The Experts below are selected from a list of 83223 Experts worldwide ranked by ideXlab platform
Jiming Chen - One of the best experts on this subject based on the ideXlab platform.
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Time Synchronization for Random Mobile Sensor Networks
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Jianping He, Jiming Chen, Peng Cheng, Ling Shi, Rongxing LuAbstract:Mobile Sensor nodes have a wide spectrum of applications, such as social networks and habitat monitoring. Time synchronization is a critical issue for most applications using Mobile Sensor networks. However, due to the limited communication range, Mobile Sensor nodes can only exchange their information when they are sufficiently close for contact. Moreover, random movements of the nodes render the performance analysis of any time synchronization protocols challenging. In this paper, we introduce the relation graph for modeling the random contact of Mobile Sensor nodes and evaluate the probability that the network can be synchronized within an arbitrary time. We adapt the previous maximum time synchronization (MTS) protocol, which can drive the clocks of all Sensor nodes to a common value by utilizing their own neighboring information. We provide an analytical lower bound of the probability that the time synchronization can be finished within any given time. The obtained theories and algorithms are applied for several fundamental problems, and it is proven that a better connectivity is beneficial for convergence. For linearizable graph, we provide an efficient way to calculate the exact probabilities. Extensive numerical examples demonstrate the effectiveness of our results.
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Target Tracking with Limited Sensing Range in Autonomous Mobile Sensor Networks
2012Co-Authors: Jing Bai, Jiming Chen, Peng Cheng, Adrien Guenard, Ye-qiong SongAbstract:As technology advancements in robotics and wire- less communication, tracking Mobile targets using Mobile Sensors has aroused widespread concern in recent years. In this paper, we propose a novel coordinative moving strategy for autonomous Mobile Sensor networks to guarantee the target can be detected in each observed step while minimizing the amount of moving Sensors. The proposed scheme consists of obtaining the current position of the target, which is then used to predict the next time-step location of the target. Once the uncertainty region of the target's position is defined, the proposed method allows the Mobile Sensors to cover it in an optimal way. Therefore, we can assign each Mobile Sensor to an optimal location to cover the uncertainty region while minimizing the total traveled distance of Sensors. Extensive simulations are given to evaluated performance and demonstrate the efficiency of the proposed strategy.
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Cost-Effective Barrier Coverage by Mobile Sensor Networks
2012Co-Authors: Shibo He, Jiming Chen, Xu Li, Youxian SunAbstract:Barrier coverage problem in emerging Mobile Sensor networks has been an interesting research issue. Existing solutions to this problem aim to decide one-time movement for individual Sensors to construct as many barriers as possible, which may not work well when there are no sufficient Sensors to form a single barrier. In this paper, we try to achieve barrier coverage in Sensor scarcity case by dynamic Sensor patrolling. In specific, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by Mobile Sensors. Based on the insight from PMS, we then propose a coordinated Sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each Sensor's current movement strategy is decided based on the past intruder arrival information. By jointly exploiting Sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that the Sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of Mobile Sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. Through extensive simulations, we demonstrate that CSP has a desired barrier coverage performance and S-DCSP and G-DCSP have similar performance as that of CSP.
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CDC - Clock synchronization for random Mobile Sensor networks
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Peng Cheng, Ling Shi, Jiming ChenAbstract:Mobile Sensor nodes have found a large range of applications, e.g., social networks, habitant monitoring. Clock synchronization is a critical issue for most applications with Mobile Sensor networks. However, due to the limited communication range, the Mobile Sensor nodes can only exchange their information once they are sufficiently close for contact. Moreover, the random movement makes the performance analysis of any time synchronization protocols more challenging. In this paper, we introduce the relation graph to model the random contact of Mobile Sensor nodes, and evaluate the performance by the probability that the network can be synchronized within an arbitrary time. We adapt the previous maximum time synchronization protocol, which can drive the clocks of all Sensor nodes to a common value by utilizing their own neighboring information. Furthermore, we provide an analytical lower bound for the probability that the time synchronization can be finished within any given time. For linearizable graph, we also provide an efficient way to calculate the exact probabilities. Extensive numerical examples show the effectiveness of our results.
Peng Cheng - One of the best experts on this subject based on the ideXlab platform.
