A Cross-Layer Routing Protocol for Balancing Energy ... - Springer Link

Wireless Pers Commun (2015) 81:1303–1320 DOI 10.1007/s11277-014-2185-1

A Cross-Layer Routing Protocol for Balancing Energy Consumption in Wireless Sensor Networks Samira Yessad · Louiza Bouallouche-Medjkoune · Djamil Aïssani

Published online: 14 November 2014 © Springer Science+Business Media New York 2014

Abstract Energy efficiency and load balancing are known to be critical design concerns in routing protocols in wireless sensor networks. We can achieve the first concern by finding the minimum energy path, while the latter, can be achieved by using multiple sub-optimal paths. In this paper, and for this purpose, we propose a routing protocol that exploits interaction between the MAC layer and the network layer. Our proposal is a simple cross-layer routing protocol that enhances the wireless sensor network lifetime by balancing the energy consumption in the forwarding task. To do so, the MAC layer informs the network layer about all the overheard communications of the neighboring nodes. According to this information, and in order to balance the energy consumption of the forwarding nodes, a node chooses its next hop among the less-used ones. Hence, the choice of the next hop is, contrary to existing multi-path routing protocols, not probabilistic and leads to better energy consumption balancing. We have used a mathematical model and simulations to evaluate the performance of our proposal. The final results have shown that our cross-layer routing protocol uses all forwarding nodes in an equitable manner compared to other routing protocols. This enables to avoid the network partitioning and to enhance the network lifetime. Keywords Wireless sensor network · Cross-layer protocol · Energy efficiency · Energy consumption balancing · Network lifetime

1 Introduction A wireless sensor network (WSN) is a type of ad hoc networks for which energy, memory and computation are crucial constraints. To minimize energy consumption in WSNs, several energy efficient MAC protocols have been proposed. We can cite among them

S. Yessad (B) · L. Bouallouche-Medjkoune · D. Aïssani Laboratory of Modelling and Optimization of Systems (LAMOS), Faculty of Exact Sciences, University of Bejaia, 06000 Bejaïa, Algeria e-mail: [email protected]

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SMAC, TMAC and R-MAC [1–3]. These protocols aim to reduce the wasted energy due to the idle listening by turning off the radio of sensor nodes. Other works have already addressed the problem at the network layer and have proposed energy efficient routing protocols. Initially, routing protocols have focused on consuming low power by finding the minimum energy path, or by finding the path with nodes having the maximum residual energy or the combination of the two. The weakness in these protocols, is that the optimal path is used for all node’s communications. Thus, the energy of nodes along those paths is quickly consumed, and, a network disconnection can occur. In order to handle with this problem, recent works have been mainly focused on maximizing the network lifetime by delaying the most later possible the occurrence of this disconnection. They balance the traffic throughout several sub-optimal paths, so that the nodes consume the energy more equitably. In order to ensure the energy consumption balancing, multi-path routing protocols have been proposed (see for example [4–8]). We have noticed, from literature survey, that this multi-path approach is not really sufficient to balance the energy consumption. In fact, a node balances the use of the multiple paths by considering only its data packets without those of other neighboring nodes. It means that a node has no knowledge about the real amount of the transmitted data by its forwarding nodes. In order to overcome this limitation, we have proposed in [6,7], two routing protocols that add two control packets. In this paper, we propose a third routing protocol that exploits the interaction between the MAC layer and the network layer giving rise to a cross-layer approach. The basic principle of cross-layer design is to make information available to all levels of the protocol stack. It allows the definition of protocols or mechanisms that do not meet the isolation layers of the OSI model [9,10]. This can be implemented using different methods. Creation of new interfaces [(bottom up approach, top-down approach, the mixed approach (Integrated)], fusion of adjacent layers, vertical calibration across all layers and completely new abstractions [9,11]. The purpose of our new cross-layer routing protocol, named CLB-routing for Cross-Layer Balancing routing, is to enhance the WSNs lifetime by balancing the energy consumption in the forwarding task. CLB-routing protocol is a bottom up approach where the network layer uses information given by the MAC layer for the choice of the next hop. It has two phases, in the initialization phase, the sink broadcasts a route request message to find sub-optimal routes from each source node to the sink. Then, in the data transmission phase, MAC layer informs the network layer about all the overheard communications of the neighboring nodes. With this information, a node can know how many times each forwarding node has routed data. According to this, and to balance the energy consumption of the forwarding nodes, a node chooses its next hop among the less-used ones. This choice is not random according to the probabilities as in FEAR [6] and BEER [7]. Thus, the energy consumption is effectively balanced. The rest of this paper is organized as follows. In the next section, the main works related to enhance the WSN lifetime at the network layer and using the cross-layer design are outlined. In Sect. 3, we describe our solution for improving the WSN lifetime. In Sect. 4, we give our network model in the Sect. 4.1, then we illustrate the functioning of CLBRouting protocol through an example in the Sect. 4.2, and at the end, we give a mathematical model and numerical results for CLB-routing and three routing protocols respectively in Sects. 4.3 and 4.4. In Sect. 5, we analyze simulation results. The paper concludes in Sect. 6.

