The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Gilles Roussel - One of the best experts on this subject based on the ideXlab platform.
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pmt 2 a predictive mobile target Tracking Algorithm in wireless multimedia sensor networks
International Symposium on Computers and Communications, 2014Co-Authors: Ibtissem Boulanouar, Abderrezak Rachedi, Stephane Lohier, Gilles RousselAbstract:In this work, we propose a new Predictive-based Mobile Target Tracking Algorithm for Wireless Multimedia Sensor Networks called PMT 2 . Resource management being a critical feature of this kind of networks, the main aim of PMT 2 is to handle the trade-off between the accuracy of the Tracking and the energy conservation. Prediction approach seems to be the best candidate to reach this objective. For this purpose, we introduce an enhanced version of the Extended Kalman Filter combined with a change detection mechanism named CuSum for Cumulative Summary. We also propose a deployment strategy to improve the efficiency of the Tracking Algorithm. Using simulations, we show the performances of the proposed coupled mechanism in the trajectory prediction and in the reactivity to abrupt direction changes. Moreover, we perform a comparative study between PMT 2 and existing works: 1) BASIC where all the Cameras Sensors are always in active mode; 2) OCNS for Optimal Camera Node Selection, a cluster-based solution with a probabilistic sensor selection; 3) PTA, another predictive solution based on standard Kalman Filter. The obtained results illustrate that PMT 2 improves the quality of Tracking by up to 35% compared to existing works, while reducing energy consumption by up to 55%.
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a collaborative Tracking Algorithm for communicating target in wireless multimedia sensor networks
Wireless and Mobile Networking Conference (WMNC) 2014 7th IFIP, 2014Co-Authors: Ibtissem Boulanouar, Abderrezak Rachedi, Stephane Lohier, Gilles RousselAbstract:In this paper, we address the problem of target Tracking in Wireless Multimedia Sensor Networks. Target Tracking is usually defined as a two stages process: 1) detecting the presence of the target and 2) locating it. We propose a cluster-based and collaborative Tracking Algorithm for a signal emitting target with the objective of finding the best trade-off between the energy consumption and the Tracking precision. In this Algorithm, each cluster component is in charge of specific tasks. More powerful sensors handle the high cost energy tasks and assume inter and intra-cluster collaboration while constraint sensors handle low-cost energy tasks and assume only intracluster communication. A probabilistic node selection method is implemented to select the best sensors which participate to the Tracking process. A deployment strategy for both sensors is also proposed. Simulation results are presented to evaluate the efficiency of the proposed Algorithm. They demonstrate a significant target Tracking accuracy improvement and energy consumption reduction comparing to existing Algorithms.
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pta a predictive Tracking Algorithm in wireless multimedia sensor networks
Global Information Infrastructure and Networking Symposium, 2013Co-Authors: Ibtissem Boulanouar, Abderrezak Rachedi, Stephane Lohier, Gilles RousselAbstract:In this paper, we propose a new Predictive Tracking Algorithm for Wireless Multimedia Sensor Networks named PTA. PTA is a complete Tracking Algorithm that implements a five-step process: wake up, detection, localization, prediction, and next sensor selection. Each step has an important role in the Tracking process. PTA attempts to find the trade-off between Tracking accuracy and energy conservation. In this Algorithm, the prediction phase is performed using a Kalman Filter, which is a recursive state estimator. Using simulations, we show the efficiency of the proposed Algorithm in both trajectory prediction as well as energy saving. Moreover, we perform a comparative study between PTA and existing solutions: 1) BASIC solution where all the Camera Sensors are always active, 2) Optimal Camera Node Selection (OCNS) which is a cluster-based mechanism based on probabilistic node election. And Finally 3) PAM, another predictive scheme based on Autoregressive Model. Our results show that PTA increases the Tracking accuracy up to 30% compared to existing solutions, while reducing energy consumption down to 589.16 Joules. Therefore, PTA yields an accurate upcoming position prediction, and is more efficient than existing predictive models.
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cta a collaborative Tracking Algorithm in wireless sensor networks
2013 International Conference on Computing Networking and Communications (ICNC), 2013Co-Authors: Ibtissem Boulanouar, Abderrezak Rachedi, Stephane Lohier, Gilles RousselAbstract:In this paper we address the problem of Object Tracking in WSN. We define Mobile Object Tracking as a two stage process: 1) detecting the presence of the object in the monitoring area and 2) locating it at each stage of its progression in the area. We propose a Collaborative Tracking Algorithm named CTA. CTA is a distributed protocol that runs on Heterogeneous WSN (HWSN) which consists of two types of sensors: Motion Sensors (MS) and Camera Sensors (CS). The MS deals with the detection phase and then activates the CS based on a probabilistic threshold. In addition to the Tracking Algorithm, we propose a deployment strategy and study its impact on CTA. The efficiency of our proposed solution is evaluated according to simulation performed. The obtained results are compared to three existing solutions: 1) BASIC solution where only CSs are deployed always in active mode; 2) OCNS (Optimal Camera Node Selection) is cluster-based approach with a CS election and 3) EAOT, our previously proposed Algorithm based on heuristic CS wake up. We observe that CTA significantly reduces the Tracking cost in term of energy consumption (up to 38.14% of energy saved compared to other Algorithms). It also performs Tracking (up to 37.5% on the Tracking accuracy).
