moreover, link breakages are also repaired proactively. This saves the time span that would have been required to identify a broken link and initiate route-rediscovery to repair the link. Therefore, the average delay in SD-EAR is much smaller than that of its competitors, as shown in Figures 14 and 19. 6. Conclusions This article presents a software deﬁned wireless sensor framework where the network is divided into multiple clusters or zones, and each zone is controlled by an SDN controller thatis aware of topology within that zone. The broadcasting of route-requests is completely eliminated in SD-EAR. The selection of the optimum route is energy efﬁcient. A sleep grant mechanism is proposed that allows nodes to go to sleep without hampering live communication sessions. These techniques greatly reduce the energy consumption in the network, and increase the number of alive nodes and network performance. 7. Future Scope We have simulated our present work in Mininet simulator. In the future, we shall implement SD-EAR in real life, and compare its performance with its state-of-the-art competitors. Author Contributions: A.B. has designed the algorithm and performed the simulation while D.M.A.H. helped in rigorous performance analysis and writing the paper Conﬂicts of Interest: The authors declare no conﬂict of interest References 1. Distefano, S. Evaluating reliability of WSN with sleep/wake-up interfering nodes. Int. J. Syst. Sci. 2013, 44, 1793–1806. [CrossRef] 2. Seeberger Company [Online]. Available online: http://www.seeberger.de/ (accessed on 16 November 2017). 3. Li, W.F.; Fu, X.W. Survey on invulnerability of wireless sensor networks. Chin. J. Comput. 2015, 38, 625–647. 4. Huang, J.; Meng, Y.; Gong, X.H.; Liu, Y.B.; Duan, Q. A novel deployment scheme for green internet of things. IEEE Int. Things J. 2014, 1, 196–205. [CrossRef] 5. Zuo, Q.Y.; Chen, M.; Zhao, G.S.; Xing, C.Y.; Zhang, G.M.; Jiang, P.C. Research on OpenFlow-based SDN technologies. J. Softw. 2013, 24, 1078–1097. [CrossRef] 6. Kreutz, D.; Ramos, F.M.V.; Verissimo, P.E.; Rothenberg, C.E.; Azodolmolky, S.; Uhlig, S. Software-deﬁned networking: A comprehensive survey. Proc. IEEE 2015, 103, 14–76. [CrossRef] 7. Open Networking Foundation. Software-Deﬁned Networking: The New Norm for Networks; ONF White Paper; Open Networking Foundation: Menlo Park, CA, USA, 2012. 8. McKeown, N. Software-deﬁned networking. INFOCOM Keynote Speech 2009, 17, 30–32. 9. Kim, H.; Feamster, N. Improving network management with software defined networking. IEEE Commun. Mag. 2013, 51, 114–119. [CrossRef] 10. Luo, T.; Tan, H.P.; Quek, T.Q.S. Sensor open ﬂow: Enabling software-deﬁned wireless sensor networks. IEEE Commun. Lett. 2012, 16, 1896–1899. [CrossRef] 11. Jacobsson, M.; Orfanidis, C. Using software-deﬁned networking principles for wireless sensor networks. In Proceedings of the 11th Swedish National Computer Networking Workshop (SNCNW), Karlstad, Sweden, 2829 May 2015. 12. Figueiredo, C.M.S.; Santos, A.L.d.; Loureiro, A.A.F.; Nogueira, J.M. Policy-based adaptive routing in autonomous WSNS. In Proceedings of the 16th IFIP/IEEE Ambient Networks Internatioanl Conference Distributed Systems: Operations and Management, Barcelona, Spain, 2426 October 2005; pp. 206–219. Appl. Sci. 2018, 8, 1013 27 of 28 13. De Gante, A.; Aslan, M.; Matrawy, A. Smart wireless sensor network management based on software-defined networking. In Proceedings of the 2014 the 27th Biennial Symposium Communications, Kingston, ON, Canada, 13 June 2014; pp. 71–75. 14. Galluccio, L.; Milardo, S.; Morabito, G.; Palazzo, S. Reprogramming wireless sensor networks by using SDN-wise: A hands-on demo. In Proceedings of the 2015 IEEE Conference Computer Communications Workshops, Hong Kong, China, 26 April1 May 2015; pp. 19–20. 