TPR: Dead end aware table less position based routing scheme for low power data-centric wireless sensor networks

Madani, Sajjad A.; Weber, Daniel; Mahlknecht, Stefan

doi:10.1109/sies.2008.4577693

Cited by 2 publications

(4 citation statements)

References 24 publications

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“…In recent years, the shortest path problem has been investigated comprehensively in the literature [9 -14] and evolutionary algorithms draw attention to many researchers to find solution to the problem. Recent studies show that researchers have done some research work to solve the shortest path problem by applying genetic algorithms [9][10][11][12][13][14][15], particle swarm optimization [22], and neural networks [23]. To solve the shortest path problem, several routing algorithms such as Dijkstra's algorithm, Bellman-Ford algorithm, breadth-first algorithm, and depth-first algorithm have been proposed, to name a few [24].…”

Section: Review Of Ga-based Routing Algorithmsmentioning

confidence: 99%

“…Therefore, shortest path problem attracts interests of many researchers. Depending on applications, in routing, forwarding schemes can be based on minimizing end-to-end delay, energy consumption, number of hops, and geographic distance [10]. In order to support time-constrained services, such as military application, to deliver information within a specified time, an efficient routing algorithm is desired that should attempt to find shortest path quickly.…”

Section: Introductionmentioning

confidence: 99%

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Towards green communication in wireless sensor network: GA enabled distributed zone approach

Kumar

Kaiwartya

et al. 2019

Ad Hoc Networks

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Section: Review Of Ga-based Routing Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards green communication in wireless sensor network: GA enabled distributed zone approach

Kumar

Kaiwartya

et al. 2019

Ad Hoc Networks

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“…It provides a rich library of modules and supports mobility and environmental dynamics. It also provides a simulation template for users to quick start simulation study Madani et al (2008); Weber et al (2007).…”

Section: Pawismentioning

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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“…e3D [56,57] In energy efficient distributed dynamic diffusion (e3D) routing algorithm, each node updates neighbours' information through distance derived from radio signal strength, battery power and load. TPR [58] No need of routing table and neighbourhood information. Topology control protocols/ algorithms GAF Rotates node functionality periodically to ensure fair energy consumption among nodes.…”

Section: Location Based Routing Protocols/ Algorithms Directmentioning

confidence: 99%

Power consumption in wireless sensor networks

Aslam

Farooq

Sarwar

2009

Proceedings of the 7th International Conference on Frontiers of Information Technology

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In wireless sensor networks (WSNs), long lifetime requirement of different applications and limited energy storage capability of sensor nodes has led us to find out new horizons for reducing power consumption upon nodes. To increase sensor node's lifetime, circuit and protocols have to be energy efficient so that they can make a priori reactions by estimating and predicting energy consumption. The goal of this study is to present and discuss several strategies such as power-aware protocols, cross-layer optimization, and harvesting technologies used to alleviate power consumption constraint in WSNs.

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TPR: Dead end aware table less position based routing scheme for low power data-centric wireless sensor networks

Cited by 2 publications

References 24 publications

Towards green communication in wireless sensor network: GA enabled distributed zone approach

Towards green communication in wireless sensor network: GA enabled distributed zone approach

Wireless Sensor Networks: Modelling and Simulation

Power consumption in wireless sensor networks

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