Effective Load Balancing for Wireless Sensor Networks with Multiple Mobile Sinks

International Journal of Information and Computer Science (IJICS) Volume 3, 2014 doi: 10.14355/ijics.2014.03.007

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Effective Load Balancing for Wireless Sensor Networks with Multiple Mobile Sinks Keiichi Endo*1, Shingo Yano2, Dai Okano3 Graduate School of Science and Engineering, Ehime University Matsuyama 790-8577, Japan endo@cs.ehime-u.ac.jp; 2yano.shingo@comp.cs.ehime-u.ac.jp; 3okano@cs.ehime-u.ac.jp

*1

Abstract In this paper, we propose a method that extends the lifetime of wireless sensor networks by using multiple mobile sink nodes. The proposed method determines the positions of multiple sink nodes based on remaining battery levels of sensor nodes. When a sink node is moved, the new position of the sink node is determined considering the distance to other sink nodes and the distance to the sensor node with the lowest remaining battery level. The proposed method also considers remaining battery levels in constructing routing tables. We conduct a comparative evaluation of the existing and proposed methods via simulation to demonstrate the effectiveness of the proposed method.

Keywords Wireless Sensor Networks; Multiple Mobile Sinks; Load Balancing; Network Lifetime

Introduction Wireless sensor networks (WSNs) are extensively used in many applications, including systems that observe natural environments and ecology, those that provide logistical information for manufacturing and traffic control, and such similar systems. Such networks have therefore received increased attention in recent years.

In general, WSNs are wireless networks that observe environments or physical conditions through the cooperation of many sensor-equipped wireless terminals (nodes) installed in a specific area. WSNs include nodes in two different positions: sink nodes and sensor nodes (Fig.1).Sensor nodes transmit the information they measure and relay information measured

FIG.1 WIRELESS SENSOR NETWORKS

by other sensors, whereas sink nodes collect observation information transmitted by sensor nodes.

A variety of information is collected by sensors, including numerical data (e.g., temperature and humidity), as well as image and video data.

Given that sensor nodes in a WSN are usually powered by batteries and require long lifespans, packet transmissions must be minimized and transmission loads must be balanced as uniformly as possible across all nodes. Therefore, in this study, we propose a method that extends the lifetime of WSNs by using multiple mobile sink nodes; more specifically, we move sink nodes on the basis of the distances between sink nodes and select packet transmission destinations considering remaining battery level. We performed a comparative evaluation of the existing and proposed methods via simulation to demonstrate the effectiveness of the proposed method.

In addition to this brief introduction, this paper is organized as follows. We first discuss related research.

Then, we explain an existing method and the proposed method. Afterwards, we present results of our simulation and discuss these findings. Finally, we summarize our study and discuss future work.

Related Work Sensor nodes in WSNs are generally operated by batteries, but because sensor nodes are spread across a wide area, charging or exchanging batteries requires extensive costs. Reducing the load on each node and generating routes for effective transmission of information is crucial in realizing long-term WSN operation.

To address this issue, research related to new routing protocols has been conducted, including a system that identifies the destination of data to balance the remaining amount of electricity in each node (Xia et al., 2005), a system that stochastically selects the

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International Journal of Information and Computer Science (IJICS) Volume 3, 2014

destination of data (Ohtaki et al., 2006), and a system that performs load balancing by using multiple sink nodes (Liang et al., 2011).Research is also being performed with the objective of measuring observation regions by using multiple sensor nodes and suppressing the occurrence of unobservable regions to achieve the long-term operation of WSNs (Katsuma et al., 2009).

Performing observations with many sensor nodes requires each sensor node to use a number of relay nodes to transmit information to a sink node, creating the possibility of packet loss due to packet collision.

Further, because sensor nodes in the vicinity of a sink node must often relay packets, their batteries become depleted more quickly than other sensor nodes.

Research has been conducted on the use of a mobile sink node to alleviate this problem (Lee et al., 2010), (Luo et al., 2006), (Safdar et al., 2012).For further alleviation, research has also been conducted through the use of multiple mobile sink nodes (De et al., 2012), (Khodashahi et al., 2010).

Existing Method In this section, we explain an existing method (Marta et al., 2009)that determines the positions of multiple sink nodes based on remaining sensor node battery levels and transmission histories.

In the existing method, when the remaining battery level of a sensor node adjacent to a sink node falls below a given threshold đ??¸th , a search is performed for a place to move the sink node. In this method, a sink node moves to the same position as one of the sensor nodes from which it is receiving packets (Fig. 2).

equal to đ??¸th ′ given by the following formula, the sink node moves to the position of the sensor node among these with the highest number of recent packet transmissions. đ??¸th ′ = (1 + đ?›ź)đ??¸th

(� > 0).