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2 Related Work There have been a lot of MAC and routing protocols already proposed to reduce energy consumption and enhance wireless sensor networks lifetime. In flat networks, most of the proposed MAC protocols, as those presented in [2,3], are variants of IEEE 802.11 [12] and SMAC [1]. The proposed routing protocols, as those of references [13,14], are variants of Directed Diffusion proposed in [15]. These routing protocols try to find an optimal path between each source node and the sink and use it for all their communications. This leads to energy imbalance since the traffic is not uniformly distributed in the network. Hence, some recent works have focused on balancing energy consumption between sensor nodes. In [16], the authors improve Energy Balanced Routing Protocol (EBRP) proposed in [17], and propose Delay Aware Energy Balanced Dynamic Routing Protocol (DA-EBDRP) that achieves better performance in terms of end-to-end delay, throughput, portion of living node and network lifetime. In [18], authors proposed BEAR in order to improve SEER [19] in terms of energy balancing and network lifetime; they use learning automata concept to ensure a fair tradeoff between energy balancing and optimal distance considered in SEER. Other works use multi-path routing in order to overcome the imbalance energy problem. In [8], authors use the mechanism of the meta-heuristic Tabu search to select the next hop for routing data based on a cost function that considers the energy and the visibility of the sensor compared to the sink. In [4], authors derive the set of paths to be used by each sensor node and the associated weights that maximize WSNs lifetime by developing a mathematical model for load-balanced systems. EAR [5] try to distribute the traffic uniformly in the network by assigning a high probability to paths having lowest cost, so that they will be used more frequently than other sub-optimal paths. We have noticed in EAR, that the distribution of the traffic is not really uniform. To get more equity in the use of the sub-optimal paths, we have already improved EAR by proposing two novel routing protocols in [6,7]. The limitation of these protocols is the overhead, since we have used two new control packets to have information about the number of nodes that use a given forwarding node. In this view, we propose, in this paper, a new routing protocol that uses the existing control packets such as CTS packets of MAC protocols. We have noticed that the network layer requires to know the exact number of data packets sent by each forwarding node to balance the distribution of its traffic in its neighborhood. This information can be known only at the MAC layer when receiving CTS messages. To exploit it at the network layer, we should define a new communication interface between the MAC and the network layers. This approach is the cross-layer design which is very used recently to enhance the WSNs lifetime. Recent works in WSNs have revealed important interactions between different layers of the protocol stack. This has led to several propositions for the cross-layer design [20]. In [21,22], the exploitation of the cross-layer interactions are classified based on layers they aim at replacing in the classical OSI network stack. Therefore, there are protocol designs based on interactions between MAC and physical layers as in [23,24], between network and physical layers as in [25,26], between transport and physical layers [27], between physical, MAC and network layers as in [28,29], and even between all layers as in [30–33], etc. In this review, we focus on the interactions between MAC and network layers. We present, in what follows, the proposed protocols in literature that exploits these interactions to minimize energy consumption and enhance the sensor networks lifetime, as it is the aim of our proposal. These protocols can be classified into two classes, Receiver-based forwarding [20] and duty cycle operation based. Generally, the former uses the fusion approach while the latter uses top-down interactions.

