Balance energy-efficient and real-time with reliable communication protocol for wireless sensor network

The Journal of China Universities of Posts and Telecommunications February 2013, 20(1): 37–46 www.sciencedirect.com/science/journal/10058885

http://jcupt.xsw.bupt.cn

Balance energy-efficient and real-time with reliable communication protocol for wireless sensor network LIU Zhi-xin1 ( ), DAI Li-li1, MA Kai1, GUAN Xin-ping1,2 1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China 2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract In many wireless sensor network applications, it should be considered that how to trade off the inherent conflict between energy efficient communication and desired quality of service such as real-time and reliability of transportation. In this paper, a novel routing protocols named balance energy-efficient and real-time with reliable communication (BERR) for wireless sensor networks (WSNs) are proposed, which considers the joint performances of real-time, energy efficiency and reliability. In BERR, a node, which is preparing to transmit data packets to sink node, estimates the energy cost, hop count value to sink node and reliability using local information gained from neighbor nodes. BERR considers not only each sender’ energy level but also that of its neighbor nodes, so that the better energy conditions a node has, the more probability it will be to be chosen as the next relay node. To enhance real-time delivery, it will choose the node with smaller hop count value to sink node as the possible relay candidate. To improve reliability, it adopts retransmission mechanism. Simulation results show that BERR has better performances in term of energy consumption, network lifetime, reliability and small transmitting delay. Keywords wireless sensor network, tradeoff, energy efficiency, real-time, reliable communication, retransmission, lifetime of network

1

Introduction

WSNs are a set of communication networks consisting of different independent sensors that cooperatively monitor some physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations [1–2]. Each node in a WSN is typically equipped with one or more sensors, a wireless communications device, a processor, and an energy source, usually a battery [3]. Considering that those nodes are energy constrained with limited battery, energy efficient routing algorithms are very important in multi-hop WSNs, so many protocols and algorithms for enhancing energy efficiency and prolong network lifetime are proposed [4–6]. However, due to various factors like fading, interference, weather, collisions, the transmission power, and so on, wireless communication links are unreliable and often Received date: 01-08-2012 Corresponding author: LIU Zhi-xin, E-mail: [email protected] DOI: 10.1016/S1005-8885(13)60005-9

unpredictable [7–9]. If a poor path is chosen for data delivery, loss rate will be heavy and retransmissions will cause extra energy consumption, and consequently, less network lifetime. Furthermore, a number of applications in WSNs require a quick response from the source node to the sink node. For example, a surveillance system needs to alert authorities of an intruder within a few seconds of detections [10]. Similarly, a fire-fighter may rely on timely temperature updates to remain aware of current fire conditions [11]. Considering the performance metrics mentioned above, it is important to present a protocol to consider energy efficiency, reliable and real-time transporting which are usually referred to quality of service (QoS) requirements [12–13]. So in this paper, we propose a novel routing protocol with joint performance indexes, BERR (balance energy-efficient and real-time with reliable communication for wireless sensor network), which is designed to achieve the aforementioned requirements

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metrics (QoS) in WSNs. We propose power aware routing protocol considering some specific parameters of both sensor node (e.g., itself and its neighbors’ residual energy), and the link (e.g., link error rate and the packet transmission energy for reliable communication across the link). Besides, to increase the lifetime of the network, and enhance the transport velocity, the real-time parameter (e.g., the hop count to the sink node) as well in each link is taken into account. The rest of the paper is organized as follows. Sect. 2 gives some related works. Sect. 3 gives the network model and radio model which our protocols perform on. Sect. 4 describes the proposed routing protocol, BERR, in detail; and routing maintaining mechanism is introduced further based on BERR which is called M-BERR. Sect. 5 describes the results of simulation that evaluates the performance of BERR and M-BEER against other protocols. Finally, the conclusions are drawn in Sect. 6.

