Energy Optimization Algorithm for Path Selection in Wireless Body Sensor Networks

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

In today’s healthcare environment, the Internet of Things technology provides suitability among physicians and patients, as it is valuable in numerous medicinal fields. Wireless body sensor network technologies are essential technologies in the growth of Internet of Things healthcare paradigm, where every patient is monitored utilising small-powered and lightweight sensor nodes. A dual-hop, inter–wireless body sensor network cooperation and an incremental inter–wireless body sensor network cooperation with energy harvesting in the Internet of Things health-based paradigm have been investigated and designed in this work. The three protocols have been named and abbreviated as follows: energy harvesting–based dual-hop cooperation, energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based incremental inter–wireless body sensor network cooperation. Outage probabilities for the three designed protocols were investigated and inspected, and mathematical expressions of the outage probabilities were derived. The simulation and numerical results showed that the energy harvesting–based incremental inter–wireless body sensor network cooperation provided superior performance over the energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based dual-hop cooperation by 1.38 times and 5.72 times, respectively; while energy harvesting–based inter–wireless body sensor network cooperation achieved better performance over energy harvesting–based dual-hop cooperation by 1.87 times.

Wireless network of wearable biomedical sensors by human body shows great potential to enhance the biometrics performance significantly. Meanwhile, it poses prominent characteristics and challenges to physicians and engineers for its particular medical application as compared to other application of wireless sensor networks (WSN). Mobility pattern plays an important role in designing the wireless body sensor networks (WBSN) and will also affect the accuracy of modeling WBSN in health care application. Much of the mobility scenarios generated in current work of wireless body sensor networks has used fairly simple models to generate the mobile topological graph, which bear little resemblance to the actual mobility patterns. This paper is the first attempt to investigate the mobility model in WBSN based on the existing mobility models in wireless data networks and ad hoc networks. We first briefly review the existing mobility models in related research areas such as wireless ad hoc network and cellular networks. Further on, we propose a dedicated and more realistic mobility model named BAMM (Body Area Mobility Model) for wireless body sensor networks by concentrating on the unique characteristics of WBSN and finally study the effects of mobility on the performance of WBSN by simulation experiments.

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

A Wireless Sensor Network (WSN) represents a set of deployed sensors in an area to track or monitor specific activities. Sensors are utilized to gather certain data and forward it to the sink or a Base Station (BS). Sensors are operated by limited-energy batteries. Optimizing the consumed energy will prolong the WSN's lifetime. One approach to reducing the consumed energy is by applying a cluster-based routing protocol. Optimizing the Cluster Head (CH) selection represents another approach to prolonging the WSN lifetime. This paper proposes an optimization algorithm to select the CH. The residual energy, the distance between sensors, and the centrality of the sensor node are used as significant features in selecting the CHs in this paper. The proposed algorithm is based on Particle Swarm Optimization (PSO) algorithm and K-Means algorithm. This study proposed approach is called the PSO-K algorithm. It helps in reducing energy consumption and increasing the network lifetime. MATLAB is used to model and simulate this developed algorithm. the number of sensors, number of rounds, number of CHs, the first dead nodes, the half-dead nodes, and the last dead nodes are used to evaluate the WSN performance. A comparison with other closely related works such as LEACH, EAMMH, and iLEACH shows that the PSO-K algorithm is the best in energy consumption and network lifetime.

Proposing a centralized algorithm to minimize message broadcasting energy in wireless sensor networks using directional antennas

The objective of the research work is to examine energy-efficient routing in wireless sensor networks for moving nodes by contrasting new evolutionary algorithms with the Particle Swarm Optimization (PSO) Algorithm. Comparative analysis of energy efficient routing is performed by a novel genetic algorithm where there are several samples (N = 10) and a PSO algorithm where there are several samples (N = 10) techniques with pre-test power of 80% using NS2. When routing wireless sensor networks with moving nodes, Various metrics, including delay, energy use, and packet delivery ratio, are used to compare PSO and genetic algorithms. The evolutionary algorithm outperforms the PSO algorithm in these ways in terms of packet delivery ratio, energy usage, and delay. (p < 0.05) indicates a statistically significant difference. The research's findings comprised the three metrics of delay, energy consumption and packet delivery ratio. The method shows that the novel Genetic algorithm outperforms the PSO algorithm for wireless sensor network energy-efficient routing for moving nodes by identifying a set of routes that can satisfy the delay restrictions.KeywordsNovel genetic algorithmParticle Swarm Optimization (PSO)Wireless sensor networkDelayEnergy consumptionPacket delivery ratio

SENET: A novel architecture for IoT-based body sensor networks

Novel multiobjective TLBO algorithms for the feature subset selection problem

A green clustering protocol for mobile sensor network using particle swarm optimization

Node localization algorithm for detecting malicious nodes to prevent connection failures and improve end-to-end delay

The technology of Heterogeneous Wireless Sensor Networks (HWSNs) is critical to the efficient operation and deployment of a variety of real-time Internet of Things (IoT) and Mobile Ad hoc Networks (MANETs). It is crucial for reducing overall energy dissipation and ensuring consistent energy distribution throughout the network. Bio-inspired hybrid optimization algorithms are emerging as a possible option for overcoming basic difficulties in Wireless Sensor Networks (WSNs), with a focus on sensor lifespan restrictions. A significant topic that must be considered prior to network configuration is attaining energy efficiency and optimal communication. Several papers have been published on the use of bio-inspired algorithms in WSNs. Few articles, however, addressed the hybrid strategy for routing and clustering in WSNs with communication. This research focuses on hybrid bio-inspired optimization algorithms and elaborates on their taxonomy and problem domains in WSNs. Furthermore, we explored and investigated the hybridization of the Whale Optimization Algorithm (WOA) with other meta-heuristic algorithms such as the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and others. This review can assist researchers in exploring the uses of such algorithms within and outside of this study area.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Location technology is the key support technology of wireless sensor network (WSN). The hop number and hop distance information obtained by traditional distance vector hop (DV-Hop) location algorithm can only be acquired by solving the nonlinear equations, and the solution of the equation determines the accuracy of node location. Although the least squares method has better estimation performance, the solution results are sensitive to the average hop distance, which will lead to the large error in the solution of the equation. In order to solve the problem of location error caused by initial value sensitivity of least squares method in the coordinate calculation stage of unknown nodes and beacon nodes, a range-free location algorithm based on particle swarm optimization (PSO) is proposed in this paper. The proposed approach solves the problem of location error caused by initial value sensitivity of least squares method, obtains relatively accurate solution, and improves the accuracy of location algorithm. The experimental results show that the PSO algorithm has faster convergence speed and higher location accuracy than the non-optimization algorithm.