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Time Synchronization for Random Mobile Sensor Networks
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Jianping He, Jiming Chen, Peng Cheng, Ling Shi, Rongxing LuAbstract:Mobile Sensor nodes have a wide spectrum of applications, such as social networks and habitat monitoring. Time synchronization is a critical issue for most applications using Mobile Sensor networks. However, due to the limited communication range, Mobile Sensor nodes can only exchange their information when they are sufficiently close for contact. Moreover, random movements of the nodes render the performance analysis of any time synchronization protocols challenging. In this paper, we introduce the relation graph for modeling the random contact of Mobile Sensor nodes and evaluate the probability that the network can be synchronized within an arbitrary time. We adapt the previous maximum time synchronization (MTS) protocol, which can drive the clocks of all Sensor nodes to a common value by utilizing their own neighboring information. We provide an analytical lower bound of the probability that the time synchronization can be finished within any given time. The obtained theories and algorithms are applied for several fundamental problems, and it is proven that a better connectivity is beneficial for convergence. For linearizable graph, we provide an efficient way to calculate the exact probabilities. Extensive numerical examples demonstrate the effectiveness of our results.
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Target Tracking with Limited Sensing Range in Autonomous Mobile Sensor Networks
2012Co-Authors: Jing Bai, Jiming Chen, Peng Cheng, Adrien Guenard, Ye-qiong SongAbstract:As technology advancements in robotics and wire- less communication, tracking Mobile targets using Mobile Sensors has aroused widespread concern in recent years. In this paper, we propose a novel coordinative moving strategy for autonomous Mobile Sensor networks to guarantee the target can be detected in each observed step while minimizing the amount of moving Sensors. The proposed scheme consists of obtaining the current position of the target, which is then used to predict the next time-step location of the target. Once the uncertainty region of the target's position is defined, the proposed method allows the Mobile Sensors to cover it in an optimal way. Therefore, we can assign each Mobile Sensor to an optimal location to cover the uncertainty region while minimizing the total traveled distance of Sensors. Extensive simulations are given to evaluated performance and demonstrate the efficiency of the proposed strategy.
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CDC - Clock synchronization for random Mobile Sensor networks
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Peng Cheng, Ling Shi, Jiming ChenAbstract:Mobile Sensor nodes have found a large range of applications, e.g., social networks, habitant monitoring. Clock synchronization is a critical issue for most applications with Mobile Sensor networks. However, due to the limited communication range, the Mobile Sensor nodes can only exchange their information once they are sufficiently close for contact. Moreover, the random movement makes the performance analysis of any time synchronization protocols more challenging. In this paper, we introduce the relation graph to model the random contact of Mobile Sensor nodes, and evaluate the performance by the probability that the network can be synchronized within an arbitrary time. We adapt the previous maximum time synchronization protocol, which can drive the clocks of all Sensor nodes to a common value by utilizing their own neighboring information. Furthermore, we provide an analytical lower bound for the probability that the time synchronization can be finished within any given time. For linearizable graph, we also provide an efficient way to calculate the exact probabilities. Extensive numerical examples show the effectiveness of our results.
Gaurav S Sukhatme - One of the best experts on this subject based on the ideXlab platform.
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constrained coverage for Mobile Sensor networks
International Conference on Robotics and Automation, 2004Co-Authors: Sameera Poduri, Gaurav S SukhatmeAbstract:We consider the problem of self-deployment of a Mobile Sensor network. We are interested in a deployment strategy that maximizes the area coverage of the network with the constraint that each of the nodes has at least K neighbors, where K is a user-specified parameter. We propose an algorithm based on artificial potential fields which is distributed, scalable and does not require a prior map of the environment. Simulations establish that the resulting networks have the required degree with a high probability, are well connected and achieve good coverage. We present analytical results for the coverage achievable by uniform random and symmetrically tiled network configurations and use these to evaluate the performance of our algorithm.
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an incremental self deployment algorithm for Mobile Sensor networks
Autonomous Robots, 2002Co-Authors: Andrew Howard, Maja J Mataric, Gaurav S SukhatmeAbstract:This paper describes an incremental deployment algorithm for Mobile Sensor networks. A Mobile Sensor network is a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. The algorithm described in this paper will deploy such nodes one-at-a-time into an unknown environment, with each node making use of information gathered by previously deployed nodes to determine its deployment location. The algorithm is designed to maximize network ‘coverage’ while simultaneously ensuring that nodes retain line-of-sight relationships with one another. This latter constraint arises from the need to localize the nodes in an unknown environment: in our previous work on i>team localization (A. Howard, M.J. Mataric, and G.S. Sukhatme, in i>Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Switzerland, 2002s i>IEEE Transactions on Robotics and Autonomous Systems, 2002) we have shown how nodes can localize themselves by using i>other nodes as landmarks. This paper describes the incremental deployment algorithm and presents the results from an extensive series of simulation experiments. These experiments serve to both validate the algorithm and illuminate its empirical properties.
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Mobile Sensor network deployment using potential fields a distributed scalable solution to the area coverage problem
Distributed Autonomous Robotic Systems, 2002Co-Authors: Andrew Howard, Maja J Mataric, Gaurav S SukhatmeAbstract:This paper considers the problem of deploying a Mobile Sensor network in an unknown environment. A Mobile Sensor network is composed of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. Such networks are capable of self-deployment; i.e., starting from some compact initial configuration, the nodes in the network can spread out such that the area ‘covered’ by the network is maximized. In this paper, we present a potential-field-based approach to deployment. The fields are constructed such that each node is repelled by both obstacles and by other nodes, thereby forcing the network to spread itself throughout the environment. The approach is both distributed and scalable.
Soura Dasgupta - One of the best experts on this subject based on the ideXlab platform.
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Target Tracking and Mobile Sensor Navigation in Wireless Sensor Networks
IEEE Transactions on Mobile Computing, 2013Co-Authors: Zhi Ding, Soura DasguptaAbstract:This work studies the problem of tracking signal-emitting Mobile targets using navigated Mobile Sensors based on signal reception. Since the Mobile target's maneuver is unknown, the Mobile Sensor controller utilizes the measurement collected by a wireless Sensor network in terms of the Mobile target signal's time of arrival (TOA). The Mobile Sensor controller acquires the TOA measurement information from both the Mobile target and the Mobile Sensor for estimating their locations before directing the Mobile Sensor's movement to follow the target. We propose a min-max approximation approach to estimate the location for tracking which can be efficiently solved via semidefinite programming (SDP) relaxation, and apply a cubic function for Mobile Sensor navigation. We estimate the location of the Mobile Sensor and target jointly to improve the tracking accuracy. To further improve the system performance, we propose a weighted tracking algorithm by using the measurement information more efficiently. Our results demonstrate that the proposed algorithm provides good tracking performance and can quickly direct the Mobile Sensor to follow the Mobile target.
Nak Young Chong - One of the best experts on this subject based on the ideXlab platform.
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locally communicative interaction framework for adaptively self organizing Mobile Sensor networks
Conference on Automation Science and Engineering, 2012Co-Authors: Kazutaka Tatara, Geunho Lee, Hiroaki Ono, Nak Young ChongAbstract:We address the adaptive self-organization problem for Mobile robotic Sensors creating wireless ad hoc networks while adapting to topological changes. Our challenge is placed on how to exploit locally communicative interactions with minimal conditions such as locality and implicit coordination. Each Sensor node organizes and updates its partially-connected network through selecting specific neighboring nodes with higher connectivity. The effectiveness of the proposed framework is verified by extensive simulations and experiments with RFID Sensor networks that contain Mobile Sensor nodes. The most notable features of our approach include self-organization, topological adaptation, and self-healing, enabling self-organization of Mobile Sensor networks in an efficient way.
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CASE - Locally communicative interaction framework for adaptively self-organizing Mobile Sensor networks
2012 IEEE International Conference on Automation Science and Engineering (CASE), 2012Co-Authors: Kazutaka Tatara, Geunho Lee, Hiroaki Ono, Nak Young ChongAbstract:We address the adaptive self-organization problem for Mobile robotic Sensors creating wireless ad hoc networks while adapting to topological changes. Our challenge is placed on how to exploit locally communicative interactions with minimal conditions such as locality and implicit coordination. Each Sensor node organizes and updates its partially-connected network through selecting specific neighboring nodes with higher connectivity. The effectiveness of the proposed framework is verified by extensive simulations and experiments with RFID Sensor networks that contain Mobile Sensor nodes. The most notable features of our approach include self-organization, topological adaptation, and self-healing, enabling self-organization of Mobile Sensor networks in an efficient way.