2

Related works

Despite the considerable amount of researches on QoS for wireless sensor networks have been proposed in literatures, they just involve some unique QoS requirement [4–5,14–19]. In the proposed protocols in Refs. [4–5, 14–16], energy efficient routing only consider the energy efficiency of routing, and do not take into account how to ensure real-time, reliable packet delivery. In protocol [5], Rajesh and Palit propose an algorithm for minimizing the cost of network by selecting a path whose nodes have the smallest transmission consumption. This may result in fewer lifetimes and more transmission delay. In Ref. [17], named REER, it investigates reliable and energy efficient communication in unreliable situation. It attempts to find a path which has the minimum energy consumption from source node to sink node. In this case, due to neglecting each node’s residual energy, some nodes will soon exhaust, which deteriorates the network lifetime. Some protocols have been proposed for real-time routing in WSNs [18–21]. In Ref. [18], a routing protocol improves performances of real-time routing by using performance indexes of the least cost and finite time delay. In Ref. [19], SPEED provides soft real-time, computes the velocity which has the highest speed among the relay nodes, and set those members’ velocity is larger than a certain desired speed. These routing schemes perform the real-time transmission by the hop number on data route.

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Consequently, reduction of hop number from source node to the destination is an effective approach to obtain the real time data transmission. However, requirement on reliable transmission is usually ignored when real-time data transmission is considered to be the main factor in routing design. In Ref. [22], multi constrained multi path (MCMP) algorithm is proposed with consideration of reliability and delay, which delivers packets to the sink node utilizing the multiple paths. The purpose of the protocol is to utilize the multiple paths to improve network performance with moderate energy cost. However, to satisfy the required QoS, MCMP always routes the packets over the path which has the minimum hops, and this operation can cause more energy consumption. In Ref. [23], it investigates multi-path transmission from source to destination and considers an end-to-end recovery mechanism which includes the end-to-end reliability and energy consumption. The protocol trades off between the reliability and the energy cost, but the results will cause high end-to-end delay. In our proposal, we make a comprehensive consideration on energy efficiency, reliable communication and average path delay. One main characteristic is that adopting retransmission scheme, which is based on information of link error rate, can guarantee reliable transmission. Further, we achieve energy efficiency characteristic by considering residual energy and the expected energy requirement jointly. The protocol also develops probabilistic model for real-time by introducing hop count value to the sink node, and searches the forwarding node with the maximum aggregated weight. Therefore, the proposed protocol not only improves the real-time, but also enhances the utilization of energy.

3

Network model and radio model

3.1 Network model The network model we adopted in this paper has the following features: 1) A large number of sensors stochastically distributed in the sensing field. 2) The nodes in the network are stationary during their lifetime. 3) All nodes are equal in terms of transmission range, initial energy, processing and communication capabilities,

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radio, battery and memory, as well as equal significance. 4) The sink node is assumed with infinite energy. 5) The network has full connections, i.e. each node can communicate with its neighbors. 6) The lifetime is defined as the time when the first node is depleted of its battery power. 3.2

Radio model

The radio model used in this paper is same as that in Ref. [4], which is a common model. In this model, the dissipated energy for radio electronics and the power amplifier are Eelec , ε amp , respectively. Transmission and receiving costs are calculated as follows ⎧⎪lEelec + lε amp d 2 ; d < d 0 ⎫ ⎪⎪ ETx ( l , d ) = ⎨ 4 (1) ⎪⎩lEelec + lε amp d ; d≥d 0 ⎬ ⎪ ERx ( l , d ) = lEelec ⎪⎭ where l is the length of the transmitted/received message in bits; d is the distance between transmitter and receiver node, and 2, 4 are the path loss exponent, where 2 represents the free space model and 4 represents the multi-path fading channel models; d 0 is the distance

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4.1 UPMs When the network is deployed, each node knows its information, but doesn’t acknowledge its neighbors. BERR algorithm can get its neighbors’ information by flooding UPMs to the whole network. When a node accepts UPMs, it will set up a neighbor list and regards the transmitter as its neighborhood, then store the node’s information into neighbor list. The node compares its new receiving value of hop count (HC) to the stored one. If the new value of HC is lower than the stored one, it will replace the old one and update its memory. Then the value of HC increases one, and the node transmits HC to its neighbor nodes and the process keeps on until each node update the UPMs. Fig. 1 shows the process of HC.

threshold. It can be seen, the transmitter expends energy to run the radio electronics and power amplifier, while the receiver only expends energy to run the radio electronics.