15. Olivier, F.; Carlos, G.; Florent, N. SDN based architecture for clustered WSN. In Proceedings of the 2015 the 9th International Conference Innovative Mobile and Internet Services in Ubiquitous Computing, Blumenau, Brazil, 810 July 2015; pp. 342–347. 16. Modieginyane, K.M.; Letswamotse, B.B.; Malekian, R.; Abu-Mahfouz, A.M. Software deﬁned wireless sensor networks application opportunities for efﬁcient network management: A survey. Comput. Electr. Eng. 2018, 66, 274–287. [CrossRef] 17. Arumugam, G.S.; Ponnuchamy, T. EE-LEACH: Development of energy-efﬁcient leach protocol for data gathering in WSN. Eur. J. Wirel. Commun. Netw. 2015, 2015, 76. [CrossRef] 18. Wang, Y.W.; Chen, H.N.; Wu, X.L.; Shu, L. An energy-efﬁcient SDN based sleep scheduling algorithm for wsns. J. Netw. Comput. Appl. 2016, 59, 39–45. [CrossRef] 19. Jayashree, P.; Princy, F.I. Leveraging SDN to conserve energy in WSN-AN analysis. In Proceedings of the 2015 the 3rd International Conference Signal Processing, Communication and Networking (ICSCN), Chennai, India, 26–28 March 2015; pp. 1–6. 20. Ejaz, W.; Naeem, M.; Basharat, M.; Anpalagan, A.; Kandeepan, S. Efﬁcient wireless power transfer in software-deﬁned wireless sensor networks. IEEE Sens. J. 2016, 16, 7409–7420. [CrossRef] 21. Xiang, W.; Wang, N.; Zhou, Y. An energy-efﬁcient routing algorithm for software-deﬁned wireless sensor networks. IEEE Sens. J. 2016, 16, 7393–7400. [CrossRef] 22. Levendovszky, J.; Tornai, K.; Treplan, G.; Olah, A. Novel load balancing algorithms ensuring uniform packet loss probabilities for WSN. In Proceedings of the 2011 IEEE the 73rd Vehicular Technology Conference (VTC Spring), Yokohama, Japan, 1518 May 2011; pp. 1–5. 23. Sachenko, A.; Hu, Z.B.; Yatskiv, V. Increasing the data transmission robustness in WSN using the modiﬁed error correction codes on residue number system. Elektron. Elektrotech. 2015, 21, 76–81. [CrossRef] 24. Jin, X.; Kong, F.X.; Kong, L.H.; Wang, H.H.; Xia, C.Q.; Zeng, P.; Deng, Q.X. A hierarchical data transmission framework for industrial wireless sensor and actuator networks. IEEE Trans. Ind. Inform. 2017, 13, 2019–2029. [CrossRef] 25. Islam, M.M.; Hassan, M.M.; Lee, G.W.; Huh, E.N. A survey on virtualization of wireless sensor networks. Sensors 2012, 12, 2175–2207. [CrossRef] [PubMed] 26. Sherwood, R.; Gibb, G.; Yap, K.K.; Appenzeller, G.; Casado, M.; McKeown, N.; Parulkar, G. Flowvisor: A Network Virtualization Layer; OPENFLOW-TR-2009-1; Deutsche Telekom Incorporation R&D Lab, Stanford University: Stanford, CA, USA, 2009. 27. Chen, B.J.; Jamieson, K.; Balakrishnan, H.; Morris, R. Span: An energy-efﬁcient coordination algorithm for topology maintenance in ad hoc wireless networks. Wirel. Netw. 2002, 8, 481–494. [CrossRef] 28. Kumar, S.; Lai, T.H.; Balogh, J. On k-coverage in a mostly sleeping sensor network. In Proceedings of the 10th Annual International Conference Mobile Computing and Networking, Philadelphia, PA, USA, 26 September1 October 2004; pp. 144–158. 29. Wang, L.; Yuan, Z.X.; Shu, L.; Shi, L.; Qin, Z.Q. An energy-efﬁcient CKN algorithm for duty-cycled wireless sensor networks. Int. J. Dis. Sens. Netw. 2012. [CrossRef] 30. Floodlight [Online]. Available online: http://www.projectﬂoodlight.org/ﬂoodlight/ (accessed on 16 November 2017). 31. Mininet [Online]. Available online: http://mininet.org/ (accessed on 16 November 2017). 32. Yuan, Z.X.; Wang, L.; Shu, L.; Hara, T.; Qin, Z.Q. A balanced energy consumption sleep scheduling algorithm in wireless sensor networks. In Proceedings of the 2011 the 7th International Wireless Communications and Mobile Computing Conference (IWCMC), Istanbul, Turkey, 48 July 2011; pp. 831–835. 