DAR: An energy-balanced data-gathering scheme for wireless sensor networks

Computer Communications 30 (2007) 2812–2825 www.elsevier.com/locate/comcom

DAR: An energy-balanced data-gathering scheme for wireless sensor networks Yanzhong Bi

a,b

, Na Li c, Limin Sun

a,*

a

Institute of Software, Chinese Academy of Sciences, Beijing 100080, People’s Republic of China Graduate School of Chinese Academy of Sciences, Beijing 100039, People’s Republic of China Computer Network Information Center, Chinese Academy of Sciences, Beijing 100080, People’s Republic of China b

c

Available online 20 June 2007

Abstract A wireless sensor network faces special challenges due to its inherent features, such as the limited energy. The energy constraint drives research on how to utilize energy efficiently to prolong the lifetime of the network. Because a sink node takes the responsibility of collecting data from other nodes, a usual conception is to transfer data towards the sink node by multihop. However, conventional datagathering schemes based on the conception give rise to the hotspot problem because of the nodes that run out of their energy sooner than other nodes, which results in accelerating the end of the whole network. The closer sensor nodes are to the sink, the more quickly they exhaust their energy, which leaves an upper bound to the lifetime of the whole network. Because of the bottleneck nodes, the network loses its service ability regardless of a large amount of residual energy of the other nodes. In this paper, we propose a novel data-gathering scheme, DAR, to handle the hotspot problem, in which all the nodes participate in the workload of gathering data from the whole network and transferring the data directly to the sink. In our scheme, the forwarding behavior of all the nodes is scheduled to balance their burden of aggregating and transmitting the network data and the nodes may send their data back against the sink, which differs from the conventional schemes. We performed simulation experiments to evaluate the performance of the DAR scheme, and the results show that our data-gathering scheme can balance the energy consumption among all the nodes and extend the network lifetime notably.  2007 Elsevier B.V. All rights reserved. Keywords: Wireless sensor network; Data-gathering scheme; Lifetime; Energy balancing

1. Introduction Wireless sensor networks have the potentiality to innovate the manner in which we sense the real physical world. The envisioned applications of wireless sensor networks range widely, such as environment monitoring, disaster surveillance, military target tracking, and medical treatment, among others. Therefore, technologies related to wireless sensor networks are under active research and development in recent years [1]. There are many challenges when designing and deploying wireless sensor networks in those applications, and an *

Corresponding author. Tel.: +86 10 6264 5408. E-mail addresses: [email protected] (Y. Bi), [email protected]. ac.cn (N. Li), [email protected] (L. Sun). 0140-3664/$ - see front matter  2007 Elsevier B.V. All rights reserved. doi:10.1016/j.comcom.2007.05.021

unavoidable one is how to make full use of the limited energy to provide a long span of service. Because of the requirement of maintaining working ability without battery recharging for a longtime, a network has to be designed to utilize its energy efficiently. Many efforts have been devoted to the energy efficiency of wireless sensor networks, most of which proposed new network protocols to preserve energy as much as possible during the process of collecting data [2,3]. Many researchers focused on nexthop selection strategies, which made one-hop neighboring nodes or multiple link candidates consume energy evenly for transmitting data [4–6]. Some researchers engaged in scheduling sensor nodes by switching the mode of nodes between sleep and work mode to save energy [7–9]. Benefiting from power control technologies, each sensor node can adjust its output power, respectively [10,11]. This has

Y. Bi et al. / Computer Communications 30 (2007) 2812–2825

attracted many researchers to study on minimizing the radio transmission power of sensor nodes without declining the network connectivity [12–14]. To the best of our knowledge, almost all previous work was based on the assumption that data packets always congregate towards the sink at every hop during the process of collecting data, which we call Centrally Aggregating (CA) scheme. In the CA scheme, relaying data from far nodes as well as delivering local data, the nodes closer to the sink have heavier workloads, which makes them hotspots and drain of energy quickly. The hotspot problem causes an early rupture of the network because the hotspot nodes no longer relay data for far nodes when they run out of their energy. Although optimizing energy efficiency in local areas of the network can prolong the lifetime to some extent, it can hardly guarantee the energy balance for the whole network. There are several feasible ways to handle the hotspot problem. The first one is to move some workloads from the hotspots to light-hearted ones. The workloads may include data delivery, heavy computation, and frequent access to memory, among others. The second way is to make sink move. A mobile sink can redirect the traffic flow and help to equilibrate energy consumption among sensor nodes. Several studies have been performed on this subject recently [15–18]. The third way is to deploy multiple sinks. Multiple sinks can cooperate with each other to lighten the workloads of hotspots and enhance energy efficiency [19,20]. Nevertheless, in this paper, we focus on the sensor network that has only one stationary sink in its coverage area, which is a common scenario in environment monitoring applications, and make an effort to shift some heavy work from the nodes near the sink onto others that are far from the sink. We propose a novel data-gathering scheme called Data Aggregating Ring (DAR) in this paper. In the DAR scheme, all sensor nodes are classified according to hop grades, their hop counts to sink. Unlike the CA scheme, sensor nodes do not always send their data to those nearer to the sink than themselves at every hop. The nodes in different hop grades, ordered in a certain sequence, would spend different periods of time taking charge of gathering the data packets from the nodes in other hop grades and transmitting them to the sink directly by one hop, respectively. The sequence is elaborated according to the traffic characteristic and routing strategy of the given network to balance the workloads between the nodes in different hop grades. As a result, the nodes at a distance of only one hop to sink tend to consume equal energy to those with more hops on delivering data. Therefore, the DAR scheme can nearly balance the energy consumption over a whole network range, increase energy efficiency and extend network lifetime notably. The rest of the paper is organized as follows. In Section 2, we review some previous work on energy-balanced data-gathering protocols for wireless sensor networks. In Section 3, we present the DAR scheme in