Ibtissem Boulanouar - One of the best experts on this subject based on the ideXlab platform.
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pmt 2 a predictive mobile target Tracking Algorithm in wireless multimedia sensor networks
International Symposium on Computers and Communications, 2014Co-Authors: Ibtissem Boulanouar, Abderrezak Rachedi, Stephane Lohier, Gilles RousselAbstract:In this work, we propose a new Predictive-based Mobile Target Tracking Algorithm for Wireless Multimedia Sensor Networks called PMT 2 . Resource management being a critical feature of this kind of networks, the main aim of PMT 2 is to handle the trade-off between the accuracy of the Tracking and the energy conservation. Prediction approach seems to be the best candidate to reach this objective. For this purpose, we introduce an enhanced version of the Extended Kalman Filter combined with a change detection mechanism named CuSum for Cumulative Summary. We also propose a deployment strategy to improve the efficiency of the Tracking Algorithm. Using simulations, we show the performances of the proposed coupled mechanism in the trajectory prediction and in the reactivity to abrupt direction changes. Moreover, we perform a comparative study between PMT 2 and existing works: 1) BASIC where all the Cameras Sensors are always in active mode; 2) OCNS for Optimal Camera Node Selection, a cluster-based solution with a probabilistic sensor selection; 3) PTA, another predictive solution based on standard Kalman Filter. The obtained results illustrate that PMT 2 improves the quality of Tracking by up to 35% compared to existing works, while reducing energy consumption by up to 55%.
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a collaborative Tracking Algorithm for communicating target in wireless multimedia sensor networks
Wireless and Mobile Networking Conference (WMNC) 2014 7th IFIP, 2014Co-Authors: Ibtissem Boulanouar, Abderrezak Rachedi, Stephane Lohier, Gilles RousselAbstract:In this paper, we address the problem of target Tracking in Wireless Multimedia Sensor Networks. Target Tracking is usually defined as a two stages process: 1) detecting the presence of the target and 2) locating it. We propose a cluster-based and collaborative Tracking Algorithm for a signal emitting target with the objective of finding the best trade-off between the energy consumption and the Tracking precision. In this Algorithm, each cluster component is in charge of specific tasks. More powerful sensors handle the high cost energy tasks and assume inter and intra-cluster collaboration while constraint sensors handle low-cost energy tasks and assume only intracluster communication. A probabilistic node selection method is implemented to select the best sensors which participate to the Tracking process. A deployment strategy for both sensors is also proposed. Simulation results are presented to evaluate the efficiency of the proposed Algorithm. They demonstrate a significant target Tracking accuracy improvement and energy consumption reduction comparing to existing Algorithms.
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pta a predictive Tracking Algorithm in wireless multimedia sensor networks
Global Information Infrastructure and Networking Symposium, 2013Co-Authors: Ibtissem Boulanouar, Abderrezak Rachedi, Stephane Lohier, Gilles RousselAbstract:In this paper, we propose a new Predictive Tracking Algorithm for Wireless Multimedia Sensor Networks named PTA. PTA is a complete Tracking Algorithm that implements a five-step process: wake up, detection, localization, prediction, and next sensor selection. Each step has an important role in the Tracking process. PTA attempts to find the trade-off between Tracking accuracy and energy conservation. In this Algorithm, the prediction phase is performed using a Kalman Filter, which is a recursive state estimator. Using simulations, we show the efficiency of the proposed Algorithm in both trajectory prediction as well as energy saving. Moreover, we perform a comparative study between PTA and existing solutions: 1) BASIC solution where all the Camera Sensors are always active, 2) Optimal Camera Node Selection (OCNS) which is a cluster-based mechanism based on probabilistic node election. And Finally 3) PAM, another predictive scheme based on Autoregressive Model. Our results show that PTA increases the Tracking accuracy up to 30% compared to existing solutions, while reducing energy consumption down to 589.16 Joules. Therefore, PTA yields an accurate upcoming position prediction, and is more efficient than existing predictive models.
