A Robust Frame of WSN Utilizing Localization Technique

Wireless sensor networks are becoming increasingly popular due to their low cost and wide applicability to support a large number of diverse application areas. Localization of sensor nodes is a fundamental requirement that makes the sensor data meaningful. A wireless sensor network (WSN) consist of spatially distributed autonomous devices using sensors to monitor cooperatively physical or environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants at different locations. The development of wireless sensor networks was originally motivated by a military application like battlefield surveillance. Node localization is required to report the origin of events, assist group querying of sensors, routing and to answer questions on the network coverage. So one of the fundamental challenges in wireless sensor network is node localization. This paper discusses different approaches of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Keywords: Localization, Particle Swarm Optimization, Received Signal Strength, Angle of Arrival

An Integrated Software Framework for Localization in Wireless Sensor Network

Devices that form a wireless sensor network (WSN) system are usually remotely deployed in large numbers in a sensing field. WSNs have enabled numerous applications, in which location awareness is usually required. Therefore, numerous localization systems are provided to assign geographic coordinates to each node in a network. In this paper, we describe and evaluate an integrated software framework WSNLS (Wireless Sensor Network Localization System) that provides tools for network nodes localization and the environment for tuning and testing various localization schemes. Simulation experiments can be performed on parallel and multi-core computers or computer clusters. The main component of the WSNLS framework is the library of solvers for calculating the geographic coordinates of nodes in a network. Our original solution implemented in WSNLS is the localization system that combines simple geometry of triangles and stochastic optimization to determine the position of nodes with unknown location in the sensing field. We describe and discuss the performance of our system due to the accuracy of location estimation and computation time. Numerical results presented in the paper confirm that our hybrid scheme gives accurate location estimates of network nodes in sensible computing time, and the WSNLS framework can be successfully used for efficient tuning and verification of different localization techniques

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Distributed Maximum Likelihood Sensor Network Localization

We propose a class of convex relaxations to solve the sensor network localization problem, based on a maximum likelihood (ML) formulation. This class, as well as the tightness of the relaxations, depends on the noise probability density function (PDF) of the collected measurements. We derive a computational efficient edge-based version of this ML convex relaxation class and we design a distributed algorithm that enables the sensor nodes to solve these edge-based convex programs locally by communicating only with their close neighbors. This algorithm relies on the alternating direction method of multipliers (ADMM), it converges to the centralized solution, it can run asynchronously, and it is computation error-resilient. Finally, we compare our proposed distributed scheme with other available methods, both analytically and numerically, and we argue the added value of ADMM, especially for large-scale networks