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GERAF (Geografic Random Forwarding) [34] assumed that each node knows its position and that of the sink. Each transmitter sends an RTS message whenever it has a packet to be transmitted. The message reaches all the neighbors and triggers contention. The nodes in the most distant region from the transmitter, first respond with a CTS message. In [35], authors propose an approach to reduce the overhead. The routing decision is made by the receiver, not by the sender. Whenever the transmitter broadcasts an RTS to its neighbors containing its location and the destination, each neighbor calculates its optimality and maps it into a delay, the optimal neighbor responds first with CTS. The less optimal nodes turn off their radio. In MACRO (MAC/Routing Protocol) [36], all sensor nodes follow a duty cycle. The choice of a relay is based on a weighted progress factor, ie, the distance between the source node and the next hop, divided by the power required to reach the next hop. These protocols don’t account for energy consumption balancing and require the knowledge of the nodes’ geographical positions. However, our proposal aims to balance the traffic distribution at maximum and doesn’t require any knowledge of nodes’ geographical positions. The authors in [29] have proposed RBF (RSSI-Based Forwarding). Nodes in RBF do not require knowledge of nodes’ geographical positions or maintaining the routing table. The next hop is determined together with the process of contention. For a signal Beacon transmitted by the sink, each node in the network calculates the Corresponding Received Signal Strength Indicator (RSSI). This RSSI is recorded and is used as a decision parameter for the routing. Unlike our proposed protocol, this protocol doesn’t balance energy consumption. ALBA (Adaptive Load-Balanced Algorithm) [37] is designed to consider load balancing and congestion. All eligible nodes of a source node calculate two indices, namely the GPI (Geographic Priority Index), i.e. area of the priority wherein the node is located, and the QPI (Priority Queue Index), which is the measure of the queue length. The load balancing viewed in this work doesn’t take into account the energy consumption. Its aim is congestion avoidance and not energy consumption balancing as it is the case in our protocol. A strategy with energy-balancing guarantees is proposed in [38]. Authors aim to investigate the benefits of a joint metric which considers hop count, link quality, and remaining battery power metric. They have proposed an algorithm to build a routing tree by means of an heuristic metric. The aim of our proposal is to balance energy consumption by using an accurate information about the amount of routed data by each node. Thus, our approach is not probabilistic and doesn’t use any heuristic method. Most of the presented protocols above are based on fusion approach. They define in the same layer the MAC and the routing functions. However, our protocol defines just a communication interface between the MAC layer and the network layer. This is intended to facilitate improvements in both MAC and network layers separately and considering easily other design concerns as delay, quality of service and congestion avoidance. In MAC-CROSS [39] and CL-MAC [40] protocols, only a few nodes concerned by the actual data transmission are asked to wake-up. The neighboring nodes belonging to the path extend their wake-up time while other nodes prolong their sleep time. The destination address is given by a routing table in the network layer. CLEEP [41] adopts cross-layer strategy that considers physical layer, MAC layer, and network layer jointly. In the physical layer, the transmission power is coordinated between two nodes and the nodes’ neighbor tables are maintained periodically to save the transmission energy. Then, the optimal routing path is constructed. MAC layer make use of the routing information to determine the node’s duty-cycle, in order to prolong the node’s sleep time. ECLP [42] is an integrated MAC and routing protocol for energy efficient data delivery to the sink node with adaptive duty cycling and tree-based energy aware routing algorithm while minimizing overhead cost and latency.

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Our protocol aims to balance energy consumption and to enhance the network lifetime, whereas, the protocols presented above ([39–42]) aim to prolong nodes sleep time.

3 CLB-Routing Protocol Details The aim of our solution is to enhance the WSN lifetime by balancing energy consumption during the forwarding task. To achieve this goal, sensor nodes must be used fairly. For example, if two nodes have the same cost for routing data of a source node, they must be used the same number of times as relay. To achieve this goal, we have already proposed in [6,7] two routing protocols FEAR (Fair Energy Aware Routing) and BEER (Balanced Energy Efficient Routing) which are the improvements of EAR (Energy Aware Routing) [5]. By proposing FEAR, we have improved the network lifetime by reducing the probability use of the highly demanded sensor nodes in the network (Nodes belonging to several routes). And more, BEER reduces the probability use of nodes belonging to a unique route of a source node. Even though, FEAR and BEER provide more equity between sensor nodes comparing to EAR, they suffer from the overhead. On the other hand, in the three protocols, the sub-optimal paths are chosen randomly according to the probabilities. So, the load balancing is probabilistic. In order to achieve an accurate energy consumption balancing without any overhead, we propose, in this paper, the solution (CLB-Routing) at the network layer that exploits information given by the MAC layer. CLB-Routing operates in two phases. In the first phase, after the deployment of the network, sensor nodes establish their forwarding tables as in EAR [5]. The sink broadcasts a route request message with a field cost initialized to “0”. Each node, receiving the route request message, updates the cost field according to its residual energy and the power required for the communication between that node and the sender of the route request and, then broadcasts it. If a given node i receives a route request from a node j with the cost field equal to cost j , it calculates costi j as follows: costi j = cost j + Ci j

