Bio Inspired Optimal Relocation of Mobile Sink Nodes in Wireless Sensor Networks

Sumit Kataria

Department of Electronics and Communication Ambedkar Institute of Advanced Communication Tech. & Research,

Govt. of N.C.T. of Delhi, Delhi sumitkataria1.24@gmail.com

Abstract—In wireless sensor networks (WSNs), during data

transmission, sensor nodes which are closer to the sink nodes use up their energy earlier than nodes which are away as they relay more data packets. This cause to the energy imbalance in between sensor nodes, and leads to the connectivity holes and coverage holes, and finally lead to network failure. In this paper, we introduce a new method to tackle with this problem by optimal relocation of the mobile sink nodes and hence to balance load between the sensors. The sink nodes relocation is performed by using the bio-inspired Digital Hormone Model. Through this method the sink nodes are being guided to move in an intelligent way towards the optimal location, which basically improves the network lifetime and reduces the energy imbalance. After simulations, has been observed that the proposed method greatly improves the network lifetime on comparing with other available methods. The simulations are performed in java using jdk 6 and jre 6 version software.

Keywords—Wireless sensor network, Sink node relocation, Network lifetime, Digital Hormone Model.

I. INTRODUCTION The wireless sensor network consists of the large number

of the resource constrained wireless sensor nodes which are able to take various environmental measurement i.e. light, sound, humidity etc. Nodes are generally self-configured and they mutually decide their actions. The basic task of these nodes is to transmit their sensed or measured data to the sink nodes of a running application. Sink nodes makes decision based on these received sensor readings. In literature, a lot of applications have been proposed for wireless sensor networks [1][2][3][4][5]. Many of these applications have specific quality of service (QoS) requirements like high reliability, low access time, low bit error rate etc., thus they offer additional challenges to the application designer.

The communication in the WSN is performed by the ad hoc communication network and data packets are transmitted to the sink nodes or base station using the multi hopping process. The sink nodes are always rich powered and have sufficient resources. Whereas, being small in size, sensor nodes have the limited battery power and computational resources.

In WSN, the most of the energy of the sensors are utilized in the data transmission rather than the data processing. However, while transmission, due to contention for shared

Aarti Jain Department of Electronics and Communication

Ambedkar Institute of Advanced Communication Tech. & Research, Govt. of N.C.T. of Delhi, Delhi

medium access, interference among transmitting nodes or due physical conditions of channel itself, data transmission may fail, which will impact on network lifetime. Hence, In order to overcome the above mentioned and to improve life of sensor nodes, multi-hopping routing scheme is opted for data transmission. Although Multi hopping technique has many advantages, but it has some inherent disadvantages also. The sensor nodes which are placed near to sink nodes have to carry the most number of data packets toward the sink nodes as compare to nodes which are placed far away. Hence, these nodes will spend more energy than far off placed sensor nodes and thus will die earlier. Therefore this process tends to increase the energy imbalance or mismatch between the sensor nodes and can produce connectivity holes and coverage holes and finally there is whole network failure. Hence the lifetime of the network will be affected and special measures are required to tackle this problem. One such solution is to deploy multiple sinks and then relocate them periodically [6][7]. However, in literature there is a little research work available to support our solution. Also, the solutions present are based upon exhaustive search methods, which are not suitable for large scale WSNs.

Therefore in this paper we have proposed a new sink relocation method to increase the lifetime of the WSNs and hence to balance load between the sensors nodes. Our proposed method is based upon Digital Hormone Model (DHM) [8], which is a bio-inspired distributed control method and is computationally inexpensive. Therefore, through this proposed work the sink nodes are being given the movement facility which allows the sinks to move in an intelligent way towards the optimal location or position which basically improves the network lifetime. Digital hormone model is applied on the sinks for their movement and optimal relocation.

Further, rest of the paper is structured as follows. Section II contains the related work about existing sensor deployment strategies. Section III describes the proposed work for optimal deployment method for mobile sink nodes in the WSN using DHM. Section IV gives the performance evaluation and simulation. Section V concludes our work.

II. RELATED WORK The optimal deployment of the sink nodes In the Wireless

sensor networks is one of the serious issues because it directly

affects the lifetime or performance of the network. The sink deployment must also be crucial for the aspect of coverage or connectivity holes and most importantly for optimal energy consumption.