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detail. In Section 4, we provide a description of the routing protocol employed in the DAR scheme. We give out some experimental results in Section 5 and discuss some issues in Section 6. Finally, we conclude this paper in Section 7. 2. Related work Energy balancing and network lifetime of wireless sensor network have drawn much attention in recent years. Many previous data-gathering mechanisms considered energy efficiency and adopted different means to balance energy consumption among sensor nodes to prolong network lifetime. Energy Aware Routing (EAR) [5] builds multiple paths from data sources to a sink node. Using a stochastic approach, it selects sub-optimal next hops for each node, but it can only gain energy balance locally. LEACH [21], a cluster-based protocol, randomly selects a few sensor nodes as cluster heads and rotates the roles to consume sensor nodes energy evenly. In LEACH, cluster heads collect the data of the nodes that belong to respective clusters and transmit compressed packets to a sink directly. ENCAST [22] re-construct spanning trees rooted at a sink during gathering data to balance the traffic load of the nodes. This implies the same idea as LEACH that sensor nodes have to rotate their roles in data-collecting process to equilibrate their energy consumption. In [23], the authors tried to find an energy-balanced solution for data propagation in wireless sensor networks, but they did not exert the function of adjusting output power of nodes adequately. The authors of Ref. [24] suggested a re-clustering strategy and a redirection scheme for cluster-based wireless sensor networks. The re-clustering protocol adjusts the transmission power of sensor nodes to control the number of their neighbors. However, their protocol can only guarantee the local energy equilibrium within a cluster. Considering the traffic characteristic of the whole network in a data-gathering application, the authors of PODA [25] mechanism assigned higher output power to the nodes far from sink than those near sink to make them consume energy equally. Although PODA can gain energy balance in whole network, it has a trivial advantage in prolonging network lifetime. On the basis of analyzing the cause of the hotspot problem in wireless sensor networks, we attempt to break the conventional data-forwarding mode that sensor nodes always transmit their data packets towards a sink at every hop, inversely, we allow sensor nodes to send data back against the sink in some conditions. As a result, the residual energy of the nodes farther from the sink can be utilized adequately if the data-gathering process is scheduled well. Furthermore, the energy consumption of all nodes can be balanced from the perspective of the whole network in our DAR scheme; consequently, the network lifetime can be lengthened.

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3. Data aggregating ring scheme In this section, we present the Data Aggregating Ring scheme after describing the network model and energy model adopted, and then we analyze the network lifetime performance of the DAR scheme and compare it with that of the CA scheme. 3.1. Network model In this paper, we assume a wireless sensor network model, which is appropriate for data-gathering applications such as environment monitoring, and the network model has the following properties: • A large number of energy-constrained sensor nodes are deployed uniformly in the network area and are equipped with power control capabilities to vary their output power. • A sink node without energy constraints is located in the center of the network area, and it can enlarge its communication radius to cover the whole network region. • Each sensor node sends fixed-length data packets to the sink node periodically. • The sensor nodes can switch into a sleep mode or a lowpower mode to preserve their energy when they do not need to receive or send data. The sensor nodes can construct a tree-like topology to forward data by performing some hop-based routing protocol. In our wireless sensor network model, the sink starts a data-gathering process, and the sensor nodes set up a data-forwarding topology hop by hop. All the nodes that have the same hop count to the sink form a nodes group, which we call a hop grade. Fig. 1 shows the hop grades formed with a small communication radius r used by all nodes in the network. The sink informs its one-hop neighbors at the start of the topology construction process, and the one-hop-grade nodes (e.g. Node A in Fig. 1) announce their hop-counts information to the nodes within their communication ranges to make them two-hop-grade nodes. The process will go on until all sensor nodes have connected into a data-forwarding tree. Assuming the nodes are distributed uniformly, Fig. 1a can be predigested to

Fig. 2. Value of parameter d varies with different next-hop selection strategies.

Fig. 1b in which nodes with the same hop count form a regular ring-like area. In Fig. 1b, the width of each hop grade is less than the radio radius r because it is hardly to make every node has at least one next-hop node at the edge of communication range. In this paper, we consider the width of each hop grade as w¼dr

ð1Þ

where d is a parameter in (0, 1], which is determined by the deployment density of nodes, communication quality and topology construction algorithm. For a given sensor network, the strategy of selecting next-hop nodes affects the value of d dominantly, which is illustrated in Fig. 2. Suppose all the nodes in Fig. 2 are experiencing the topology construction process. Both Node B and C are in the communication range of Node A, which belongs to the uppermost hop grade. If Node C is always inclined to select its neighbor as near to the sink as possible, it likely chooses Node A, which is near the margin of its communication range, as its next hop, and it will be assigned to the adjacent hop grade of the uppermost grade. In this case, the value of d is about 0.9. However, if Node C considers other factors, such as energy level and link quality, when selecting next hops, it probably selects a node closer to it than Node A as its next hop. In this case, Node C may belong to a higher hop grade than the middle grade. Consequently, the nodes like Node B will determine the width of the middle grade and the value of d will be about 0.7, as shown in Fig. 2. In this paper, we consider d as an average value of those in all hop grades.

3.2. Energy-balanced data-gathering scheme

Fig. 1. Hop grades in our wireless sensor network.

In the CA scheme, sensor nodes are disposed to select the nodes nearer to the sink than themselves as their next hops to send data, as shown in Fig. 3. The nodes within the one-hop grade take charge of transmitting the data generated by nodes in all other grades. Therefore, the sensed data always flow towards the sink, which exhausts the

Y. Bi et al. / Computer Communications 30 (2007) 2812–2825

Fig. 3. Data flows in the Centrally Aggregating scheme.

energy of the nodes in the first-hop grade, G0, as early as possible. To balance the workloads between the nodes in different hop grades, we break through the traditional idea and schedule the data flows backwards. In the scheme proposed, each hop grade has the opportunity to take the responsibility of gathering all network data and transmitting them directly to the sink, which is similar to the burden taken only by the nodes with one hop to the sink in the CA scheme. Considering the scenario in which the sensor nodes report their data to the sink periodically, we schedule each hop grade to be an in-charge grade in different data-gathering periods. No matter which hop grade is in charge, the nodes in other hop grades that are not in charge would send their data to the in-charge grade by multihop. However, the nodes in the one-hop grade always send data to the sink directly no matter which grade is in charge. The nodes of the in-charge grade would transmit the data, including their own sensed data and those received from other hop grades, to the sink directly using enlarged communication radii. Fig. 4 shows the two steps of one data-gathering period in the DAR scheme. In Fig. 4a, the hop grade G3 is the current in-charge grade, thus G1, G2, and G4 send their data to G3; meanwhile G0 sends its data to the sink. Fig. 4b shows the second step that G3 sends both its gen-

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erated data and the received data to the sink by a single hop. The main idea underlying the DAR scheme is that since transmitting almost all network data costs much energy, all the nodes should partake of the job to balance the energy consumption in a whole network range. It may be a little waste of energy to send data back against the sink some nodes; however, it aims to make the best use of the energy of the nodes far away from the sink. Consequently, the network lifetime can be prolonged by avoiding driving a few nodes to be hotspots. Based on this idea, we need to determine the proportion of the time that each hop grade should be in charge in long-running applications. We define the in-charge time of each hop grade as the number of data-gathering periods during which the hop grade is in charge before the network lifetime ends. With various distances to the sink, different hop grades consume much different amount of energy on sending data to the sink by single hop when they are in charge. Therefore, the in-charge time for each hop grade is related to its hop count and the data amount to transmit. 3.2.1. Energy model In this subsection, we present the energy model for communication in the DAR scheme. However, what should be pointed out is that the DAR scheme does not restrict the energy model employed. In other words, it can work with diverse energy models adaptive to different applications. We adopt the practical radio energy model described in [26]. In this model, the transmitter needs energy to run the radio electronics and the power amplifier, and the receiver consumes energy to run the radio electronics. For relatively short distances, the propagation loss is modeled as being inversely proportional to d2, whereas for longer distance, the propagation loss is modeled as being inversely proportional to d4. Power control can be used to invert this loss by setting the power amplifier to ensure a certain power at the receiver. Therefore, to transmit and to receive an L-bit packet in a distance d, the radio expends the following energy, respectively:

Fig. 4. Data flows in the Data Aggregating Ring scheme. (a) Step 1: the hop grades not in-charge transmit their data to the in-charge hop grade by multihop. (b) Step 2: the in-charge hop grade sends the data to the sink by single hop.

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( ETx ðL;dÞ ¼

L  Eelec þ L  efriis-amp  d 2 ; if d < d crossover 4

L  Eelec þ L  etwo-ray-amp  d ; if d P d crossover

ERx ðLÞ ¼ L  Eelec

ð2Þ ð3Þ

where dcrossover is the crossover distance for Friis and Two-ray ground attenuation models. Eelec is the electronics energy and depends on factors such as digital coding, modulation, and filtering of the signal before it is sent to the transmit amplifier. The parameters efriis-amp and etwo-ray-amp depend on the required sensitivity and the noise figure of the receiver. For the experiments described in this paper, we adopted the parameters of the radio chips similar to those in [10,27] to determine the parameters values in Eqs. (2) and (3). Therefore, we have Eelec ¼ 1:16 lJ=bit; efriis-amp ¼ 5:46 pJ=bit=m2 ; etwo-ray-amp ¼ 0:00325 pJ=bit=m4 ; d crossover ¼ 40:8 m Assuming the minimum output power is 20 dBm for the sensor nodes, we obtained a minimum communication radius of 47.43 m, which is beyond the dcrossover, so the Tworay ground attenuation model would always be used in our network. Therefore, we can rewrite the energy consumption for transmitting as follows: ETx ðL; dÞ ¼ L  Eelec þ L  e  d 4

ð4Þ

where e stands for etwo-ray-amp. 3.2.2. DAR model In this subsection, we present DAR model with the notations listed in Table 1. We divide the problem into three cases to discuss, respectively:

1. The case in which the first hop grade G0 is in charge. 2. The case in which the last hop grade GH1 is in charge. 3. The case in which a middle hop grade Gk (0 < k < H  1) is in charge. Case 1: G0 is in charge. According to Table 1, the total number of nodes in the network can be expressed as N ¼ q  p  R2 ¼ q  p  ðH  wÞ

2

ð5Þ

and the number of nodes in the first hop grade, G0, is N 0 ¼ q  p  w2 Assuming each node sends a data data-gathering period, the amount of by the nodes in grade G0 is equal is in charge, the energy consumed G0 is   EIC0 ¼ ER  q  p  H 2  w2  q  p  w2

ð6Þ packet packets to N0. by the

þ E T  q  p  H 2  w2   ¼ q  p  w2  ER  ðH 2  1Þ þ ET  H 2

in every generated When G0 nodes in

ð7Þ

where the former item denotes the energy consumed on receiving the data from the nodes in other hop grades, and the latter item denotes that the nodes in grade G0 have to send the data of the entire network. Meanwhile, the other hop grades, which are not in charge, send their data to G0 hop by hop. For the hop grade Gk (0 < k < H  1), it has to receive the data from the hop grades farther from the sink than itself and send the data to its neighboring grade Gk1. Thus, the energy consumed by the nodes in Gk is

Table 1 Notations definition in DAR model Notation

Definition

R H R d w q N ET ER EDirTxk Tk Nk EICk ENICki

Radius of the network region Maximum hop count in the network Radio radius used to communicate with one-hop neighbors A parameter in (0, 1], which is described in Section 3.1 Average width of all the hop grades; w = d · r Node density in the uniform deployment Total number of the nodes in the network Energy consumed in sending an L-bit-long data packet using the radio radius r Energy consumed in receiving an L-bit-long data packet Energy consumed in sending an L-bit-long data packet directly to the sink from the nodes in hop grade Gk (0 6 k < H) In-charge time of hop grade Gk (0 6 k < H) during the whole network lifetime Number of nodes in hop grade Gk (0 6 k < H) Total energy consumed by the nodes in the in-charge hop grade Gk (0 6 k < H) in a data-gathering period Total energy consumed by the nodes in the not-in-charge hop grade Gi (0 6 i < H) in a data-gathering period when hop grade Gk (k„i) is in charge Total energy consumed by the nodes in hop grade Gk (0 6 k < H) in a data-gathering period Total energy consumed by the nodes in hop grade Gk (0 6 k < H) during the whole network lifetime

Ek Etotalk

Y. Bi et al. / Computer Communications 30 (2007) 2812–2825

h i ENIC0k ¼ ER  q  p  H 2  w2  q  p  ððk þ 1Þ  wÞ2 h i þ ET  q  p  H 2  w2  q  p  ðk  wÞ2 n o ¼ q  p  w2  ET  ðH 2  k 2 Þ þ ER  ½H 2  ðk þ 1Þ2  ð0 < k < H  1Þ

ð8Þ The hop grade GH1, which is the farthest grade from the sink, only needs to send its data to GH2 without receiving data from any grades. Hence, its energy consumption is 2

ENIC0ðH1Þ ¼ q  p  w2  ET  ½H 2  ðH  1Þ 

ð9Þ

Synthesizing Eqs. (8) and (9), we obtain the general expression for the energy consumption of each not-in-charge hop grade when G0 is in charge as follows: n o 2 ENIC0k ¼ q  p  w2  ET  ðH 2  k 2 Þ þ ER  ½H 2  ðk þ 1Þ  ð0 < k < H Þ

ð10Þ

Case 2: GH1 is in charge. In this case, the energy consumption of G0 is only for sending its own data to the sink, which is ENICðH1Þ0 ¼ ET  q  p  w2

ð11Þ

However, all the other hop grades that are not in charge send their data to GH1. For grade Gk (0 < k < H  1), the energy consumption can be written as n o 2 ENICðH1Þk ¼ q  p  w2  ET  ½ðk þ 1Þ  1 þ ER  ðk 2  1Þ ð0 < k < H  1Þ

ð12Þ

The in-charge grade GH1 sends the data gathered to the sink directly, thus the energy consumption is n EICðH1Þ ¼ q  p  w2  EDirTxðH1Þ  ðH 2  1Þ o 2 ð13Þ þ ER  ½ðH  1Þ  1 where EDirTx(H1) denotes the energy consumed in sending a data packet directly to the sink. For simplicity, we assume the radio radius for Gk (0 < k < H) to communicate with the sink to be (k + 1) · w, so we can calculate EDirTxk by using Eq. (4). Case 3: Gk (0 < k < H1) is in charge. In this case, nodes in G0 behave the same as in Case 2, so the energy consumption is the same as that shown in Eq. (11). Similarly, nodes in GH1 behave the same as in Case 1, and the energy consumption can be expressed by Eq. (9). The other hop grades that are not in charge send their data towards Gk by multihop. Because Gi (0 < i < k) has to transmit data for Gi1 (0 < i < k), the energy consumption of Gi is n o ENICki ¼ q  p  w2  ET  ½ði þ 1Þ2  1 þ ER  ði2  1Þ ð0 < i < kÞ

