Performance evaluation of power optimization in wireless sensor networks using particle swarm optimization

Developments in last decade in the field of electronics & communication, computer science and information technology field have resulted in the new technology paradigm, Wireless Sensor Networks. WSN is becoming increasingly popular due to their wide range of applications. For successful adaption of WSN in real field applications, there are certain issues which need to be addressed particularly the issue of energy consumption associated with sensor nodes. This limitation combined with node deployment has added many challenges to design and management of wireless sensor networks. The WSNs are involved in applications such as remote environment monitoring, event tracking in areas where providing electrical energy is very difficult. Moreover, the battery fed sensor nodes have very small energy associated with them, so preserving that energy and maximizing the usage of that energy is most critical issue in WSNs. Here, we present a survey of various energy optimization and lifetime enhancement techniques in wireless sensor networks and makes the reader aware about various energy saving techniques in WSN. They are analyzed from various points of view: Device hardware, Transmission, MAC protocols and Routing protocols.

In the modern world with the advancement of technologies in solid-state electronics, the use of power electronics devices is playing a crucial role in the conversion and control of electric power. By using these innovative devices in 3-phase voltage source inverters, precise and smooth control over the conversion of electrical power can be achieved. But on the other hand, this leads to Total Harmonic Distortion (THD) leading to an adverse effect on the overall system performance. The optimization technique has emerged as a newly popular method for selective harmonic elimination. The main focus of this paper is on simulation study for harmonic analysis of 3-phase voltage source inverter by using particle swarm optimization (PSO) technique. By considering the expression of THD as an objective function, optimization is achieved in the PSO algorithm. This paper also discusses the harmonic analysis of the proposed inverter for conventional PWM techniques such as sinusoidal pulse width modulation (SPWM) and trapezoidal pulse width modulation (TPWM). The harmonic spectrum of the output voltage of the inverter is compared for SPWM, TPWM, and PSO by using MATLAB. From THD analysis, it is observed that PSO gives better performance than other conventional techniques.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

The path planning of mobile beacon node is a basic part of node localization supported by mobile beacon nodes. In this paper, we use the principle of the shortest path in the graph theory as well as the particle swarm algorithm which has the characteristics of simplicity, fast convergence and memorizing in both position and velocity information in the procession of evolution to propose a mobile beacon node optimal path access method based on particle swarm optimization. The discussion of the realization of path planning, and optimal path searching throughout the sensing region are also included in the paper. The experimental results show the method is effective.

Objectives: Randomly moving target search in an unknown location is a complex problem which does not have a linear solution. This paper proposes Energy aware - Bio-inspired Hybrid technique for target finding Area surveillance application. Methods/Statistical Analysis: In recent years, many researchers tried to find a generic solution which requires minimum energy for target searching problem. For the proposed work four algorithms are proposed which are MSN’s Movement Prediction Algorithm (MMPA), Leader Selection Algorithm (LSA), and Leader’s Movement Predication Algorithm (LMPA), and Follower’s Algorithm. Particle Swarm Optimization (PSO) technique is used to move the Mobile Sensor Nodes (MSN) in searching area. Findings: There is a need to study the hybrid or heterogeneous Sensor network (combination of the Static sensor network and Mobile sensor network) as it is less studied in research literature. The major hurdle is communication between heterogeneous sensor nodes. Due to the mobility of MSN, topology changes frequently and it affects the communication in the network. In area surveillance domain, finding the randomly moving target using sensor node is a complex and non-linear problem. There is no straight forward solution. Nature based techniques may be more effective for such problem. Bio-inspired Hybrid WSN technique is to be studied for its suitability for target finding and target tracking application in area surveillance domain. To perform the work, two scenarios are considered and calculated their energy to prove that ifmore number of SSNs is planted, the target can be found in less time and ultimately overall energy of network reduces. Through the simulation, it is shown that E-BHAS reduces the energy when more number of SSNs is involved in the network. Application/ Improvements: Borderline Surveillance, Disaster Management, under water Surveillance, Traffic Monitoring etc. Keywords: Area Surveillance, Energy, Hybrid Wireless Sensor Network, Particle Swarm Optimization

