Bio-Inspired Load Balancing In Large-Scale WSNs Using Pheromone Signalling

Wireless sensor networks (WSNs) consist of multiple, distributed nodes each with limited resources. With their strict resource constraints and application-specific characteristics, WSNs contain many challenging tradeoffs. This paper proposes a bioinspired load balancing approach, based on pheromone signalling mechanisms, to solve the tradeoff between service availability and energy consumption. We explore the performance consequences of the pheromone-based load balancing approach using (1) a system-level simulator, (2) deployment of real sensor testbeds to provide a competitive analysis of these evaluation methodologies. The effectiveness of the proposed algorithm is evaluated with different scenario parameters and the required performance evaluation techniques are investigated on case studies based on sound sensors

Advancing Evolutionary Coordination for Fixed-Wing Communications UAVs

In this paper we present advances to our previously proposed coordination system for groups of unmanned aerial vehicles that provide a network backbone over mobile ground-based vehicles. Evolutionary algorithms are employed in order to evolve flying manoeuvres that position the aerial vehicles. The updates to the system include obstacle representation, a packing mechanism to permit efficient dynamic allocation of ground-based vehicles to their supporting aerial vehicles within large-scale environments, and changes to time synchronisation. The experimental results presented in this paper show that the system is able to adaptively form sparse formations that cover as many ground-based vehicles as possible, optimising the use of the available power