ð14Þ

Meanwhile, Gj (k < j < H1) has to transmit data for Gj+1 (k < j < H  1), so we have

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n o 2 ENICkj ¼ qpw2  ET  ðH 2  j2 Þ þ ER  ½H 2  ðj þ 1Þ  ðk < j < H  1Þ

ð15Þ

The in-charge grade Gk gathers data from all other hop grades except G0 and sends them to the sink using a large enough radio radius. Therefore, the energy consumption of Gk can be expressed as 2

EICk ¼ q  p  fEDirTxk  ½ðH  wÞ  q  p  w2  2

2

2

þ ER  ½ðk  wÞ  w2 þ ðH  wÞ  ððk þ 1Þ  wÞ g ¼ q  p  w2  fEDirTxk  ðH 2  1Þ 2

þ ER  ½H 2  ðk þ 1Þ þ k 2  1g

ði < k < jÞ ð16Þ

Synthesizing all the equations derived from the three cases above, we obtain the following equations: • When G0 is in charge, the energy consumption of Gk is n o Ek ¼ g  ET  ðH 2  k 2 Þ þ ER  ½H 2  ðk þ 1Þ2  ð0 6 k 6 H  1Þ

ð17Þ

• When Gi (0 < i < H) is in charge, the energy consumption of Gk is 8 g  ET ; > n o > > > 2 2 > < g  ET  ½ðk þ 1Þ  1 þ ER  ðk  1Þ ;   Ek ¼ > g  EDirTxi  ðH 2  1Þ þ ER  ½H 2  2i  2 ; > > n o > > : g  E  ðH 2  k 2 Þ þ E  ½H 2  ðk þ 1Þ2  ; T R

k¼0 0
where g = q · p · w2. Therefore, assuming Tk (0 < k < H) is the in-charge time of hop grade Gk during the whole network lifetime, the total energy consumed by Gk is X ðENICik  T i Þ ð19Þ Etotalk ¼ EICk  T k þ i6¼k

Aiming to equalize the energy consumption among the nodes with different hop counts to the sink, we make the average energy consumption of the nodes in each hop grade equal, which can be expressed as EtotalðH1Þ Etotal0 Etotal1 Etotal2 ¼ ¼ ¼  ¼ N0 N1 N2 N H1

ð20Þ

where Nk (0 6 k < H) denotes the number of nodes in hop grade Gk. According to Eqs. (3), (4), and (19), we have

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( ! k1 X Etotalk g 2 2 ¼  b  ðH  k Þ  Ti Nk Nk i¼0 ! k1 X þ T i  fa  ½k 2 þ ðk þ 1Þ2   Eelec g i¼0 4

þ T k  ½a  ð2  k þ 3Þ  Eelec þ ðk þ 1Þ  d4  b  ðH 2  1Þ þ

H1 X

2

2

)

2

T i  fb  ½ðk þ 1Þ  1 þ Eelec  ½k þ ðk þ 1Þ  2g

i¼kþ1

ð21Þ where a = 2 Æ Eelec ÆH2 and b = e Æ r4. Given a network, the value of the parameters q, r, w, and H can be determined; thus, we can derive the proportion of Tk in the form of (T0:T1:T2:   :TH1) by solving multi-variable simple equations, which are set up by Eqs. (20) and (21). Considering the node numbers in hop grades of real networks can hardly match those in the ideal model, we substitute the actual nodes numbers into Eq. (21) when calculating the in-charge time proportion. The actual number of nodes can be counted by the sink from the data packets, which include the hop counts information of the data sources. After obtaining Nk, the sink can solve the equations to get the in-charge time proportion and make a schedule for every hop grade, which will be broadcast to the whole network. Therefore, the hop grades can switch between in-charge status and not-in-charge status automatically according the in-charge schedule. 3.3. Network lifetime comparison with the CA scheme In this subsection, we focus on the comparison of the lifetime performance between the DAR scheme and the conventional CA scheme in theory. Table 2 lists the notations used in this subsection, which are not involved in Table 1. In this paper, the network lifetime is defined as the period of time until the first node dies [13,28]. In the CA scheme, sensor nodes intend to select the neighbors that are closer to the sink than they are as their next-hop nodes. As a result, the nodes nearer to the sink carry heavier traffic

loads than those farther away, which leads to the early deaths of the overworking nodes, usually those in the hop grade G0. However, in the DAR scheme, nodes in different hop grades consume their energy equally and incline to die at the same time. Therefore, we regard the average lifetime among the nodes in hop grade G0 as the network lifetime in respective schemes approximately. We first formulate the average energy consumption of the nodes in the hop grade G0 in the two schemes, respectively, and then compare them to prove that the DAR scheme has a better performance on network lifetime than the CA scheme. Average energy consumption in G0 in the DAR scheme On the basis of the analysis above, we have T DAR  T DAR0 ¼

EInit EDAR0

ð22Þ

Considering EDAR0 consists of two parts, one is the average energy consumption when G0 is in charge, and the other is the energy consumption when it is not in charge, we write EDAR0 as EDAR0 ¼ a  EDARIC0 þ ð1  aÞ  EDARNIC0

ð23Þ

When G0 is in charge, the average energy consumption of each node in G0 can be calculated as follows: EDARIC0 ¼

EIC0 N0 2

2

p  ðH  wÞ  q  ET þ p  ½ðH  wÞ  w2   q  ER p  w2  q 2 2 ð24Þ ¼ H  ET þ ðH  1Þ  ER

¼

Since ET denotes the energy consumption on sending an L-bit packet with a radio radius r and ER denotes the energy consumption on receiving the packet, as described in Table 1, according to Eqs. (3) and (4), we have ET ¼ L  Eelec þ L  e  r4

ð25Þ

ER ¼ L  Eelec

ð26Þ

Thus, we can rewrite Eq. (24) as EDARIC0 ¼ L  H 2  ðEelec þ e  rn Þ þ L  ðH 2  1Þ  Eelec

ð27Þ

Table 2 Notations definitions for lifetime performance comparison Notation

Definition

EInit a EDAR0 ECA0 EDARIC0 EDARNIC0 TDAR0 TCA0 TDAR TCA

Initial energy of every node Proportion of the in-charge time to the lifetime of the node in G0 Average energy consumption of the nodes in G0 in the DAR scheme during a data-gathering period Average energy consumption of the nodes in G0in the CA scheme during a data-gathering period Energy consumption of each node in G0 when G0 is in charge during a data-gathering period in the DAR scheme Energy consumption of each node in G0 when G0 is not in charge during a data-gathering period in the DAR scheme Number of data-gathering periods experienced by the nodes in G0 during the whole network lifetime in the DAR scheme Number of data-gathering periods experienced by the nodes in G0 during the whole network lifetime in the CA scheme Lifetime of the network in the DAR scheme Lifetime of the network in the CA scheme

