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In this paper a Heuristic Based Improved Linearly Decreasing Weight Particle Swarm Optimization algorithm (ILDW-PSO) is proposed to solve real time task assignment in heterogeneous processors. To achieve better quality solution to the particles, existing LDW-PSO encompasses to ILDW-PSO, which includes Genetic algorithm mutation operator. The objective is to minimize the maximum utilization of the processors with energy aware load balance condition which reduces the energy consumption. Experimental results show that ILDW-PSO outperforms the existing PSO algorithms interms of maximum utilization and normalized energy consumption for both consistent utilization matrix and inconsistent utilization matrix.

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The Next Generation Wireless Networks (NGWN) should be compatible with other communication technologies to offer the best connectivity to the mobile terminal which can access any IP based services at any time from any network without the knowledge of its user. It requires an intelligent vertical handover decision making algorithm to migrate between technologies that enable seamless mobility, always best connection and minimal terminal power consumption. Currently existing decision engines are simple, proprietary and its handover is only based on the received signal strength which has been proven unintelligent. The proposed decision algorithm gains intelligence by combining fuzzy logic system to handle imprecise data, multiple attribute decision making to handle multiple attributes for decision making and context aware strategies to reduce unnecessary handover. The proposed intelligent decision algorithm detects new network which offers best connectivity than current network and does authentication and mobile IP registration before making the handover; thereby reducing the packet loss to ensure high quality of service. This algorithm is capable of forwarding data packets to appropriate attachment point to maximize battery lifetime and also to maintain load balancing. The performance analysis shows that the proposed algorithm efficiently uses the network resources by switching between 3G and Wi-Fi under the different RF environmental conditions to offer best connectivity with minimal service cost to the users. It is observed that average handover delay for the experiment is 30-40ms and the integration of cellular network with WLAN using the proposed intelligent decision algorithm reduces the call dropping rate (<0.006) and call blocking probability (<0.00607) as well as unnecessary handover in heterogeneous networks.

WSN plays a major role in the design of IoT system. In today’s internet era IoT integrates the digital devices, sensing equipment and computing devices for data sensing, gathering and communicate the data to the Base station via the optimal path. WSN, owing to the characteristics such as energy constrained and untrustworthy environment makes them to face many challenges which may affect the performance and QoS of the network. Thus, in WSN based IoT both security and energy efficiency are considered as herculean design challenges and requires important concern for the enhancement of network life time. Hence, to address these problems in this paper a novel secure energy aware cluster based routing algorithm named Trusted Energy Efficient Fuzzy logic based clustering Algorithm (TEEFCA) has been proposed. This algorithm consists of two major objectives. Firstly, the trustworthy nodes are identified, which may act as candidate nodes for cluster based routing. Secondly, the fuzzy inference system is employed under the two circumstances namely selection of optimal Cluster Leader (CL) and cluster formation process by considering the following three parameters such as (i) node’s Residual Energy level (ii) Cluster Density (iii) Distance Node BS. From, the experiment outcomes implemented using MATLAB it have been proved that TEEFCA shows significant improvement in terms of power conservation, network stability and lifetime when compared to the existing cluster aware routing approaches.