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��ࡱ�>��	�����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������#`	��04�bjbjm�m�w����K)�������hhhhhhh$�///Pd/l�/��]B��0�0"�0�0�0�2�2�2�A�A�A�A�A�A�A$Ch{E��A]h5m2^�255�Ahh�0�0�B8=8=8=5,h�0h�0�A8=5�A8=8=hh8=�0t0@-7<��/D:�8=@�-B0]B8=3F<�3F8=3Fh8=��2vA3T8=�3D�3?�2�2�2�A�A�<d�2�2�2]B5555����!���!���hhhhhh����Energy-Distance Based Clustering for Wireless Sensor Networks
M.-L. Messai
Doctoral school in computer science, UAMB, Bejaia, 6000, Algeria
messai.amine@gmail.com


Abstract
To extend the networks lifetime in wireless sensor networks, energy saving becomes the aim of all proposed approaches. Organize wireless sensor networks into clusters reduces energy consumption. In this paper, we propose a novel Energy-Distance based Clustering algorithm (EDC) for prolong the wireless sensor network lifetime. Our algorithm achieves a good performance in terms of lifetime by balancing the energy load among all the sensor nodes in the network. EDC introduces the energy factor for cluster head selection and distance factor for non-cluster heads to select its cluster head. Simulation results show that the proposed algorithm, increase the network lifetime in compare of the well-known LEACH and HEED protocols.

Keywords: Clustering, Distributed Algorithm, Energy Saving, Wireless Sensor Network.


1. Introduction
A wireless sensor network (WSN for short) consists of tiny sensing devices called sensor nodes. Sensor nodes have predetermined positions or randomly deployed in the field of interest. Each sensor node has sensing and wireless communication capabilities, which enable it to sense and gather information from the environment and then send messages of sensing data to other sensor nodes in the sensor network or to the base station (sink) [1]. WSNs can be used in monitoring applications such as surveillance, human health care, and many other applications [2, 3, 4].

Sensor nodes have limited resources in memory space, computation capability and especially in energy. The limited energy at senor nodes creates requirement to propose an energy aware communication protocols.

To enhance the WSNs lifetimes, energy consumption must be minimized as much as possible. In most WSNs applications, the power source of a sensor node is a tiny battery which furnishes the electric power to all sensor node sub-systems (sensing sub-system, processing sub-system �etc). The sensor node is in life even the battery energy is not entirely consumed. In addition, sensor nodes maybe deployed in unattended areas like volcano, so we need to maximize the lifetime of sensor nodes by optimizing the use of energy. 

An efficient technique for prolonging the lifetime of WSNs is clustering. Clustering reduces energy consumption in WSNs, and it is known as an energy-efficient structure for data routing in WSNs [11].

We consider a WSN in which sensors nodes transmit sensing data to a base station (sink) through cluster heads. Cluster heads are chosen during the network lifetime according to energy of sensor nodes. In EDC, the time is divided into rounds and every round is composed of two phases: cluster formation and data transmission. In the first phase of each round, each sensor node broadcasts its residual energy value, a sensor node elects itself a cluster head if its energy is the highest compared with neighbors energies. A sensor node sends a join message to its nearest neighbor sensor node that has the greatest energy budget. After that, the cluster head constructs a TDMA schedule, this TDMA schedule is sent to cluster members. During the second phase, each sensor node sends data packets to its cluster head in its allocated TDMA schedule. When cluster head receives data packets from all its cluster members, it uses non-persistent CSMA to send its data packets to the base station. Our algorithm leans on high energy sensor nodes and avoids that low energy sensor nodes send the sensing data directly to base station that might be far away. Obviously, a cluster head consumes a greater amount of energy than a non cluster head sensor node. EDC does not make any assumptions regarding the distribution of the sensor nodes or their capabilities, it assumes only that sensor nodes can vary their transmission power. The main idea of EDC is to relay on sensor nodes with highest energies budgets and reduces the energy consumption of sensor nodes with lower energies budgets, so prolong network lifetime.

The rest of the paper is organized as follows: section 2 presents the related work and motivation. Energy-Distance based Clustering for WSNs (EDC) is outlined in Section 3. Section 4 presents the simulation results by comparing energy consumption and network lifetime of EDC, LEACH [5], and HEED [10]. Finally, the conclusion is presented.

2. Related Work and Motivation
The main advantage of clustering is data aggregation that decreases the number of packets transmitted to the base station. In [7], the authors analyze the energy consumption and the lifetime of WSNs within the two approaches. They show theoretically and empirically that the clustering-based schemes outperforms the flat schemes in term of energy saving. Detailed surveys of clustering schemes in WSNs can be found in [7, 8]. 

To enhance the WSNs lifetimes, energy consumption must be minimized as much as possible. In most WSNs applications, the power source of a sensor node is a tiny battery which furnishes the electric power to all sensor node sub-systems (sensing sub-system, processing sub-system �etc). The sensor node is in life even the battery energy is not entirely consumed. In addition, sensor nodes maybe deployed in unattended areas like volcano, so we need to maximise the lifetime of sensor nodes by optimising the use of energy. 