Y. Bi et al. / Computer Communications 30 (2007) 2812–2825

When G0 is not in charge, the average energy consumption of each node in G0 is equal to the consumption on transmitting its local data. Hence, we have EDARNIC0 ¼ ET ¼ L  Eelec þ L  e  rn

ð28Þ

Substituting Eqs. (27) and (28) into Eq. (23), we obtain EDAR0 ¼ a  L  ½H 2  ðEelec þ e  rn Þ þ ðH 2  1Þ  Eelec  þ ð1  aÞ  L  ðEelec þ e  rn Þ

ð29Þ

3.3.1. Average energy consumption in G0 in the CA scheme Similarly, in the CA scheme, we have T CA  T CA0 ¼

EInit ECA0

ð30Þ

In the CA scheme, since G0 takes the same task as that in the DAR scheme when it is in charge, the average energy consumption of the nodes in G0 during a data-gathering period is equal to EDARIC0. Therefore, we have ECA0 ¼ EDARIC0 ¼ L  H 2  ðEelec þ e  rn Þ þ L  ðH 2  1Þ  Eelec ¼ a  L  ½H 2  ðEelec þ e  rn Þ þ ðH 2  1Þ  Eelec  þ ð1  aÞ  L  ½H 2  ðEelec þ e  rn Þ þ ðH 2  1Þ  Eelec  ð31Þ Comparison of the average energy consumption For convenience, we define the difference between the average energy consumption in the two schemes as DCA-DAR ¼ ECA0  EDAR0 ¼ ð1  aÞ  L  ðH 2  1Þ  ð2Eelec þ ern Þ

ð32Þ

Because a is a number in (0, 1) and H > 1, the DCA-DAR is positive, namely, ECA0 is larger than EDAR0. Consequently, given the same initial energy for each node in the two schemes, we can draw the conclusion that TDAR0 is greater than TCA0 from Eqs. (22) and (30), which means that the DAR scheme can provide a longer network lifetime than the CA scheme. 4. Geographical TDMA-like routing protocol In most of conventional routing protocols, after sensor nodes pack data packets, they send the data immediately or delay a small random time. However, in dense sensor networks, this kind of simple data-gathering method, which lacks scheduling at a system level, often causes much waste of energy of nodes and network bandwidth, especially when the communication radii of sensor nodes increase. If all the sensor nodes in the network transmit data almost at the same time, the burst of data traffic will lead to low packet reception rate at the sink because of serious transmission collisions [31]. Meanwhile, since the acknowledgements to the data packets successfully received

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may also be destroyed, the senders have to retransmit the data without receiving acknowledgements. Furthermore, disorganized retransmissions probably aggravate collisions, thereby wasting more energy and bandwidth. Considering the long interval between two data-collecting processes in data-gathering sensor networks, we can disperse data transmissions over the interval to reduce the effect of transmission collisions. Therefore, we suggest a geographical routing protocol, called Geographical TDMA-like Routing (GTR), to reduce the negative impact of enlarging radii and increase the packet reception rate at the sink. In the proposed scheme, sensor nodes sample, send and receive data at different time. They sample data at the beginning of each data-gathering period to keep the snapshot character of the data, and then enter the sleep state till the time they should wake up to receive data. 4.1. GTR protocol overview We assume that each sensor node knows its own geographic location, by either GPS services or self-configuring localization techniques. The routing protocol consists of two stages: topology construction stage and data collection stage. Topology construction Using a small radio radius r, the sink broadcasts a notification packet to start constructing a data-forwarding tree. A notification packet includes the information of node ID, hop count to the sink, and the position of the node. When a senor node receives notification packets, it adds the sender’s information to its neighbor list and marks whether the sender can be considered as a candidate for its next hop. We define a routing-available range as a coverage range that is smaller than the communication range of a sensor node and has high reception rate. The candidates for next hops are restricted within the routing-available range. Although this makes data pass through more hops to arrive the sink, it can improve the energy efficiency of the whole network [29]. After receiving several notification packets from one-hop neighbors, the sensor node selects its next-hop node from the candidates using a probabilistic method, thereby determining its own hop count. The node then broadcasts a notification packet that includes its information, using the same radio radius r. Once the sensor node determines its hop count, it classifies its one-hop neighbors into three groups. The first group consists of the neighbors whose hop counts to the sink are one less than that of the sensor node itself, and this group is regarded as the forward routing table. The second group consists of the neighbors whose hop counts are one more than that of the node itself, and this group is considered as the backward routing table. The third group contains all other neighbors that are in neither of the routing tables. When all the nodes in either of the two routing tables fail, the nodes in the third group can be picked up to become possible next-hop nodes.

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By spreading the notification packets in the network, every sensor node would be assigned to a certain hop grade. In order to adapt to node failure, the sink performs topology reconstruction periodically with a long interval. Data collection The sink broadcasts a beacon packet that includes an incharge schedule, as described in Section 3.2.2, to start the data collection stage. Unlike in the topology construction stage, the radio radius used by the sink to send beacon packets is large enough to cover the whole network region. However, if the network is well time-synchronized, the beacon packets can also be broadcast using a small radio radius and spread in the network. Therefore, all sensor nodes can receive the bean packets and enter the process of collecting data at almost the same time. The sink can broadcast beacon packets periodically during gathering data to synchronize the data-reporting behavior of all the sensor nodes. The data collection stage consist of continuous datagathering periods. After receiving a beacon packet, a sensor node updates the in-charge schedule in its memory, and decides which routing tables should be used for the current data-gathering period according to the schedule. Considering the transmission reliability in dense sensor networks [29], the sensor node selects one neighbor from the in-use routing table by a distancebased stochastic method. The nearer the neighbor is, the higher probability of being selected is. Thus, the sensor node can send its data to the next hop after a delay, which is calculated by the mechanism described in Section 4.2. 4.2. Geographical TDMA-like transmission scheduling To reduce collisions when sensor nodes report data, especially when the nodes in the in-charge grade send data directly to the sink, we introduce a three-level TDMA-like transmission scheduling mechanism into the DAR scheme. The basic idea of the scheduling mechanism is to make sensor nodes decide their transmission delay according to their positions. Assuming a data-gathering period is long

a

B

5.1. Simulation setup We implemented the DAR scheme in GloMoSim [27]. In our experiments, 500 sensor nodes were distributed uni-