If no sensor node fulfills the above condition, a search is conducted for a destination two hops away from the sink node. If this also fails to find a destination, the algorithm increases the number of hops by one and searches once again. If no sensor nodes transmitting data to the sink node fulfill the above condition, the threshold đ??¸th is multiplied by đ?&#x203A;˝, with 0 < đ?&#x203A;˝ < 1.If the remaining battery level of a sensor node adjacent to a sink node falls below the new threshold đ??¸th , a search is performed for a place to move the sink nodeusing the same procedure described above.

Packets transmitted from sensor nodes are relayed according to routing tables. To construct these routing tables at the initiation of network operations, control packets that are called hop count packets are sent from every sink node. Hop count packets include integers called hop counts that represent the number of hops to the sink node. When hop count packets are transmitted from the sink node, the hop count is initially set to one. Sensor nodes that receive the hop count packet update the routing table if it is the first hop count packet that they have received or if the hop count value of the packet is lower than previously received packets. If the routing table is updated, the sensor node increases the hop count by one and forwards the hop count packet to adjacent sensor nodes. This procedure lets each sensor node know the optimal destination sink node and the next hop node (i.e., the node to which the sensor node should forward packets).If a sink node moves, it sends out hop count packets again to update the routing tables.

Proposed Method

FIG.2 AN EXAMPLE OF THE SINK NODE MOVEMENT IN THE EXISTING METHOD

Below, we explain the scheme used to search for the position to which a sink node moves. To minimize the distance the sink node moves, the existing method searches for destinations from among sensor nodes one hop away from the sink nodeâ&#x20AC;&#x201D;i.e., sensor nodes that are adjacent to the sink node. Among those sensor nodes, if there exist nodes such that the remaining battery level of every adjacent node is higher than or

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In this section, we explain the proposed method, which adds three improvements to the existing method explained in the previous section. When determining the destination for a moving sink node, the proposed method considers the distance to other sink nodes and the distance to the sensor node with the lowest remaining battery level. It also uses standby sink nodes to preserve the optimal state of routing tables. Further, in our approach, routing tables are constructed considering the remaining battery levels.

We explain each of these respective improvements in the subsections below.

International Journal of Information and Computer Science (IJICS) Volume 3, 2014

Position Determination Based on Distance Metrics As with the existing method, when the remaining battery level of a sensor node adjacent to a sink node falls below a given threshold đ??¸th , a search is performed for a place to move the sink node. At this point, the existing method expands the search range by one hop, but in the proposed method, we simultaneously search all sensor nodes transmitting to the sink node. If there exist multiple sensor nodes such that the remaining battery level of every adjacent sensor node is higher than or equal to đ??¸th â&#x20AC;˛ given by the same formula as the existing method, the destination is determined by using one of the three methods Proposed1, Proposed2, and Proposed3explained below. In Proposed1, the destination is set to the sensor node with the largest distance evaluation value among the top ten sensors in terms of the remaining battery levels. The distance evaluation value here refers to the sum of the distance to the closest other sink node and the distance to the sensor node transmitting to the sink node with the lowest remaining battery level. In Proposed2, the destination is set to the node with the highest remaining battery level among the top ten sensor nodes in terms of the distance evaluation values. In Proposed3, the destination is set to the node with the largest number of recent packet transmissions among the top ten sensor nodes in terms of the distance evaluation values. We show the result of comparative evaluations for these three methods later.

Using Standby Sink Nodes In the existing method, when a sink node is moved, it sends hop count packets to update the routing tables; however, even if the number of hops to another destination sink node has become smaller than the number of hops to the current destination sink node because of the movement of the current destination sink node, it cannot change the destination. It is possible to maintain the routing tables in the optimal state by flooding hop count packets from all the sink nodes whenever one of the sink nodes is moved; however, the increased transmissions speed up battery consumption.

Thus, the proposed method constructs a route to not only the destination sink node that can be reached in the smallest number of hops, but also a standby sink node that can be reached in the second smallest number of hops. When the destination sink node is moved, a sensor node refers to the hop count included

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in the hop count packet received from that node. If the hop count to the standby sink node is smaller than that value, the node requests the standby sink node to send hop count packets. This changes the destination sink node when the number of hops to the standby sink node is smaller, allowing the sensor nodes to maintain the routing tables in the optimal state.

Constructing Routing Tables Considering Remaining Battery Levels In the existing method, the routing table is updated only if the hop count of the hop count packet received by the sensor node is smaller than those previously received. We extend this in the proposed method by selecting the node with the higher remaining battery level as the next hop node if the hop count in the received hop count packet is equal to the smallest hop count in the packets previously received. For example, node X in Fig. 3 selects node A as the next hop node because the remaining battery level of node A is higher than that of node B. The aim here is to extend the overall lifetime of the network.

