data gathering in wireless sensor networks using...

International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, Volume-1, Issue-10, Dec-2013

Data Gathering In Wireless Sensor Networks Using Mobile Sink 1

DATA GATHERING IN WIRELESS SENSOR NETWORKS USING MOBILE SINK

1P.MADHUMATHY, 2D.SIVAKUMAR

Department of CSE, Easwari engineering college, Chennai, India

Email: [email protected]

Abstract -- In WSN, data aggregation is an important process for reducing the energy consumption. We can reduce energy consumption by using the efficient technique for data aggregation. Also, achieving the reliability and robustness of transferring data is one of the major issues and challenges. in order to produce both reliability and energy efficiency, in this paper we propose to design a Mobile Sink for Data Gathering process for WSN. Initially, a Biased Random Walk method is used to determine the next position of the sink. Then a rendezvous point selection with splitting tree technique is used to determine the optimal data transmission path. Keywords--- mobile sink, Data Gathering, rendezvous point, Random walk. I. INTRODUCTION Wireless sensor networks (WSNs) are formed from sensor nodes which as limited resources that are deployed to detect any physical phenomena. These nodes generate data and operate in a multi-hop fashion to relay data from other nodes. Towards a more balanced and energy efficient data collection sink mobility can be used. Two challenging issues in using mobile sinks are: seamless data collection and energy conservation. Since the location of the sink keeps changing, data reports from the sensor nodes can be lost because an existing path can become invalid when the sink moves. To remedy that situation, communication paths, or routing trees should be exhaustively rebuilt. However, frequent updates of the routing tree cause large energy consumption and significant delays for restarting communication. Since the sinks are collecting data only from the nodes inside a certain range, a protocol should have policies for sleeping and waking up the sensor nodes to save energy[1,2] 1.1 Data Gathering using Mobile Sink There have been many proposals in recent literature that studied using mobile element(s) to reduce the energy consumption in a sensor network. Based on the categorization given in [3], the three major approaches. In a typical flat-topology network, the nodes around the sink are likely to be the first to die due to having to forward the data traffic from all other sensors. Based on this observation, several proposals [6-8] have investigated using mobile sink(s) to reduce the energy consumption in the network. By varying the path to the sink(s), the residual energy in the nodes becomes more evenly balanced throughout the network, leading to a higher network lifetime. However, in order to be effective, this technique requires the sink location (and routes thereto) to change regularly, which places a potentially prohibitive overhead on the nodes due to the frequent recomputation of the routes[11-13]

II. RELATED WORKS

In [3] the clustering routing algorithm is used to find out intra cluster and inter cluster link in wireless sensor network clusters are acted as a router, which maintain and distribute of the routing information After node is selected as cluster head, it will broadcast information that he is the cluster head to the rest of the nodes in the same cluster. The remaining nodes decide to join the cluster according to the size of the received signal.

The approach which is presented in [4] is based on multiple spanning trees. The authors in this research define a method to provide fault tolerance to packet losses by forming a Directed Acyclic Graph (DAG). DAG allows having multiple parent nodes at each sensor. By using multiple parents as intermediate nodes, the method provides tolerance for the failure in wireless transmission. In our performance evaluation, we call this approach Method1.There are also other approaches which have worked on reliable data collection in sensor networks.

In cellular approach to fault detection and recovery [5], network is partitioned into a virtual grid of cells, where each cell consists of a group of sensor nodes. A cell manager and a secondary manager are chosen in each cell to perform fault management tasks. Secondary manager works as back up node which will take control of the cell when cell manager fails to operate. This protocol handles only those failures which are caused by energy depletion.

