Parallel energy-efficient coverage optimization using WSN with Image Compression

Energy efficiency is a primary problem in wireless sensor networks which employ a large number of intelligent sensor nodes to accomplish complicated tasks. Focused on the energy consumption problem in target tracking applications, this paper proposes a dynamic energy management mechanism with an improved particle filter prediction in wireless sensor networks. The standard particle filter is improved by combining the radial-basis function network to construct the process model and the novel algorithm is adopted to predict the prior position of target. For dynamic awakening, the idle interval of each sensor node is estimated according to its sensing tasks. A cluster head rotating approach is introduced from low-energy adaptive clustering hierarchy for collecting data through the large sensing field. A group of sensor nodes which are located in the vicinity of target will wake up and have the opportunity to report their data. Distributed genetic algorithm is performed on cluster heads to optimize the sensor node selection. In target tracking simulations, we verify that the improved particle filter has more robustness than standard particle filter against the sensing error and dynamic energy management enhances energy efficiency of wireless sensor networks.

Detecting and measuring holes in Wireless Sensor Network

Greedy modular eigenspaces (GME) has been developed for the band selection of hyperspectral images (HSI). GME attempts to greedily select uncorrelated feature sets from HSI. Unfortunately, GME is hard to find the optimal set by greedy operations except by exhaustive iterations. The long execution time has been the major drawback in practice. Accordingly, finding an optimal (or near-optimal) solution is very expensive. In this study we present a novel parallel mechanism, referred to as parallel particle swarm optimization (PPSO) band selection, to overcome this disadvantage. It makes use of a new particle swarm optimization scheme, a well-known method to solve the optimization problems, to develop an effective parallel feature extraction for HSI. The proposed PPSO improves the computational speed by using parallel computing techniques which include the compute unified device architecture (CUDA) of graphics processor unit (GPU), the message passing interface (MPI) and the open multi-processing (OpenMP) applications. These parallel implementations can fully utilize the significant parallelism of proposed PPSO to create a set of near-optimal GME modules on each parallel node. The experimental results demonstrated that PPSO can significantly improve the computational loads and provide a more reliable quality of solution compared to GME. The effectiveness of the proposed PPSO is evaluated by MODIS/ASTER airborne simulator (MASTER) HSI for band selection during the Pacrim II campaign.

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.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

Path planning of the unmanned aerial vehicle (UAV) is one of the complex optimization problems due to the model complexity and a high number of constraints. Evolutionary algorithms are a conventional method to solve complex optimization problems with multiple constraints. However, they are time-consuming algorithms which make their implementation difficult. UAV path planning is usually a real-time problem that is needed to solve in limited time steps. Parallelization is one of the most effective methods for reducing the computation time of evolutionary algorithms. In the industrial applications, usually there is a communication platform between devices which can be used for the implementation of the parallelization process. For example, controller area network (CAN) is a protocol used for reliable communication in aerospace systems. Due to the development of this protocol on UAVs and its characteristics, it can be used for parallel optimization of path planning problem. In this paper, an asynchronous distributed multi-master parallel genetic algorithm and a parallel particle swarm optimization algorithm are implemented on CAN bus to improve the speed and performance of the UAV path planning.

Wireless sensor networks (WSNs) are employed in a variety of applications. One of the key applications of WSNs, which gained much attention, is the target tracking. Directional sensor networks (DSNs) are a subset of WSNs with some unique characteristics. Since optimizing the tracking system under the energy and coverage constraints in DSNs is of paramount importance, in this paper, we introduce a reliable algorithm for tracking mobile targets using directional WSNs. First, by selecting a minimum set of boundary and borderline sensor nodes, we achieve the desired coverage for an incoming detection. Second, for both deterministic ordered and random node deployments, we propose an efficient mechanism for determining the minimal interior sensor nodes that should be activated. Doing so, the network lifetime can be maximized by the employment of much fewer sensor nodes. Third, we use a geometric method for collecting data using two active sensors at a time. Accordingly, target position is estimated using the extended Kalman filter (EKF). Finally, we compare the proposed algorithm with a genetic algorithm and present the comparative simulation results of the EKF and the random walk. The results demonstrate the effectiveness of our proposed scheme in terms of the energy efficiency, coverage, and tracking accuracy.

This paper presents a parallel particle swarm optimization (PSO) technique for reactive power and voltage control (Volt/Var Control: VVC) investigating dependability. High penetration of renewable energies and deregulation of power systems force power flow to change suddenly and operators in control centers have to control voltage in wider power systems. Therefore, VVC is required to shorten the control interval and handle larger-scale power systems. One of the solutions for the problem is applications of parallel and distributed computing. Since power system is one of the infrastructures of social community, sustainable voltage control is crucial and dependability is strongly required for VVC. This paper investigates not only fast computation by parallel computation but also dependability of parallel PSO for VVC with IEEE 14, 30, 57, and 118 bus systems.

Node placement for connected target coverage in wireless sensor networks with dynamic sinks

Huge data processing contributes many factors in wireless sensor network such as network traffic and energy constraint. Using compressive sensing a new technique in data acquisition which reduced the required sampling rate to reconstruct the original signal will therefore lessen the power consumption. This paper will implement the compressive sensing algorithm of the wireless sensor network installed in the greenhouse. The primary objective of the design is to reduce the power consumption on wireless system network by maximizing the data packet payloads while minimizing the transmission activity of the Wireless Sensor Network. The sensor and receiver node consumes more power when transmission of data is taking place. The main contributions of this paper are to apply the compressive sensing algorithm in the greenhouse monitoring system to lessen the power consumption of the WSN, to serve as a reference for the new design of analog to digital converter using sampling rate lower than the traditional Nyquist rate and to give ideas to other researchers that compressive sensing can be applied to other WSN applications. This study will do the compression of the measured data in the sensor node and transmit it over a specified time. Matlab will be used to simulate and recover the original signal for verification of the results. The results will compare the power consumption of the existing system of the Precision Agriculture group in our laboratory.

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

The sensor nodes in wireless sensor network (WSN) come with limited battery life, thus energy usage in WSN is to be handled with great care. The routing protocols are the major research area to work on the QoS of the network such as network lifetime, scalability, energy consumption, packet overhead, etc. Since sensor node posses limited battery life, to use nodes efficiently yet not loosing connectivity in the network becomes a major issue in designing the routing protocols. To achieve energy efficiency and better network lifetime, grouping of sensor nodes into small clusters and selecting one cluster head seems to have more advantages in comparison with other network models to get better scalability, robustness and end-to-end data delivery. In this paper, we have discussed and studied the different energy-efficient routing protocols for clustering of sensor nodes in wireless sensor network, its merits, demerits and applications.

As a portable energy storage system, lithium-ion batteries (LIBs) are widely used in wireless sensor networks, electric vehicles and other fields. To ensure the continuity of power supply, it is necessary to monitor the state of charge (SOC) of LIBs. However, due to the nonlinearity of battery operation, accurate SOC estimation has become a challenging task. In this paper, a SOC estimation method based on long-term short-term memory (LSTM) network and improved particle filter (IPF) is proposed, which maps the easily observed voltage, current and temperature to the target SOC. Firstly, through a layer of the LSTM network, the timing characteristics of the data are fully utilized to obtain the SOC variation trend of LIBs. Then, the noise variance adaptive algorithm and particle distribution optimization algorithm are introduced to improve the standard particle filter (PF). On this basis, the estimation results of the LSTM network are optimized by IPF. In addition, the performance of the proposed LSTM-IPF method is compared with other methods. The results show that the estimation performance of the proposed model is excellent, and the root mean squared error (RMSE) and maximum error (MAX) are controlled below 1% and 2% respectively, which meets the requirements of SOC estimation and verifies the effectiveness of the proposed method.

Devices with sensors are rapidly becoming indispensable. As the technology behind sensors has improved, a wide variety of sensor-based automated systems have emerged. By relaying data from sensors to actuators, these systems can perform actions automatically. Researchers face difficulties in obtaining long-lasting efficacy from sensor nodes into wireless sensor networks (WSNs) because of inadequate resources, energy constraints, and interference. For WSNs to continue operating for as long as possible, it is essential that their limited supply of energy be conserved. technique to organize groups of sensor nodes into clusters of varying sizes, each of which will then send its data on to the central station. First, the CEOMS algorithm build energy density task of the network nodes, & using the energy density function, it designates the sensor nodes having maximum left-over energy like cluster heads.

SummaryThis paper addresses the problem of nonlinear time‐varying state and parameter estimation of induction machines (IMs) on the basis of a third‐order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non‐Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non‐Gaussian processes withouta prioristate information, by utilizing a time‐varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results. Copyright © 2014 John Wiley & Sons, Ltd.