Results 1 - 10
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40
Mobility limited flip-based sensor networks deployment
- in Proceedings of IEEE MASS
, 2005
"... An important phase of sensor networks operation is deployment of sensors in the field of interest. Critical goals during sensor networks deployment include coverage, connectivity, load balancing etc. A class of work has recently appeared, where mobility in sensors is leveraged to meet deployment obj ..."
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Cited by 26 (0 self)
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An important phase of sensor networks operation is deployment of sensors in the field of interest. Critical goals during sensor networks deployment include coverage, connectivity, load balancing etc. A class of work has recently appeared, where mobility in sensors is leveraged to meet deployment objectives. In this paper, we study deployment of sensor networks using mobile sensors. The distinguishing feature of our work is that the sensors in our model have limited mobilities. More specifically, the mobility in the sensors we consider is restricted to a flip, where the distance of the flip is bounded. We call such sensors as flip-based sensors. Given an initial deployment of flip-based sensors in a field, our problem is to determine a movement plan for the sensors in order to maximize the sensor network coverage, and minimize the number of flips. We propose a minimum-cost maximum-flow based solution to this problem. We prove that our solution optimizes both the coverage and the number of flips. We also study the sensitivity of coverage and the number of flips to flip distance under different initial deployment distributions of sensors. We observe that increased flip distance achieves better coverage, and reduces the number of flips required per unit increase in coverage. However, such improvements are constrained by initial deployment distributions of sensors, due to the limitations on sensor mobility.
Deploying Wireless Sensor Networks under Limited Mobility Constraints
"... Abstract—In this paper, we study the issue of sensor network deployment using limited mobility sensors. By limited mobility, we mean that the maximum distance that sensors are capable of moving to is limited. Given an initial deployment of limited mobility sensors in a field clustered into multiple ..."
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Cited by 23 (0 self)
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Abstract—In this paper, we study the issue of sensor network deployment using limited mobility sensors. By limited mobility, we mean that the maximum distance that sensors are capable of moving to is limited. Given an initial deployment of limited mobility sensors in a field clustered into multiple regions, our deployment problem is to determine a movement plan for the sensors to minimize the variance in number of sensors among the regions and simultaneously minimize the sensor movements. Our methodology to solve this problem is to transfer the nonlinear variance/movement minimization problem into a linear optimization problem through appropriate weight assignments to regions. In this methodology, the regions are assigned weights corresponding to the number of sensors needed. During sensor movements across regions, larger weight regions are given higher priority compared to smaller weight regions, while simultaneously ensuring a minimum number of sensor movements. Following the above methodology, we propose a set of algorithms to our deployment problem. Our first algorithm is the Optimal Maximum Flow-based (OMF) centralized algorithm. Here, the optimal movement plan for sensors is obtained based on determining the minimum cost maximum weighted flow to the regions in the network. We then propose the Simple Peak-Pit-based distributed (SPP) algorithm that uses local requests and responses for sensor movements. Using extensive simulations, we demonstrate the effectiveness of our algorithms from the perspective of variance minimization, number of sensor movements, and messaging overhead under different initial deployment scenarios. Index Terms—Sensor networks, deployment, limited mobility sensors. 1
Sensor networks deployment using flip-based sensors.
- In Mobile Adhoc and Sensor Systems Conference, 2005. IEEE International Conference on,
, 2005
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Tru-alarm: Trustworthiness analysis of sensor networks in cyber-physical systems
- In ICDM
, 2010
"... Abstract—A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational) components to form a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios. One key issue in CPS research is trustwort ..."
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Cited by 19 (10 self)
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Abstract—A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational) components to form a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios. One key issue in CPS research is trustworthiness analysis of the observed data: Due to technology limitations and environmental influences, the CPS data are inherently noisy that may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this paper, we propose a method called Tru-Alarm which finds out trustworthy alarms and increases the feasibility of CPS. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inferences based on linked information in the graph. Extensive experiments show that Tru-Alarm filters out noises and false information efficiently and guarantees not missing any meaningful alarms. I.
