In large scale wireless sensor networks, energy consumption and optimization are a major challenge. Most of the protocols designed for cluster based routing problems suffer from unbalanced energy consumption. In this research work, an energy efficient clustering and routing protocol has been devised. An improved cuckoo search algorithm which uses a novel multiobjective fitness function have been proposed for optimal cluster head selection. Further, Monkey search algorithm has been used to find the optimal path from the cluster head to the sink nodes. The algorithm has been compared with some existing metaheuristics and significant improvements in number of alive nodes, energy efficiency has been observed. © 2019 IEEE.

Shankar T

Department of Communication Engineering

School of Electronics Engineering

Vellore Campus

Rajesh A

Mageshvaran R

Department of Electrical Engineering

School of Electrical Engineering

Eappen G

Sahani S

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

2019 Innovations in Power and Advanced Computing Technologies (i-PACT)

Automatic generation control or AGC system is significant controlling system that operates efficiently to balance the load and generation in power system at minimum cost for economical operation. System frequency will vary from nominal value if there is mismatch occurs between generation and demand. Due to this high frequency deviation system may breakdown. A very fast, reliable and accurate controller is needed to maintain the system frequency within the range to maintain stability. In this paper the proposed model consisting of PID controller whose parameters have been optimized using PSO tuned Fuzzy Logic Controller and it’s been compared with conventional PSO-PID controller. Each control area in power systems includes the dynamics response of the systems. The results contained in this paper present the strength of the particle swarm optimizer for tuning the Fuzzy based PID controller parameter for two area power system network, for better performance PSO trained SSSC and TCPS has been introduced to the system. The enhancement in the dynamic response of the power system network is verified. The output response of the proposed work is compared with conventional PSO-PID & PSO Fuzzy-PID based AGC system. Simulation experiments so conducted in MATLAB showed that the proposed system outperformed the conventional one by achieving better response.

Fulltext

International Journal of Engineering & Technology

Optimization of Two Area AGC based Power System Using PSO Tuned Fuzzy PID Controller and PSO Trained SSSC And TCPS

The major concerns in Wireless Sensor Networks (WSN) are energy efficiency as they utilize small sized batteries, which can neither be replaced nor be recharged. Hence, the energy must be optimally utilized in such battery operated networks. One of the traditional approaches to improve the energy efficiency is through clustering. In this paper, a hybrid differential evolution and simulated annealing (DESA) algorithm for clustering and choice of cluster heads is proposed. As cluster heads are usually overloaded with high number of sensor nodes, it tends to rapid death of nodes due to improper election of cluster heads. Hence, this paper aimed at prolonging the network lifetime of the network by preventing earlier death of cluster heads. The proposed DESA reduces the number of dead nodes than Low Energy Adaptive Clustering Hierarchy (LEACH) by 70%, Harmony Search Algorithm (HSA) by 50%, modified HSA by 40% and differential evolution by 60%. © 2016 Faculty of Engineering, Ain Shams University

Ain Shams Engineering Journal

Lifetime Improvement in Wireless Sensor Networks using Hybrid Differential Evolution and Simulated Annealing (DESA)

Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle Swarm Optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA–PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm. © 2016 Elsevier B.V.

Swarm and Evolutionary Computation

Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

Applied Mechanics and Materials

Markov Process Modeling for Wireless Sensor Network Availability with QOS Constraints

Journal of Network and Computer Applications

Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison

Integrated Cuckoo and Monkey Search Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

Journal	Data powered by Typeset2019 Innovations in Power and Advanced Computing Technologies (i-PACT)
Publisher	Data powered by TypesetIEEE
Open Access	No