4

Fig. 1 Example of HC

The BERR routing protocol

The protocol BERR is a distributed algorithm that provides a robust transmission environment based on the energy-aware routing and real-time mechanisms with hop count information in the network layer. In BERR, data packets are classified into three different categories: update messages (UPMs), retransmission request packets (RRPs) and sensing data information (SDI). Sink node transmits UPMs throughout the whole network and each node can obtain neighbor nodes’ information (including node ID, geographic location, hop-count to sink node). When one node receives the UPMs, its neighbor nodes’ relative information is updated dynamically. When one node cannot receive successfully the sensing packet, it will send RRPs to the transmitter asking for retransmitting the loss packet. SDI is transported from source node to sink node with hop-by-hop retransmissions (HBH).

4.2

RRPs

When one node receives packets, it will store the packets in sequence in its buffer, and then relays packet to sink node. If one node cannot successfully receives the packet, it will send RRPs to the transmitter and ask for retransmitting until receives successfully. After that, transmitter will abandon the packet from its buffer, and achieve more memory space to the following packets. Now, we analyze the effect of retransmissions on the resultant probability that a packet will transported successfully to the sink node. Denote pi , j as the probability of unsuccessful transmission from node i to its neighbor node j, and R as the number of the transmitter i tries to send the packer to node j, and the probability of successful transmissions can be described as R

Plink = ∑ (1 − pi , j )

r

(2)

r =1

This simplifies to the probability of not failing at all R

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transmissions Plink = 1 − (1 − pi , j ) R

(3)

When considering H hops for a message flow transmitted successfully from source node to base station, the probability of arrival is H psucc = 1 − plink (4)

Here a simple example is given. We compare the probability of arrival across 7 hops for the repeated mechanism. Fig. 2 shows the comparison result, where the number of retries is set to be three. In each retransmission process, the routing is set up according to the rules described later. From Fig. 2, one can get that arrival probability of considering repeated mechanism is higher than that of no consideration.

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density (noise power per Hz), f is the raw channel bit-rate. Because the received signal power is inversely proportional to D k , where D is the link distance, and k is the same constant as 2. Thus Pr can be replaced by

Pt D k , where Pt is the transmitter power. Function Fc ( x ) and is given by

2

∫ π

Fc ( x) = 1 −

x 0

2

e − t dt

(6)

The packer error rate can be expressed as

pi , j = 1 − (1 − ei , j )

l

(7)

where l is the packet length. 4.3.1.2 Energy- efficiency analysis BERR algorithm adopts hon-by-hop transmitting mechanism. Link quality effects the retransmission time. If the path has good quality link, it will have small retransmission time. For a link ( i, j ) with the packer error rate P i , j , to ensure the successful transmission of a packet between link

( i, j ) ,

the number of transmission

R (including retransmissions) necessary can be described as (8) Pr { x = R} = piR, −j 1 (1 − pi , j ) ; ∀R Fig. 2 Probability of arrival

4.3 SDI 4.3.1 Energy awareness with reliable transmission 4.3.1.1 Link loss model The link reliability is the probability of successful transmitting data packets from the current node to the next node [24]. Reliability will be affected by the channel quality, information congestion, the size of the packet and so on. We now define the packet error rate pi , j . Let ei , j and Di , j denote the bit error rate and the distance of a link between nodes i and j, respectively. Using the typical channel modulation scheme, binary phase shift keying (BPSK), the bit error rate can be given as: ⎛ ⎛ pr ⎞ pt ⎞ ⎟ (5) ei , j = 0.5Fc ⎜ = 0.5 Fc ⎜ 2 ⎜ Nf ⎟⎟ ⎜ Di , jη f ⎟ ⎝ ⎠ ⎝ ⎠ where pr denotes the received power per bit and it is a function of the modulation scheme, N is the noise spectral

So the expected number of transmission (including retransmissions) for the successful transfer of a single packet is calculated as N exp = 1 (1 − pi , j ) . Hence, the expected energy requirement to reliably transmit a packet across the link is given by Ei , j Elink (i , j ) = (9) 1 − pi , j where Ei , j is the transmission energy consumed in node i to transmit a packet over link

( i, j )

to node j. Let Eres(i )

as the residual energy of node i. Therefore, the energy efficiency (denoted as Ee ) for node i over the link ( i, j ) is clearly calculated as: Eres(i ) (1 − pi , j ) Eres(i ) Ee (i ) = = Elink (i , j ) Ei , j

(10)

So, we can get the energy cost (denoted as EC(i , j ) ) EC(i , j ) =

1

Ee ( i )

=

Ei , j

Eres( i ) (1 − pi , j )

(11)

Before elaborating on energy efficiency, some definitions are introduced. For node i, the neighbor set of

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node N i is the set of nodes that are inside the radio range of node i. The forwarding candidate set of node Fi is the set of nodes that belong to N i and are closer to the destination, i,e., Fi = { j | D ( i, d )≥D ( j , d ) , j ∈ N i } .