Using multiple sink nodes in WSNs leads to decrease in the energy required to communicate between sensor nodes and their respective sinks. Also for long distance transmission of data in large scale wireless sensor networks, it results in less number of hopes between sensor nodes and sink. In literature, the optimal deployment of these sink nodes basically minimize the distance between the sink nodes with sensors. In [9], the multiple sink nodes are deployed by using the genetic expression but the sink nodes was static in nature. Also, being a centralized approach, genetic expression method leads to excessive overhead. Several mathematical models [10], clustering algorithms [11], data-centric paradigms [12][13] has been proposed. The basic aim behind all these techniques is to determine the sink location so that the average communication distance is minimized. Also most of these methods are based on exhaustive search, which is not suitable for large scale wireless networks.

In [14], the optimization is also done by using the genetic algorithm, but method presented is onerous and not explained succinctly. The BSL and MSPOP problem for sink deployment is solved in [15]. In [16],[17], [18], [19], methods based on a movable sink node are introduced to gather data through moving the sink node to the data source node, which reduces energy consumption through avoiding the multi-hop data transmission and increases the life time .but this increase in lifetime is at the cost of response time. In [20], the optimal position of the sink nodes is achieved by a local search algorithm which drives an optimal sinks distribution.

In [21] the sinks nodes are static in nature and hence have not proposed an optimal solution because once they deployed optimally then they were not able to move to another location. In the related literature, Sink relocation is not based upon current activity of sensor network and thus dynamic nature of sensor network is not taken into account. Also, node degree i.e. number of nodes in a particular area, in sensor network is not uniform, but random and also dynamic in nature. This has not been considered for relocation. In our work, we have considered recent activity in sensor network as well as node degree for sink relocation.

III. PROPOSED WORK

In the many applications like environmental monitoring a large scale wireless sensor network is required which contains several thousand of sensor nodes spread over a several kilometres of monitoring region. In these cases the network should be scalable [15]. To provide the scalability to the network the sensor network is divided into the number of clusters by using various clustering algorithms [20]. However in most of the clustering algorithms, one of the sensor nodes is given the responsibility of cluster head. Selected cluster head is to manage the communication of nodes resides in its range, collect and aggregate data received from them and finally

transmit it to sink node. This technique goes well for small scale networks, where numbers of nodes are small. However, for very large scale networks, an ordinary sensor node can’t handle all these responsibilities, so a powerful node is required in place of ordinary node. In our case, we have also consider a wireless sensor network which is divided into the clusters but unlike the clustering techniques proposed in literature, each cluster has a mobile sink node as a cluster head ,which is dedicated to it. Number of clusters is same as number of sink nodes in the network. Each mobile sink node will serve in its own cluster.

A. The Digital Hormone Model The DHM is basically inspired from biological system. In

biological systems, different cells respond differently to different hormones because different cells have different receptors designed to bind with particular hormones. The different types of hormones and target cells present in vertebrates are so great that virtually every cell can processes or responds to every hormone received. Hormones provide the common mechanism that makes it possible for cells to communicate without identifiers and addresses, and they support a broad spectrum of seemingly diverse biological effects [8].

In our case, the basic idea is that sink nodes in the WSN can dynamically change their links in the network to connect with the sensor nodes based upon the Digital Hormone Model. The mobile sink nodes use hormone-like messages to communicate, collaborate, and accomplish global behaviours. The hormone-like messages do not have addresses but propagate through network. All the sensor nodes are running on the same protocol. On receiving hormones, sensor nodes, react according to their hormone received, their local topology and state information. Thus, sinks can communicate with the sensor nodes through hormones and can efficiently take decisions for future actions.

According to our work, the basic idea for using the DHM is that the sinks will generate or propagate the hormone-like message throughout its communication range in the network. The sensors which receive these hormones will transmit the respective response hormone to the sink node by sending their coordinates, their centrality (load information) and remaining energy. On the basis of the received information through response hormones, the sinks will perform its action to achieve the optimal location or position and will move to this location or position to serve the sensors in its cluster.

In the proposed work, our DHM consists of three components: (i) A dynamic network of mobile sink nodes, (ii) a probabilistic function for individual sink node behaviour and (iii) a set of rules and equations for hormone reaction and propagation.

B. Dynamic Network of Mobile Sink Nodes And Sensor Nodes(DNMSS) The dynamic network proposed in our method is composed

of mobile sink nodes and sensor nodes. We have taken of the traditional wireless sensor network representation for sensing and monitoring applications. The term dynamic refers to

mobile sink nodes which are dynamically connected with the sensor nodes through a set of dynamic links. Through these dynamic set of links, sink nodes establish the dynamic communication links with the sensor nodes.