PurposeThis paper aims to employ particle swarm optimization (PSO) technique for optimum design of single‐phase induction motor (SPIM) on the basis of maximizing the efficiency of the motor simultaneously satisfying a set of performance constraints.Design/methodology/approachThe design problem of a SPIM is presented as a nonlinear optimization problem on the basis of maximizing the efficiency of the motor. A set of performance constraints are imposed in the optimization procedure. Particle swarm optimization technique is used as an optimization tool for obtaining the motor dimensions corresponding to maximum efficiency. Incorporation of PSO as a derivative free optimization technique in solving SPIM optimum design problem significantly relieves the assumptions imposed on the optimized objective function.FindingsThis approach has been applied to two sample motors and the results are compared with the evolutionary programming (EP) results. It is observed that the proposed approach is effective and robust.Originality/valueThe fields of application of SPIMs are expanding day by day. The emphasis on energy conservation demands an improvement of the efficiency of single‐phase induction motors. Hence, the design optimization of these motors with efficiency as objective function assumes great importance.

There has been ongoing experiments on a two-wheeled self-balancing robot for several years, and it is almost similar to the model of an inverted pendulum on a cart. The presented paper describes the model, design, working and simulation of a two-wheeled self-balancing robot. Properties of the system under study are extremely non-linear. Moreover, there is a great deal of instability in the controller of proposed system. To improve the stability of mobile robots, this paper introduces fractional order PID (FOPID) or PI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sup> D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sup> controller for a non-linear and high-order system and compares it with the conventional PID. FOPID controller is designed specifically to adjust the parameters of the system more accurately according to desired specifications. The factorrs of the considered controller are optimized using both Nelder-Mead (N-M) technique and Particle Swarm Optimization (PSO) technique. The results obtained by N-M technique are compared with those of PSO technique. N-M tuned FOPID controller significantly improves the quality of controller in contrast to PSO technique. Simulation results also indicate that Nelder-Mead tuned FOPID is a better and more stable controller as compared to classical PID or PSO tuned FOPID, for a non-linear system like a two-wheeled self-balancing robot.

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.

This paper presents performance comparative study of various particle swarm optimization (PSO) techniques for the placement of generator units in the distributed generation (DG) system. For the installation of generator units in the distributed generation system, it is very important to know the generator sizing and its placement in the network system for reducing the line losses and hence the cost. Various PSO techniques such as Canonical PSO, Hierarchical PSO (HPSO), Time varying acceleration coefficient (TVAC) PSO, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), Stochastic inertia weight (Sto-IW) PSO and Time varying inertia weight (TVIW) PSO have been used for comparative study. Here the main objective function (OF) is to minimize the system cost. These techniques have been tested on the standard IEEE-14 bus, IEEE-30 bus and IEEE-57 bus network system by the use of MATLAB software.

In wireless sensor networks (WSN), generally sensors closest to the sink or base station tend to deplete their battery energy faster than other sensors and create an energy hole around the sink. In order to overcome this problem, a mobile data gathering using PSO and minimum covering spanning tree (MDG-PSO-MCST) is proposed. In this technique, multiple sensors are arranged to form clusters. Two SenCars with multiple antennas are deployed in the cluster location and space division multiple access (SDMA) technique is applied for data gathering with energy efficiency. The anchor points are selected for intermediate data collection based on the connectivity and node degree, node compatibility and the distance between the sensors of two adjacent clusters parameters using particle swarm optimisation (PSO) technique. The visiting tour of each Sencars among the selected anchors is performed using the MCST algorithm. By simulation results, we show that the proposed technique minimises the energy consumption and delay, and enhances the network efficiency.

In the network field, Wireless Sensor Networks (WSN) contain prolonged attention due to afresh augmentations. Industries like health care, traffic, defense, and many more systems espoused the WSN. These networks contain tiny sensor nodes containing embedded processors, Tiny OS, memory, and power source. Sensor nodes are responsible for forwarding the data packets. To manage all these components, there is a need to select appropriate parameters which control the quality of service of WSN. Multiple sensor nodes are involved in transmitting vital information, and there is a need for secure and efficient routing to reach the quality of service. But due to the high cost of the network, WSN components have limited resources to manage the network. There is a need to design a lightweight solution that ensures the quality of service in WSN. In this given manner, this study provides the quality of services in a wireless sensor network with a security mechanism. An incorporated hybrid lightweight security model is designed in which random waypoint mobility (RWM) model and grey wolf optimization (GWO) is used to enhance service quality and maintain security with efficient routing. MATLAB version 16 and Network Stimulator 2.35 (NS2.35) are used in this research to evaluate the results. The overall cost factor is reduced at 60% without the optimization technique and 90.90% reduced by using the optimization technique, which is assessed by calculating the signal-to-noise ratio, overall energy nodes, and communication overhead.