(1)

where β

Ci j = eiαj Ri

(2)

where ei j is the power required for the communication between the nodes i and j, and Ri is the residual energy of the node i normalized to its initial energy. The weighting factors α and β can be chosen to find the minimum energy path or the path with nodes having the maximum residual energy or the combination of the above. After the reception of the route request message from all neighbors, a node can establish its forwarding table by adding neighbors with minimal cost. Then, it calculates the average cost that it puts in the cost field for forwarding the route request message with the following formula: k∈F Ti Costik costi = (3) |F Ti | where, |F Ti | is the number of routes recorded in the forwarding table of node i. This phase ends when the route request message is broadcasted in the whole network and all nodes have set their forwarding tables with routes to the sink. In the second phase, sensor nodes sense phenomena in the interest field according to their application and send data to the sink. For the first time, nodes send data over the neighbor in

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their forwarding table having the minimal cost. Progressively that there are communications in the network, the network layer of each node will have information about the use of its neighbors for routing data. We suppose that the nodes use a CSMA/CA based MAC protocol (SMAC [1], TMAC [2] or R-MAC [3]) with RTS/CTS sequence. When a sensor node wants to send data over a neighbor node, it sends an RTS message and then receives a CTS message from the forwarding node. The latter will be received by all its neighbors. In our protocol, if the node receiving a CTS message is the destination, it sends the data packet to the sender of the CTS message. Otherwise, instead of dropping the CTS as it is the case in layered protocol, it sends the source address of the CTS message to the network layer. Receiving this information, the network layer increments the variable N associated to the sender of the CTS message. The variable N is a field in the forwarding table which counts the number of times that each neighbor node has routed data. Whenever, a given node j has data to send, it calculates the value of B associated for each forwarding node i. Then, it sends its data over the neighbor having the greater value of B. Bi =

1/Cost ji × Ni k∈F T j 1/Cost jk × Nk

(4)

4 Performance Evaluation of CLB-Routing In this section, we propose a mathematical model to evaluate and compare the performance of our solution with those of three routing protocols (EAR, FEAR and BEER). But first, we give our particular network model and an illustrative example. 4.1 Network Model Our wireless sensor network model has the following properties: – The sensor network is composed of M sensor nodes scattered in a field of interest in flat manner, that means, all sensor nodes play the same role in the network. – There are k source nodes that send the sensed data in the environment to the sink. The network can be divided into levels of k nodes (the first level L 1 is the one composed of source nodes), and we assume that the ith node of the jth level (L j ) noted N ji has i forwarding nodes in the next level L j+1 (see Fig. 1). This assumption will allow us to have different values for the variables N and T of FEAR and BEER and, have the maximum possible cases for our analysis. – There is one sink to gather the sensed data by sensor nodes. – The nodes and the sink are not mobile. – The sensor node is not rechargeable. – There is no method to get location information of sensor nodes. – The network application can be either query driven, event driven, time driven or the hybridization of the three. 4.2 Illustrative Example In this section, we give an example to illustrate the functioning of CLB-Routing in the network model given above with k = 4. We illustrate in Fig. 2 a scenario of six communications between levels 2 and 3. To further explain more this example, we present in Table 2

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Fig. 1 Network model CTS

N21

CTS

N22

CTS

RTS

N23

DATA1

RTS

N31

DATA1

CTS

RTS

N32

CTS CTS

DATA2

CTS

RTS CTS

DATA3 RTS

CTS

DATA4 RTS

CTS

CTS

RTS RTS

RTS

N33

CTS CTS

CTS

RTS

CTS

N24

CTS

RTS

RTS

CTS

DATA4

RTS

CTS

DATA5

RTS

RTS

N34

T1

T2

T3

T4

RTS

T5

T6

T7

T8

T9

T10

T11

T12

T13

DATA6

RTS

RTS

RTS

CTS

RTS

RTS

RTS RTS

DATA2

DATA5

CTS

CTS DATA3

CTS

T14

T15

T16

CTS

T17

DATA6

T18

Fig. 2 Illustrative example Table 1 Routing tables of N21 , N22 , N23 and N24 N21

N22

N23

N24

Next hop

Cost

N

Next hop

Cost

N

Next hop

cost

N

Next hop

cost

N

N31

C

1

N31

C

1

N31

C

1

N31

C

1

N32

C

1

N32

C

1

N32

C

1

N33

C

1

N33

C

1

N34

C

1

the communications details. We assume that, initially, we have the routing tables of nodes N21 ,N22 , N23 and N24 as given respectively in Table 1. The value of N is initialized to 1 and the cost of all nodes is assumed to be the same. If the value of B of many neighbors in the routing table is the same as it is the case for the first communication, the node chooses the first neighbor in the routing table.