One of the basic ideas of the proposed routing algorithm is to find a path with more energy efficiency, this means the minimum EC is expected. When a sensor i sends data to the base station, it can transmit data to one of its neighbors (including sink node). In other word, the sensor determines which sensor is the best candidate within its neighbors in aspect of minimum energy consumption. But it is not neglected such condition that the selected best candidate may not have next better candidate than others. So we not only consider its neighbors’ condition but also the whole link. Fig. 3 can show this idea.

1 2 3

EC(i , j )

EC(i ,r )

20/9 20/9

20/9 20/9 20/9

in our algorithm we also consider node j, k’s neighbors. EC(i , j ) + EC ( j , k * ) j∈Fi

PE(i , j ) =

k ∈F j

∑ EC(i, j )

(12)

j∈Fi

*

where k is the best candidate of node j, namely k * = Arg min ( EC(i , j ) ) j∈ N i

4.3.2 Real-time analysis We want to find the best candidate which not only has the best energy efficiency, but also has the best real-time performance as the relay node. As shown in Fig. 3, route 3 is the more energy-efficient one, but it does not imply the good real time performance. For another example, there exist two data routes that originate from node i to sink node in Fig. 4. If just considering energy efficiency, route 2 is better than route 1, and node k will be chosen as the relay node. If packets process on the energy-efficient route 2, the direction of routing is gradually far away the sink, which leads to more hop counts to the sink node, and more delay will be involved in the transmission. Herein, HC is introduced into the real-time routing. The velocity of packet delivery can be expressed as Eq. (13). H j ,d (13) Vi , j = H max j∈Fi where H i , d indicates the hop count of node i to the sink

(a) The topology of network

Route

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∑E

node which can be get from hop count messages (HCMs). And H max is the maximum value of hop count.

C( i , j )

2.2 2.5 1.67

(b) The energy cost of link Fig. 3

Illustration of energy cost on data route

In Fig. 3, given some data, node i wants to select a relay node from its neighbor nodes to transmit the data to node r, where j , k , r ∈ Fi , r ∈ Fj , Fk . Node i has three optimal data routes towards to node r , and they are route 1 ( Li , r ), route 2 ( Li , j , L j , r ) and route 3 ( Li , k , Lk , r ) respectively. From Fig. 3(b), we can get that route 3 is the least energy cost among all the optional routes, so node i will select node k as the next-hop relay node based on the selection probability PE(i , j ) in Eq. (13). If we just consider node i’s neighbors, we can find that it is difficult to select from node j and node k, because EC(i , j ) = EC(i , k ) ,

Fig. 4 The example of real-time

4.3.3 BERR routing protocol The proposed routing algorithm uses a path satisfying QoS metrics (such as energy efficiency, real time and reliability). Now, the problem of routing a packet from the source node to the destination clearly can be transformed to that of finding the downstream node that has the

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minimum aggregated weight. The delay-energy tradeoff can be described as the following. f * = arg min {α f1 + (1 − α ) f 2 } = ⎧⎪ ⎫⎪ arg min ⎨α PE(i , j ) + (1 − α ) Vi , j ⎬ (14) j∈Fi ⎪ ⎪⎩ ⎭ where, the coefficient α ∈ [ 0,1] is the weighted factor

