A price-based adaptive task allocation for Wireless Sensor Network

Applications for wireless sensor networks may be decomposed into the deployment of tasks on different sensor nodes in the network. Task allocation algorithms assign these tasks to specific sensor nodes in the network for execution. Given the resource-constrained and distributed nature of wireless sensor networks (WSNs), existing static (offline) task scheduling may not be practical. Therefore there is a need for an adaptive task allocation scheme that accounts for the characteristics of the WSN environment such as unexpected communication delay and node failure. In this paper, we focus on task allocation in WSNs which is performed with the aim of achieving a fair energy balance amongst the sensor nodes while minimizing delay using a market-based architecture. In this architecture, nodes are modeled as sellers communicating a deployment price for a task to the consumer. To address this task allocation problem, proposed price formulation is used as it continuously adapts to changes of the availabilities of resources. This scheme also accommodates for the node failure during task assignment. The centralized and distributed message exchanged mechanisms between the nodes (sellers) and task allocator (consumer) are proposed to determine the winner among the sellers with the goal of reducing overhead and energy consumption. Simulation results show that, compared with a static scheduling scheme with an objective in energy balancing, the proposed scheme adapts to new environmental changes and uncertain network condition more dynamically and achieves a much better performance on energy balancing.