A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks

Abstract

Wireless sensor networks (WSNs) with nodes spreading in a target area have abilities of sensing, computing, and communication. Since the GPS device is expensive, we used a small number of fixed anchor nodes that are aware of their locations to help estimate the locations of sensor nodes in WSNs. To efficiently route sensed data to the destination (the server), identifying the location of each sensor node can be of great help. We adopted a range-free color-theory based dynamic localization (CDL) [Shen-Hai Shee, Kuochen Wang, I.L. Hsieh, Color-theory-based dynamic localization in mobile wireless sensor networks, in: Proceedings of Workshop on Wireless, Ad Hoc, Sensor Networks, August 2005] approach, to help identify the location of each sensor node. Since sensor nodes are battery-powered, we propose an efficient color-theory-based energy efficient routing (CEER) algorithm to prolong the life time of each sensor node. The uniqueness of our approach is that by comparing the associated RGB values among neighboring nodes, we can efficiently choose a better routing path with energy awareness. Besides, the CEER has no topology hole problem. Simulation results have shown that our CEER algorithm can save up to 50–60% energy than ESDSR [Mohammed Tarique, Kemal E. Tepe, Mohammad Naserian, Energy saving dynamic source routing for ad hoc wireless networks, in: Proceedings of Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, April 2005, pp. 305–310] in mobile wireless sensor networks. In addition, the latency per packet of CEER is 50% less than that of ESDSR.

Introduction

With the advance of recent wireless and VLSI technologies, small and low-cost sensor nodes have become feasible in our daily life. These sensor nodes sense data in the environment surrounding them and transmit the sensed data to the sink (or the server). The way to transmit sensed data to the server affects the power usage of each node deeply. Typically, wireless sensor networks (WSNs) contain hundreds or thousands of sensor nodes and these sensor nodes have the ability to communicate with either one another or directly to the destination node (sink). Using a larger number of sensor nodes allows for sensing over wider geographical regions with greater accuracy. Sensor nodes may use up their energy so that they become unavailable in a WSN during communications or measurements. With more and more sensor nodes becoming unavailable, the WSN may be separated into several sub-networks or become sparse, which is not desirable. Therefore, energy saving is an important issues in the WSN.

It is a great challenge for routing in a WSN due to the following reasons. First, since it is not easy to grasp the whole network topology, it is hard to find a routing path. Secondly, sensor nodes are tightly constrained in terms of energy, processing, and storage capacities. Thus, they require effective resource management policies, especially efficient energy management, to increase the overall lifetime of a WSN.

The proposed clustered-based color-theory-based energy efficient routing (CEER) algorithm is based on a range-free color-theory-based dynamic localization algorithm, CDL [1], in which the location of a sensor node is represented as a set of RGB values. With known RGB values for each sensor node, we can find out the most possible position of a node by looking up the location database in the server. To keep track of a sensor node’s location, frequently updating the RGB values of each sensor node and delivering the update to the server is necessary. However, if battery-powered nodes frequently update and report their positions, they may consume energy quickly and also waste bandwidth. The CEER selects those cluster members that are closer to the anchor than itself as next possible hops by comparing their RGB values. Among the selected cluster members, the sensor node with the highest energy level is chosen as the next hop.

The remainder of this paper is organized as follows. Section 2 briefly introduces the CDL algorithm that is the basis of our routing algorithm. Section 3 introduces five related approaches and explains how they work. The network model and the proposed CEER algorithm are detailed in Section 4. Section 5 evaluates and compares our approach with a classical approach. Simulation results demonstrate the merits of our algorithm. Finally, concluding remarks are given in Section 6.