Wireless sensor network design for tactical military applications : Remote large-scale environments

Lee, Sang Hyuk; Lee, Soo Bin; Song, Heecheol; Lee, Hwang Soo

doi:10.1109/milcom.2009.5379900

Cited by 157 publications

(96 citation statements)

References 15 publications

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“…Some examples of these application like robotic navigation Fu et al (2009), aircraft corrosion monitoring Demo et al (2010) , direct load control in residential areas Molina-Garcia et al (2007), personal mobile physiological monitoring and management system for chronic disease Toh et al (2008), Wildfire detection Antoine-Santoni et al (2006), www.intechopen.com in-service motor monitoring and energy management Hu (2008), application in petrochemical industry Ke et al (2008), application for critical infrastructure risk analysis of fossil fuelled power stations Isreb (2006) and tactical military applications Lee et al (2009) can be found in the litrature.…”

Section: Wireless Sensor Network Applicationsmentioning

confidence: 99%

Wireless Sensor Networks: Modelling and Simulation

Mahlknecht¹,

Madani²,

Kazmi³

2010

Discrete Event Simulations

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Although different modeling techniques have been proposed during the last 300 years, the differential equation formalism proposed by Newton and Leibniz has been the tool of choice for modeling and problem solving Taylor (1996);Wainer (2009). Differential equations provide a formal mathematical method (sometimes also called an analytical method) for studying the entity of interest. Computational methods based on differential equations could not be easily applied in studying human-made dynamic systems (e.g., traffic controllers, robotic arms, automated factories, production plants, computer networks, VLSI circuits). These systems are usually referred to as discrete event systems because their states do not change continuously but, rather, because of the occurrence of events. This makes them asynchronous, inherently concurrent, and highly nonlinear, rendering their modeling and simulation different from that used in traditional approaches. In order to improve the model definition for this class of systems, a number of techniques were introduced, including Petri Nets, Finite State Machines, min-max algebra, Timed Automata, etc. Banks & Nicol. (2005) (2000); Toffoli & Margolus. (1987). Wireless Sensor Network (WSN) is a discrete event system which consists of a network of sensor nodes equipped with sensing, computing, power, and communication modules to monitor certain phenomenon such as environmental data or object tracking Zhao & Guibas (2004). Emerging applications of wireless sensor networks are comprised of asset and warehouse * madani@ciit.net.pk † jawhaikaz@ciit.net.pk ‡ mahlknecht@ict.tuwien.ac.at 1 www.intechopen.com management, automotive, home and building automation, civil infrastructure monitoring, healthcare, industrial process control, military battlefield awareness, and security and surveillance Cerpa et al. (2001). As discussed earlier, modeling and simulation is a mean to verify the working and to measure the effectiveness of the different techniques proposed for WSNs. Analytical modeling provides quick insight for the techniques developed for WSNs but fail to give realistic results because of WSN specific constraints like limited energy and sheer number of sensor nodes Chen et al. (2006). Real world implementation and test beds are the most accurate method to verify the concepts but are restricted by costs, effort, and time factors as well as repeating environmental conditions is also not possible Zeigler (1976). Simulations provide a good approximation to verify different schemes and applications developed for WSNs at low cost and in less time. To have credible results through simulation, the choice of models and the simulation environment is important. There is always a tradeoff between credible simulation results and the time required to get these simulation results. The results always depend upon the level of abstraction of the models. The more detailed is the model, the better the accuracy of results but higher the amount of time required for simulation. The models used for simulation ...

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Section: Wireless Sensor Network Applicationsmentioning

confidence: 99%

Wireless Sensor Networks: Modelling and Simulation

Mahlknecht¹,

Madani²,

Kazmi³

2010

Discrete Event Simulations

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“…They have been used in a wide variety of contexts, from safety critical purposes such as military [1] to non-critical applications such as habitat monitoring [2]. For both applications privacy is often a key component.…”

Section: Introductionmentioning

confidence: 99%

“…To catch the criminals, WSNs have been deployed to monitor large areas where these animals roam [3]. A recent implementation of an asset monitoring network is the Wildlife Crime Technology Report 1 . Whilst the content of this data is protected via encryption, it is also important to protect context such as a location when routing packets.…”

Section: Introductionmentioning

confidence: 99%

“…The class of context-privacy focused on in this work is called source location privacy (SLP). 1 worldwildlife.org/projects/wildlife-crime-technology-project Source Location Privacy SLP is the process of keeping the location of the source hidden from a potential attacker in an asset monitoring network. When a sensor node, called the source, detects the presence of an asset, it will begin to broadcast messages to be collected at a node called the sink.…”

Section: Introductionmentioning

confidence: 99%

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Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks

Kirton

Bradbury

Jhumka

2017

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

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“…The rapid deployment, self-organization, and fault tolerance can provide a promising sensing method for certain functions, such as control, communication, and computing [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Selective Forwarding Attacks over Wireless Sensor Networks

Alajmi¹,

Elleithy²

2015

IJCA

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The potential use of wireless sensor networks (WSNs) in many technological situations is extensive. WSNs are employed in numerous areas, such as battlefields, traffic surveillance, healthcare, and environmental monitoring. Many studies have been conducted to improve various aspects of the performance of WSNs, such as energy efficiency, quality of service (QoS), reliability, mobility, scalability, and extended network lifetime. However, few studies have sought to advance the security of WSNs by focusing on medium access control (MAC), physical, and transport stacks. The network layer is the middle layer that coordinates the lower layers and the upper layers. Thus, the network layer is of paramount significance to the security of WSNs to prevent exploitation of their confidentiality, privacy, availability, integrity, and authenticity. The objective of this study is to address the security laps of WSNs on the network layer, particularly selective forwarding attacks. This paper includes a survey performed from 2003 to 2014. Significant constraints and hazards of physical attacks at the network layer are extensively addressed. The survey includes a benchmark for the comparison of existing approaches for handling the security of WSNs on the network layer and their challenges. Future directions are also comprehensively highlighted in the document. This study will help researchers to understand attacks on the network layer. KeywordsWireless sensor network, selective forwarding, and denial of services attacks.

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Wireless sensor network design for tactical military applications : Remote large-scale environments

Cited by 157 publications

References 15 publications

Wireless Sensor Networks: Modelling and Simulation

Wireless Sensor Networks: Modelling and Simulation

Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks

Comparative Analysis of Selective Forwarding Attacks over Wireless Sensor Networks

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