III. SINK MOBILITY This data collection paradigm has many attractive properties. The major advantage of having a mobile sink (or more) is an increase in system lifetime. In sensor networks, the sustainable lifetime is severely limited by the battery capacity of the sensor nodes, since replacing or recharging of batteries can be extremely costly and impractical. A mobile agent that



moves closer to the nodes can help conserve energy since data is transmitted over fewer hops, thus reducing the number of transmitted packets[9,10] The extra energy spent for the operation and movement of the sink doesn’t affect overall sensor network lifetime since the mobile sink is considered an external to the network factor e.g. could be a man navigated vehicle or an unmanned robot that periodically returns to a fixed point in order to recharge itself. Another important advantage is that connectivity of the network is not required, thus sparse networks can be better handled, and additionally, fewer sensor devices may be deployed, to reduce the operational cost of the network. Also, the sensor devices can reduce their transmission range to the lowest value required to reach the mobile infrastructure, thus saving energy. Moreover, the mobile sink can navigate through or bypass problematic regions where sensor devices can not operate, such as small lakes, large boulders that block the propagation path. However, many apparent difficulties arise as well since traversing the network in a timely and efficient way is critical. Failure to visit some areas will result in data loss, while infrequently visiting some regions will result in high delivery delays. Also, routing and localization problems in the case of mobile sinks become more difficult to cope with. Additionally, critical issues arise in node to sink communications. For single hop communication, the sink should eventually come within range of every sensor. Furthermore,to successfully complete the communication, it must remain within the range of the transmitter for the entire period that the message is transmitted[14-16] This problem can be severe when there is high density of sensors in an area or when some sensors have recorded a significant amount of data. In such cases, the communication time between the nodes and the sink is not enough to upload their data, thus they need to wait for the sink to return. This results in high delivery delays or even data loss when the nodes have limited buffers. This problem can be mitigated or even completely eliminated if the sink pauses the network traversal in order to collect the data. In our work we investigate sink mobility strategies that follow this approach, since we introduce adaptive stop times which are proportional to the local data traffic[17]. IV. PROPOSED SOLUTION In paper [6] there is a problem of energy consumption. This scheme consumes more energy. But it reduces the possibility of detection on the sensor network and to protect the mobile sink against tracking. On the other hand, paper [1] use biased, adaptive sink mobility scheme for data collection and adjusting the local network conditions minimizing the energy consumption. But it selects the visiting schedule of the sink based on only the distance and node density. Moreover it does not ensure reliability.

From the existing works, it can be seen that providing both reliability and energy efficiency in mobile sink based data gathering is a challenging task and very rarely handled. In order to solve the above issues, we propose to design a Mobile Sink Based Reliable and Energy Efficient Data Gathering technique for WSN. For solution first we use Biased Random Walk [1] to determining the next position of the sink. It is a random walk that uses probabilistic transitions between the cells. The next position of sink is determined by applying the rendezvous point selection with splitting tree technique [8]. Biased Random Walk: However, in the general case it may not be feasible to traverse the network in a deterministic way as the one we already presented. The presence of obstacles may hinder the movement of the sink while it may be desirable to move in an unpredictable way to avoid mischiefs from adversaries. Also, the network topology may not be known to the sink or may change dynamically. We thus propose a form of random walk that uses probabilistic transitions between the cells. The next position of the sink is determined by selecting (with some appropriate probability) one of the neighbors of the current vertex/cell in the graph . Let CNi be the counter for every vertex i

Let i be the degree of vertex i

Initially JiCN i 0 The steps involved in the sink’s position

estimation are as follows. 1) When a mobile sink S enters the area related to i, it increments the counter CNi by 1. This reveals that the recurrent visits of each area is estimated and stored in the sink. 2) The sink selects the next area to visit based on biased random manner using the following equation (2) If S is on vertex i with i , Then

),(:,)(nei jijCNiCNj

j (2)

End if 3) The probability of visiting neighbor vertex j is estimated using the following equation (3)

.0for,1

)(/1Pr nei

nei

CN

iCNCN

i

jj

(3)

