Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Information exchange in randomly deployed dense WSNs with wireless energy harvesting capabilities

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As large-scale dense and often randomly deployed wireless sensor networks (WSNs) become widespread, local information exchange between colocated sets of nodes may play a significant role in handling the excessive traffic volume. Moreover, to account for the limited life-span of the wireless devices, harvesting the energy of the network transmissions provides significant benefits to the lifetime of such networks. In this paper, we study the performance of communication in dense networks with wireless energy harvesting (WEH)-enabled sensor nodes. In particular, we examine two different communication scenarios (direct and cooperative) for data exchange and we provide theoretical expressions for the probability of successful communication. Then, considering the importance of lifetime in WSNs, we employ state-of-the-art WEH techniques and realistic energy converters, quantifying the potential energy gains that can be achieved in the network. Our analytical derivations, which are validated by extensive Monte-Carlo simulations, highlight the importance of WEH in dense networks and identify the tradeoffs between the direct and cooperative communication scenarios.Peer ReviewedPostprint (author's final draft

A Review of Range-based RSSI Algorithms for Indoor Wireless Sensor Network Localization

The secure localisation of unknown nodes in Wireless Sensor Networks (WSNs) is a crucial research topic due to the vast range of applications of WSNs. These applications drive the development of WSNs, as real-world obstacles typically motivate them. WSN technology is rapidly evolving, and this paper provides a brief overview of WSNs, including key research findings on energy conservation and node deployment. The paper discusses the applications of WSNs in medical health, environment and agriculture, intelligent home furnishing and construction, and military, space, and marine exploration. The paper focuses on the research of RSS-based locating algorithms in WSNs and is divided into two sections. Firstly, accurate location depends on the accurate RSSI received from nodes. This experiment analyses the distribution trend of RSSI and derives the loss model of signal propagation by processing experimental data. Secondly, Gaussian fitting calculates the distance between receiving and sending nodes by processing individual RSSI at different distances. The primary challenge in studying this RSSI range-based technique is the low positioning accuracy, low energy, and high error rate. To solve this problem, a recommended GA is used to find the optimal site by minimising error, providing the best feasible solution, and being energy-sensitive, with accuracy based on the least error inside the network. The proposed approach aims to optimise sensor placements for improved performance

Data Collection in Smart Communities Using Sensor Cloud: Recent Advances, Taxonomy, and Future Research Directions

The remarkable miniaturization of sensors has led to the production of massive amounts of data in smart communities. These data cannot be efficiently collected and processed in WSNs due to the weak communication capability of these networks. This drawback can be compensated for by amalgamating WSNs and cloud computing to obtain sensor clouds. In this article, we investigate, highlight, and report recent premier advances in sensor clouds with respect to data collection. We categorize and classify the literature by devising a taxonomy based on important parameters, such as objectives, applications, communication technology, collection types, discovery, data types, and classification. Moreover, a few prominent use cases are presented to highlight the role of sensor clouds in providing high computation capabilities. Furthermore, several open research challenges and issues, such as big data issues, deployment issues, data security, data aggregation, dissemination of control message, and on time delivery are discussed. Future research directions are also provided

Reliable load-balancing routing for resource-constrained wireless sensor networks

