Intelligent Real-Time Adaptation for Power Efficiency in Sensor Networks

Podpora, J.; Reznik, Leon; Pless, G. Von

doi:10.1109/jsen.2008.2006699

Cited by 14 publications

(12 citation statements)

References 19 publications

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“…Death of central point is the end of the system. In contrast to it, decentralized architecture needs powerful autonomous entities on one hand, and [44], fault detection [45], self-con�guration [46] Arti�cial immune network [34] Immune system [47] Misbehavior detection [48], image pattern recognition [49] Genetic algorithm [35] Natural evolution system Dynamic shortest path routing [50], lifetime maximization [51] Cellular automata [29] Life Life like/ game of life Large network simulations [52], area coverage scheme [53] Rendering (computer graphics) [30] Patterns of animal skins, birds feathers, mollusk shells, and bacterial colonies [54] Range-free localization [55] Fractal geometry Clouds, river networks, snow�akes, cauli�ower or broccoli, and systems of blood vessels and pulmonary vessels, ocean waves Antenna designing [56] Communication networks and protocols Epidemiology Cross-layer communication protocol [57,58] [61], target tracking [62] then it also re�ects the behavior of adaptation (�exible to changing environment), self-assembly (unit to be one), selforganizing (interaction for the one), and self-regulation, (keeping the process tunably smooth) [25] as in WSN due to the distributed knowledge, distributed control and scalable properties on the other hand. Although the architecture is application dependent, decentralized system has variety of advantages over the centralized system.…”

Section: � �Rom �Eal To �Rti��ialmentioning

confidence: 99%

Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

Jabbar

Iram

Minhas

et al. 2013

International Journal of Distributed Sensor Networks

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is survey article is a comprehensive discussion on Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing. e marvelous perfection of biological systems and its different aspects for optimized solutions for non-biological problems is presented here in detail. In the current research inclination, hiring of biological solutions to solve and optimize different aspects of arti�cial systems' problems has been shaped into an important �eld with the name of bio-inspired computing. We have tabulated the exploitation of key constituents of biological system for developing bio-inspired systems to represent its importance and emergence in problem solving trends. We have presented how the metaphoric relationship is developed between the two biological and non-biological systems by quoting an example of relationship between prevailing wireless system and the natural system. Interdisciplinary research is playing a splendid contribution for various problems' solving. e process of combining the individuals' output to form a single problem solving solution is depicted in three-stage ensemble design. Also the hybrid solutions from computational intelligence-based optimization are elongated to demonstrate the emergent involvement of these inspired systems with rich references for the interested readers. It is concluded that these perfect creations have remedies for most of the problems in non-biological system.

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Section: � �Rom �Eal To �Rti��ialmentioning

confidence: 99%

Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

Jabbar

Iram

Minhas

et al. 2013

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

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“…SPOTs [19] while more information on the Crossbow Telos sensor kits implementation is given in [20] and [21]. The system design is split into the host and the sensor node components.…”

Section: System Implementationmentioning

confidence: 99%

Distributed Neural Networks for Signal Change Detection: On the Way to Cognition in Sensor Networks

2011

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Cognition is a fundamental feature of natural intelligence, which a modern technology has not yet been able to reproduce in full capacity. Sensor networks provide a new technological support for a substantial increase in an amount and quality of information that might be collected and communicated in complex adaptive systems. Their application may significantly raise the degree of intelligence in system design and implementation into the levels where effects of cognition will start kicking in. The paper describes the results of an empirical study aiming to demonstrate that a cognition ability may be treated as a generic sensor network feature. The new architecture with neural networks distributed over the sensor network platforms was developed for sensor network engineering applications. The detection system learns to detect the change of not only the signal levels but also sensor signal shapes and parameters that represent a more complicated task. The architecture allows for a significant reduction in resource consumption without compromising the change detection performance. Implemented as an agent controlling the sensor network self-adjustment to the objects under measurement in the sensor network composed from typical sensor motes, the novel neural network structures may achieve a significant saving in power consumption and an increase in a possible network deployment time from a few days to a few years. The experiments prove that a neural-network-based change detection system is feasible for sensor networks application designs and could be successfully implemented on the technological platforms currently available on the market.Index Terms-Artificial neural networks (ANNs), distributed artificial intelligence, sensor networks, signal change detection.

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“…However, it works only for a small-scale ring-topology network. [19] also takes a system-wide view of WSN for the overall energy efficiency. Based on the geographic routing, it adaptively coordinates the senor nodes by switching them between active and sleep modes in order to save their energy consumptions while guaranteeing the correct detection of important events.…”

Section: B Comparison With a Pgps-based Solutionmentioning

confidence: 99%

Combined Scheduling and Routing for Deterministic Guarantee of End-to-End Deadlines in Cell Structured Sensor Networks

et al. 2009

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Wireless sensor networks have great potentials to be applied to mission critical real-time applications such as target tracking, emergency detection, and disaster management. For such real-time applications, sensory data should be delivered within the end-to-end deadlines; otherwise, the data may become useless or misleading. In order to provide such deterministic guarantee of end-to-end deadlines, not only packet scheduling within a single hop but also routing across multiple hops have to be jointly considered. In this paper, we propose a combined scheduling and routing mechanism that provides a deterministic guarantee of end-to-end deadlines leveraging real-time scheduling and analysis techniques. Our experimental study shows that the proposed mechanism can accommodate a larger workload meeting all the end-to-end deadlines than a pretty comprehensive set of other possible solutions of scheduling and routing that are known to deterministically guarantee end-to-end deadlines.

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Intelligent Real-Time Adaptation for Power Efficiency in Sensor Networks

Cited by 14 publications

References 19 publications

Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

Distributed Neural Networks for Signal Change Detection: On the Way to Cognition in Sensor Networks

Combined Scheduling and Routing for Deterministic Guarantee of End-to-End Deadlines in Cell Structured Sensor Networks

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