Prolonging Network Lifetime via Intra-Cluster Routing in Wireless Sensor Networks g Trong Thua Huynh, Choong Seon Hong Department of Computer Engineering, Kyung Hee University Giheung-Eup, Yongin-Si, Gyeonggi-Do, 449-701, Korea
[email protected],
[email protected]
ABSTRACT An important challenge in wireless sensor networks (WSN) is that how to disseminate data packets from sensor nodes to base station energy efficiently. Inspiring data fusion, an outstanding feature of clustering approach, and multi-hop routing, in this paper, we propose a clustering approach called PLIR (Prolonging Network Lifetime via Intra-Cluster Routing) for saving energy and distributing data dissipation evenly by improving base station cluster formation algorithm of LEACH-Centralized and providing 3-hop routing algorithm within clusters. Hence, it prolongs the lifetime of the network. Our simulation results show that PLIR consumes less energy and reduces communication overhead, and extends the network lifetime compared with other approaches. Keywords: sensor network, cluster, energy, intra-cluster, routing, network lifetime.
1 INTRODUCTION In wireless sensor networks, where sensors are deployed densely in inhospitable environments, the proximate nodes will sense the identical data. Data aggregation from many of correlative data will reduce a large amount of data traffic on network, avoid information overload, produce a more accurate signal and require less energy than sending all the unprocessed data throughout the network. In various literatures, clustering approach is addressed as a routing method using the data aggregation feature effectively. The second version of protocol in [1] called LEACHCentralized shows a base station (BS) cluster formation approach but the communication between sensor nodes is one hop. This causes more interference to proximate sensor nodes and the energy dissipation is distributed unevenly. In addition, each node sends information about its current location and energy level to the BS during the cluster formation phase. This causes high delay in network, the amount of overhead transmitted in the network will g
This work was supported by University ITRC Project of MIC Dr. C.S. Hong is the corresponding author
increase significantly. Moreover, after several rounds, direct communication from all sensor nodes to BS is unfeasible. Another method of wireless communication is to use multihop approach. There has been much work on multi-hop routing protocols for wireless networks [4, 5]. In these protocols, authors discusses strategies for choosing multi-hop routes to minimize the power dissipated in the sensor nodes along the route and find optimal routes by relying on the energy at each node along the route. However they do not take full advantage of data aggregation feature in WSNs. Furthermore, applications requiring efficient data aggregation are natural candidates for clustering. Clustering can be extremely effective in one-many, many-to-one, one-to-any, or one-to-all communication. For instance, in many-to-one (suit with our model), clustering can support data aggregation and reduce communication interference. Lifetime of the WSN can be defined as the time lasted until the last sensor node in the network depletes its energy. Energy consumption in a sensor node can be due to many factors such as sensing event (data), transmitting or receiving data, processing data, listening to the media (avoid the conflict), communication overhead, ect. Considering the sensor’s energy model in [11], the energy used to send r bits a distance d from one node to another node is given by ETx = (α1+α2dn)*r. Where α1 is energy dissipated in transmitter electronics per bit, α2 is energy dissipated in transmitter amplifier. For relatively short distances, the propagation loss can be modeled as inversely proportional to d2, whereas for long distances, the propagation loss can be modeled as 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. Obviously, energy consumption in a sensor node will be significant if it transmits data to the node that is at long distance. That is why we suggest an intra-cluster 3-hop routing approach here. However, why is it must 3 hops but not 2 or more than 3 hops. Firstly, if we use more than 3 hops, relaying nodes will need much more energy to forward data in the network, the computation complexity is also higher. It brings about the higher latency. This is really not an expected concern in WSNs. Secondly, why do not we use 2 hops for routing within each cluster. Actually, 2-hop routing prefers to use to 3-hop routing. The reason it was not called 2-hop was simply that 3-hop
routing will be used in some cases where 2-hop routing is not a good solution. Easily speaking, 2- hop routes occur more frequently than 3-hop routes. And notation 3-hop routing implies to the maximum number of hops can be used in routing scheme. Motivated by above mentioned issues, in this paper, we improve BS cluster formation approach of LEACHCentralized protocol [1]. Then, we provide an intra-cluster 3-hop routing approach for WSN. We partition sensors within each cluster into sets of nodes based on their location and remaining energy level. Each set of nodes has its own task. Then, we use the Shortest Path algorithm to determine the best route from sensor nodes to CH node through these sets of nodes. The remainder of the paper is organized as follows. Section 2 mentions about related work. Section 3 describes the network model. In section 4, our approach will be addressed. We present the performance evaluation in section 5. Finally, we conclude the paper in section 6.