Links used in any wireless communication network is through radio communication links. A link between two nodes is established whenever a direct communication is possible between them. In our method, a sink node transmits the start-up hormone to establish links. Any active nodes on receiving this hormone acknowledge the link by responding back with its own response hormone. Let the Nt denotes the set of mobile sink nodes and Lt denotes the set of links that exist in a dynamic network at time t. The DNMSS is defined as:

DNMSS = ( Nt, Lt) (1)

Through this DNMSS, the nodes exchange the hormones.

C. Probabilistic Function For Individual Sink Node Behaviour The behaviour of the each sink node is perfectly defined by

the conditional probabilistic function which based on the current state of events [23].

A mobile sink node will select its action A based on the probabilistic function P, which is based on the two local factors S, state information of the sink node, and H, hormone level,.

P (A/S, H) (2)

1. State information(S) defines the current state of the mobile sink node and the sink node can be in one of the three states, named as Setup State, Steady State and Move State. In the set up state sink decides about its next action, steady state the sink node will transmit the hormone, receive the response hormone from the connecting sensors, receive data from sensor node, and in the move state the sink node will start moving toward that sensor node to reduce the energy imbalance between the sensor nodes in the network.

2. The sink nodes will be in the Steady state for a time T, before the execution of P and also the sink node will be in the Move state till it does not reach its target location.

3. H denotes the hormone that is transmitted from a sink node to a sensor node and also denote as the response hormone when transmitted from the sensor nodes to the sink node. Each H has five tuples containing the x,y coordinate of the transmitting node, energy left (Es), sensor reading as well as the load information (LOAD).

Table1. Response Hormone

Load information is calculated by number of nodes connected by that node.

4. Each sensor node knows its location in a global coordinate system and that information is transmitted along with the load information, sensed reading and its own remaining energy, whenever a hormone H is transmitted.

D. Actions The actions to be performed are as follows:

1. A0 (Relocation Rule) If S=Setup state AND received hormone from node Xj , Es

< upper threshold or LOAD > THRESHOLD then S = Move towards Xj .

Else if no hormone received S= Move (Random) 2. A1 (Hormone Transmission/Reception rule)

If sink node in state S = Steady state, it transmits and receives a data/information/hormones from the sensor nodes in its range. If no hormone/data received for δT times then S=setup state.

3. A2 (Gradient Move Rule)

A sink node in S = Steady state accumulates/collects all the hormones Hk it received from its sensor nodes for the duration T. After that, the sink node calculates the gradient, gk, towards each of the sensor node in its vicinity that transmitted the hormones.

Where, g E LOAD (3)

Based on the values of gk ,gk

max is determined. And then direction for movement of sink is towards the node with maximum gradient. Then the sink node set S=move.

4. A3 (Destination arrival Rule)

If S = Move then S = Steady if (xk, yk) = (xi, yi) else S = Move Where, xk, yk is the coordinate of sink nodes.

E. Probabilities For Execution Of Actions The probability function P (HTx) is defined for a sink node

to transmit a hormone against the rule A1

P (HTx) = P(S = Steady state) (4) The probably of a sink node to be in the Steady, setup and Move states are as follows: P(S= Steady) = [1-P(S= Move)] [P (HTx)]m (5) (when sink node transmitting the hormone) or P(S= Steady) = [1-P(S= Move)] n [P (HTx)] (6)

x y Load information (LOAD)

Reading sensed by sensor (ri(tx))

Remaining sensor energy(Es)

(When sink node receiving the hormone from the connecting sensor nodes) Where, n = number of the neighbours i.e. the sensor nodes within the communication range of the sink node.

P(S = Move) = (1- P (A0)) +P (A3) (7)

P(S = Steady) + P(S= setup) +P(S = Move) = 1 (8) The above equation implies that a sink node will be exactly in one state at a given time. The probability of each action is defined on considering the above equations:

P (A0) = P(S= Setup state) (9)

P (A0) = P 1 δ (10) δ is a an implementation constant which specifies that for how much duration a sink node should be wait at a particular location before going to setup state for moving to a new location when there is low sensor activity and no hormone received.

P (A1) = P (HTx) (11)

P (A2) = P (HTx)n [1-P(S = Move)] (12)

P (A3) = P(S = Move) (13)

F. Rules For Hormone Reaction And Propagation The hormones propagation and reaction occurs in the two

dimensional medium[8] and they are propagate throughout the network through the radio links and this just follows the rules laid down for the radio signal propagation. In the biological organism system the hormones diffuses through the cellular interactions whereas in the case of mobile sink nodes radio/microwave transmission and reception help in the hormones diffusion.