When CNnei = 0, Prj = 1/ i . Thus, less frequently visited regions are favored when the sink is located at a nearby region. An Optimal Solution –RP-MS Generally, for a specific node, the optimal trajectory either passes through node, or lay in one of the sub-trees by removing the node v from the tree. Based on this observation, we design a recursive algorithm to find the optimal trajectory. First, the algorithm finds the optimal length-constrained trajectory which must pass through a stationary point called median , by



”removing” the median, the algorithm finds the current optimal trajectory in the sub-trees which rooted atone of median’s children. In the same way, the algorithm keeps on ”splitting” each sub-tree. For each sub-tree, we find the optimal length-constrained trajectory which passes through its root until every sub-tree has been examined, restore these optimal trajectories candidates into the candidate set .Finally ,we get the global optimal one by examining the candidate setas shown in fig 1,2,3. In the following, we present the pseudo code of our optimal solution named Rendezvous Planning with a Mobile Sink (RP-MS) algorithm.

Fig. 1. Tree-shaped network topology

Fig. 2. First iteration of optimal path.

Fig. 3. Optimal path (P0g).

1: procedure RP-MS(G, L/2) 2: Find median(m) of the tree-shaped topology; 3: Orient tree-shaped topology into directed one which is rooted at m ; 4: push m into queue q ; 5: while q≠ NULL do 6: Find the optimal trajectory opt p by calling RP−MR (G, L, v), where v ═.pop() ;

7: Insert opt p into the candidate set SET ; 8: Insert the children of v into queue q ; 9: end while 10: Find the global optimal trajectory with minimum C(R opt )from the candidate set SET ; 11: return Popt and C(R opt); 12: end procedure Data Gathering linked with Pause time for mobile sink

An adaptive pause time technique is used which involves the computation of pause time with respect to the local density of the network and reliability of the nodes. The estimation of pause time is described below.

Let h be the time required for the sink to visit all the nodes. n be the number of sensors in the network, Tts is the maximum time that the sink is stationary, Mtx is the total message transmitted, Etx is the maximum amount of energy utilized for data transmission, ψ is the average data generation rate, Eia is idle energy, Texp is the total time taken for the experiment.

The initial energy of all Ni in the network,

iatxtstxtxi EETnEME V. RESULTS PERFORMANCE METRICS: The performance of MSREEDG is compared with the BSMASD [1] scheme. The performance is evaluated mainly, according to the following metrics

Energy: It is the average energy consumed for the data transmission.

Delay: It is the average time taken by the packets to reach the destination.

Fig. 4. Nodes vs. energy.

Fig. 4 shows the energy consumption of MSREEDG and BSMASD techniques for different number of nodes scenario. We can conclude that the energy consumption of our proposed MSREEDG approach has 10% of less than BSMASD approach.

Fig. 5. Rate vs. energy.

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Fig. 5 shows the energy consumption of MSREEDG and BSMASD techniques for different rate scenario. We can conclude that the energy consumption of our proposed MSREEDG approach has 9% of less than BSMASD approach.

Fig. 6. Nodes vs. delay.

Fig. 6 shows the delay of MSREEDG and BSMASD techniques for different number of nodes scenario. We can conclude that the delay of our proposed MSREEDG approach has 34% of less than BSMASD approach.

Fig. 7. Rate vs. delay.

Fig. 7 shows the delay of MSREEDG and BSMASD techniques for different rate scenario. We can conclude that the delay of our proposed MSREEDG approach has 42% of less than BSMASD approach.

CONCLUSION .

In this paper, we have proposed to design a Mobile Sink Based Reliable and Energy Efficient Data Gathering technique for WSN. This technique considers tree based network topology. Initially, a Biased Random Walk method is used to determining the next position of the sink. Then a rendezvous point selection with splitting tree technique is used to determine the optimal data transmission path. The increased packet losses in a specific region of the network can be prevented by increasing the pause time of the sink. By simulation results, we have shown that the proposed technique increases the reliability and energy efficiency

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data gathering in wireless sensor networks using...

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