used to adjust the relationship between the energy and delay. Note that smaller α tends to favor end-to-end delay performance, while the larger one can assign traffic to nodes with higher energy level and get better energy balance. The rationale of the function in Eq. (14) is explained as follows. The first parameter Pi , j use retransmissions to guarantee reliable transmission which can be achieved through calculating the expected number of transmission, and it as well as realize energy efficiency through minimizing Eq. (11). This part contributes to ensure the balanced energy consumption among the nodes. The second parameter Vi , j represents each forward node’s velocity. In general, the closer a node it is to the sink node, the better performance of real-time will be, and the smaller value of node’s hop count to the sink node represents the short distance to the sink node. So, minimizing Eq. (14) means maximizing the packet transmission efficiency and enhancing the transmission real-time. So according to Eq. (14), one can get that the neighbor node in Fi who has the high velocity and lifetime will be chosen as the next hop node with higher probability. Fig. 5 gives the flow chart of BERR algorithm. We make a further research on protocol BEER. It is obvious that if the transmission path changes frequently, the unstable route will be a great disadvantage to the algorithm performance and whole network. So we introduce routing maintaining mechanism into BERR. In other words, when transmitting a packet, the best transmission path based on Eq. (14) will be selected, and then it is stored first. If there are packets to be ready to transmit, the stored path will be directly used. When one of the nodes’ residual energy in the selected transmission path is lower than a threshold, we will reselect the transmission path, and this process is repeated until all nodes’ energy exhaust. The introduced routing maintaining mechanism can avoids energy consumption caused by frequent changing path, and prolong the network lifetime. So we call it as M-BERR based on BERR.

Fig. 5

5

BERR algorithm flow chart

Experimental analysis

In this section, we provide several experimental results to validate the effectiveness of the BERR algorithm. The comparison of the algorithm is with three other algorithms discussed in: reliability consider routing (DETR) [25], reliable and energy-efficient routing (REER) [16], and SPEED (K=2, K=10) [18]. 5.1 Simulation model In simulation, the number of ordinary nodes varies from 50 to 100 to examine the performance with different node densities, and these nodes are distributed randomly in the range from 50 m×50 m to 100 m×100 m, for example 50 ordinary nodes corresponding 50 m×50 m field and 100 ordinary nodes corresponding 100 m×100 m field. For

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simply but without loss of generality, it is assumed that there is only one source and one sink node. The source node is located at the bottom left corner of the square, and the sink node is located at the top right corner. The initial energy and communication range of each node are set identically as 1 J and 30 m, respectively. Data packet is generated by the source nodes with the rate of 4 kbit/s. We use the following metrics to evaluate the performance of our protocol BERR and compare the results with the three algorithms. Network lifetime [26]: The network lifetime is defined as the smallest time that it takes for at least one node in the network to drain its energy. Average delay [27]: The average delay is the average time required to transfer a data packet from source node to sink node. Energy efficiency [28]: Energy efficiency is measured as the ratio of total consumed energy by all to the number of packets received by sinks. Note that in this case, the higher, the value is, lower the efficiency is. Average delivery ratio: The delivery ratio is the number of packets received by the sink node to that generated by the source node. Since a is regarded as the weighted factor to adjust the relationship between the energy and delay in Eq. (15), the value of a effects the performance of the proposed protocol. So how to choose appropriate a is a significant and key operation. Fig. 6 and Fig. 7 show the average path delay and the average path energy consumption when source node transmits a data to sink node.

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value of a, the phenomenon can be explained using Eq. (15) that the smaller a is, the more emphasis on velocity will be. So, to enhance real-time, the value of a is set 0.1 in the following simulations. 5.2 Simulation results 5.2.1 Network lifetime The most significant improvement of protocol BERR is to prolong the network lifetime with balancing the energy consumption and enhancing energy available. Fig. 8 and Fig. 9 show the network lifetime of BERR and other comparative protocols under different node numbers and packet numbers respectively.

Fig. 8 Network lifetime with different number of nodes

Fig. 6 Average path energy consumption with different a Fig. 9 Network lifetime with packet number

Fig. 7

Average path delay with different a

It is clear that the value of the average delay and the average energy consume increase with the increment of the

From Fig. 8 and Fig. 9, it is clear that protocol BERR outperforms DETR, REER and SPEED (K=2, K=10). For the fact that DETR has only considered its one hop neighbor nodes’ information, it is slightly poor to our protocol. And REER just wants to find a path with the minimum energy consumption ignoring each node’s residual energy, which results in some nodes dying out quickly. SPEED only considers their velocity to enhance real-time delivery and ignore the energy consumption,

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which result in enhancing real-time delivery at cost of network lifetime. While, to enhance the network lifetime, BERR considers the sender’s energy efficiency and neighbors’ energy efficiency, when transmit a data packet from the current node to sink node.