Data-gathering Period For not-in-charge grades

A

Sector 4

5. Experimental results

b

Sector 2

Sector Y 3

enough, we disperse data transmissions over the whole interval to reduce collisions. In Level-1 scheduling, we divide a data-gathering period into two sub-periods. In the first sub-period, all the nodes in the grades that are not in charge send data to the incharge grade. The nodes in the in-charge grade store the data received and send them to the sink in the second sub-period, which benefits data aggregation in the course of collecting data. To reduce the transmission collisions in the same hop grades, in Level-2 scheduling, we set up a coordinate system that takes the sink as its origin, and then divide the coordinate system into eight sectors with radiate lines, as shown in Fig. 5a. Accordingly, we divide each sub-period into eight slots, as shown in Fig. 5b. Each sector of a hop grade is corresponding to a slot. For example, Sector 2 of the hop grade G2 in Fig. 5a is assigned to the second slot in the first sub-period in Fig. 5b. Since all sensor nodes know the positions of their own and the sink, they can calculate the sectors that they belong to; consequently, they will know the slots in which they can send data. Level-3 scheduling is used to arrange the transmissions of the nodes in the same sectors. We divide each slot into several slices, as shown in Fig. 5b. The number of the slices in each slot is greater than the possible maximum hop count in a given network. The sensor nodes in the same sector pick up a slice to send data according to their hop counts to the sink. Therefore, the collision range is limited to the nodes that are in the same sector and have the same hop count. In Fig. 5a, both Node A and Node B belong to Sector 2 of G2 and have three hops to the sink. When G2 is not in charge, they would compete for transmitting in the third slice of the second slot of the first sub-period. To handle this problem, we employ a small coordinate-based stochastic delay to reduce collisions further.

Sector 1 Slots

Sink

1

2

3

4

For in-charge grade

5

6

7

8

X Sector 5

Sector 8 Sector 6

Sector 7

Slices

1 2 3 4 5 6 7 Node A

Collision Regions

Node B

Fig. 5. Geographical TDMA-like scheduling mechanism. (a) Spatial division in the coordinate system where the sink is the origin. (b) Temporal division in a data-gathering period.

Y. Bi et al. / Computer Communications 30 (2007) 2812–2825 Hop Count

1

2

3

4

5

6

7

Initial Values 1.4276 1.1394 1.1081 1.0594 1.5071 1.4251 1.0000 Period #1 0.4276 1.1394 1.1081 1.0594 1.5071 1.4251 1.0000 -1.0 Period #2 0.4276 0.1394 1.1081 1.0594 1.5071 1.4251 1.0000 -1.0

…

…

Period #7 0.4276 0.1394 0.1081 0.0594 0.5071 0.4251 0.0000 -1.0

+

Period #8 0.8552 1.2788 1.2162 1.1188 2.0142 1.8502 1.0000 -1.0

…

Period #9 0.8552 0.2788 1.2162 1.1188 2.0142 1.8502 1.0000 -1.0

…

formly in a circular area, which had a radius of 200 m and had a sink node at the center. In our simulated data-gathering application, each sensor node reported a data packet to the sink every 30 min. The main simulation parameters are listed in Table 3. In each experiment of the DAR scheme, the sink sent two beacon packets to the whole network. The first beacon was used to inform all the sensor nodes to enter the data collection stage, and the second beacon was used to update the in-charge schedule stored in the sensor nodes. An example of the in-charge time proportion is shown in Table 4, which was calculated by the sink after getting the number of nodes in different hop grades according to the hop count information included by the data packets. In addition, in this experiment, the maximum hop count was controlled at seven (H = 7). Every sensor node stores the in-charge time proportion into an array and determines which hop grade should be in charge in the current data-gathering period by updating the values of the elements in the array. At the beginning of each data-gathering period, each sensor node accesses the element that is next to the latest in-charge hop. As shown in Fig. 6, if the value of the element is no less than 1.0, the node will subtract 1.0 from the value and mark the corresponding hop grade in-charge in the current period. If the value is less than 1.0, the sensor node will go ahead to check the next element in the array. If the values of all the elements are less than 1.0, the node will add the initial values of the incharge time proportion to the current values in the array and continue searching for an element to subtract 1.0. This

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Fig. 6. An example of how a sensor node uses the in-charge time proportion to determine the run-time scheduling.

method can guarantee the run-time scheduling as close as possible to the in-charge time proportion. In our experiments, we compared the DAR scheme with the CA scheme and the energy-balanced data propagation protocol proposed in [23]. In this section, we use the acronym EBDP to denote the energy-balanced data propagation protocol. We adopted the solution under simplifying assumptions, which had been proved to be very tight in [23]. For the free parameter x (x 2 (0, 1)) in the solution of EBDP, we set x to be 0.2, 0.5, and 0.8 to perform simulations in the same settings, respectively, and we calculated the averages of the results as the result of EBDP in one simulation. 5.2. Energy balance

Table 3 Main simulation parameters Parameter

Value

Network radius (R) Number of nodes Length of the data packet (L) Time interval for reporting data MAC protocol Radio frequency Radio bandwidth Normal transmission power Maximum transmission power Eelec in the energy model dcrossover in the energy model efriis-amp in the energy model etwo-ray-amp in the energy model

200 m 500 88 bytes 30 min CSMA 433 MHz 19.2 Kbps 20 dBm 10 dBm 1.16 lJ/bit 40.8 m 5.46 pJ/bit/m2 0.00325 pJ/bit/m4

Table 4 An example of in-charge time proportion in our experiments Hop to sink

Number of nodes

In-charge time

1 2 3 4 5 6 7

25 46 68 85 103 102 70

1.4276 1.1394 1.1081 1.0594 1.5071 1.4251 1.0000

Fig. 7 shows a statistical histogram for the average residual energy of the nodes with different hop counts to the sink in the networks, which employed DAR, CA, and EBDP, respectively. The scale of horizontal axis is the hop count to the sink, and the height of square columns denotes the average residual energy of nodes in a corresponding hop grade. We set the initial energy of each node as 4 J and record the residual energy of all the nodes at the same time during the simulations were running. As shown in Fig. 7, in the network that adopted the CA scheme, the average residual energy of the nodes with small hop counts to the sink is obviously lower than that of the nodes with large hop counts. However, in the network that adopted the DAR scheme, the differences among the average residual energy of the nodes are much smaller. In addition, the EBDP solution also achieved a much better balance than the CA scheme, but the nodes near the edge of the network still had much higher residual energy than the nodes near the sink. This is because that (1) we employed a close solution of EBDP but not the complicated general solution in [23], (2) the close solution does not consider the energy consumption of message reception, and (3) EBDP is seemed to be more suitable for large-scale sensor networks from the analysis in [23].

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4000000 3600000 3200000

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4000000 3600000 3200000 2800000 2400000 2000000 1600000 1200000 800000

400 350 300 250 200 150 100 50

400000 0

)

Figs. 8–10 show the snapshots of the energy consumption of the sensor nodes at the same simulation time in the experiments that adopted different solutions. In the figures, axis X and Y decide the locations of the nodes, while axis Z denotes the total energy consumption of the node. Obviously, in the CA scheme, the sensor nodes near the sink depleted their energy much faster than the nodes far away from the sink did, as shown in Fig. 8. However, in the networks adopting EBDP and DAR, the sensor nodes consumed their energy much equally. Therefore, the experimental results show that the CA scheme brings dramatic disparity among the nodes with different distances to the sink; in contrast, the DAR scheme can balance the energy consumption of the sensor nodes in a whole network range efficiently.

