Distributed Artificial Intelligence and Adaptive Fish Swarm Optimization based Resource Allocation in Wireless Sensor Network

The Wireless Sensor Network (WSN) is a decentralized and distributed ad-hoc network that comprises the powerful computing, sensing and processing nodes. In WSN, the sensor nodes are limited in terms of computational latency, communication range, bandwidth, storage and battery power. The efficient utilization of WSN resource is a difficult task for improve network lifetime, throughput, minimize the computational delay and overhead. Various intelligent strategies are developed through adopting intelligent resource management approaches. Due to the sensor heterogeneity, the inter and intra-cluster cooperative communication among sensor node in terms of response time and energy consumption. In this paper, a Distributed Artificial Intel ligence (DAI) with a hierarchical resource allocation for address the resource allocation issues. For inter-cluster power allocation, this paper considers the Quality of Service (QoS) and energy consumption with DAI. Additionally, for intra-cluster resource allocation, the Adaptive Fish Swarm Optimization (AFSO) is utilized which utilizes its objective functions as node distance and corresponding energy loads. The proposed DAI and AFSO less energy consumption of 7.86%, packet delivery ratio of 99.4% which is better than other existing methods.