Search results for "Wireless Sensor Network"

showing 10 items of 363 documents

Probabilistic Anomaly Detection for Wireless Sensor Networks

2011

Wireless Sensor Networks (WSN) are increasingly gaining popularity as a tool for environmental monitoring, however ensuring the reliability of their operation is not trivial, and faulty sensors are not uncommon; moreover, the deployment environment may influence the correct functioning of a sensor node, which might thus be mistakenly classified as damaged. In this paper we propose a probabilistic algorithm to detect a faulty node considering its sensed data, and the surrounding environmental conditions. The algorithm was tested with a real dataset acquired in a work environment, characterized by the presence of actuators that also affect the actual trend of the monitored physical quantities.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniKey distribution in wireless sensor networksBrooks–Iyengar algorithmComputer scienceNode (networking)Sensor nodeReal-time computingProbabilistic logicintelligent data analysis probabilistic reasoning wireless sensor networksAnomaly detectionWireless sensor network
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A Monitoring Framework Exploiting the Synergy between Actual and Virtual Wireless Sensors

2006

This paper describes a framework that allows realistic monitoring of a wireless sensor network in order to assess its behavior before actually deploying all the nodes. Designing a wireless sensor network for a specific application typically involves a preliminary phase of simulations that rely on specialized software, whose behavior does not necessarily reproduce what will be experienced by an actual network. On the other hand, delaying the test phase until deployment may not be advisable due to unreasonable costs. This paper suggests the adoption of a hybrid approach that involves coupling an actual wireless sensor network composed of a minimal set of actual nodes with a simulated one; we …

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniKey distribution in wireless sensor networksWireless site surveyComputer scienceSoftware deploymentVisual sensor networkbusiness.industryDistributed computingMobile wireless sensor networkWirelessbusinessWireless sensor networkWireless sensor networks2006 International Conference on Parallel Processing Workshops (ICPPW'06)
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Secure random number generation in wireless sensor networks

2011

The increasing adoption of wireless sensor networks as a flexible and inexpensive tool for the most diverseapplications, ranging from environmental monitoring to home automation, has raised more and more atten-tion to the issues related to the design of specifically customized security mechanisms. The scarcity ofcomputational, storage, and bandwidth resources cannot definitely be disregarded in such context, and thismakes the implementation of security algorithms particularly challenging. This paper proposes a securityframework for the generation of true random numbers, which are paramount as the core building blockfor many security algorithms; the intrinsic nature of wireless sensor nodes …

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniLeader electionleader election random numbers generation wireless sensor networksRandom number generationbusiness.industryComputer scienceDistributed computingKey distribution in wireless sensor networksTrue random numbers generatorRobustness (computer science)Mobile wireless sensor networknetwork securityWirelessbusinesswireless sensor networksWireless sensor networkRandomnessComputer Science::Cryptography and SecurityComputer network
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Enabling Technologies on Hybrid Camera Networks for Behavioral Analysis of Unattended Indoor Environments and Their Surroundings

2008

This paper presents a layered network architecture and the enabling technologies for accomplishing vision-based behavioral analysis of unattended environments. Specifically the vision network covers both the attended environment and its surroundings by means of multi-modal cameras. The layer overlooking at the surroundings is laid outdoor and tracks people, monitoring entrance/exit points. It recovers the geometry of the site under surveillance and communicates people positions to a higher level layer. The layer monitoring the unattended environment undertakes similar goals, with the addition of maintaining a global mosaic of the observed scene for further understanding. Moreover, it merges …

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniNetwork architecturebusiness.industryReliability (computer networking)Computer laboratorydistributed video surveillanceSMART CAMERA NETWORKSBehavioral analysisMULTI-MODAL SENSOR FUSIONECamera networkGeographyHuman–computer interactionmulti-modal surveillance; wireless sensor networksEMBEDDED SMART CAMERASmulti-modal surveillanceMULTI-MODAL SENSOR FUSIONE; SMART CAMERA NETWORKS; EMBEDDED SMART CAMERASComputer visionArtificial intelligenceLayer (object-oriented design)businesswireless sensor networks
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A machine learning approach for user localization exploiting connectivity data

2016

The growing popularity of Location-Based Services (LBSs) has boosted research on cheaper and more pervasive localization systems, typically relying on such monitoring equipment as Wireless Sensor Networks (WSNs), which allow to re-use the same instrumentation both for monitoring and for localization without requiring lengthy off-line training. This work addresses the localization problem, exploiting knowledge acquired in sample environments, and extensible to areas not considered in advance. Localization is turned into a learning problem, solved by a statistical algorithm. Additionally, parameter tuning is fully automated thanks to its formulation as an optimization problem based only on co…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniOptimization problemSupport vector machineRange-free localizationbusiness.industryComputer science020206 networking & telecommunicationsSample (statistics)02 engineering and technologyMachine learningcomputer.software_genreSupport vector machineSoftware deploymentArtificial IntelligenceControl and Systems Engineering0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceInstrumentation (computer programming)Electrical and Electronic EngineeringbusinessWireless sensor networkcomputerWireless sensor network
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Real-Time Object Detection in Embedded Video Surveillance Systems