A Geometric Transversal Approach to Analyzing Track Coverage in Sensor Networks
, 2007
"... This paper presents a new coverage formulation addressing the quality of service of sensor networks that cooperatively detect targets traversing a region of interest. The problem of track coverage consists of finding the positions of n sensors such that the amount of tracks detected by at least k se ..."
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Cited by 14 (9 self)
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This paper presents a new coverage formulation addressing the quality of service of sensor networks that cooperatively detect targets traversing a region of interest. The problem of track coverage consists of finding the positions of n sensors such that the amount of tracks detected by at least k sensors is optimized. This paper studies the geometric properties of the network, addressing a deterministic track-coverage formulation and binary sensor models. It is shown that the tracks detected by a network of heterogeneous omnidirectional sensors are the geometric transversals of non-translates families of circles. A novel methodology based on cone theory is presented for representing and measuring sets of transversals in closed-form. By this methodology, the solution of the track-coverage problem can be formulated as a nonlinear program (NLP). The numerical simulations show that this approach can improve track coverage by up to two orders of magnitude compared to grid and random deployments. Also, it can be used to reduce the number of sensors required to achieve a desired detection performance by up to 50%, and to optimally replenish or reposition existing sensor networks.
Sampling-based coverage path planning for inspection of complex structures
- In Proceedings of the International Conference on Automated Planning and Scheduling
, 2012
"... We present several new contributions in sampling-based cov-erage path planning, the task of finding feasible paths that give 100 % sensor coverage of complex structures in obstacle-filled and visually occluded environments. First, we establish a framework for analyzing the probabilistic completeness ..."
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Cited by 13 (3 self)
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We present several new contributions in sampling-based cov-erage path planning, the task of finding feasible paths that give 100 % sensor coverage of complex structures in obstacle-filled and visually occluded environments. First, we establish a framework for analyzing the probabilistic completeness of a sampling-based coverage algorithm, and derive results on the completeness and convergence of existing algorithms. Sec-ond, we introduce a new algorithm for the iterative improve-ment of a feasible coverage path; this relies on a sampling-based subroutine that makes asymptotically optimal local im-provements to a feasible coverage path based on a strong gen-eralization of the RRT ∗ algorithm. We then apply the algo-rithm to the real-world task of autonomous in-water ship hull inspection. We use our improvement algorithm in conjunc-tion with redundant roadmap coverage planning algorithm to produce paths that cover complex 3D environments with un-precedented efficiency.
Track coverage in sensor networks
- in Proc. Amer. Control Conf., 2006
"... Abstract—So far coverage problems have been formulated to address area coverage or to maintain line-of-sight visibility in the presence of obstacles (i.e., art-gallery problems). Although sensor networks often are employed to track moving targets, none of the existing formulations deal with the prob ..."
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Cited by 12 (7 self)
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Abstract—So far coverage problems have been formulated to address area coverage or to maintain line-of-sight visibility in the presence of obstacles (i.e., art-gallery problems). Although sensor networks often are employed to track moving targets, none of the existing formulations deal with the problem of allocating sensors in order to achieve track-formation capa-bilities over a region of interest. This paper investigates the problem of finding the configuration of a network with n sensors such that the number of tracks intercepted by k sensors is optimized without providing redundant area coverage over the entire region. This problem arises in applications where proximity sensors are employed that have individual detection capabilities, and that obtain limited measurements from each track, possibly at different moments in time. By assuming that the target travels along a straight unknown path, and that the sensors are omnidirectional with limited range (i.e., their visibility can be represented by a circle), it can be shown that the tracks detected by one or more (k) sensors always are contained by a coverage cone. Therefore, the track coverage of the network can be measured through the opening angle of the coverage cone and formulated in terms of unit vectors that depend on the sensors ’ range and location. Through this approach, the coverage of a given network configuration can be rapidly assessed. Also, a coverage function is obtained that, when maximized with respect to the sensor location, optimizes the number of tracks detected over a rectangular area of interest. The same approach can potentially be applied to other convex polygons and to three-dimensional Euclidian space. I.
Acoustic sensor network design for position estimation
, 2007
"... In this paper, we develop tractable mathematical models and approximate solution algorithms for a class of integer optimization problems with probabilistic and deterministic constraints, with applications to the design of distributed sensor networks that have limited connectivity. For a given deploy ..."
