Abstract
The Ant Colony System (ACS) algorithm has been applied in solving packet routing problems in Wireless Sensor Networks (WSNs). Solving these problems is complicated as packets need to be submitted through sensor nodes which are spatially distributed and heterogeneous by nature. Without an effective packet routing algorithm, energy consumption will be increased while network lifetime will be reduced. Most researches are focused on optimizing the routing process by using predefined parameters within a certain range. However, this approach will not guarantee optimal performance. This paper presents the parameter adaptation values for ACS experimental set-up in validating its performance.Possible values of each parameter within a defined range were employed. Experiments were conducted to obtain the best value of each parameter to be used for throughput, energy consumption, and latency.Results of this study can be adopted to achieve optimal performance for the packet routing process.
Highlights
A Wireless Sensor Network (WSN) consists of tiny-sized sensor nodes that can communicate between each other, perform basic computation operations, and sense any changes in a system (Cecílio & Furtado, 2014)
This paper presents the analysis of parameter adaptation that can be used by Ant Colony System (ACS) in WSN
EEABR uses two types of ants, the forward ant that finds the high capacity sensor nodes during the search process and the backward ant that is responsible for updating the pheromone value on the sensor nodes along the path that leads to the destination node
Summary
A Wireless Sensor Network (WSN) consists of tiny-sized sensor nodes that can communicate between each other, perform basic computation operations, and sense any changes in a system (Cecílio & Furtado, 2014). ACS uses a heuristic function to construct routing solutions in dynamically-distributed environments It consists of three main phases: solution construction, local pheromone and global pheromone updates that are influenced by the values of the parameters. The Energy–Efficient Ant-Based Routing (EEABR) algorithm was proposed by Camilo et al (2006) to minimize energy consumption and communication load in WSNs. EEABR uses two types of ants, the forward ant that finds the high capacity sensor nodes during the search process and the backward ant that is responsible for updating the pheromone value on the sensor nodes along the path that leads to the destination node. ACO is responsible for finding the optimal path between the cluster head and the destination node where the pheromone update technique is executed on a selected path to overcome stagnation problems in the WSN. Re ρ (0
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