2008

In this paper we report a new method to detect both moving objects and new stationary objects in video sequences. On the basis of temporal consideration we classify pixels into three classes: background, midground and foreground to distinguish between long-term, medium-term and short-term changes. The algorithm has been implemented on a hardware platform with limited resources and it could be used in a wider system like a wireless sensor networks. Particular care has been put in realizing the algorithm so that the limited available resources are used in an efficient way. Experiments have been conducted on publicly available datasets and performance measures are reported.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniPixelBasis (linear algebra)business.industryComputer scienceReal-time computingVideo sequencevideo surveillance embedded systemsObject detectionTerm (time)Statistical classificationComputer visionArtificial intelligencebusinessWireless sensor networkLimited resources2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
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A distributed Bayesian approach to fault detection in sensor networks

2012

Sensor networks are widely used in industrial and academic applications as the pervasive sensing module of an intelligent system. Sensor nodes may occasionally produce incorrect measurements due to battery depletion, dust on the sensor, manumissions and other causes. The aim of this paper is to develop a distributed Bayesian fault detection algorithm that classifies measurements coming from the network as corrupted or not. The computational complexity is polynomial so the algorithm scales well with the size of the network. We tested the approach on a synthetic dataset and obtained significant results in terms of correctly labeled measurements.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniPolynomialBrooks–Iyengar algorithmComputer scienceBayesian probabilityReal-time computingFault DetectionSoft sensorWireless sensor networkFault detection and isolation2012 IEEE Global Communications Conference (GLOBECOM)
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QoS-Aware Fault Detection in Wireless Sensor Networks

2013

Wireless sensor networks (WSNs) are a fundamental building block of many pervasive applications. Nevertheless the use of such technology raises new challenges regarding the development of reliable and fault-tolerant systems. One of the most critical issues is the detection of corrupted readings amidst the huge amount of gathered sensory data. Indeed, such readings could significantly affect the quality of service (QoS) of the WSN, and thus it is highly desirable to automatically discard them. This issue is usually addressed through “fault detection” algorithms that classify readings by exploiting temporal and spatial correlations. Generally, these algorithms do not take into account QoS re…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniQA75Article SubjectComputer Networks and CommunicationsComputer scienceQuality of serviceReal-time computingGeneral EngineeringBayesian networkcomputer.software_genreMulti-objective optimizationFault detection and isolationlcsh:QA75.5-76.95Distributed algorithmData mininglcsh:Electronic computers. Computer scienceWireless Sensor NetworksWireless sensor networkcomputerBlock (data storage)International Journal of Distributed Sensor Networks
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A TRNG Exploiting Multi-Source Physical Data

2010

In recent years, the considerable progress of miniaturization and the consequent increase of the efficiency of digital circuits has allowed a great diffusion of the wireless sensor network technology. This has led to the growth of applications and protocols for applying these networks to several scenarios, such as the military one, where it is essential to deploy security protocols in order to prevent opponents from accessing the information exchanged among sensor nodes. This paper analyzes security issues of data processed by the WSN and describes a system able to generate sequences of random numbers, which can be used by security algorithms and protocols. The proposed True Random Number G…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniRandom number generationbusiness.industryNetwork securityComputer scienceDistributed computingNode (networking)Cryptographic protocolTrusted third partyKey distribution in wireless sensor networksWireless Sensor Networks Random Number Generator Network SecurityNISTbusinessWireless sensor networkComputer network
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Robust and Efficient Data Gathering for Wireless Sensor Networks

2006

This paper describes a new strategy for data gathering in wireless sensor networks that takes into account the need for both energy saving, typical of such networks, and for a reasonable tradeoff between robustness and efficiency. The proposed algorithm implements an efficient strategy for retransmission of lost packets by discovering alternative routes and making clever use of multiple paths when necessary; in order to do that we build upon the general framework presented in recent works, that provided a formulation of duplicate and order insensitive aggregation functions, and by taking advantage of some intrinsic characteristics of the wireless sensor networks, we exploit implicit acknowl…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniRouting protocolbusiness.industryNetwork packetComputer sciencePacket transmissionDistributed computingRetransmissionWireless sensor networksBase stationKey distribution in wireless sensor networksIntelligent sensorRobustness (computer science)Aggregation functionbusinessWireless sensor networkComputer networkProceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)
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