150

Fig. 9. Energy consumed by the sensor nodes during an experiment running in the EBDP solution (Initial Energy of Node = 4 J, H = 7).

) Energy Consumed (uJ

Fig. 7. Average residual energy of the nodes with different distances to the sink in the three solutions (Initial Energy of Node = 4 J, H = 7).

100

250 Posit 300 ion.X 350 ( m) 400

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ion

3250000

s it

3500000

0

Po

Average Residual Energy (uJ)

4250000

Fig. 10. Energy consumed by the sensor nodes during an experiment running in the DAR scheme (Initial Energy of Node = 4 J, H = 7).

5.3. Network lifetime

4000000 3600000

) Energy Consumed (uJ

3200000 2800000 2400000 2000000

DAR EBDP CA

100000 90000 80000 70000 60000 50000 40000 30000 20000 10000

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itio n. Y

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)

400000 0

110000

Average Network Lifetime (s)

In this paper, the network lifetime is defined as the period of time until the first node dies. Fig. 11 gives the comparison of the network lifetimes among the three solutions varying

Fig. 8. Energy consumed by the sensor nodes during an experiment running in the CA scheme (Initial Energy of Node = 4 J, H = 7).

1.5

2.0

2.5

3.0

3.5

4.0

Initial Energy for Each Sensor Node (J)

Fig. 11. Lifetime performance of the three schemes (H = 7).

with the initial energy for each sensor node. Every value dot in the figure is the average of the results of five experiments in different node deployments using the same topology construction strategy. As shown in Fig. 11, the DAR

Y. Bi et al. / Computer Communications 30 (2007) 2812–2825

50000

DAR EBDP CA

60000 55000

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5000 0

1

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0 4

Topology No.

5

6

7

8

9

Maximum Hop Count in the Network

Fig. 12. Lifetime performance of the three schemes varied with different nodes deployments (Initial Energy of Node = 1.5 J, H = 7).

Fig. 13. Lifetime performance of the three schemes varied with different maximum hop count in the network (Initial Energy of Node = 1.5 J).

scheme performed better than both the other solutions. Moreover, the lifetimes of the networks in the DAR scheme increased faster than that of the network in the CA scheme. The reason is that if the sensor nodes have low initial energy, when the network lifetime ends, the real time proportion of the in-charge hop grade did not match the proportion expected well. However, if the sensor nodes have enough energy to work for a longtime, they would have more chances to try to match the expected proportion. Therefore, we can conclude that the DAR scheme can extend network lifetime notably, especially in long-running applications that require many data-gathering periods. When performing the experiments, we found that the network lifetime performance of the DAR scheme varied much with different network topologies. To evaluate the performance of the DAR scheme objectively, we present several experimental results here. As shown in Fig. 12, the network lifetimes of the DAR scheme swing with different node deployments. The reason for this is that the first dead node is not always among the one-hop neighboring nodes of the sink, but often among the quasi-bottleneck nodes that are perhaps located far from the sink [30]. Nevertheless, the DAR scheme can still extend the network lifetime by a factor of three over the CA scheme in the worst case.

the routing protocol to obtain different values of d and calculated the different in-charge time proportions for different experiments. Fig. 13 shows the comparison of the lifetime performance among the three solutions when the maximum hop count of the network increased. We can see that the DAR scheme worked better in the networks with different maximum hop counts than the other two solutions.

5.4. Influence of parameter d The DAR scheme can combine with various routing protocols that may form different data-collecting topologies. As a result, the parameter d in the scheme may vary with different routing protocols. We performed several experiments to investigate the lifetime performance of the DAR scheme with different values of d in the geographical TDMA-like routing protocol mentioned in Section 4. From Eq. (1), we know that different values of d can bring in different average widths of the hop grades, and consequently result in different maximum hop counts in the network. In this group of experiments, we controlled the maximum hop count by adjusting the parameters of

6. Discussion Considering the limited communication ability of actual sensor nodes, the DAR scheme seems more suitable for the sensor networks that are not very large. In a large-scale network that consists of many hop grades, it may be unreasonable to make the nodes in G1 and G2 send their data to the farthest hop grade from the sink when GH1 is in charge. In such case, the energy waste of the nodes that are near the sink may overwhelm the improvement in energy efficiency made by scheduling the data flows on a system level. Therefore, when adopting the DAR scheme in a large-scale sensor network, we suggest deploying multiple sinks to form hierarchy architecture and applying the scheduling in each sub-network. In this paper, we aim to present a technique that improves energy efficiency of sensor networks by scheduling the traffic flows during gathering data, even if the data may be transmitted away from the sink sometimes. There is a wide research space behind this paper and several issues can be explored further, such as whether the nodes in the hop grade G1 should also send data to the sink all the time, like the nodes in G0, whether the network should be divided into several groups of adjacent rings so as to design a twolevel scheduling algorithm, and so forth. In the future, we will first extend our work to model the DAR scheme combined with the routing protocols without any collision avoiding mechanisms. Moreover, we will introduce data aggregation into the DAR scheme to reduce the total energy consumption of the network.

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7. Conclusion In this paper, we proposed a novel data-gathering scheme, DAR, to tackle the hotspot problem of a wireless sensor network in the conventional data-gathering scheme that always drives data to flow towards a sink. In the DAR scheme, all sensor nodes with different hop counts to the sink will participate in gathering all the sensed data and transferring them to the sink. Because all the nodes in the network partake of the heavy workload, the sensor nodes consume their energy almost equally and the hotspot problem can be significantly relieved. We modeled the DAR scheme and presented the method of determining the incharge time for the nodes with different hop counts. The experimental results show that the DAR scheme can balance the energy consumption in a whole network range and prolong the network lifetime dramatically. Acknowledgements This work was supported by the National Science Foundation of PR China (Grant No. 60673178) and the National Basic Research Program of China (973 Program, Granted No. 2006CB303000). The authors express sincere appreciation to the reviewers of this paper for their helpful recommendations. References [1] I.F. Akyildizy, M.C. Vuran, O.B. Akan, W. Su, Wireless sensor networks: a survey revisited, Elsevier Computer Networks Journal (2005). [2] J.N. Al-Karaki, A.E. Kamal, Routing techniques in wireless sensor networks: a survey, IEEE Wireless Communications 11 (2004) 6–28. [3] D. Niculescu, Communication paradigms for sensor networks, IEEE Communications Magazine 43 (3) (2005) 116–122. [4] J.H. Chang, L. Tassiulas, Energy conserving routing in.wireless adhoc networks, in: Proceedings of IEEE INFOCOM, March 2000. [5] R.C. Shah, J.M. Rabaey, Energy aware routing for low energy ad hoc sensor networks, in: IEEE Wireless Communications and Networking Conference (WCNC), March, 2002, Orlando, IEEE Communications Society, 2002. [6] S. Lindsey, C. Raghavendra, K.M. Sivalingam, Data gathering algorithms in sensor networks using energy metrics, IEEE Transactions on Parallel & Distributed Systems 13 (9) (2002) 924–935. [7] W. Ye, J. Heidemann, D. Estrin, An energy-efficient MAC protocol for wireless sensor networks, Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’02) 3 (2002) 1567–1576. [8] T.V. Dam, K. Langendoen, An adaptive energy-efficient MAC protocol for wireless sensor networks, in: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys’03), Los Angeles, CA, November 2003, pp. 171–180. [9] G. Lu, B. Krishnamachari, C. Raghavendra, An adaptive energyefficient and low-latency MAC for data gathering in sensor networks, in: Proceedings of the 4th International IEEE Workshop on Algorithms for Wireless, Mobile, Ad Hoc and Sensor Networks (WMAN’04), April 2004. [10] CC1000 RF Transceiver, . [11] CC2420 RF Transceiver, .