Authors in [9] divide the clustering schemes on tow kinds: homogenous and heterogeneous schemes. Clustering algorithms applied in homogenous or heterogeneous WSNs. The heterogeneity here means that sensor nodes are equipped with different amount of energy. In the same paper authors propose DEEC (Distributed Energy Efficient Clustering for heterogeneous wireless sensor networks). DEEC algorithm chooses the cluster head sensor node based on residual energy and the average energy of the network. The value of this average energy is estimated based on the total energy and the lifetime of the network. The base station broadcasts this tow last values each epoch (or round) which consume an additional energy. Also authors presume that every sensor node consumes the same amount of energy in each round, although this is cannot be achieved in all region of the network, particularly in event driven WSNs applications, where sensor nodes only send data when events occur in the environment.

LEACH (Low-Energy Adaptive Clustering Hierarchy) is a clustering protocol proposed in [5]. LEACH is divided into rounds, a round is composed of tow steps, one for cluster formation and the second for data collection. LEACH particularity is the no need of communications in cluster heads designation, each sensor node decide independently to be or not a cluster head. After cluster nodes election, non-cluster heads sensor nodes choose the appropriate cluster head in term of energy consumption (minimum communication energy according to received signal strength). In LEACH, a low power sensor node might elect it self a cluster head, and also in some cases a cluster head cannot reach the base station. 

LEACH authors propose LEACH-C (LEACH-Centralized) in [6]. LEACH-C differs form LEACH in the way to choose the cluster head sensor node in the beginning of each round. In LEACH-C, the base station executes a centralized algorithm to determine the cluster heads sensor nodes in the network. Sensor nodes send the location information to the base station, which decides the set of sensor nodes acting as cluster heads. Centralized selecting of cluster heads provides better performance than LEACH. However, base station fail is not tolerated. In both LEACH and LEACH-C, authors assume that all sensor nodes are with in the communication rang of each other and the base station, which is not possible in large scale and large sensing areas WSNs.

HEED (Hybrid Energy-Efficient Distributed Clustering) [10] use the combination of residual energy and the communication cost as a criterion to select the cluster heads sensor nodes. HEED do not suppose that the energy consumption is uniform for all sensor nodes, and the cluster heads sensor nodes are well distributed in the network avoiding that the cluster heads being in the same rang of each other. HEED's authors suppose that the initial percentage of cluster heads among all sensor nodes in the network can be calculated a priori, which is difficult to calculate in networks with post-deployments of new sensor nodes. In addition, the calculation method is not indicated. 

3. EDC: Energy-Distance based Clustering for WSNs
3.1. Assumptions

The following properties are assumed in regard to the WSN being studied: 

� N homogeneous sensor nodes are randomly dispersed within a m x m field.

� Sensor nodes are homogeneous and have same capability of processing, memory and power consumption.

� The sensor nodes can use power control to change the amount of transmit power.

� Distance can be measured based on the wireless radio signal power.

� Sensor nodes and the base station are stationary after deployment.

� Sensor nodes are unaware of their locations after deployment.

� Base station has no constraint on energy and sensor nodes are energy-constrained and are left unattended after deployment. 

� Communication is symmetric: meaning if the sensor node Si can listen to the sensor node Sj, Sj can listen to Si. 



Figure 1. A WSN with 100 sensor nodes deployed randomly in 100m x 100m and base station in centre.

We use a simplified model shown in [5, 6] for the radio hardware energy dissipation as follows. Figure 2 illustrates the radio energy dissipation model used in WSNs [5, 6]. To transmit a k bits packet to a distance d, the radio expands:

ETx(k, d ) = k x Eelec + k x �fs  x d2           d  <  d0               
ETx(k, d ) = k x Eelec + k x �amp  x d4        d  >=  d0        
The first item presents the energy consumption of radio dissipation, while the second presents the energy consumption for amplifying radio. Depending on the transmission distance both the free space �fs and the multi-path fading �amp  channel models are used. 

When receiving k bits packet, the radio expends:
ERx(k)  = k  x Eelec
And when the cluster head aggregates data of its cluster members, it consumes the energy EDA.


Figure 2. Radio energy dissipation model.

3.2 EDC Algorithm
	
In this paper, an energy-distance clustering for wireless sensor networks (EDC) is proposed for balancing the energy consumption in WSNs. In EDC, sensor node is selected as cluster head according to its remaining energy. Also non-cluster heads sensor nodes select its cluster heads according to the distance to neighbors cluster heads.

The operation of EDC is divided into rounds and each round into two phases: the setup phase, which includes cluster heads election and consequently clusters formation. In addition, in this phase, every cluster head coordinates with its members to send sensing data packet during the following phase. The cluster head builds a TDMA schedule for communication in its own cluster. The steady state phase, which is broken up into TDMA frames. During each frame, every member sensor node, at the time of its respective time slot, sends sensing data packet to its cluster head. At the end of each TDMA frames, every cluster head forwards the aggregated data to the BS. 

