An Energy Efficient Chain-Based Clustering Routing Protocol for Wireless Sensor Networks Jae Duck Yu, Kyung Tae Kim, Bo Yle Jung, and Hee Yong Youn School of Information and Communications Engineering Sungkyunkwan University, Suwon, Korea
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Abstract— Wireless sensor network consisting of a large number of sensors is effective for gathering data in a variety of environments. Since the sensors operate on battery of limited power, it is a challenging task to design an efficient routing scheme which can minimize the delay while offering high energy efficiency and long network lifetime. In this paper we propose a new routing protocol and data gathering scheme in which the sensor nodes form chains in each cluster. The cluster and chain construction occur only once, and the cluster-head rotates locally inside the cluster without re-clustering. Simulation results show that the proposed routing scheme significantly reduces energy consumption and increases the lifetime of sensor network compared to other hierarchical routing schemes such as Low-Energy Adaptive Clustering Hierarchy (LEACH) and Power-Efficient Gathering in Sensor Information System (PEGASIS). Keywords - Chain; clustering; energy-efficiency; network lifetime; wireless sensor networks.
I.
INTRODUCTION
Wireless sensor network (WSN) consists of tiny sensor nodes which form an ad hoc distributed sensing and data propagation network to collect the context information on the physical environment. WSN is widely used in both military and civilian applications such as target tracking, surveillance, and security management [1, 2]. The sensor node has four basic components: sensing unit, processing unit, radio unit, and power unit. With their capabilities for monitoring and control, the sensors are expected to be deployed in vast area. They can provide a fine global picture of the target area through the collaboration of sensors collecting a coarse local view [3, 4]. One of the main applications of sensor network is to periodically gather data from a remote terrain where each node continually senses the environment and sends back the data to the Base Station (BS) for further analysis, which is usually located considerably far from the target area [5]. The most restrictive factor in the life-time of wireless sensor network is limited energy resource of the deployed sensor nodes. Because the sensor nodes carry limited and generally irreplaceable power source, the protocols designed for the WSN must take the issue of energy efficiency into consideration. Also, the network protocol should take care of other issues such as self-configuration, fault tolerance, delay, etc [6-12]. Another important criterion in the design of a sensor network is data delivery time since it is critical This research was supported by a grant (07High Tech A01) from High tech Urban Development Program funded by Ministry of Land, Transportation and Maritime Affairs of Korean government. Corresponding author: Hee Yong Youn
in many applications including battle field and medical/security monitoring system. Such applications require to receive the data from sensor nodes within some time limit [7, 13]. In this paper we introduce a novel data gathering approach employing the chain-based clustering scheme and data aggregation at each sensor node. The proposed routing scheme enhances network lifetime by evenly distributing energy consumption among the nodes. The scheme distributes the energy load of the cluster-head to the member sensors so that energy consumption of the sensors is balanced. In the proposed routing scheme, the number of clusters is decided such that the energy efficiency can be maximized. In each cluster the chains of nodes are constructed for data transmission to the cluster-head. All nodes in a cluster send the sensed data to their neighbor node instead of the cluster-head, while each node aggregates the data to reduce the amount of data transferred. The cluster-head fuses the data received from the member nodes and then transmits them to the BS. Here, the cluster and chain construction occurs only once, and the cluster-head is rotated among the member nodes in each cluster without reclustering. It is to reduce the energy waste caused by repeated cluster set-up in the existing clustering scheme. Using several short chains in each cluster, we can avoid excessive delay occurring in PEGASIS while achieving higher energy efficiency by reducing inter-node distance. As a result, it can significantly reduce energy consumption and increase the lifetime of the sensor network compared to the existing schemes. Computer simulation confirms this with practical operational environment. The remainder of the paper is organized as follows. The following section reviews the work published in the literature. Section Ⅲ presents the proposed routing scheme including the system model and energy model. Simulation results for different routing schemes and network sizes are compared and discussed in Section Ⅳ. Finally, Section Ⅴconcludes the paper and outlines future research directions. II.
RELATED WORK
A number of routing protocols have been proposed which try to maximize the lifetime of sensor network of constrained resources. We review some of the most relevant designs [14-16]. In LEACH [14], sensor nodes are organized into clusters with one node in each cluster working as cluster-head. The cluster-head receives data
from all other sensors in the cluster, aggregates the data, and then transmits the aggregated data to the BS. LEACH rotates the cluster-head in order to evenly distribute the energy consumption. The operation of LEACH is organized into rounds. Each round begins with a set-up phase followed by a steady-state phase. During the set-up phase, each node decides whether it becomes a cluster-head or not according to a predefined criterion. After that, the rest sensor nodes decide the cluster-head they will belong to for that round. The cluster-head then creates a TDMA schedule for all the number nodes in its cluster. During the steady-state phase, each member node transmits data to the cluster-head within its assigned time slot. LEACH has some drawbacks. Firstly, the cluster set-up and TDMA scheduling overhead in every round is significant. Secondly, the distance between the cluster-head and member node can be long causing large transmission delay and energy consumption. Proxy-Enable Adaptive Clustering Hierarchy for wireless sensor network (PEACH) [15] improved LEACH by selecting a proxy node which can assume the role of the current cluster-head of weak power during one round of communication. It is based on the consensus of healthy nodes for the detection and manipulation of failure of any cluster-head. It allows considerable improvement in the network lifetime by reducing the overhead of re-clustering. PEGASIS [16] forms a chain covering all nodes in the network using a greedy algorithm so that each node communicates with only the neighboring nodes. In each round of communication, a randomly selected node in the chain takes turn to transmit the aggregated information to the BS to save the energy. Also, the elimination of cluster set-up phase allows considerable energy saving. However, the communication delay can be large due to long single chain. When the network size is relatively large, the delay might be intolerable. Also, as the nodes in the chain cannot be relocated, the inter-node distance gets larger as the network size grows, which cause increased energy consumption. These issues motivated the proposed scheme. III.
THE PROPOSED SCHEME
We first discuss the system model and energy model adopted in the proposed routing scheme. A. The System Model In this paper we consider the wireless sensor network consisting of one sink node and a large number of immobile sensor nodes. The sensor nodes are uniformly deployed over the target area to continuously monitor the environment. We make some assumptions about the sensor nodes and the underlying network: · There is a BS (i.e., sink) located far away from the square shape sensing area. Sensors and the BS are all stationary after deployment.
· All nodes are homogeneous and have the same capabilities. Each node is assigned a unique identifier (ID). · The nodes can vary the amount of transmission power depending on the distance to the receiver. Each node can reach the BS directly. · Data are periodically transmitted from the sensor node to the remote BS. · The links are bi-directional. B. The Energy Model of a Sensor We adopt the radio energy model described in [7], where the transmitter needs energy to run the radio electronics and power amplifier while the receiver needs energy to run the radio electronics. Both the free space (d2 power loss) and multi-path fading (d4 power loss) channel models are used depending on the distance between the transmitter and receiver, d. For relatively short distances, the propagation loss is modeled as inversely proportional to d2, whereas it is modeled as inversely proportional to d4 for longer distances. Thus, power must be controlled to compensate the loss and ensure a certain power level at the receiver by setting the power amplifier properly. To transmit a k-bit packet for a distance of d, the radio expends the following energy: ETx ( k,d ) = ETx-elec ( k ) + ETx-amp ( k , d ) 2 ⎧ kE ⎪ elec + k ε fs d ,
=⎨
⎪ kEelec + k ε mp d 4 , ⎩
if d < do
(1)
if d ≥ d o
For receiving k-bit data, the energy consumed is E Rx ( k,d ) = E Rx-elec ( k ) E Rx ( k ) = kEelec
(2)
Here, Eelec is the energy consumed by the electronic circuitry which depends on various factors related to coding, modulation, and filtering of signal occurring before it is sent to the transmission amplifier. The parameters εfs and εmp depend on the receiver sensitivity and noise figure. For the experiments presented in this paper, we adopt the values given in [7]: Eelec = 50 nJ/bit, εfs = 10 pJ/bit/m2 and εmp=0.0013 pJ/bit/m4. A sensor node also consumes 5 nJ/bit/signal for data aggregation [7, 14]. Also, we assume that all data packets are same sizes. C. The Proposed Routing Schemes In this subsection we describe the proposed hybrid routing scheme which employs chain-based clustering. Each cluster contains self-organizing chains which distribute the energy load evenly among the sensors in the cluster. The structure of the proposed routing scheme for wireless sensor networks is shown in Figure 1.
Figure 1. The structure of the proposed chain-based clustering scheme for WSNs
The operation of the proposed routing scheme consists of two phases: the cluster and chain set-up phase followed by data collection and transmission phase. In the cluster and chain set-up phase, clusters are formed. Here the number of clusters is decided using the scheme developed by the author [18], which allows the longest network lifetime. In each cluster one node is elected as cluster-head with the proposed cluster-head selection method. After cluster-head selection, chains of nodes are formed which are connected to the cluster-head. Then the TDMA schedule is transmitted to the member nodes. After the completion of cluster and chain set-up phase, the data collection and transmission phase begins where each node transmits the data to the upstream neighbor node in the established chain. Once the cluster-head receives all the data from the nodes in its cluster, it aggregates the data and then transmits the compressed data to the BS. Each sensor node also performs data fusion before forwarding the data to the neighbor node. In the existing clustering algorithm, a new cluster-head is elected in each round. In the proposed scheme, however, the cluster-head is elected with the Round-Robin algorithm where the time quantum is larger than one round of communication.
· The Cluster and Chain Set-up Phase In the existing algorithm like LEACH, cluster-heads are stochastically selected. In order to select cluster-heads each node determines a random number between 0 and 1. If the number is smaller than a threshold T, the node becomes a cluster-head for the current round. The threshold is set as follows: T =
P 1 − P × ( r mod
1 P
)
(3)
with P as the cluster-head probability and r as the number of the current round. The nodes that have not been clusterheads in the last 1/P rounds participate in the selection process. This algorithm ensures that every node becomes a cluster-head exactly once within 1/P rounds. In particular, the LEACH circulates the role of cluster-head and thus
distributes the overhead among the sensor nodes in the network. In the sensor network the distance from the cluster-heads to the BS and the distance from the sensors to the clusterhead depend on the number of sensors and clusters, and size of target area. The distance between a sensor and the cluster-head in a cluster decreases while that between the cluster-head and BS increases as the number of clusters increases in a bounded region. An opposite phenomenon is observed when the number of clusters decreases. Therefore, an optimal value of P, Popt, in terms of energy efficiency needs to be decided by properly taking account the tradeoff between sensor-to-cluster-head and cluster-head-to-BS communication overhead. In [18], we have developed a model finding Popt, which allows significant improvement over the existing cluster-based schemes including LEACH. With Popt, the new threshold of node_n, Tnew(n), is decided as follows: Tnew ( n ) =
Popt 1 − Popt × ( r mod
1 Popt
, ∀n ∈ G )
(4)
(5) where G is the set of nodes. Each node determines a random number between 0 and 1. If the number is smaller than the threshold, the node becomes the cluster-head in that round. This algorithm ensures that one node becomes the clusterhead in the first round. Once a node has elected itself as the cluster-head, it informs all other nodes in the network of this. It first generates a cluster-head token, and then broadcasts an advertisement message (ADV) to the rest of the nodes. The message contains the node ID and a header identifying the message as announcement message. Upon receiving the advertisement messages, the non-cluster-head nodes compare the signal strengths. They decide to join the cluster-head of the highest signal power. Then each node transmits a join-request message (Join-REQ) back to the cluster-head it selected. Now cluster formation is complete and the chain formation step begins. In the proposed scheme cluster formation occurs only once in the first round. From the next round, cluster-head is selected inside the cluster already formed. After the clusters are formed, chains are constructed using the member nodes in each cluster. For chain construction, the cluster-head applies the shortest path algorithm to find the best route to each member node. Recall that, in cluster formation phase, each non-cluster-head node sends the location information with Join-REQ message to the cluster-head. The location information is used in establishing the best route and finally constructing the chains of nodes. Figure 2 shows an example of chain construction. Chain construction occurs only once, and therefore the overhead is not significant. The chain information is Tnew ( n ) = 0
, ∀n ∉ G
transmitted to the member nodes in the cluster.
Figure 2.
An example of chain constrction.
constructed inside the clusters and the rotation order of the nodes in the chains is decided. Then the cluster-heads broadcast the schedule information to the member nodes in the cluster, which includes {node_id, rotation_number, next_node}. Here, node_id is unique identification of each node, rotation_number is the order of the node to become the cluster-head, and next_node is the neighbor node to which data transmission occurs. For example, {20, 5, 16} indicates that node-20 is the 5th cluster-head and needs to send data to node-16. Figure 4 shows an example of clusterhead rotation in a cluster with the proposed scheme.
After chain construction is over, the cluster-head nodes decide the order of cluster-head rotation and create the TDMA schedule. The schedule includes the scheduling information, order of cluster-head rotation and chain information. The schedule ensures no collision among data transmissions and also allows the radio components of each non-cluster-head node to be turned off at all times except during transmission time, thus minimizing the energy consumption. During this phase, all cluster-heads keep their receivers on. After the chain-rotation schedule is received by all the nodes in the cluster, the cluster and chain set-up phase is complete, and the data collection and transmission phase can begin.
(a)
A cluster
·The Cluster-head Rotation In the existing cluster-based scheme such as LEACH, clusters are reconstructed every round for load distribution among the member nodes. During this process, large energy is consumed. In the proposed scheme, thus, the sensor nodes take turn to assume the role of cluster-head in each round without re-clustering. This approach can significantly improve the energy efficiency. The timeline of LEACH and the proposed scheme are shown in Figure 3. Figure 3(a) shows that the set-up process is repeated with LEACH, while Figure 3(b) of the proposed scheme does not. In the proposed scheme the cluster-head transmits a token to inform cluster-head change in the last frame of each round to the member nodes in the cluster.
(a) LEACH
(b) Cluster-head rotation Figure 4. An example of cluster-head rotation.
·Data Collection and Transmission Phase After the cluster and chain set-up phase is over, the data collection and transmission phase starts. At the beginning of this phase every node collects local data and each clusterhead accumulates the data sent from the member nodes of its cluster. For initiation of data transmission, we adopt the token passing mechanism similar to [17]. The cluster-head sends a token to the end nodes of the chains. As the size of the token is very small, the associated cost for transmitting the token is negligible. Each end node in a chain starts to transmit the data to the next node when it receives the token. Each node receives data from the neighbor, fuses with its own data, and transmits the data to the upstream neighbor in the chain. Finally, the cluster-head transmits the data to the BS after applying data fusion. IV.
Figure 3.
(b) The proposed scheme The time-line of LEACH and the proposed scheme.
The initial set-up phase of the proposed scheme is similar to LEACH. Here clusters are formed and clusterheads are elected. After cluster formation, chains are
PERFORMANCE EVALUATION
In this section we evaluate the energy efficiency of the proposed scheme via computer simulation. We compare the proposed scheme with LEACH and PEGASIS. In the implementation of PEGASIS, the chains are constructed according to [16-17]. For LEACH, the probability for a
node to be selected as a cluster-head is decided using the model presented in [14]. For the proposed scheme, the probability for a node to be selected as a cluster-head is decided using the model explained in Section Ⅲ. For the simulation we consider a sensor network of 100 nodes randomly located in a 100 × 100 region. A BS is located at (75, 180). The simulation parameters are given in Table I, in which the parameters of radio model are same as those in [7]. THE PARAMETERS USED IN THE SIMULATION.
Initial energy
0.25 J / 0.5 J
Eelec
50 nJ/bit
LEACH
2
10 pJ/bit/ m
εfs
4
0.00013 pJ/bit/m
do
87 m
EDA
5 nJ/bit/signal
Data packet size
2000 bits
Table Ⅱ lists the round a node begins to die and the round the last node dies. Notice that the proposed scheme is consistently better than the others. We ran the simulator with different energy thresholds, and obtained similar results. Notice that the time when the node begins to die with the proposed protocol is about 73% and 13% longer than that of LEACH and PEGASIS, respectively. The time when all the nodes die with the proposed protocol is about 53% and 10% longer than that of LEACH and PEGASIS, respectively.
Energy (J/node)
0.25
0.5
Proposed
90 80 70 60 50 40 30 20 10 0 10 0
εmp
TABLE II.
PEGASIS
100
Figure 5.
Time steps (rounds)
Comparison of the number of live sensors as the round continues.
Figure 6 compares the distribution of live nodes after 600 rounds are over with the three schemes. Note that the proposed protocol allows more uniform distribution of live nodes than the other two in addition to a larger number of live nodes of 80 than LEACH (no live one) and PEGASIS(61 live ones). This improvement was able to be achieved by reducing the communication distance between the member nodes in the cluster.
THE NETWORK LIFETIMES WITH DIFFERENT INITIAL ENERGIES OF THE SENSORS.
Protocol
The round a node begins to die
The round all the nodes die
LEACH
276
497
PEGASIS
423
702
Proposed
478
768
LEACH
385
612
PEGASIS
715
1428
Proposed
782
1506
80 0
100
75 0
Nodes
65 0
(75, 180)
55 0
Base station location
49 0
100 × 100
40 0
Network size
30 0
Value
20 0
parameter
Number of sensor still alive
TABLE I.
The improvement offered by the proposed protocol over LEACH and PEGASIS can be clearly seen from Figure 5, which shows the number of sensors alive as the round continues with 0.25 J/node initially. In LEACH, the clusterheads directly communicate with the BS. When the BS is located far from the cluster-heads, large amount of energy is consumed. Therefore, the farther a cluster-head is from the BS, the quicker it exhausts the power than the nearby ones. In PEGASIS, the nodes take turn to become the leader. Therefore, the nodes far away from the BS consume more energy when they become the leader nodes than the nodes stay near the BS. In the proposed scheme, each cluster has a cluster-head which is chosen at random. It allows uniform distribution of energy consumption among the nodes, and thus dead nodes will be uniformly distributed in the target area.
(a) LEACH
REFERENCES
(b)
PEGASIS
(c) The proposed scheme Figure 6.
The distribution of live nodes after 600 rounds are over.
V.
CONCLUSION AND FUTURE WORK
In this paper we have proposed a novel chain-based clustering scheme for wireless sensor network. In the proposed routing scheme the number of clusters allowing the highest energy efficiency is decided, and the nodes in the cluster are connected in the chain structure. Here, the cluster and chain construction occur only once. The energy efficiency is maximized by the rotation of cluster-head locally inside the cluster without re-clustering. Also, chaining inside the cluster reduces the communication distance and thereby reduces energy consumption of the nodes. Computer simulation showed that the proposed approach allows much longer lifetime than the existing schemes such as LEACH and PEGASIS. The proposed approach will be more important when the wireless sensor network is deployed in large area and the BS is far from the network. As future work we will consider different chain-based protocols such as linear-chain and binary combining-chain. Also, we will consider dynamic multiple-chaining to enhance the energy efficiency.
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