Quality of Deployment in Surveillance Wireless Sensor Networks

Onur, Ertan; Ersoy, Cem; Delic, H.

doi:10.1007/s10776-005-5152-7

Cited by 11 publications

(3 citation statements)

References 8 publications

(12 reference statements)

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“…-highly correlated: due to the spatial-temporal dynamics of the network, the collected data may be redundant and highly correlated. An example may be the correlation of temperature measurements in a field by sensor networks or target presence decisions in surveillance applications [7].…”

mentioning

confidence: 99%

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

Onur

Durmus

Hawas

et al. 2011

Wireless Pers Commun

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Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Link to publication• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract In this paper, we present a visionary concept referred to as Collaborative and Cognitive Network Platforms (CCNPs) as a future-proof solution for creating a dependable, self-organizing and self-managing communication substrate for effective ICT solutions to societal problems. CCNP creates a cooperative communication platform to support critical services across a range of business sectors. CCNP is based on the personal network (PN) technology which is an inherently cooperative environment prototyped in the Dutch Freeband PNP2008 and the European Union IST MAGNET projects. In CCNP, the cognitive control plane strives to exploit the resources to better satisfy the requirements of networked applications. CCNP facilitates collaboration inherently. Through cognition in the cognitive control plane, CCNP becomes a self-managed substrate. The self-managed substrate, in this paper, is defined as cognitive and collaborative middleware on which future applications run without user intervention. Endemic sensor networks may be incorporated into the CCNP concept to feed its cognitive control plane. In this paper, we present the CCNP concept and discuss the research challenges related to collaboration and cognition.

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mentioning

confidence: 99%

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

Onur

Durmus

Hawas

et al. 2011

Wireless Pers Commun

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“…Onur vd. [12] algılayıcı dağıtım kalitesinden bahsederek dağıtımın yeterli kapsamayı sağlayıp sağlamadığını veya yeniden dağıtımın gerekli olup olmadığını gösteren kaliteli ölçümü önermişlerdir. Cheng vd.…”

Section: İlgi̇li̇ çAlişmalar (Related Work)unclassified

Kablosuz algılayıcı ağlarda hedef kapsama problemi için algılayıcı dağıtımı ile ağın yaşam süresinin optimizasyonu

Ozdag¹

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Ağ yaşam süresi Kablosuz Algılayıcı Ağ (KAA)'ların etkinliğini belirleyen kritik bir faktördür. Askeri ve sivil uygulamalarda KAA'ların kapsanmasının sürekliliği açısından hedefleri izleyen algılayıcı düğümlerin pil ömürlerinin optimizasyonu ağın yaşam süresinin uzatılmasında önemli bir rol oynar. KAA'ları oluşturan algılayıcı düğümlerin sınırlı pil ömürleri bulunduğu için algılayıcıların kendi aralarında haberleşmeleri ve ilgili alanı algılamaları neticesinde enerjileri gittikçe azalır. Nihayetinde düğüm enerjisini tamamıyla tüketerek KAA'nın işlevini yerine getirememesine neden olur. Bu sebeple KAA'ların yaşam sürelerinin optimizasyonu literatürde sıklıkla çalışılan konulardan biri olmuştur.Bu makalede KAA'lardaki hedef kapsama problemine çözüm bulmak için hedeflerin maksimum dört algılayıcı düğüme kadar olan kapsama gereksinimlerinin (1 ≤ k ≤ 4) sağlanması şartıyla, düğümlerin dinamik dağıtımları yapılarak ağın yaşam süresinin optimizasyonu amaçlanmıştır. Hedeflerin kapsama gereksinimleri sağlandığı anda düğümlerin kalan pil ömürleri ile ağın yaşam süresinin üst sınırı hesaplanarak ağın ulaşılabilir yaşam süresinin tespiti hedeflenmiştir. Ayrıca algılayıcı düğümlerin dinamik dağıtımlarının yapılmasında meta-sezgisel olan Elektromagnetizma -Benzer (EM) algoritması temel alınmış ve enerji verimli yeni bir algoritma geliştirilmiştir. Bu algoritma ile hesaplanan ulaşılabilir ağ yaşam süreleri literatürdeki Yapay Arı Kolonisi (ABC) ve Parçacık Sürü Optimizasyonu (PSO) algoritmaları ile karşılaştırılmıştır. Ulaşılan benzetim sonuçlarına göre ağın yaşam süresinin üst sınırına ulaşmada geliştirilen algoritmanın daha optimum sonuçlar verdiği tespit edilmiştir.

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“…In [31], Zhang and Hou studied the system lifetime of a k-covered sensor network, and proved that it is upper bounded by k times node continuous working time. In [20] and [21], Onur et al investigated the effect of false alarm rate and path-loss exponent on the quality of deployment using a probabilistic approach, and proposed a method to determine the required number of sensors being deployed under the weakest breach path problem. In [18], Megerian et al proposed the optimal polynomial time worst and average case algorithm for the coverage calculation of homogeneous isotropic sensors, by combining computational geometry and graph theoretic techniques.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks

Ren

Li²,

Wang³

et al. 2007

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Partial coverage, where a sensing field is partially sensed by active sensors at any time, is an appropriate approach to balancing the two conflicting design requirements of monitoring applications. Under partial coverage, we develop an analytical framework for object detection in sensor networks, and mathematically analyze average-case object detection quality in random and synchronized sensing scheduling protocols. Our analytical framework facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Furthermore, we propose three wave sensing scheduling protocols to achieve bounded worst-case object detection quality. We justify the correctness of our analyses through rigorous proof, and validate the effectiveness of the proposed protocols through extensive simulation experiments.

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Quality of Deployment in Surveillance Wireless Sensor Networks

Cited by 11 publications

References 8 publications

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

Kablosuz algılayıcı ağlarda hedef kapsama problemi için algılayıcı dağıtımı ile ağın yaşam süresinin optimizasyonu

Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks

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