2 RELATED WORK Many wireless network protocols have been developed for increasing energy efficiency, especially clustering algorithms in recent years. Some of these literatures can be listed out here, such as: A clustering architecture based on a distributed algorithm for WSNs is provided in [1], where sensor nodes elect themselves as CH nodes with some probability based on residual energy of sensors for each round. This approach allows only 1-hop clusters to be formed, but CH nodes can rotate at each round. Then, authors improved clustering algorithm by using a center cluster algorithm to form the clusters. In this approach, BS will control almost operations in the network, compute and determine optimal clusters. Clustering architecture introduced in [9] provides two threshold parameters (hard, soft) in other to reduce number of transmission in the networks. The main drawbacks of two approaches are the overhead and complexity of forming clusters in multiple levels, implementing thresholdbased functions. Younis et al, address hierarchical routing architecture in [10] based on 3-layer model. Clusters are formed by a lot of factors such as communication range, number and type of sensor nodes and geographical location, that can base on GPS [8] or other techniques. However, cluster formation problem is not mentioned by authors. They only focus on the issue of network management within the cluster, particularly energy-aware routing. In [2], authors provide a protocol called HEED. This approach selects well-distributed CH nodes using information about residual energy and a second parameter such as node proximity to its neighbors or node degree.
3 NETWORK MODEL A clustering model for WSN is shown in figure 1.
Base Station
Cluster head node Sensing node Level-1 relaying node Level-2 relaying node
Fig. 1. 3-hop clustered sensor network example. Relaying node has both sensing data and relaying data packet to next nodes while sensing node is only sensing data. In a typical WSN, the sensor nodes’ locations are fixed and the instability of cluster due to mobility of sensor is not an issue. It means that sensor nodes are mobile but not much and with low rate. Hence, in this network model, we assume the sensors are quasi-stationary. Each tiny sensor has a sensing module, a computing module, memory and wireless communication module. Sensor nodes are left unattended after deployment. BS has adequate energy to communicate directly with all sensor nodes in the network. However, the sensor nodes cannot always do this because of their limited energy supply and it may be impossible to recharge batteries for them. The sensors are homogeneous and begin with the equal initial energy. They can use power control to vary the amount of transmit power to reduce the possibility of interfering with nearby cluster and its own energy dissipation. Also, each node has computation power to support different MAC protocols and performs signal processing functions. The sensor nodes located close to each other sense correlated data and they always have data to send to BS.
4 THE PROPOSED APPROACH (PLIR) 4.1. Clustering Model In the figure 1, the sensor nodes are grouped into clusters controlled by a BS. Each cluster has a cluster-head (CH) node that aggregates all data sent to it by all its members and transmits data to the remote BS. Therefore, the CH node must have much more energy than the non-CH node (sensor node, relaying node). BS performs cluster formation in the network, and informs all sensor nodes of clustering information afterwards.