33. Fu, X.W.; Li, W.F.; Fortino, G.; Pace, P.; Aloi, G.; Russo, W. Autility oriented routing scheme for interest-driven community-based opportunistic networks. J. Univ. Comput. Sci. 2014, 20, 1829–1854. 34. Duan, Y.; Li, W.F.; Fu, X.W.; Luo, Y.; Yang, L. A methodology for reliability of WSN based on SDN in adaptive industrial environment. IEEE/CAA J. Autom. Sin. 2018, 5, 74–82. [CrossRef] Appl. Sci. 2018, 8, 1013 28 of 28 35. Banerjee, A. Sensor Networks Summarized; Lambert Academic Publishing: Sarbrogen, Germany, 2016; ISBN 978-3-659-94609-7. 36. Barabde, K.; Gite, S. Big Energy Efﬁcient and Optimal Path Selection in LEACH Algorithm. Int. J. Appl. Eng. Res. 2015, 10, 44. 37. Gill, R.K.; Chawla, P.; Sachdeva, M. Study of LEACH routing protocol for Wireless Sensor Networks. In Proceedings of the International Symposium on Contamination Control (ICCCS 2014), Seoul, Korea, 13–17 October 2014. 38. Wireless Sensor Networks. Available online: http://sensors-and-networks.blogspot.it/2011/10/spin- sensor-protocol-for-information.html (accessed on 14 March 2018). 39. Available online: http://www.ncbi.nlm.nih.gov (accessed on 15 March 2018). 40. Pattani, K.M.; Chauhan, P.J. SPIN Protocol for Wireless Sensor Networks. Int. J. Adv. Res. Eng. Sci. Technol. 2015, 2, 2394–2444. 41. Leccese, F. Remote-control system of high efﬁciency and intelligent street lighting using a zig bee network of devices and sensors. IEEE Trans. Power Deliv. 2013, 28, 21–28. [CrossRef] 42. Leccese, F.; Cagnetti, M.; Calogero, A.; Trinca, D.; di Pasquale, S.; Giarnetti, S.; Cozzella, L. A new acquisition and imaging system for environmental measurements: An experience on the Italian cultural heritage. Sensors (Switzerland) 2014, 14, 9290–9312. [CrossRef] [PubMed] 43. Leccese, F.; Cagnetti, M.; Tuti, S.; Gabriele, P.; de Francesco, E.; Ðurovic-Pej ´ cev ˇ , R.; Pecora, A. Modiﬁed LEACH for Necropolis Scenario. In Proceedings of the IMEKO International Conference on Metrology for Archaeology and Cultural Heritage, Lecce, Italy, 23–25 October 2017. 44. Available online: https://arxiv.org/ftp/arxiv/papers/1501/1501.07135.pdf (accessed on 14 March 2018). 45. Murillo, A.F.; Peña, M.; Martínez, D. Applications of WSN in Health and Agriculture. In Proceedings of the 2012 IEEE Colombian Communications Conference (COLCOM), Cali, Colombia, 16–18 May 2012; Available online: https://ieeexplore.ieee.org/document/6233678/ (accessed on 14 March 2018). 46. Pitchai, K.M. An Energy Efﬁcient Routing Protocol for extending Lifetime of Wireless Sensor Networks by Transmission Radius Adjustment. Acta Graph. 2016, 7, 33–38. Available online: https://hrcak.srce.hr/ﬁle/ 265118 (accessed on 14 March 2018). © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Applied Sciences Multidisciplinary Digital Publishing Institute

http://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/sd-ear-energy-aware-routing-in-software-defined-wireless-sensor-5ndbTrY1zS

SD-EAR: Energy Aware Routing in Software Defined Wireless Sensor Networks

Loading next page...

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher: Multidisciplinary Digital Publishing Institute
ISSN: 2076-3417
DOI: 10.3390/app8071013
Publisher site: See Article on Publisher Site

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

SD-EAR: Energy Aware Routing in Software Defined Wireless Sensor Networks

SD-EAR: Energy Aware Routing in Software Defined Wireless Sensor Networks

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

SD-EAR: Energy Aware Routing in Software Defined Wireless Sensor Networks

SD-EAR: Energy Aware Routing in Software Defined Wireless Sensor Networks

References

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies