No abstract

Designing an energy-efficient routing makes the Wireless Sensor Networks (WSN) more effective and attractive for different applications. The WSN communication system power consumption mainly depends on three aspects such as routing cost computation, signal interference, and routing distance. All three factors are equally important in order to improve the network performance. The system reliability and deployment cost depends on the energy efficiency of the WSN. The energy related cost assignment and shortest paths identification are used in existing routing techniques. In the existing routing techniques maximum achievable lifetime and optimal link cost are low. Hence greatest possible performance can be achieved in distributed routing algorithm by finding shortest path. Maximum lifetime and best cost link can be generally obtained using distributed shortest path routing algorithm. In this paper high speed reconfigurable distributed Lifetime-Efficient Routing algorithm is designed to provide route selection outline with low complexity and obtain better performance compared to existing routing algorithm.

Objective: To propose an effective hybrid model for predictive control (EHMPC) to efficiently manage demand and supply of energy for a microgrid operating in islanded mode operation. Due to the intermittent nature of renewable energy sources and variation in load in the microgrid, maintaining the system stability and reliability along with the economy is a critical issue to be addressed. In the islanded mode of operation, the voltage and frequency have to be monitored in addition to managing energy storage units. The different uncertainties occurring at various stages of microgrid should be taken into account to operate the microgrid with reliability under critical condition Methods: This paper proposes an effective algorithm to efficiently control the operation of microgrid and to operate it with optimal efficiency and reliability. In this work, we have proposed a three-stage control of the microgrid where the first stage consists of the arbitrarily distributed generation (ADG) stage, the second stage has energy storage unit (ESU), and the final stage has the energy management scheme (EMS). Finding: A case study has been carried out and the proposed method is found to be better in performance, economical and robust in comparison with the conventional two-stage model predictive control (MPC) optimization approach. Further important parameters have been analyzed. Novelty: To overcome the limitations of conventional MPC algorithm, we propose a three-stage EHMPC algorithm it consists of three stages, ADG had the first stage of the algorithm the main features of this stage. To optimize the placement, sizing, power factor, minimize network losses and maximize DG integration. The second stage has ESU, the main features of this stage to improve control strategy for optimal power management of microgrid and the final stage has the EMS, to improve tested analysis for a real-time islanded microgrid under various load conditions.

Background/objectives: The objective of the study is to increase the resolution and radiate sharper beam towards the user in mobile communication using Smart Antenna. Methodology: The Conventional and Subspace Algorithms from the literature are studied and simulated in MATLAB so that the foundation is laid for better detection of algorithms and radiation formation. The results are explained for varying number of antenna elements and mobiles sources placed close to far. Findings: The classical direction of arrival algorithms namely CAPCON, Maximum Entropy Method, Maximum Likelihood Method are used to find the direction of mobile users based on the computation of the power spectrum. Several methods namely Least Mean Square, Griffiths Method, Variable Step Size Griffiths and Recursive Least Square are used to form the main beam for the user detected by the direction of arrival algorithms. Novelty/improvements: In order to take further the research on enhancing the resolution and having a higher convergence rate with reasonable step size, this paper presents the well-known conventional and modern algorithms from the literature. The results are simulated are well described for performing parameters.

Objective: Energy efficiency aspect in wireless sensor networks (WSN) can be achieved by small sized rechargeable and easily replaceable batteries. The lifetime of wireless sensor network can be improved by identifying the efficient and reliable nodes as a cluster heads using Hybrid Simulated Annealing algorithm. The proposed algorithm identifies cluster head to reduce overhead and is capable of handling high volume of nodes with minimum node death rate. Methods: This study proposed initialization of population vectors using the opposite point procedure, self-adaptive control approach by node mutation rate, crossover rate, node capacity and cluster head allocation Methods. Findings: A case study in the proposed work is found to be better in throughput, accuracy, efficiency, energy utilization, batteries recharge ability and replacement procedures compared to the conventional methods. By the analysis and comparison of the proposed method with existing methods, it is identified that the reduction of the number of dead nodes gradually increases the throughput and lifetime of the nodes with respect to the number of iterations. Novelty: To overcome the limitations of conventional Low Energy Adaptive Clustering Hierarchy (LEACH), harmony search algorithm (HSA), modified HSA and differential evolution, we propose a hybrid optimal model using simulated annealing algorithm which includes a node capability function. It is used to improve the network lifetime of the cluster heads and sensor nodes. The proposed method have capability of batteries recharge ability and replacement option to improve network throughput and reliability of network.