Multi-objective genetic algorithms for computing fast fourier transforms over wireless sensor networks
Wireless sensor networks (WSNs) consist of sensor nodes which are geographically distributed with the capacity to communicate and re-organize themselves within the network. Each sensor node possesses wireless communication capabilities in the form of radio signals and some level of intelligence for signal processing and networking data. In most practical implementations, WSNs are deployed with limited battery sources and it is time consuming to replace the battery sources in practice which constrain the energy requirements of the WSN and inhibit network performance. While researches in traditional WSN applications are primarily focused on achieving high quality of service provisions, this current research focuses on protocols aimed at power conservation. This research initially identifies and_ describes the nature of problems that arise in deploying WSNs due to their limited battery sources and complications that arise in replacing the battery sources in practice. Therefore, we explore problems associated with sensor communication and distributed processing in WSNs. Our initial approach was to review existing techniques from literature applicable to distributed signal processing that resolves the challenge associated with efficient energy consumption in WSNs. The review indicates that data redundancy in communication is significantly due to spatio-temporal correlations. Among other techniques used, comprehensive sensing technique was discovered to be more effective in reducing the amount of redundant data communicated. Also, the literature survey indicates that the bulk of WSNs energy consumption is principally due to ineffective node communication resulting in poor load balancing. Furthermore, the bulk of energy expended during that ineffective node communication is associated with complex number multiplication. Yet, existing literature has failed to handle these issues of ineffective node communication resulting in poor load balancing and complex number multiplication which leads to increased energy depletion. Consequently, our novel technique is through a simulated approach where we developed a solution through the application of Multi-Objective Genetic Algorithm for Field Programmable Gate Arrays in order to resolve the challenge of complex number multiplication during signal processing at the node level. In handling the challenge of ineffective node communication, resulting in poor load balancing between nodes, we design a stochastic context-aware energy consumption and distribution model that effectively resolves the problem of load balancing. In sum, this research work contributes to knowledge in that it has developed a Multi-Objective Genetic Algorithm based on field programmable gate arrays to resolve the challenge of complex multiplication associated with the computation of Fast Fourier Transform during signal processing at the node level. Another novel technique achieved by this research is the design of Stochastic Context-aware energy consumption and distribution model capable of resolving the problem of load balancing in a WSN. These two novel techniques are aimed at reducing computational complexity, hence energy consumption during signal processing in WSNs.