Network lifetime is divided into rounds. Each round begins with cluster formation phase followed by data transmission phase. In each frame of data transmission phase, non-CH node is assigned its own time slot to transmit data to CH node. A description is depicted in the figure 2. Cluster formation
Slot for node i
Slot for node i
Time
Cluster formation
Data transmission
Round
Fig. 2. Network lifetime for PLIR
4.2. Algorithms The operation of PLIR is depicted in figure 3. Sensor nodes send information (location, energy level) to BS
BS forms clusters, determines route, and creates TDMA schedule for sensor nodes, then send clustering and routing information to sensor nodes
Header
Fig. 3. Flowchart of PLIR PLIR distinguishes between the first round and the remaining rounds. In the first round, all sensors must send information about their location and current energy level to BS directly. The BS, based on this information, uses a cluster formation algorithm as will be described below to choose CH nodes and distribute remaining nodes into associated clusters. In subsequent rounds, to form clusters, the sensor nodes do not need to resend the information about location and residual energy to BS anymore. Instead of this, information will be extracted from the INFOR part in the data packets received from CH nodes at previous round. The last packet from each node at the end of each round is the only one that carries information about residual energy level of that node. The other packets carry data normally. CH nodes receive data packets from non-CH nodes, perform data integration then send data packet to BS directly. This packet format is depicted in figure 4.
INFOR
Node ID Node Energy Node Information
Fig. 4. The Packet format. The INFOR part includes information about {ID, energy} of nodes that packet passed. After having information about location and energy level of all sensor nodes in WSN, BS begins determining CH nodes and using the simulated annealing algorithm [3] to find out associated clusters. Determining optimal clusters from sensor nodes is a problem that is known to be NP-Hard property. Approximation algorithms, such as local search, taboo search or simulated annealing [3], can be applied to optimal solutions in polynomial time. Simulated annealing is an algorithm based on thermodynamics principles. If a solid material is melted and allowed to cool, the energy of the system enters several intermediate states settling at the low-energy final state. If the system enters a state that is lower in energy than its previous state, the system remains there. However, if the system enters a state that is higher in energy than its previous state, the system remains there with a probability given by:
p=e
next round
Sensor nodes send data to BS (include residual energy)
Data
−
δ k BoltzT
Where kBoltz is the Boltzman constant and T is a fixed temperature. This algorithm can be applied to optimal problems where δ is replaced with the difference in cost between the new state and the old state, and kBoltzT is a parameter that must be picked to ensure that the algorithm converges. Firstly, BS computes the average node energy (ANE) among all the nodes. Whichever nodes have energy more than ANE are candidate for CH nodes. After that, BS runs the simulated annealing algorithm [3] to find out k nodes that are the best to be CH nodes for next round and the associated clusters. This algorithm minimizes the amount of energy the non-CH nodes will have to use to transmit their data to the CH, by minimizing the total sum of squared distances between all the non-CH nodes and the closest CH. At each iteration, the next state, which consists of a set of nodes in C’, is determined from the current state, the set of nodes in C, by randomly perturbing the x and y coordinates of the nodes c in C to get new coordinates x’ and y’. The nodes that have location closest to the (x’,y’) become the new set of CH nodes c’ that make up set C’. Given the current state at iteration k, represented by the set of CH nodes C’ with cost f(C’), will become the current state with probability:
⎧ e − ( f ( C ') − f ( C )) / α k pk = ⎨ ⎩1
: :
f (C ' ) ≥ f (C ) f (C ' ) < f (C )
Where: αk: the control parameter (equivalent temperature parameter in the thermodynamic model).
