Wireless sensor networks for battlefield surveillance

2004

An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The system consists of a large number of cheap sensors communicating through an ad-hoc wireless network, thus it is capable of tolerating multiple sensor failures, provides good coverage and high accuracy, and is capable of overcoming multipath effects. The performance of the proposed system is superior to that of centralized countersniper systems in such challenging environment as dense urban terrain. In this paper, in addition to the overall system architecture, the acoustic signal detection, the most important middleware services and the unique sensor fusion algorithm are also presented. The system performance is analyzed using real measurement data obtained at a US Army MOUT (Military Operations in Urban Terrain) facility.

FP6 IST research project Ubiquitous Sensing and Security in the European Homeland (UbiSeq&Sens) aims at providing a comprehensive architecture for medium and large scale Wireless Sensor Networks (WSN)s, with the full level of security and reliability required to make them trusted and secure for all applications, while considering early-warning and tracking in a Homeland Security/Defense context (e.g., support of anti-terrorist SWAT team operations) as one of the scenarios for system demonstration. This paper extrapolates from this scenario, defining an architecture for WSNs supporting Military Operations in Urban Terrain (MOUT) in the context of XXIst century Operations Other Than War (OOTH). Based on the defined architecture, the authors identify the main WSN Networking and Security issues and challenges that must be overcome to provide the assurance, efficiency and reliability required by the warfighter, which constitute the focus of ongoing work in IST FP6 UbiSeq&Sens. The work described in this paper is based on results of IST FP6 project UbiSec&Sens (http://www.istubisecsens.org/). UbiSec&Sens receives research funding from the European Community's Sixth Framework Programme. Apart from this, the European Commission has no responsibility for the content of this paper.

IEEE signal processing …, 2002

We outline a framework for collaborative signal processing in distributed sensor networks. The ideas are presented in the context of tracking multiple moving objects in a sensor field. The key steps involved in the tracking procedure include event detection, target classification, and estimation and prediction of target location. Algorithms for various tasks are discussed with an emphasis on classification. Results based on experiments with real data are reported which provide useful insights into the essential nature of the problems. Issues, challenges and directions for future research are identified.

IEEE Transactions on Mobile Computing, 2010

The work reported in this paper investigates the performance of the Particle Filter (PF) algorithm for tracking a moving object using a wireless sensor network (WSN). It is well known that the PF is particularly well suited for use in target tracking applications. However, a comprehensive analysis on the effect of various design and calibration parameters on the accuracy of the PF has been overlooked. This paper outlines the results from such a study. In particular, we evaluate the effect of various design parameters (such as the number of deployed nodes, number of generated particles, and sampling interval) and calibration parameters (such as the gain, path loss factor, noise variations, and nonlinearity constant) on the tracking accuracy and computation time of the particle-filter-based tracking system. Based on our analysis, we present recommendations on suitable values for these parameters, which provide a reasonable trade-off between accuracy and complexity. We also analyze the theoretical Cramér-Rao Bound as the benchmark for the best possible tracking performance and demonstrate that the results from our simulations closely match the theoretical bound. In this paper, we also propose a novel technique for calibrating off-the-shelf sensor devices. We implement the tracking system on a real sensor network and demonstrate its accuracy in detecting and tracking a moving object in a variety of scenarios. To the best of our knowledge, this is the first time that empirical results from a PF-based tracking system with off-the-shelf WSN devices have been reported. Finally, we also present simple albeit important building blocks that are essential for field deployment of such a system.

2009

In recent years, advances in signal processing have led to small, low power, inexpensive Wireless Sensor Network (WSN). The signal processing in WSN is different from the traditional wireless networks in two critical aspects: firstly, the signal processing in WSN is performed in a fully distributed manner, unlike in traditional wireless networks; secondly, due to the limited computation capabilities of sensor networks, it is essential to develop an energy and bandwidth efficient signal processing algorithms. Target localisation and ...

12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06), 2006

Target tracking systems, consisting of thousands of low-cost sensor nodes, have been used in many application domains such as battlefield surveillance, wildlife monitoring and border security. These applications need to meet certain real-time constraints in response to transient events, such as fast-moving targets. While the real-time performance is a major concern in these applications, it should be compatible with other important system properties such as energy consumption and accuracy. Hence, it is desirable to have the ability to exploit the tradeoffs among them. This work presents the real-time design and analysis of VigilNet, a large-scale sensor network system which tracks, detects and classifies targets in a timely and energy efficient manner. Based on a deadline partition method and theoretical derivations of each sub-deadline, we are able to make guided engineering decisions to meet the end-to-end tracking deadline. To confirm our design and obtain an empirical understanding of these tradeoffs, we invest significant efforts to perform large-scale simulations with 10,000 nodes as well as a field test with 200 XSM motes, running VigilNet. The results from both analysis and evaluation can serve as general design guidelines to build similar real-time systems.

2020 IEEE 23rd International Multitopic Conference (INMIC)

This article outlines the military applications of wireless sensor networks (WSN). The combat activities in modern military operations are divided into different categories, which give the requirements and restrictions for wireless sensor networks. The type of sensor and its function can adjust and limit the use of WSN. A WSN military application's capacity depends on various factors, including capabilities of sensors, type of sensors, the architecture of wireless communications, its range, and appropriate data processing. We performed a categorization of WSN applications used in the military according to the strategic scenario and sensor type. In this paper, military WSN is classified, involving military combat, the sensor type, and defines the vital kind of WSN. This article introduces the research and engineering issues of next-generation WSN military applications.

Wireless Sensor Networks (WSNs) received enormous attention in recent years due to its phenomenal ability of implementation in various fields. WSNs consist of a large number of small sensor nodes. These nodes are very cheap in terms of cost. In military operations, there is always a threat of being attacked by enemies. So, the use of these cheap sensor nodes will help to reduce the loss. In this paper, we analyze the existing literature of using WSNs for military applications. We will discuss the available scenarios of using sensor nodes in the military uses. We aim to present a better deployment of sensor nodes for military purposes with the help of existing research work. We will try to identify different areas in which we can reduce the damage in case of militant's attack or enemy's outbreak using an intelligent deployment of nodes.