In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource Clustering is one of the effective methods of increasing the network lifetime However, improper cluster head (CH) selection easily drains the energy early in network nodes With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime The T2FL is highly useful in providing the accurate solution in uncertain network environments Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ) The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%–15% and the packet transmission ratio by 10% Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime

/pdf/t2fl-pso-type-2-fuzzy-logic-based-particle-swarm-48jhukvjob.pdf

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks

Evaluation of EDM process parameters on titanium alloy through Taguchi approach

An Energy-Proficient Clustering-Inspired Routing Protocol using Improved Bkd-tree for Enhanced Node Stability and Network Lifetime in Wireless Sensor Networks

Due to the emerging applications of unmanned aerial vehicle (UAV)-based technologies, UAV-based wireless communication techniques, such as UAV-based coverage extension, UAV-based data distribution and UAV-based relaying, are being used to collect information in different processing sectors. In particular, UAV-based data gathering and distribution can be executed using a UAV-based wireless sensor network (WSN). In UAV-based WSNs, the cluster heads (CHs) serve important functions in both data gathering and data transfer between members and UAVs. Due to the important functions of CHs, many attackers attempt hack CH nodes. Typically, a hacked CH utilizes excess energy compared to a normal CH since it performs the CH function of delivering information to a sink greedily. To resolve this, this paper develops a novel UAV-based CH selection (CHS) algorithm for use in WSNs, namely, the Fitness-based Fuzzy C-Means (Fit-FCM) algorithm, which gathers the remaining energy of nodes and utilizes the energy for selecting new CHs while neglecting the nodes with the lowest energy. Initially, UAV-based WSN nodes are simulated, and then, CHS is performed using the developed Fit-FCM algorithm, in which fitness functions such as energy, distance and trust are considered. After CHS, information is transmitted through the selected CHs. Experimental results demonstrate that the developed Fit-FCM achieves better results in terms of distance, energy, and trust, with values of 51.9076 m, 0.4882 J, and 0.536439, respectively.

Trust- and energy-aware cluster head selection in a UAV-based wireless sensor network using Fit-FCM

Clustering is considered as one of the most primitive technique that aids in prolonging the lifetime expectancy of wireless sensor networks (WSNs). But, the process of cluster head selection concerning energy stabilization for the purposed of prolonging the network life expectancy still remains a major issue in WSNs. In this paper, a hybrid grey wolf and crow search optimization algorithm-based optimal cluster head selection (HGWCSOA-OCHS) scheme was proposed for enhancing the lifetime expectancy of the network by concentrating on the minimization of delay, minimization of distance between nodes and energy stabilization. The grey wolf optimization algorithm is hybridized with the crow search optimization algorithm for resolving the issue of premature convergence that prevents it from exploring the search space in an effective manner. This hybridization of GWO and CSO algorithm in the process of cluster head selection maintains the tradeoff between the exploitation and exploration degree in the search space. The simulation experiments are conducted and the results of the proposed HGWCSOA-OCHS scheme is compared with the benchmarked cluster head selection schemes with firefly optimization (FFO), artificial bee colony optimization (ABCO), grey wolf optimization (GWO), firefly cyclic grey wolf optimisation (FCGWO). The proposed HGWCSOA-OCHS scheme confirmed minimized energy consumption, improved network lifetime expectancy by balancing the percentage of alive and dead sensor nodes in the network.

A Hybrid Grey Wolf and Crow Search Optimization Algorithm-Based Optimal Cluster Head Selection Scheme for Wireless Sensor Networks

Artificial neural network based prediction of responses on eglin steel using electrical discharge machining process

Sea Turtle Foraging and Hydrozoan Optimization Algorithm-based NLOS Node Positioning Scheme for Reliable Data Dissemination in Vehicular Ad hoc Networks

VANET (Vehicular Ad Hoc Network), a rapidly expanding wireless network using multiple routing systems to provide vehicle network connectivity Interconnection between vehicles is rapidly changing because of the frequent movement of VANETs. This frequently results in network topologies, resulting in path errors on these networks. Furthermore, the movement-induced track failure problem is a key priority in VANET to provide efficient network access to vehicles with an optimal QoS without disrupting service. This paper has randomly roamed the performance of various ad hoc mobile node routing protocols that have not been reported in previous studies of IEEE 802.11g VANET based on OFDM and that have a high data rate of 54 Mbps. This work showed the implementation of the DCF-MAC protocol to load VoIP traffic with OPNET Modeler 14.5.

An Implementation of the DCF-MAC protocol based VANET

Every year, 62 million tons of garbage is generated in India, and 45 million tons of that waste is disposed of in an unhygienic manner, resulting in serious health consequences and environmental distress. In smart cities, an efficient waste management system is crucial. To address the current issues, this paper proposes an Internet of Things (IoT) based waste management system which aims to segregate the waste materials of different streams such as wet, dry, plastic, paper, metal, and glass. It increases the potential for recovery and consequent recycling processes. This system is implemented using Arduino microcontroller, which controls the entire process with ease and simplicity. This segregator system consists of various stages including of infrared sensor, inductive proximity sensor, rain drop sensor, photoelectric sensor, and the segregation bins. Each waste is detected by the relevant sensor and drops into the bins allocated to it for further processing. The status of the segregated data is made available in the cloud for monitoring and controlling purposes. These enabled policies empower cities to manage waste collection effectively.

J. Martin Sahayaraj

Papers

A Hybrid Grey Wolf and Crow Search Optimization Algorithm-Based Optimal Cluster Head Selection Scheme for Wireless Sensor Networks

Artificial neural network based prediction of responses on eglin steel using electrical discharge machining process

Sea Turtle Foraging and Hydrozoan Optimization Algorithm-based NLOS Node Positioning Scheme for Reliable Data Dissemination in Vehicular Ad hoc Networks

An Implementation of the DCF-MAC protocol based VANET

IoT based Waste Management System