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Table 2 Illustrative example T1

For N23 , B N31 =B N32 =B N33 = 1/3. So, it sends RTS to N31 and its neighbors N31 ,N32 and N33 receive this RTS

T2

N31 responses by CTS and its neighbors N21 , N22 , N23 and N24 receive this CTS, these lasters send the address N31 to the network layer which sets the value of N N31 to 2

T4

For N24 , B N32 = B N33 = B N34 = 2/7 and B N31 = 1/7. So, N24 sends RTS to N32 and its neighbors N31 ,N32 , N33 and N34 receive this RTS

T5

N32 responses by CTS and its neighbors N22 , N23 and N24 receive this CTS, these lasters send the address N32 to the network layer which set the value of N N32 to 2

T7 T8

For N21 , B N31 = 1. So, N21 sends RTS to N31 N31 responses by CTS and its neighbors N21 , N22 , N23 and N24 receive this CTS, these lasters send the address N31 to the network layer which set the value of N N31 to 3

T10

For N22 , B N31 = 2/5 and B N32 = 3/5. So, it sends RTS to N32 and its neighbors N31 and N32 receive this RTS

T11

N32 responses by CTS and its neighbors N22 , N23 and N24 receive this CTS, these lasters send the address N32 to the network layer which set the value of N N32 to 3

T13

For N23 , B N31 = B N32 = 1/5 and B N33 = 3/5. So, it sends RTS to N33 and its neighbors N31 ,N32 and N33 receive this RTS

T14

N33 responses by CTS and its neighbors N23 and N24 receive this CTS, these lasters send the address N33 to the network layer which set the value of N N33 to 2 For N24 , B N31 = B N32 = 2/13, B N33 = 3/13 and B N34 = 6/13. So, it sends RTS to N34 and its neighbors N31 ,N32 , N33 and N33 receive this RTS

T16 T17

N34 responses by CTS and its neighbors N24 receive this CTS, these lasters send the address N33 to the network layer which set the value of N N34 to 2

After the six communications illustrated in the example, we can easily note that our solution leads to a fairly use of sensor nodes. For the coming communications, nodes will route their data through the less-used neighbors and the difference in their use will stay constant to the end of the network functioning. There will be no large disparity in the energy levels of nodes. 4.3 Mathematical Model Let’s calculate the energy consumed E by a given node N ji in our network model to route S packets of data to the sink. In EAR, FEAR and BEER, node N ji can route data sent by nodes N( j−1)(i) , N( j−1)(i+1) , N( j−1)(i+2) , …, and N( j−1)(k) with, respectively, the following probabilities: PN( j−1)(i) N ji , PN( j−1)(i+1) N ji , PN( j−1)(i+2) N ji ,…, and PN( j−1)(k) N ji . Then, we can calculate the energy consumed by the node as follows: E = PN( j−1)(i) N ji ∗ (Er + E t ) + PN( j−1)(i+1) N ji ∗ (Er + E t ) + · · · + PN( j−1)(k) N ji ∗ (Er + E t )

(5)

where Er and E t are, respectively, the energy required for the reception and the transmission of data by the node N ji .

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4.3.1 The Model of EAR In EAR protocol, we calculate E as follows: ⎡ E = (Er + E t ) ∗ ⎣