to
the
The parameter αk must be chosen to be increasing with increasing k to ensure that the algorithm converges. However, if αk increases too quickly, the system will get stuck in local minima. On the other hand, if αk increases too slowly, the system will take a very long time to converge. The simulations show that αk = 1000ek/20 is best choice for determining good clusters. f(.): cost function defined by:
Our routing problem can be considered as determining the shortest route (least cost) from one node to a set of nodes because not all sensor nodes are active simultaneously. A study in [6] suggested that the best approach for solving this kind of problem is Dijkstra’s algorithm. Moreover, Dijktra’s algorithm is shown to suit centralized routing. Therefore, ee use Dijkstra’s algorithm for solving this routing approach with the link cost Cij for the link between the nodes i and j defined as follows:
N
5
f (C ) = ∑ min d 2 (i, c)
C ij = ∑ C k
Where: c ∈ C; d(i,c) is distance between the nodes i and c; N is number of sensors in the network. After determining CH nodes and associated clusters, BS performs 3-hop routing within each cluster as following algorithm:
Where: C1 = c1*(dij)2 : data communication cost (energy) from node i to node j where c1 is a weighting constant. This parameter reflects the cost of the wireless transmission power. Where dij = distance between the nodes i and j. C2 = data sensing cost of node j. C3 = c3*dij : delay cost because of propagation between the nodes i and j where c3 is a constant which describes the speed of wireless transmission. C4 = 1/(residual energy of node j). This parameter reflects cost of energy. C5 = 1/(number of connections to node j). It is worth to notice that routing setup is dynamically adjusted to optimally respond to changes in the sensor network. After each round, routes will be determined by BS relying on the remaining energy of sensors. Therefore, the “single-point of failure” is not problem in our approach. Moreover, since these computations are being performed at the base station, energy dissipation is not a concern. After determining routes for sensor nodes within each cluster, BS creates the TDMA schedule for sensor nodes. As such, each cluster uses a unique spreading code; each non-CH node assigned its own time slot to transmit data to CH node uses this spreading code as depicted in figure 2. The other sensors turn off their receivers for saving energy. However, the relaying node that is the next hop for the node transmitting data at its time slot must be in active mode to receive data from the transmitting node. The other nodes are idle to save energy. For example, in figure 6, when node k3 sends data to node j3, only j3 becomes active to receive data, the other nodes must be in idle mode (sleeping). Similarly, if node j2 is transmitting data, only node i1 is active to receive data packet, the others are in idle mode. If i2 takes turn to send (its slot time is assigned by TDMA schedule – figure 2), only CH can receive data packet, the other nodes are sleeping.
i =1
AD = average distance from non-CH nodes to associated CH node; CAE = average energy for each cluster; I1 = {}: set of nodes that are level-1 relaying nodes (sense data, relay data packet from J1, J2 to CH); J1 = {}: set of nodes that are level-2 relaying nodes (sense data, relay data packet from K to I1); I2 = {}, J2 = {}, K = {}: sets of sensing nodes; J = union of J1 with J2. I = union of I1 with I2. Ei : Energy of node i; If (Di,CH < AD) then // Di,CH : distance from node i to CH If (Ei >= CAE) then I1 = I1 U i; Else I2 = I2 U i; Else If (Di,CH >= AD and Di,CH < 2*AD) then If (Ei >= CAE) then J1 = J1 U i; Else J2 = J2 U i; Else K = K U i; Apply the Shortest Path Algorithm to determine the best route from CH node to J(J1 U J2), K using the set nodes I1, J1 respectively. The example of network within each cluster is depicted in the figure 5. I1 CH
J2
k =1
i1
I2 J1
I
J
Fig. 5. Intra-cluster network example.
CH K
j1
i2
i3
j2 I
j3 J
k3
K Fig. 6. An example for TDMA schedule within each cluster
5
PERFORMANCE EVALUATION
In this section, we analyze performance evaluation compared with LEACH-Centralized protocol (LEACH_C) in terms of communication overhead, the number of nodes alive, total amount of energy dissipated in the system over time using a simulator based on SENSE simulator [12]. The simulation uses MAC IEEE 802.11 DCF that SENSE implements.
In these experiments, each node begins with only 2J of energy and an unlimited amount of data to be sent to the BS. We ran simulations for 1000s. Each round lasts for 20s. We choose 20s because it effects mainly on the performance. If round-time is short, it causes high communication overhead for cluster formation process. If round-time is long, it makes CH nodes deplete energy quickly. This reduces significantly the burden for data packets. Table 1 summarizes parameters used in our simulation. 5.2 Simulation Result Some of the simulation results are demonstrated as follow. 100
Number of nodes alive
CH nodes use a fixed spreading code and CSMA approach to send data to BS. It means that, when CH node has data to send, it must sense the channel to see if anyone else is transmitting using the BS spreading code. If so, CH node has to wait. Otherwise, the CH node sends data using the BS spreading code. Finally, BS informs clustering information to all sensors in the network. The sensor nodes perform data transmission according to clustering information received. Data packet transmitted to BS will be added information about residual energy of nodes that packet passed. So, from the second round, sensor nodes will not send any information to BS anymore. BS will extract information from data packet received at previous round as described above in order to re-determined routes and TDMA schedule for all sensor nodes.