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outperforms than DETR. REER and SPEED don’t consider each node’s energy availability.

5.2.2 Average delay Fig. 10 shows the results for the average path delay as the number of ordinary nodes increases from 50 to 100. And Fig. 11 illustrates the average path delay when the occurred time changes from 100 seconds to 1 900 seconds with 100 nodes. From two figures, we can see that protocol BERR slightly outperforms the other three protocols. This is because protocol BERR adopts the information of each node’s hop count to sink node, so the smaller hop count is, the less path delay is gained. While DETR and REER do not consider their velocity so that they have a longer time to send a packet to the sink node.

Fig. 12

Energy efficiency with time consumption

Fig. 13

Packet arrival rate

5.2.4 Packet delivery ratio

Fig. 10

Average delay with different number of nodes

Fig. 11 Average delay vs. time consumption

5.2.3 Energy efficiency The main characteristic of our protocol BERR is to enhance energy efficiency by balancing energy consumption among the nodes. Fig. 12 can explain the trait. Form the figure we can see that protocol BERR outperform the DETR, REER and SPEED protocols in term of energy consumption. One of improvements of our protocol better than DETR is that our protocol considers more nodes’ energy information, so our protocol slightly

Fig. 13 shows the comparison of average packet delivery ratio of BERR and SPEED (K=2, K=10) protocols. In these experiments, we test this performance with same ordinary nodes (N=100) and different time consumptions. Obviously, BERR has a high packet arrival ratio which approximates one. This is because BERR considers retransmissions to avoid packet being discarded. While SPEED uses neighborhood feedback loop, if there is no node can meet the metric, it will considers the metric of dropping data packets. For this performance, we don’t compare BERR with DETR and REER, this is because that DETR chooses the next node which has the best link quality with no dropping metrics, and REER has the same retransmission metric with our protocol. 5.3 M-BEER Next compare the performance of improved protocol (M-BEER) with BEER. Fig. 14 shows the network lifetime performances of BERR and M-BERR protocols varying the size of the network. And Fig. 15 also shows the network lifetime performance when the delivering packet number is varied from 5 to 33. As shown in the two figures, the M-BERR protocol has the better network lifetime. Fig. 16 shows the experimental results for the case of energy efficiency with occurring time increasing. It is clear to find out that the M-BERR protocol

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has a better performance than BERR protocol in both the network lifetime and energy efficiency. For the fact that M-BERR adopts one novel tactics, (i.e., on the one hand, adopting maintaining mechanism can avoid unnecessary energy consumption; on the other hand, this mechanism selects an other transmission path timely when finding that some nodes’ energy value have lighter or approaching to a threshold value of energy), it can improve energy use ratio and enhance the network lifetime and energy efficiency. Fig. 17 shows the average path delay performance of BERR and M-BERR protocols varying the size of the network. This figure shows that as the network gets larger, the performance of M-BERR on average path delay outperforms BERR. The characteristic that the transmission paths are relatively stable can improve the real-time in some extent.

Fig. 14

Network lifetime with different number of nodes

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Fig. 17 Average delay with different number of nodes

6

Conclusions

In this paper, we propose an energy efficient routing protocol (BERR) for real-time, reliable communication for WSNs. It uses the joint performance indexes of the residual energy, hop count value and the retransmission mechanism with link error rate to choose the next hop node. BERR achieves the efficient tradeoff among different performances. Through simulation, we have evaluated and analyzed the performances of our routing protocol under different network conditions and compared them with the DETR, REER and SPEED protocols. Simulation results have shown that BERR achieves better performance in balancing energy consumption, prolonging network lifetime, enhancing energy efficiency and improving delivery ratio. At the same time, it also pays attention to the real-time. How to combine the three factors (real-time, energy efficiency and reliability delivery) and analyze their interaction and tradeoff in both stationary and dynamic scenarios are the future works. Acknowledgements This work was supported by the National Natural Science Foundation of China (61104033, 61174127, 60934003), the Hebei Provincial Natural Science Fund (F2012203109, F2012203126).

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Fig. 16

Network lifetime with packet number

Energy efficiency with time consumption

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(Editor: ZHANG Ying)