FIG.3CONSTRUCTING ROUTING TABLES CONSIDERING REMAINING BATTERY LEVELS

Simulation In this section, we explain the simulation we performed in this study and present the results, and then discuss the findings.

Simulation Conditions We implemented a simulator in C++.In the simulation, we first found optimal values for the initial value of the threshold đ??¸th and reduction rate đ?&#x203A;˝ .Next, we performed a comparative evaluation of the proposed and existing methods for the case in which sensor nodes are uniformly distributed (uniform distribution) and the case in which sensor nodes are distributed according to a grid (grid distribution).Finally, we confirmed that all of the three aforementioned improvements to the existing method contributed to extending the lifetime of the network.

The simulation area was 200 m Ă&#x2014; 200 m, and 400

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sensor nodes and four sink nodes were placed in the area. In the initial conditions, the simulation area was partitioned into four square regions with a sink node placed in the center of each region. In the uniform distribution case, the simulation area was partitioned into 400 squares with a sensor node placed randomly within each region (Fig. 4).In the grid distribution case, the regions were divided in the same manner as the uniform distribution case, but sensor nodes were placed in the center of each square region (Fig. 5).The transmission radius of the sensor nodes was set as short as possible while preventing node isolation. In the uniform distribution, the radius was set to 18 m, whereas in the grid distribution, the radius was set to 15 m.

We represented time in the simulation via rounds.

During one round, all sensor nodes observed their surrounding environment once each then transmitted a sensing data packet to the destination sink node.

The remaining battery level was initially set to 1.

Transmitting a control packet ora sensing data packet consumed 0.001 of the battery, receiving a packet consumed 0.0005 of the battery, and sensing consumed 0.0005 of the battery.

Parameter đ?&#x203A;ź in the existing and proposed methods was set to 0.05.

We conducted our simulation 100 times, and evaluation was conducted by using the average results.

We identify the proposed methods by using an S when we used standby sink nodes and B for routing table construction considering remaining battery levels. We attach these S and B indicators to the position determination methods Proposed1, Proposed2 and Proposed3.For example, the case in which position determination method Proposed1 was used in conjunction with standby sink nodes and routing table construction considering remaining battery levels is written as Proposed1SB. Evaluation Indices

FIG. 4 NODE PLACEMENT IN THE UNIFORM DISTRIBUTION

We used network lifetime, minimum remaining battery level, and average remaining battery level as our evaluation indices. Network lifetime measures the number of rounds that elapsed until the remaining battery level of one of the sensor nodes in the network reached zero. The minimum remaining battery level measures the lowest remaining battery level of the sensor nodes in the network. The average remaining battery level measures the average remaining battery level of all sensor nodes in the network.

Results and Discussion

FIG. 5 NODE PLACEMENT IN THE GRID DISTRIBUTION

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Fig.6 shows simulation results given various values for the threshold đ??¸th in both the existing and proposed methods.When the initial value of the threshold đ??¸th was too high, the number of updates to the routing table associated with sink node movement increased, thus shrinking network lifetime. When the initial value of the threshold đ??¸th was set too low, the first movement of the sink node was delayed, running down the remaining battery level and shrinking the network lifetime. Fig. 6 shows that setting the initial value of the threshold đ??¸th to 0.5 resulted in an increased network lifetime. Thus, we set the initial value of the threshold đ??¸th to 0.5 in our simulations.

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First, we consider the results of the simulation using the uniform distribution (Fig. 8).As we set the initial value of the threshold đ??¸th to 0.5, the minimum remaining battery level decreased more slowly after reaching 0.5.Decreases in the average remaining battery level were faster in the proposed method, but decreases in the minimum remaining battery level were slower. Because the minimum remaining battery level affects the network lifetime, we conclude that the proposed method is better for long-term observations.

FIG. 6RELATIONSHIP BETWEEN INITIAL BATTERY THRESHOLD AND NETWORK LIFETIME

FIG. 7 RELATIONSHIP BETWEEN THRESHOLD REDUCTION RATE AND NETWORK LIFETIME

Fig. 7 shows our simulation results given various values for the threshold reduction rateđ?&#x203A;˝.These results show that setting the threshold reduction rateđ?&#x203A;˝to 0.6 extended the network lifetime. Thus, we set the threshold reduction rateđ?&#x203A;˝to 0.6 in our simulations.

Results of our simulation using the grid sensor node distribution (Fig. 9) show that just as with the uniform distribution, the proposed method maintained a higher minimum remaining battery level. In the existing method, the minimum remaining battery level in the uniform distribution was approximately 0.24 in round 25, but was approximately 0.19 in the grid distribution. We attributed this difference to the fact that because sensor nodes in the grid distribution were placed at equal intervals, the number of adjacent nodes was almost uniform. The existing method determines destinations by considering the number of packets transmitted from sensor nodes, but because there was no major difference in the number of packet transmitted when the number of adjacent nodes was uniform, the sink node movement has a minimal load balancing effect. Conversely, in the proposed method, the minimum remaining battery levels in both uniform and grid distributions were approximately 0.35 with little variation in round 25.Thus, we conclude that the proposed method had a large impact on load balancing regardless of node distribution.

Next, we compared transitions of minimum remaining battery levels and average remaining battery levels in the proposed method (Proposed1SB) and the existing method. Fig. 8 shows results for the uniform sensor node distribution, and Fig. 9 shows results for the grid distribution.

FIG. 9 REMAINING BATTERY LEVEL TRANSITION IN THE GRID DISTRIBUTION

FIG. 8 REMAINING BATTERY LEVEL TRANSITION IN THE UNIFORM DISTRIBUTION

The above results show that the proposed method is superior to the existing method. We further show how the three improvements to the existing method contributed to increase in performance. In the simulation below, the sensor nodes were placed in the uniform distribution.

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Fig. 10 compares the Existing SB, an improvement on the existing method by using standby sink nodes (S) and considering remaining battery levels in construction of the routing tables (B), with the proposed methods Proposed1SB, Proposed2SB, and Proposed3SB.In the figure, we observe that among the proposed methods, Proposed1SB had the slowest decrease in minimum remaining battery levels. Thus, we conclude that among the three proposed methods for determining the destination position of the sink node, the method that set the destination to the node with the highest distance evaluation value among the ten nodes with the highest amount of remaining battery level was superior. We also observe that the Proposed1SB maintained higher minimum remaining battery levels than the Existing SB.

FIG. 10 COMPARING METHODS FOR DETERMINING SENSOR NODE POSITION

Fig. 11 compares the use of standby sink nodes (Proposed1SB) with not using standby sink nodes (Proposed1B).Fig. 12 compares the construction of the routing tables considering remaining battery levels (Proposed1SB) with not considering remaining battery levels (Proposed1S).From these results, we observe that the use of standby sink nodes and construction of routing tables considering remaining battery levels both contributed to elongating network lifetime.

Based on the above simulation results, we conclude that all of the three improvements to the existing method are effective in lengthening network lifetime.

Conclusion In this study, we proposed a load balancing method for lengthening the lifetime of WSNs by using multiple mobile sink nodes. Specifically, we implemented the following three improvements to the existing method: (1) determining the destination for a moving sink node on the basis of the distance evaluation values, (2) using standby sink nodes, and (3) constructing routing tables considering remaining battery levels. We conducted a comparative evaluation of the existing and proposed methods via simulation and demonstrated that the proposed method preserved a higher remaining battery level in the sensor node with the lowest remaining battery level.

We also confirmed that all three improvements contributed to lengthening the network lifetime.

Our future research includes investigating methods to further improve load balancing in the proposed method by, for example, considering the number of adjacent nodes. We also aim to confirm the effectiveness of the proposed method through a proof of concept experiment as opposed to just simulation.

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FIG. 11EFFECTIVENESS OF USING STANDBY SINK NODES

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FIG. 12 EFFECTIVENESS OF CONSTRUCTING ROUTING TABLES CONSIDERING REMAINING BATTERY LEVELS

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Keiichi Endo was born in Osaka, Japan in 1980.He received his B.S. degree in Engineering, M.S. and Ph.D. in Informatics from Kyoto University, Japan, in 2003, 2005, and 2008, respectively.

He is currently a Senior Assistant Professor at the Graduate School of Science and Engineering, Ehime University, Japan. His current research interests are in the area of peer-to-peer and wireless networks.

Dr. Endo is a member of IPSJ, IEICE, and JSIAM. Shingo Yano was born in Ehime, Japan in 1990.He received his B.S. degree in Engineering from Ehime University, Japan in 2013.

He is currently a graduate student at the Graduate School of Science and Engineering, Ehime University, Japan.H is current research interests are in the area of wireless sensor networks.

Dai Okano had B.Eng. and M.Eng. in Applied Physics, Dr.Eng. in Information Science and Technology from the University of Tokyo.

He is currently an Associate Professor at the Graduate School of Science and Engineering, Ehime University, Japan. His research interests are in Mathematics, Computations, and their applications.

Dr. Okano is a member of JSIAM, IPSJ, and SIAM. He received the 40th Anniversary Best Paper Award in 2000 from IPSJ.

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