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5.1 Simulation Model
Fig. 7. Number of nodes alive over the simulation time
Parameter Network size Number of sensors Packet generating rate Eelec εmp εfs EDA Initial energy (for each node) Round time Data packet size Header size Infor_packet_size Simulation time
Value 100x100 100 1 packet/sec 50nJ/bit 0.0013pJ/bit/m4 10pJ/bit/m2 5nJ/bit/signal 2 Joule 20 sec 500 bytes 25 bytes 25 bytes 1000 sec
Table 1. Simulation Parameters
Since PLIR does not allow all non-CH nodes to communicate directly with CH node, number of nodes died after each round will be reduced significantly. It means that our algorithm optimizes the issue that many nodes died early while the other nodes are still alive; hence, it prolongs the lifetime of the network. N umber of overhead (byte)
Our sensor field spans an area of 100x100m2 wherein 100 sensors are scattered randomly. A node is considered non-functional if its energy level reaches 0. For a node in the sensing state, packets are generated at a constant rate of 1 packet/sec [7]. For the purpose of our simulation experiments, the values for the parameters {ck} in the link cost Cij are initially picked based on sub-optimal heuristics for best possible performance. The communication environment is contention and error free; hence, sensors do not have to retransmit any data. To compute energy consumption for each transaction sending and receiving, we use the radio energy dissipation model in [1].
5000 4000
LEACH_C 3000
PLIR
2000 1000 0 100
64
36
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4
Number of nodes alive
Fig. 8. Number of communication overhead in the network Since PLIR does not allow sensors to send information required for cluster formation to BS at the beginning of each round, amount of overhead will be reduced significantly.
easily conclude that, the five clusters is the best choice for the 100 sensor network. In addition, in LEACH_C approach, when there is only 1 cluster, the non-CH nodes often have to transmit data very far to reach the CH node. However, in PLIR approach, energy consumption is reduced for using 3-hop routing within each cluster. When there are more than 5 clusters, the average energy dissipation is almost same for both LEACH_C and PLIR approaches.
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6
CONCLUSIONS
0 0
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Fig. 9. Total of amount energy dissipated in the network.
Number of data signals received at BS (x1000)
Since we use 3-hop routing within each cluster, the relaying nodes have the capability to aggregate data from sensors which reduces a larger amount of the same data passed unnecessarily in the network. This is one of the reasons that the total amount of energy dissipated in the network is reduced significantly. 90 80 70 60 50 40 30 20 10 0
REFERENCES
LEACH_C
PLIR
0
20 40 60 80 Number of nodes alive
100
Fig. 10. Number of data signals received at BS vs. Number of nodes alive. PLIR delivers data to the BS more efficiently than LEACH_C. Average energy dissipation per round (J)
Clustering approach in WSNs has more advantages than other approaches such as direct transmission or multi-hop routing for saving energy. In this paper, we improve the BS cluster formation of the second version of LEACH and provide a 3-hop routing approach within each cluster in order to balance the energy consumption for all sensor nodes in the network, extend lifetime of network and reduce the number of communication overhead in the network. Simulation results demonstrate that our algorithm (PLIR) consistently performs well with respect to energy-based metrics, e.g. network lifetime compared with LEACH-Centralized.
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Fig.11. Average energy dissipation per round as a function of the number of cluster. Goal of this simulation is try to find the optimal number of clusters for the 100 sensor network. From figure 11, we
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