Wireless sensor networks (WSNs) are energy and resource constrained. Energy limitations make it advantageous to balance radio transmissions across multiple sensor nodes. Thus, load balanced routing is highly desirable and has motivated a significant volume of research. Multihop sensor network architecture can also provide greater coverage, but requires a highly reliable and adaptive routing scheme to accommodate frequent topology changes. Current reliability-oriented protocols degrade energy efficiency and increase network latency. This thesis develops and evaluates a novel solution to provide energy-efficient routing while enhancing packet delivery reliability. This solution, a reliable load-balancing routing (RLBR), makes four contributions in the area of reliability, resiliency and load balancing in support of the primary objective of network lifetime maximisation. The results are captured using real world testbeds as well as simulations. The first contribution uses sensor node emulation, at the instruction cycle level, to characterise the additional processing and computation overhead required by the routing scheme. The second contribution is based on real world testbeds which comprises two different TinyOS-enabled senor platforms under different scenarios. The third contribution extends and evaluates RLBR using large-scale simulations. It is shown that RLBR consumes less energy while reducing topology repair latency and supports various aggregation weights by redistributing packet relaying loads. It also shows a balanced energy usage and a significant lifetime gain. Finally, the forth contribution is a novel variable transmission power control scheme which is created based on the experience gained from prior practical and simulated studies. This power control scheme operates at the data link layer to dynamically reduce unnecessarily high transmission power while maintaining acceptable link reliability

Determining the Best Sensing Coverage for 2-Dimensional Acoustic Target Tracking

Distributed acoustic target tracking is an important application area of wireless sensor networks. In this paper we use algebraic geometry to formally model 2-dimensional acoustic target tracking and then prove its best degree of required sensing coverage. We present the necessary conditions for three sensing coverage to accurately compute the spatio-temporal information of a target object. Simulations show that 3-coverage accurately locates a target object only in 53% of cases. Using 4-coverage, we present two different methods that yield correct answers in almost all cases and have time and memory usage complexity of Θ(1). Analytic 4-coverage tracking is our first proposed method that solves a simultaneous equation system using the sensing information of four sensor nodes. Redundant answer fusion is our second proposed method that solves at least two sets of simultaneous equations of target tracking using the sensing information of two different sets of three sensor nodes, and fusing the results using a new customized formal majority voter. We prove that 4-coverage guarantees accurate 2-dimensional acoustic target tracking under ideal conditions

Time constrained fault tolerance and management framework for k-connected distributed wireless sensor networks based on composite event detection

Wireless sensor nodes themselves are exceptionally complex systems where a variety of components interact in a complex way. In enterprise scenarios it becomes highly important to hide the details of the underlying sensor networks from the applications and to guarantee a minimum level of reliability of the system. One of the challenges faced to achieve this level of reliability is to overcome the failures frequently faced by sensor networks due to their tight integration with the environment. Failures can generate false information, which may trigger incorrect business processes, resulting in additional costs. Sensor networks are inherently fault prone due to the shared wireless communication medium. Thus, sensor nodes can lose synchrony and their programs can reach arbitrary states. Since on-site maintenance is not feasible, sensor network applications should be local and communication-efficient self-healing. Also, as per my knowledge, no such general framework exist that addresses all the fault issues one may encounter in a WSN, based on the extensive, exhaustive and comprehensive literature survey in the related areas of research. As one of the main goals of enterprise applications is to reduce the costs of business processes, a complete and more general Fault Tolerance and management framework for a general WSN, irrespective of the node types and deployment conditions is proposed which would help to mitigate the propagation of failures in a business environment, reduce the installation and maintenance costs and to gain deployment flexibility to allow for unobtrusive installation

Implementation of coverage problem in wireless sensor network based on unit Disk model

Wireless sensor networks (WSNs) have a wide range of applicability in many industrial and civilian applications such as industrial process monitoring and control, environment and habitat monitoring, machine health monitoring, home automation, health care applications, nuclear reactor control, fire detection, object tracking and traffic control. A WSN consists of spatially distributed autonomous sensors those cooperatively monitor the physical or environmental conditions including temperature, sound, vibration, motion, pressure or pollutants. In sensor networks where the environment is needed to be remotely monitored, the data from the individual sensor nodes is sent to a central base station (often located far from the network), through which the end-user can access data. The number of sensor nodes in a Wireless Sensor Network can vary in the range of hundreds to thousands. Such a network may have many challenges like low energy consumption, functional independence, efficient distributed algorithms, transmission routes, coverage, synchronization, topology control, robustness and fault tolerance, cost of maintaining the sensors and lifetime of the network