TLBO-Based Routing Approach for Wireless Sensor Networks

Abstract

Routing in Wireless Sensor Networks (WSN) differs from routing in classical networks, as in WSN links are unreliable. Indeed, there is no infrastructure and the sensors have limited energy resources and low processing capacity. Routing problems in many cases are considered NP-hard problems. In these cases, metaheuristics can be the best solution for this kind of problems. Thus, many metaheuristics are used for routing in WSNs. Ant colony optimization (ACO) is among the most used metaheuristics, as it showed an excellent performance for routing. Due to the adaptation difficulties related to the ACO parameters, in this paper we propose a new optimization approach for routing problem using the Teaching Learning Based Optimization (TLBO). TLBO is a recent nature inspired metaheuristic based on the influence of the teacher in the class and learners interactions. In this work, TLBO algorithm is used for routing as it was proposed for continuous optimization problems, despite the fact that the routing in WSNs is a discrete optimization problem. Our proposed approach is shown to be competitive with ACObased approaches in terms of energy consumption and WSN lifetime. The experiments performed in varied scenarios and the results found, illustrate the efficiency of the TLBO approach against the well-known ACO approaches: Energy Efficient Ant Based Routing (EEABR) Algorithm and the Improved Ant Colony Optimization Routing Protocol (IACO).

Enhanced Ant Colony Optimization for Routing in WSNs An Energy Aware Approach

“…At the beginning of network operation, all paths have an equal probability for the ants to choose among them since there is no pheromone. After finding the food a backward ant is sent for putting the pheromone along the path from the food location to the source [19]. Over times, pheromone concentration reduces due to evaporation, and the right path to food will gain high pheromone concentration thus will be followed by other ants because the ant follows the path with the highest pheromone and the bad paths will be neglected [20].…”

Towards a context-aware Wireless Sensor Networks

“…1) Case 1: Static nodes and one static sink This case is the standard case (see Fig. 2) where nodes are deployed randomly or not and communicate the information detected to the single base station, this structure caused some problems related to the routing, the reliability, and the communication delay [24]. 2) Case 2: Static nodes and one mobile sink In this case some flexibility is added in the network, as the sink is mobile, so the communication of the data is somehow easier.…”