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Cited by 7 (4 self)
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In this paper, we develop tractable mathematical models and approximate solution algorithms for a class of integer optimization problems with probabilistic and deterministic constraints, with applications to the design of distributed sensor networks that have limited connectivity. For a given deployment region size, we calculate the Pareto frontier of the sensor network utility at the desired probabilities for d-connectivity and k-coverage. As a result of our analysis, we determine (i) the number of sensors of different types to deploy from a sensor pool, which offers a cost vs. performance trade-off for each type of sensor, (ii) the minimum required radio transmission ranges of the sensors to ensure connectivity, and (iii) the lifetime of the sensor network. For generality, we consider randomly deployed sensor networks and formulate constrained optimization techniques to obtain the localization performance. The approach is guided and validated using an unattended acoustic sensor network design. Finally, approximations of the complete statistical characterization of the acoustic sensor networks are given, which enable average network performance predictions of any combination of acoustic sensors. Categories and Subject Descriptors: C.2.1 [Computer-Communication Networks]: Distributed networks; G.1.6 [Numerical Analysis]: Optimization—Constrained optimization, convex programming, integer programming, nonlinear programming; G.3 [Probability and Statistics]: Experimental design
Robust deployment of dynamic sensor networks for cooperative track detection
- IEEE SENSORS
, 2009
"... The problem of cooperative track detection by a dynamic sensor network arises in many applications, including security and surveillance, and tracking of endangered species. Several authors have recently shown that the quality-of-service of these networks can be statically optimized by placing the s ..."
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Cited by 7 (3 self)
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The problem of cooperative track detection by a dynamic sensor network arises in many applications, including security and surveillance, and tracking of endangered species. Several authors have recently shown that the quality-of-service of these networks can be statically optimized by placing the sensors in the region of interest (ROI) via mathematical programming. However, if the sensors are subject to external forcing, such as winds or currents, they may be rapidly displaced, and their quality-of-service may be significantly deteriorated over time. The novel approach presented in this paper consists of placing the sensors in the ROI based on their future displacement, which can be estimated from environmental forecasts and sensor dynamic models. The sensor network deployment is viewed as a new problem in dynamic computational geometry, in which the initial positions of a family of circles with time-varying radii and positions are to be optimized subject to sets of algebraic and differential equations. When these equations are nonlinear and time-varying, the optimization problem does not have an exact solution, or global optimum, but can be approximated as a finite-dimensional nonlinear program by discretizing the quality-of-service and the dynamic models with respect to time. Then, a near-optimal solu-tion for the initial sensor positions is sought by means of sequential quadratic programming. The numerical results show that this approach can improve quality-of-service by up to a factor of five compared to existing techniques, and its performance is robust to propagated modeling and deployment errors.
Optimal Control of an Underwater Sensor Network for Cooperative Target Tracking
- IEEE JOURNAL OF OCEANIC ENGINEERING
, 2009
"... Optimal control (OC) is a general and effective ap-proach for trajectory optimization in dynamical systems. So far, however, it has not been applied to mobile sensor networks due to the lack of suitable objective functions and system models. In this paper, an integral objective function representin ..."
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Cited by 6 (4 self)
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Optimal control (OC) is a general and effective ap-proach for trajectory optimization in dynamical systems. So far, however, it has not been applied to mobile sensor networks due to the lack of suitable objective functions and system models. In this paper, an integral objective function representing the quality of service of a sensor network performing cooperative track detec-tion over time is derived using a geometric transversals approach. A set of differential equations modeling the sensor network’s dynamics is obtained by considering three dependent subsystems, i.e., underwater vehicles, onboard sensors, and oceanographic fields. Each sensor-equipped vehicle is modeled as a bounded subset of a Euclidian space, representing the sensor’s field of view (FOV), which moves according to underwater vehicle dynamics. By this approach, the problem of generating optimal sensors’ trajectories is formulated as an OC problem in computational geometry. The numerical results show that OC significantly improves the network’s quality of service compared to area-coverage and path-planning methods. Also, it can be used to incorporate sensing and energy constraints on the sensors’ state and control vectors, and to generate fronts of Pareto optimal trajectories.