Firefly algorithm (FFA) optimization technique is a popular continuous optimization technique and also widely applied to tune controller gain values of proportional–integral–derivative (PID) controller. In the chaos-based firefly algorithm, the chaotic maps are included to improve the randomness when generating new solutions and thereby to increase the diversity of the population. It increases global search mobility for robust global optimization. The investigated power system incorporates two thermal power systems, and two areas are interconnected via tie-line. Also, this work system is equipped with a secondary (PID) controller. The gain value of the controller is optimized by implementing chaos-based firefly algorithm (CFFA) by applying step load in area 1. The effectiveness and supremacy of proposed optimization technique tuned controller performance are verified by comparing genetic algorithm (GA), particle swarm optimization (PSO) technique and firefly algorithm tuned PID controller performance in the same interconnected power system. The proposed optimization technique-based controller response is evaluated by considering time domain specifications parameters of the proposed optimization technique-based controller response. Further, effectiveness of recommended optimization technique is verified by adding nonlinearity’s [generation rate constraint (GRC) and governor dead band (GCB)] in the investigated power system with connecting unified power flow controller (UPFC) is applied, and it is a flexible alternating current transmission system (FACTS) controller family device connected parallel to the tie-line. Further, hydrogen aqua electrolyzer (HAE) energy storage unit with a fuel cell is connected in area 2 for improving the performance of the system during sudden load demand. Finally, simulation result proved that the proposed CFFA technique gives better controlled performance compared with GA, PSO and FFA technique tuned controller performance in terms of fast settled response with a minimum peak over and undershoot, damping oscillations during unexpected load demand conditions.

Objectives: This research paper is fixated to evaluate the performance of wireless sensor network by employing Bio inspired optimization techniques. In this work, it has been proposed to explore the possibilities of optimization procedures in order to improve the performance of wireless sensor networks. Methods/Analysis: We seek to optimize the Quality of service in wireless sensor network via routing. In order to raise the lifetime of the wireless sensor network load balancing of cluster heads is implemented here in this research work with this the energy consumption could be reduced long with less Error Rate and less Routing Overhead, Minimization of End to end delay and improving Throughput. Findings: In this work, the performance analysis has been evaluated for the different optimization techniques like Genetic algorithm, Particle Swarm optimization, and bacterial foraging optimization and Hybrid approach of GA-PSO optimization. First of all, the optimization techniques such as GA, PSO and BFO are adopted separately on WSN setup and after that the hybridization of GA and PSO is employed. In the existing work Load balancing was employed with GA optimization but in this work other techniques are also taken along with hybridization of GA and PSO. A comparison on the performance analysis of all the optimization algorithms is specified and to infer which of the techniques performs better in order to maximizing the network lifetime and minimizing the end to end delay of the wireless sensor network so that packets transferring is carried efficiently with a reduced amount of error rate so that there will be a lesser chance of the node failure and extend the network lifetime for the awareness of routing optimization. Improvement: Further hybridization of other optimization techniques can be implemented for the improvements of wireless sensor networks. Keywords: Wireless Sensor Networks, Network Performance in Terms of Network Lifetime and End to End Delay, BFO, GA, GA-PSO, PSO

In wireless sensor networks, the use of energy efficient infrastructure such as clustering may be used to lengthen the network lifetime and prevent network connectivity degradation. In such systems, the performance of the clustering scheme is generally influenced by the cluster head selection method and the number of clusters. This paper presents a dynamic clustering method with multi-objectives that automatically determines the optimum number of clusters in the network. The algorithm, which is based on binary Particle Swarm Optimization (PSO), eliminates the need to set the number of clusters a priori. In addition, a multi-objective approach is utilized in the cluster head selection algorithm in order to select the best set of cluster heads. Simulation results demonstrate that the proposed protocol can achieve an optimal number of clusters, as well as prolong the network lifetime and increase the data delivery at the base station when compared to other well known clustering algorithms.

Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN