Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.     

Localization Based Evolutionary Routing (LOBER) for Efficient Aggregation in Wireless Multimedia Sensor Networks

Efficient aggregation in wireless sensor nodes helps reduce network traffic and reduce energy consumption. The objective of this work Localization Based Evolutionary Routing (LOBER) is to achieve global optimization for aggregation and WMSN lifetime. Improved localization is achieved by a novel Centroid Based Octant Localization (CBOL) technique considering an arbitrary hexagonal region. Geometric principles of hexagon are used to locate the unknown nodes in the centroid positions of partitioned regions. Flower pollination algorithm, a meta heuristic evolutionary algorithm that is extensively applied in solving real life, complex and nonlinear optimization problems in engineering and industry is modified as Enhanced Flower Pollination Algorithm (EFPA) to fit into WMSN and enhance routing mechanism and ensure efficiency in data aggregation. The system is simulated using MATLAB and found to have a considerable improvement in the optimization process.     

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption     

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.     

蚁群循环定位算法在WSNs节点定位中的应用

针对无线传感网络中测距和定位算法复杂、精度不高的问题,提出了一种基于蚁群算法的循环定位算法。首先,利用接收信号强度指示(RSSI)方法测量距离,并建立信号衰减模型来计算测距公式。然后,利用循环定位算法对8个锚节点进行循环定位,将定位结果的平均值作为盲节点的最佳位置。最后,将该算法与加权质心定位算法和三边测量法进行对比,实验结果表明,循环定位算法的定位精度和稳定性更佳。     

基于力导向量子粒子群算法的气体源点定位研究

根据有风时气体浓度衰减模型,采用量子粒子群优化(quantum particle swarm optimization,QPSO)算法实现无线传感网络中的气体源点定位,考虑到传感器节点测量气体浓度时存在门限值的实际情况,引入力导向思想,通过使传感器节点产生虚拟力来影响QPSO算法的位置更新过程,使粒子移动更有目的性,引...     

Energy Efficient Clustering Protocol to Enhance Network Lifetime in Wireless Sensor Networks

In this paper, the energy conservation in the ununiform clustered network field is proposed. The fundamental reason behind the methodology is that in the process of CH election, nodes Competition Radius (CR) task is based on not just the space between nodes and their Residual Energy (RE), which is utilized in Energy-Aware Distributed Unequal Clustering (EADUC) protocol but also a third-degree factor, i.e., the nearby multi-hop node count. In contrast, a third-factor nearby nodes count is also used. This surrounding data is taken into account in the clustering feature to increase the network’s life span. The proposed method, known as Energy Conscious Scattered Asymmetric Clustering (ECSAC), self-controls the nodes’ energy utilization for equal allotment and un-equal delivery. Besides, extra attention is agreed to energy consumption in the communication process by applying a timeslot-based backtracking algorithm for increasing the network’s lifetime. The proposed methodology reduces the clustering overhead and node communication energy consumption to extend the network’s lifetime. Our suggested method’s results are investigated against the classical techniques using the lifetime of the network, RE, alive hop count and energy consumption during transmission as the performance metric.     

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers’ water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (), correlation coefficient (), Root Mean Square Error (), Mean Absolute Percentage Error (), and are provided.     

Research on Time Synchronization Method Under Arbitrary Network Delay in Wireless Sensor Networks

To cope with the arbitrariness of the network delays, a novel method, referred to as the composite particle filter approach based on variational Bayesian (VB-CPF), is proposed herein to estimate the clock skew and clock offset in wireless sensor networks. VB-CPF is an improvement of the Gaussian mixture kalman particle filter (GMKPF) algorithm. In GMKPF, Expectation-Maximization (EM) algorithm needs to determine the number of mixture components in advance, and it is easy to generate overfitting and underfitting. Variational Bayesian EM (VB-EM) algorithm is introduced in this paper to determine the number of mixture components adaptively according to the observations. Moreover, to solve the problem of data packet loss caused by unreliable links, we propose a robust time synchronization (RTS) method in this paper. RTS establishes an autoregressive model for clock skew, and calculates the clock parameters based on the established autoregressive model in case of packet loss. The final simulation results illustrate that VB-CPF yields much more accurate results relative to GMKPF when the network delays are modeled in terms of an asymmetric Gaussian distribution. Moreover, RTS shows good robustness to the continuous and random dropout of time messages.