The cluster head is responsible for aggregating the data received from its cluster member�s sensor nodes, and then transmitting the aggregated data to the base station. The cluster heads use non-persistent CSMA to send their data to the base station. Since the cluster head performs data processing and receive data from all members of its cluster and then transmit aggregated data to the base station, it consumes more power than the member�s sensor nodes. The cluster head must be changed through the system lifetime and powerful sensor nodes are chosen as cluster heads. Each sensor node with high energy budget must take its turn to act as a cluster head. EDC algorithm is given by algorithm 1.

� Setup phase: the main goal of this phase is to elect cluster heads and create clusters. During the setup phase, each sensor node Si in the network broadcasts its current budget of energy Ei. Each sensor node Sj receives the energies values of its neighbors, and it can calculate the distance between it and each neighbor based on the wireless radio signal power. After that, each sensor nodes sorts out the energies values including its energy value from the high to the low value. If a sensor node has the highest value of energy budget, it decides to be a cluster head for this current round. Else, it sends a join message to the neighbor that has the highest energy budget to become a member of the cluster. Then, the cluster head creates a TDMA scheduler according to cluster members sensor nodes that receive join message from them and send TDMA to all members. 

� Steady state phase: Once the clusters are formed and the TDMA schedule is fixed in all clusters of the network. The cluster member�s sensor nodes send their data packets to cluster heads sensor node. Each member sensor node send its data packets to its cluster head at its TDMA frame and the radios transmitter of other members sensor nodes is turned off until their allocated transmission time to save the energy. In each cluster, when all the data packets of members have been received, the cluster head aggregates the data and then, this data is sent to the Base station. The amount of information is reduced due to the data aggregation done at the cluster head sensor node. This round is done and the next round begins with setup and steady state phases repeatedly. Non-persistent CSMA is used as MAC protocol between cluster heads sensor nodes and the base station.

The pseudo code of EDC algorithm is given as follow:
Algorithm 1

Begin
Phase 1: Setup phase

For each sensor node Si:

� Broadcasts (Ei)

� For all sensor node Sj neighbor of Si : 
Receives (Ej)

� If (Ei is great than the all received value) then 

	- Si is the cluster head

- Si receives the join messages from its neghbors and then fixes and sends a TDMA schedule to all its cluster members

� Else

- Send join message to the neighbor that has the highest energy value

Phase 2: Steady state phase

� For each cluster member sends its data packets in its allocated TDMA slot

� For each cluster head: receives data packets from all members and then send the aggregated data to the base station
End.

4. Simulation Results
In this section, a comparison between the simulation results in EDC, LEACH and HEED algorithms is performed via MATLAB software environment. For simplicity, we assume the probability of signal collision and interference in the wireless channel is ignorable. The parameters of simulations are listed in table 1.






Table 1: Parameters of simulations.
ParameterValueArea100 m x 100 mData packet size4000 bitsControl packet size500 bitsDistance d087 mNumber of sensor nodes100Initial energy0, 5 JouleBase station location(50m, 50m)Eelec50 nJ/bitEDA5 nJ/bit/signal�fs10 pJ/bit/m2�amp0,0013 pJ/bit/m4
Figure 3 shows number of dead sensors because of finish batteries during the rounds in EDC, LEACH and HEED. Comparing of three graphs of this figure implies the network lifetime of EDC will be increase further than LEACH and HEED. This is because our proposed EDC scheme relays on powerful sensor nodes as cluster heads. The data transmission distance from each sensor node to its cluster head node is minimized. Thus, the energy consumption is saved.


Figure 3. Comparison of network lifetime.

Hence, our proposed EDC algorithm reduces the data transmission distances of sensor nodes and results in the lower energy consumption in the WSNs. 

The reason for this behaviour is that the data communications consume a large amount of energy in wireless sensor networks. However, the transmission distance between non-cluster head node and cluster head node is suitable by using our scheme. The transmission power of non-cluster head nodes is reduced. Also, in order to prove our proposed scheme is well designed, we increase the number of sensor nodes in the simulation environment.

From the simulation results, it is clear that our proposed EDC scheme strikes the appropriate performance in the energy consumption and network lifetime for WSNs. 

5. Conclusion
As WSNs are still a young research field, much activity is still on-going to solve many open issues. The energy saving issue is the most important. Energy efficient clustering schemes must be developed to extend the lifetime of WSNs. In our EDC scheme, the cluster heads and their members are selected according to two parameters: the remaining residual energy and the distance of member�s sensor nodes to theirs cluster heads sensor nodes. This parameters conduct to select the appropriate sensor nodes as cluster heads and nearest sensor nodes as members. By this way the lifetime of WSNs is extended. Simulation results indicate that our proposed scheme achieves better network lifetime. As future work, we will compare our EDC scheme with other proposed clustering schemes and test our scheme in an appropriate WSNs test bed to pass to experimentation. 

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