In our method, the sink node is guided to move towards the optimal sink location, the hormone is transmitted whenever the action A1 satisfies. In the classical DHM, the received hormone is retransmitted by a receiver to generate the cascading motion with the immediate effect. But in our approach allows only the fresh hormones transmission which is based on the local topologies is transmitted. And this improves the overall lifetime of the networks.

G. Algorithm Step1: Divide the network into n number of clusters and each

cluster contains a dedicated sink node. Step2: Sink node will transmit hormone to all sensors in its

communication range, S = Steady state Step3: Sink node will receive the response hormone from all

connecting sensors, S= Steady state. Step4: Sink node will utilize the information received through

the response hormone and will calculate gradient towards each sensor nodes.

Step5: Set S = move and sink node will move towards the sensor which has the maximum gradient value is calculated.

Step6: Set S = Steady state when (xk, yk) = (xi, yi) otherwise S = Move.

Step7: go to step1

IV. PERFORMANCE EVALUATION AND SIMULATION In this part the performance of the proposed method for

deploying the multiple mobile sink nodes is evaluated on the java and java is a most power software development kit and often use for the implementation of the WSNs.

To analysis the effectiveness of this method, the performance of the proposed algorithm for deploying the mobile sink nodes is compared with multiple node deployment using GEN-MSN.

A. Simulation Environment We consider a wireless sensor network with n sensor nodes

and m mobile sink node and the movement of the sink is governed by the digital hormone model. The sensor nodes and the sink nodes are randomly deployed in the rectangular region.

The network parameters for setting up the simulation environment are listed below:

Table2. Network parameters and Values

B. Network Lifetime The improvement in the lifetime of the network is

achieved. The lifetime of the network is analysis after performing a number of experiments under the different number of sink nodes to determine that on increasing the number of sink node extremely affect the lifetime.

Parameters Values

Area 1200*900 m2

Sensor nodes n

sink m

Sensing range of sensor 80 m

transmission range of sensor 80 m

Sensing energy (es) 100 J [9]

Transmission energy (et ) 100 J [9]

Receiving energy (er) 100 J [9]

Initial energy of sensor node 100 mJ [9]

Communication range of Sink node

103 m

Figure1. Network Lifetime with sink nodes increasing sink nodes on comparing with lifetime achieved using the GEN-MSN.

C. Network Throughput Network throughput is the average rate of successful data

packet delivery over a communication link. The throughput is usually measured in bits per second (bit/s or bps).

Throughput RWIND (14)

Where, RWIN is the TCP receiving window and the maximum window size is 65535 bytes or 524288 bits.

Figure2. Network throughput with sink nodes D. Overall Energy Consumption With Time

Figure3. Energy Consumption with Time E. Overall Remaining Energy

To evaluate the overall remaining energy of the network a number of experiments are conducted and overall remaining energy is calculate with different number of sink nodes.

Figure4. Overall remaining Energy with sink nodes

The graph shows that there is the effective utilization of the energy of the sensors with increase in the number of sink nodes. This implies that the life time of the network is improved on increasing the mobile sink nodes.

F. Time Delay For calculating the time delay, the experiments are

performed with the same simulation setting to evaluate that the optimal deployment of the sink nodes also decreases delay time and it is define as: tdelay= tend− tstart [9] (15) Where, tend is the time at which the receiving sink node has finished receiving the last bit of a packet. tstart is the time at which that the sensor node starts sending the packet.

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There is some improvement is observed in the time delay with increasing mobile sink nodes than the static sink nodes.

V. CONCLUSION In order to improve the lifetime of wireless sensor

network, the energy of the sensor nodes should be efficiently utilized. We propose a new method as optimal deployment of the mobile sink nodes using the digital hormone model to load balancing and to maximize the network lifetime and also reduces the chances of connectivity hole and coverage hole. A number of experiments were performed in support of our method and we got satisfactory results on comparing with the other available methods for sink node deployment. On comparing the experimental or simulation result, we observe that the network lifetime has been improved. There is also more scope in this area of WSNs for efficiently utilizations of sensor node’s energy for increasing the lifetime by moving the sink nodes.

In future, our intention not only to increase the lifetime of the sensor network by relocation of the moving sink nodes to balance the load among the sensor nodes but also the performance of the network such as latency, bandwidth, data delivery ratio, and end to end delay.

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