scholarly journals Review of Wireless Sensor Networks

2021 ◽  
Vol 11 (5) ◽  
Author(s):  
Khairi Salem Ahmed
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Communications ◽  
Mobile Phones ◽  
State Of The Art ◽  
Wireless Systems ◽  
Wireless Sensor ◽  
Main Concept ◽  
Simulation Tools ◽  
A Current

This article presents a study of the state of the art of sensor networks wireless systems, which continue to develop and present a wide variety of Applications. These networks constitute a current and emerging field of study where combines the development of computers, wireless communications and devices mobile phones and integration with other disciplines such as agriculture, biology, medicine, etc. I know presents the main concept, components, topologies, standards, applications, problems and challenges, then delves into security solutions and concludes with basic simulation tools.

Significant Effect of Vital Rule of Simulators in Wireless Sensor Networks

2016 ◽  
Vol 4 (2) ◽  
pp. 424
Author(s):  
Muhammad Toufique ◽  
Hongbo Jiang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Open Source ◽  
State Of The Art ◽  
Wireless Sensor ◽  
Main Stream ◽  
Specific Task ◽  
Scripting Languages ◽  
Simulation Tools ◽  
Art Techniques

<p>It is the requirement of the time, to simulate/emulate for analyzing the system. Therefore we must need some tools for this specific task. This paper is for encompassing the available tools for analyzing and evaluation purpose. Paper having all these exclusive information with detail are not available. We show ATEMU, Avrora, EmStar, J-Sim, NS-2, OMNeT++ and TOSSIM. We try to analyze the tools thoroughly. We define and discusss about the operation, functionality, advantages, disadvantages and constraints. These are the main stream superb simulation tools based on state of the art techniques. We explore on the basis of popularity, accessibility (open source), complexity, accuracy, scalability, extensibility and availability of various problems in homogenous and heterogenous WSN and other networks on different platforms and with or without using different scripting languages.</p>

Adaptive Monitor Placement for Near Real-time Node Failure Localisation in Wireless Sensor Networks

2022 ◽  
Vol 18 (1) ◽  
pp. 1-41
Author(s):  
Pamela Bezerra ◽  
Po-Yu Chen ◽  
Julie A. McCann ◽  
Weiren Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
State Of The Art ◽  
Global Analysis ◽  
Wireless Sensor ◽  
Greedy Heuristics ◽  
Network Tomography ◽  
Topology Changes ◽  
End To End

As sensor-based networks become more prevalent, scaling to unmanageable numbers or deployed in difficult to reach areas, real-time failure localisation is becoming essential for continued operation. Network tomography, a system and application-independent approach, has been successful in localising complex failures (i.e., observable by end-to-end global analysis) in traditional networks. Applying network tomography to wireless sensor networks (WSNs), however, is challenging. First, WSN topology changes due to environmental interactions (e.g., interference). Additionally, the selection of devices for running network monitoring processes (monitors) is an NP-hard problem. Monitors observe end-to-end in-network properties to identify failures, with their placement impacting the number of identifiable failures. Since monitoring consumes more in-node resources, it is essential to minimise their number while maintaining network tomography’s effectiveness. Unfortunately, state-of-the-art solutions solve this optimisation problem using time-consuming greedy heuristics. In this article, we propose two solutions for efficiently applying Network Tomography in WSNs: a graph compression scheme, enabling faster monitor placement by reducing the number of edges in the network, and an adaptive monitor placement algorithm for recovering the monitor placement given topology changes. The experiments show that our solution is at least 1,000× faster than the state-of-the-art approaches and efficiently copes with topology variations in large-scale WSNs.

Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

2020 ◽  
pp. 1580-1600
Author(s):  
Subhendu Kumar Pani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Sensor Devices ◽  
Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

Privacy and Trust Management Schemes of Wireless Sensor Networks

2010 ◽  
pp. 290-309 ◽  
Author(s):  
Riaz Ahmed Shaikh ◽  
Brian J. dAuriol ◽  
Heejo Lee ◽  
Sungyoung Lee
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Trust Management ◽  
State Of The Art ◽  
The State ◽  
Wireless Sensor ◽  
Security Issues ◽  
Management Schemes ◽  
Art Research

Until recently, researchers have focused on the cryptographic-based security issues more intensively than the privacy and trust issues. However, without the incorporation of trust and privacy features, cryptographic-based security mechanisms are not capable of singlehandedly providing robustness, reliability and completeness in a security solution. In this chapter, we present generic and flexible taxonomies of privacy and trust. We also give detailed critical analyses of the state-of-the-art research, in the field of privacy and trust that is currently not available in the literature. This chapter also highlights the challenging issues and problems.

Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

2016 ◽  
pp. 441-461
Author(s):  
Subhendu Kumar Pani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Sensor Devices ◽  
Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

scholarly journals A Combined Localization Algorithm for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaogang Qi ◽  
Xiaoke Liu ◽  
Lifang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Vehicle Tracking ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Environment And Health ◽  
Anchor Nodes ◽  
Positioning Algorithm

Wireless sensor networks (WSNs) are widely used in various fields to monitor and track various targets by gathering information, such as vehicle tracking and environment and health monitoring. The information gathered by the sensor nodes becomes meaningful only if it is known where it was collected from. Considering that multilateral algorithm and MDS algorithm can locate the position of each node, we proposed a localization algorithm combining the merits of these two approaches, which is called MA-MDS, to reduce the accumulation of errors in the process of multilateral positioning algorithm and improve the nodes’ positioning accuracy in WSNs. It works in more robust fashion for noise sparse networks, even with less number of anchor nodes. In the MDS positioning phase of this algorithm, the Prussian Analysis algorithm is used to obtain more accurate coordinate transformation. Through extensive simulations and the repeatable experiments under diverse representative networks, it can be confirmed that the proposed algorithm is more accurate and more efficient than the state-of-the-art algorithms.

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