+

1 Cost N( j−1)(i) N ji

1 m|N jm ∈F TN( j−1)(i) Cost N ( j−1)(i) N jm

1 Cost N( j−1)(i+1) N ji 1 m|N jm ∈F TN( j−1)(i+1) Cost N ( j−1)(i+1) N jm

+ ··· +

1 Cost N( j−1)(k) N ji 1

⎤ ⎦

(6)

m|N jm ∈F TN( j−1)(k) Cost N ( j−1)(k) N jm

To simplify the calculation of E, we assume that the cost of all nodes in the network is the same and it is equal to C. So E can be written as: 1 1 1 C C C E = (Er + E t ) ∗ + + · · · + (7) i ∗ C1 (i + 1) ∗ C1 (k) ∗ C1 1 1 1 + + ··· + (8) = (Er + E t ) ∗ i i +1 k = (Er + E t ) ∗

k 1 m

(9)

m=i

4.3.2 The Model of FEAR In FEAR protocol, we calculate E as follows: ⎡ E = (Er + E t ) ∗ ⎣

+

1 Cost N( j−1)(i) N ji ∗(N N ji )

1 m|N jm ∈F TN( j−1)(i) Cost N ∗(N N jm ) ( j−1)(i) N jm

1 Cost N( j−1)(i+1) N ji ∗(N N ji ) 1 m|N jm ∈F TN( j−1)(i+1) Cost N ∗(N N jm ) ( j−1)(i+1) N jm

+ ··· +

1 Cost N( j−1)(k) N ji ∗(N N ji ) 1 m|N jm ∈F TN( j−1)(k) Cost N ∗(N N jm ) ( j−1)(k) N jm

⎤ ⎦

(10)

Assuming, as above, that the cost of all nodes in the network is the same and it is equal to C. We find E as:

1 C∗(k−i+1) 1 1 1 + C∗(k) C∗(k−1) + · · · + C∗(k−i+1) 1 1 C∗(k−i+1) C∗(k−i+1) + · · · + 1 1 1 1 1 C∗(k−1) + · · · + C∗(k−i) C∗k + C∗(k−1) + · · · + C∗(1)

E = (Er + E t ) ∗ +

1 C∗k

+

(11)

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= (Er + E t ) ∗ +··· +

1 k

+

1 k−i+1 1 k

+

1 k−1

+ ··· +

1 k−i+1

+

1 k

+

1 k−i+1 1 1 k−1 + · · · + k−i

1 k−i+1 1 k−1 + · · · + 1

1 = (Er + E t ) ∗ k −i +1 1 + · · · + k 1

(12)

1

k

1 m=k−i+1 m

+ k

1

1 m=k−i m

(13)

m=1 m

4.3.3 The Model of BEER In BEER protocol, we calculate E as follows: ⎡ E = (Er + E t ) ∗ ⎣

1 Cost N( j−1)(i) N ji ∗(TN ji )

1 m|N jm ∈F TN( j−1)(i) Cost N ∗(TN jm ) ( j−1)(i) N jm

+

1 Cost N( j−1)(i+1) N ji ∗(TN ji ) 1 m|N jm ∈F TN( j−1)(i+1) Cost N ∗(TN jm ) ( j−1)(i+1) N jm

+ ··· +

1 Cost N( j−1)(k) N ji ∗(TN ji )

⎤ ⎦

1

(14)

m|N jm ∈F TN( j−1)(k) Cost N ∗(TN jm ) ( j−1)(k) N jm

Similarly, we assume that the cost of all nodes in the network is the same and it is equal to C and we find:

1 C∗(k−i+1)∗i 1 1 C∗(k−1)∗(i) + · · · + C∗(k−i+1)∗(i) 1 C∗(k−i+1)∗(i+1) + 1 1 1 C∗k∗(i+1) + C∗(k−1)∗(i+1) + · · · + C∗(k−i)∗(i+1) 1 C∗(k−i+1)∗k +··· + 1 1 1 C∗k∗k + C∗(k−1)∗k + · · · + C∗(1)

1 1 k−i+1 k−i+1 + (Er + E t ) ∗ 1 1 1 1 1 1 k + k−1 + · · · + k−i+1 k + k−1 + · · · + k−i 1 k−i+1 +··· + 1 1 k + k−1 + · · · + k

E = (Er + E t ) ∗

=

1 C∗(k)∗(i)

+

⎧ ⎪ 1 ⎪ (Er + E t ) ∗ k−i+1 k 1 1 + k 1 ⎪ ⎪ ⎪ m=k−i+1 m m=k−i ⎪ ⎨ 1 k , for i < k E = ( m=2 m1 )+k ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ , for i = k ⎩(Er + E t ) ∗ k 1 1 (

123

m=2 m∗k )+1

1 m

(15)

(16)

+ ···+ (17)


1313

4.3.4 The Model of CLB-Routing In our cross-layer solution, node N ji can route data sent by nodes N( j−1)(i) , N( j−1)(i+1) , N( j−1)(i+2) , …, and N( j−1)(k) . We assume, as in the previous calculations, that the cost of all nodes in the network is the same and it is equal to C. For the first packet, the performance of CLB-Routing are influenced by the position of the node which first sends data. We suppose that nodes will send data in the order of their identifications. This means that if the node N ji starts communication, the second will be N j ((i+1)modk) , then the third will be N j ((i+2)modk) , and so forth, until the node N j ((i+k−1)modk) sends its data at last. So, if N( j−1)m starts the communication, node N ji can be solicited by the nodes: – – – –

N( j−1)(i) and N( j−1)(m+i−1) , if i < m and m < k − i + 2 N( j−1)(m+i−1) , if i >= m and m < k − i + 2 N( j−1)(i) , if i < m and m >= k − i + 2 None, if i >= m and m >= k − i + 2

In our solution, there are no probabilities, and as we have mentioned above, for all the cases a node can route data for two, one, or none node. So, the energy consumed by a given node N ji in our solution is as follows: ⎧ (Er + E t ) ∗ 2, if i < m and m < k − i + 2 ⎪ ⎪ ⎪ ⎨ (Er + E t ), if (i >= m and m < k − i + 2) or E= ⎪ (i < m and m >= k − i + 2) ⎪ ⎪ ⎩ 0, if i >= m and m >= k − i + 2

(18)

4.4 Numerical Results The standard deviation metric calculates how far node’s energy are spread out from each other. According to the earlier equations, we calculate the standard deviation, in a first time, for sending one packet of data (S = 1) by varying the value of k, k = 1 to k = 10 and for each value of k the i takes values in the interval [1,k]. Results are presented in Fig. 3. For CLBRouting, we give multiple graphs according to the value of m (The identification of the node starting the communication). In the legend, cr ossi designates the graph for CLB-Routing with m = i. In Fig. 3, we present graphs for m = 2, 4, 6 and 8. As mentioned above, CLB-Routing performances depend on the variable m. The best case is when N j1 starts the communication. In this case, nodes are really used, as relay, the same number of times and the standard deviation is equal to ‘0’. In a second time, we calculate the standard deviation for S = 2 to S = 5. Figure 4 shows the variation of standard deviation with the variation of S, where k is fixed to ’10’. We present EAR, FEAR, BEER and CLB-Routing for m = 2 and 6. We notice that in our protocol, the standard deviation is constant while that of EAR [5], FEAR [6] and BEER [7] increase with the increase of the number of packets. In fact, the difference, between the energy level of nodes due to the initialization of communications, stays the same to the end of the functioning of the network. This, because after a certain amount of communications in a given zone, all the nodes of that zone will have information about the amount of data packets forwarded by each node and can easily balance the use of their forwarding nodes. As shown in Fig. 4, CLB-Routing uses sensor nodes in an equitable manner unlike EAR, FEAR and BEER which use some nodes more than others in the routing task.

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Fig. 3 Comparison of standard deviation with increase in sensor’s density for EAR, FEAR and BEER

Fig. 4 Standard deviation over the number of packets (S)

5 Simulation Results We evaluate the CLB-Routing performances by simulation using SENSIM simulator [43] (sensor simulator framework for OMNeT++) developed at the Sensor Networking Laboratory at Louisiana State University. We compare the performances of CLB-Routing protocol with those of EAR [5], FEAR [6] and BEER [7] under the topology given in Fig. 5, in terms of two metrics: network lifetime and standard deviation. As the aim of our protocol is to consume nodes’ energy in a fair manner to enhance the network lifetime, the network lifetime and the

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0 2

1

3

4

6

5

8

7

9 11

10 12

Fig. 5 Network topology Table 3 Seed configurations

Configuration

Seed value

1

152465

2

23605

3

100202

4

4200

5

125

6

98

standard deviation are the interesting metrics to calculate how far node’s energy are spread out from each other and to show that there is not a great difference between the remaining energy of nodes. The topology is made of 13 sensor nodes of which, node 0 is a source node and node 12 is a sink node. In this simulation scenario, we have used the IEEE 802.11 MAC protocol which is implemented in the sensor simulator framework of omnet++. The four routing protocols use the same energy metrics for path selection. This was the metric function given in the previous section with α = 1 and β = 1. Because of our topology, the T parameter of BEER [7] has the same value of the N parameter of FEAR [6] for all sensor nodes. So, the results in both FEAR and BEER are the same. 5.1 Network Lifetime In the present paper, we consider the network lifetime as the time till the first node runs out of energy. To measure the network lifetime, we run simulation six times with six different seeds values as presented in Table 3 with FEAR, CLB-Routing and EAR protocols. We present the results in the graph of Fig. 6. The figure shows that CLB-Routing enhances significantly the network lifetime comparing with EAR and FEAR. This is the impact of the load balancing. We note, also, that for CLB-Routing, the differentiation of seed values have no impact on the network lifetime. This is because EAR and FEAR are probabilistic solutions and their results depend on the seed and probabilities, unlike CLB-Routing, where, we really balance the use of sensor nodes in routing task.

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Fig. 6 Network lifetime over variation of seeds

Fig. 7 Standard deviation over simulation time

5.2 Standard Deviation To measure the Standard deviation, we run the simulation with CLB-Routing, EAR, FEAR and BEER protocols. The obtained results are shown in the graph of Fig. 7. The graph presents the variation of the standard deviation of the remaining energy of sensor nodes over simulation time. The figure shows clearly that the standard deviation is greater in EAR, BEER and FEAR. It means that in CLB-Routing, the energy of the entire nodes in the network is close to the average energy, in contrast to EAR, FEAR and BEER. The augmentation of standard

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deviation in CLB-Routing is due to the high use of nodes 1 and 2 comparing to the others. In fact, Nodes 1 and 2 are close to the source node 0 and route the half amount of its traffic while other nodes route less.

6 Conclusion To reduce the energy consumption in wireless sensor networks and enhance their lifetime, several MAC and routing protocols have been proposed. Most of them respect the layered model as OSI model. However, it is proved that the latter is still not the best method since there are important interactions between the different layers. To exploit these interactions, we have presented, in this paper, a simple cross-layer routing protocol that balances the use of sensor nodes in the routing task to enhance the network lifetime. CLB-routing protocol is a bottom up approach, where the network layer uses information given by the MAC layer in the choice of the next hop to effectively balance the energy consumption of sensor nodes. We have evaluated the performance of our solution through a mathematical model and simulations. The results have shown that CLB-routing protocol achieves better performances than traditional routing protocols. The comparison of our CLB-Routing protocol and other cross-layer routing protocols will be presented in our future work.

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Samira Yessad is currently a Lecturer at the University of Béjaïa, Algeria. She received her Master Degree in 2007 in computer science from the University of Béjaïa, (Algeria) and she is a member of a research group at the laboratory LAMOS of the University of Bejaia for the project: “Performance evaluation and optimization of distributed systems and wireless networks”. She worked in the area of sensor networks and many works to enhance network lifetime at MAC and Network layer were done.

Louiza Bouallouche-Medjkoune is currently an Associate Professor at the University of Béjaïa, Algeria. She was received the engineer degree in computer science from the University of Sétif (Algeria) and the Master Degree in applied mathematics from the University of Bejaia (Algeria). She received her Ph.D. in 2006 in computer science from the University of Sétif (Algeria). She works as a teacher at the department of Computer Science at the University of Bejaia (for Data Structures, Programming and Algorithmic, Performance evaluation, Queuing Theory and Markov chains, Simulation of Systems and Networks, Seminar on Performance Evaluation of Networks and Systems), and as a researcher at the laboratory LAMOS of the University of Bejaia. Her research interests are in: Performance Evaluation of Computer systems and telecommunication networks, Stability of Systems, Markov chains, Queuing Theory, Computer Networks (wired, wireless), Quality of Service of Networks and Systems, Routing and Protocols.

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S. Yessad et al. Djamil Aïssani was born in 1956 in Biarritz (Basque Country, France). He started his career at the University of Constantine in 1978. He received his Ph.D. in 1983 from Kiev State University (Soviet Union). He is at the University of Béjaïa since it’s opened in 1983/1984. Director of Research, Head of the Faculty of Science and Engineering Science (1999–2000), Director of the LAMOS Laboratory (Modelling and Optimisation of Systems), Scientific Head of the Doctoral Computer School (since 2004), he has taught in many universities (Algiers, Annaba, Rouen, Dijon, Montpellier,…). He has published many papers on Markov chains, queuing systems, reliability theory, performance evaluation and their applications in such industrial areas as electrical networks and computer systems. He was the president of the national Mathematical Committee (Algerian Ministry of Higher Education and Scientific Research).

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