[12] Jie Wu, Shuhui Yang, Coverage issue in sensor networks with adjustable ranges, in: 2004 International Conference on Parallel Processing Workshops (ICPPW’04), pp. 61–68. [13] Jae-Hwan Chang, Leandros Tassiulas, Maximum lifetime routing in wireless sensor networks, IEEE/ACM Transactions on Networking 12 (4) (2004) 609–619. [14] Francisco Javier Ovalle-Martı´nez, Ivan Stojmenovic´, Fabia´n Garcı´aNocetti, Julio Solano-Gonza´lez, Finding minimum transmission radii for preserving connectivity and constructing minimal spanning trees in ad hoc and sensor networks, Journal of Parallel and Distributed Computing 65 (2) (2005) 132–141. [15] F. Ye, H. Luo, J. Cheng, S. Lu, L. Zhang, A two-tier data dissemination model for large-scale wireless sensor networks, in: Proceedings of the Eighth Annual International Conference on Mobile Computing and Networks (MobiCOM 2002), Atlanta, GA, USA, September 2002, pp. 148–159. [16] Hyung Seok Kim, Tarek F. Abdelzaher, Wook Hyun Kwon, Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks, SenSys’03, Los Angeles, CA, USA, November 5–7, 2003, pp. 193–204. [17] Arnab Chakrabarti, Ashutosh Sabharwal, Behnaam Aazhang, Using predictable observer mobility for power efficient design of sensor networks, IPSN2003, LNCS 2634, Berkeley, California, USA, April 2003, pp. 129–145. [18] Z. Maria Wang, Stefano Basagni, Emanuel Melachrinoudis, Chiara Petrioli, Exploiting sink mobility for maximizing sensor networks lifetime, in: Proceeding of the 38th Hawaii International Conference on System Sciences, Big Island, Hawail, January 3–6, 2005, pp. 1–9. [19] EI Oyman, C. Ersoy, Multiple sink network design problem in large scale wireless sensor networks, in: Proceedings of the International Conference on Communications (ICC’04), Paris, France, June 20–24, vol. 6, 2004, pp. 3663–3667. [20] Haeyong Kim, Yongho Seok, Nakjung Choi, Yanghee Choi ,Taekyoung Kwon, Optimal multi-sink positioning and energy-efficient routing in wireless sensor networks, one of two best paper awards, in Proceedings of ICOIN 2005, Jeju, Korea, January, 2005, reprinted in Lecture Notes in Computer Science (LNCS), Springer-Verlag, No. 3391, 2005. [21] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS’00) 8 (2000) 8020–8029. [22] Shoudong Zou, Ioanis Nikolaidis, Janelle J. Harms, ENCAST: energycritical node aware spanning tree for sensor networks, in: Proceedings of the 3rd Annual Communication Networks and Services Research Conference (CNSR’05), Halifax, Canada, May 2005, pp. 249–254. [23] Charilaos Efthymiou, Sotiris Nikoletseas, Jose Rolim, Energy balanced data propagation in wireless sensor networks, in: 18th International Parallel and Distributed Processing Symposium (IPDPS’04) – Workshop 12, 2004, p. 225. [24] Jain-Shing Liu, Chun-Hung Richard Lin, Energy-efficiency clustering protocol in wireless sensor networks, Ad Hoc Networks 3 (3) (2005) 371–388. [25] Bi Yan-zhong, Yan Ting-Xin, Sun Li-min, Wu zhi-mei, A power graded data-gathering mechanism for wireless sensor networks, To appear in ACTA Automatic Sinica, 2006. [26] W.B. Heinzelman, Application-specific protocol architectures for wireless networks, Ph.D. Thesis, Massachusetts Institute of Technology, June 2000. [27] GloMoSim, . [28] Qiling Xie, Chin-Tau Lea, Mordecai J. Golin, Rudolf Fleischer, Maximum residual energy routing with reverse energy cost, GLOBECOM 1 (2003) 564–569. [29] A. Woo, T. Tong, D. Culler, Taming the underlying challenges of reliable multihop routing in sensor networks, in: Proceedings of the First International Conference on Embedded Networked Sensor Systems, 2003, pp. 14–27.

Y. Bi et al. / Computer Communications 30 (2007) 2812–2825 [30] Le Tian, Dongliang Xie, Lei Zhang, Shiduan Cheng, Quasi-bottleneck nodes: a potential threat to the lifetime of wireless sensor networks, APWeb Workshops 2006, 2005, pp. 241–248. [31] Jerry Zhao, Ramesh Govindan, Understanding packet delivery performance in dense wireless sensor networks, in: Proceedings of ACM SenSys 2003, Los Angeles, November 2003, pp. 1–13.

Yanzhong Bi received his B.S. degree in Computer Science and Technology from Tsinghua University, Beijing, China, in 2002. He is currently a Ph.D. candidate in the Multimedia Communication and Network Engineering Research Center of the Institute of Software, Chinese Academy of Sciences, Beijing, China. His research interests include network architecture, routing techniques and mobility in wireless sensor networks.

Na Li received her B.S. degree in Computer Science and Technology from Nankai University, Tianjin, China, in 2005. She is currently an assistant researcher in Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. Her interests involve routing and scheduling techniques, QoS and managing strategies in Wireless Sensor Network and Ad Hoc Network.

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Limin Sun received his M.S. degree in 1995 and Ph.D. degree in 1998, both from National University of Defense Technology, Hunan, China. From June 1998 to June 2000 he was a postdoctoral fellow in the Institute of Software, Chinese Academy of Sciences, Beijing, China. He is currently a professor in the Multimedia Communication and Network Engineering Research Center of the Institute of Software. His research interests include mobile IP techniques, wireless sensor networks and community network with integrated services.