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cta a collaborative Tracking Algorithm in wireless sensor networks
2013 International Conference on Computing Networking and Communications (ICNC), 2013Co-Authors: Ibtissem Boulanouar, Abderrezak Rachedi, Stephane Lohier, Gilles RousselAbstract:In this paper we address the problem of Object Tracking in WSN. We define Mobile Object Tracking as a two stage process: 1) detecting the presence of the object in the monitoring area and 2) locating it at each stage of its progression in the area. We propose a Collaborative Tracking Algorithm named CTA. CTA is a distributed protocol that runs on Heterogeneous WSN (HWSN) which consists of two types of sensors: Motion Sensors (MS) and Camera Sensors (CS). The MS deals with the detection phase and then activates the CS based on a probabilistic threshold. In addition to the Tracking Algorithm, we propose a deployment strategy and study its impact on CTA. The efficiency of our proposed solution is evaluated according to simulation performed. The obtained results are compared to three existing solutions: 1) BASIC solution where only CSs are deployed always in active mode; 2) OCNS (Optimal Camera Node Selection) is cluster-based approach with a CS election and 3) EAOT, our previously proposed Algorithm based on heuristic CS wake up. We observe that CTA significantly reduces the Tracking cost in term of energy consumption (up to 38.14% of energy saved compared to other Algorithms). It also performs Tracking (up to 37.5% on the Tracking accuracy).
Luca Schenato - One of the best experts on this subject based on the ideXlab platform.
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a hierarchical multiple target Tracking Algorithm for sensor networks
International Conference on Robotics and Automation, 2005Co-Authors: Shankar S Sastry, Luca SchenatoAbstract:Multiple-target Tracking is a canonical application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multi-hop communication, sensor management and decision making. The task of Tracking multiple objects in a sensor network is challenging due to constraints on a sensor node such as short communication and sensing ranges, a limited amount of memory and limited computational power. In addition, since a sensor network surveillance system needs to operate autonomously without human operators, it requires an autonomous Tracking Algorithm which can track an unknown number of targets. In this paper, we develop a scalable hierarchical multiple-target Tracking Algorithm that is autonomous and robust against transmission failures, communication delays and sensor localization error.
Zongzhi Cheng - One of the best experts on this subject based on the ideXlab platform.
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coda a continuous object detection and Tracking Algorithm for wireless ad hoc sensor networks
Consumer Communications and Networking Conference, 2008Co-Authors: Wangrong Chang, Huitang Lin, Zongzhi ChengAbstract:Wireless sensor networks make possible many new applications in a wide range of application domains. One of the primary applications of such networks is the detection and Tracking of continuously moving objects, such as wild fires, biochemical materials, and so forth. This study supports such applications by developing a continuous object detection and Tracking Algorithm, designated as CODA, based on a hybrid static/dynamic clustering technique. The CODA mechanism enables each sensor node to detect and track the moving boundaries of objects in the sensing field. The numerical results obtained using a Qualnet simulator confirm the effectiveness and robustness of the proposed approach.
Timothy J Hall - One of the best experts on this subject based on the ideXlab platform.
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A parallelizable real-time motion Tracking Algorithm with applications to ultrasonic strain imaging.
Physics in medicine and biology, 2007Co-Authors: Jingfeng Jiang, Timothy J HallAbstract:Ultrasound-based mechanical strain imaging systems utilize signals from conventional diagnostic ultrasound systems to image tissue elasticity contrast that provides new diagnostically valuable information. Previous works (Hall et al 2003 Ultrasound Med. Biol. 29 427, Zhu and Hall 2002 Ultrason. Imaging 24 161) demonstrated that uniaxial deformation with minimal elevation motion is preferred for breast strain imaging and real-time strain image feedback to operators is important to accomplish this goal. The work reported here enhances the real-time speckle Tracking Algorithm with two significant modifications. One fundamental change is that the proposed Algorithm is a column-based Algorithm (a column is defined by a line of data parallel to the ultrasound beam direction, i.e. an A-line), as opposed to a row-based Algorithm (a row is defined by a line of data perpendicular to the ultrasound beam direction). Then, displacement estimates from its adjacent columns provide good guidance for motion Tracking in a significantly reduced search region to reduce computational cost. Consequently, the process of displacement estimation can be naturally split into at least two separated tasks, computed in parallel, propagating outward from the center of the region of interest (ROI). The proposed Algorithm has been implemented and optimized in a Windows® system as a stand-alone ANSI C++ program. Results of preliminary tests, using numerical and tissue-mimicking phantoms, and in vivo tissue data, suggest that high contrast strain images can be consistently obtained with frame rates (10 frames s−1) that exceed our previous methods.