Coverage of Targets in Mobile Sensor Networks With Restricted Mobility
TL;DR: This paper considers the problem of covering all regions of interests (targets) by relocating a set of mobile sensors such that total movement made by them is minimized and develops heuristics to solve the problem.
Abstract: In this paper, we consider the problem of covering all regions of interests (targets) by relocating a set of mobile sensors such that total movement made by them is minimized. This problem itself is a challenging one and addressed recently by some researchers under free mobility model. We consider a more restricted version of the problem where sensors can move only in two mutually perpendicular directions. We first show that the optimal point to which a sensor must move to cover a specific target is different under this model from the one where sensors can move freely, and characterize such a point. On the basis of this observation, we have developed heuristics to solve the problem. The heuristics run in two phases; the first phase ensures coverage and the second phase, connectivity. In both the phases, the sensors can move only with restricted mobility. We have run a set of experiments to evaluate the performance of the proposed algorithm and found that the total movement made in the first phase is comparable to the solution given by an IPP ( Integer Programming Problem ). For the second phase, we have presented two heuristics MinCon and MinCon_m. The algorithm MinCon works by finding connected components of the graph consisting of sensor nodes. It then identifies destination locations where some sensors must be placed so that all necessary components become connected. Once the destinations are known, the problem is solved by mapping it to an LSAP (Linear Sum Assignment Problem). The other heuristic MinCon_m improves over MinCon by moving only a subset of sensors to their destinations using the solution of LSAP. It then finds the movement of the remaining sensors applying a technique used in the first phase.
Citations
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TL;DR: An improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method that can reduce the root-mean-squared error of the position compared with the extended Kalman filter and has better robustness against large localization and tracking errors.
Abstract: The outliers remove, the classification of effective measurements, and the weighted optimization method of the corresponding measurement are the main factors that affect the positioning accuracy based on range-based multi-target tracking in wireless sensor networks. In this paper, we develop an improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method. According to the ENNBC method, the outliers in the measurement data are removed effectively, dataset density peaks are found quickly, and remaining effective measurements are accurately classified. The ENNBC method improves the traditional direct classification method and took the dependence among continuous density attributes into account. Four common indexes of classifiers are used to evaluate the performance of the nine methods, i.e., the normal naive Bayesian, flexible naive Bayesian (FNB), the homologous model of FNB (FNB
ROT
), support vector machine, k-means, fuzzy c-means (FCM), possibilistic c-means, possibilistic FCM, and our proposed ENNBC. The evaluation results show that ENNBC has the best performance based on the four indexes. Meanwhile, the multi-target tracking experimental results show that the proposed algorithm can reduce the root-mean-squared error of the position compared with the extended Kalman filter. In addition, the proposed algorithm has better robustness against large localization and tracking errors.
17 citations
TL;DR: In this paper, a generic coverage optimization problem arising in wireless sensor networks with sensors of limited mobility was formulated and analyzed, and four algorithms were proposed to solve it heuristically or approximately.
Abstract: We formulate and analyze a generic coverage optimization problem arising in wireless sensor networks with sensors of limited mobility. Given a set of targets to be covered and a set of mobile sensors, we seek a sensor dispatch algorithm maximizing the covered targets under the constraint that the maximal moving distance for each sensor is upper-bounded by a given threshold. We prove that the problem is NP-hard. Given its hardness, we devise four algorithms to solve it heuristically or approximately. Among the approximate algorithms, we first develop randomized (1-1/e)-optimal algorithm. We then employ a derandomization technique to devise a deterministic (1-1/e)-approximation algorithm. We also design a deterministic approximation algorithm with nearly ▵-1 approximation ratio by using a colouring technique, where denotes the maximal number of subsets covering the same target. Experiments are also conducted to validate the effectiveness of the algorithms in a variety of parameter settings.
16 citations
01 Dec 2020
TL;DR: In this paper, a mobile air quality monitoring system that relies on sensors mounted on buses to broaden the monitoring area is considered, where the optimal buses to place the sensors as well as the optimal monitoring timings to maximize the number of critical regions that are monitored are investigated.
Abstract: So far, air quality monitoring is usually handled by monitoring stations located at fixed locations. However, due to the cost of installation, deployment, and operation, the number of monitoring stations deployed is often tiny; thus, the monitored area is limited. To deal with this problem, in this paper, we consider a mobile air quality monitoring system that relies on sensors mounted on buses to broaden the monitoring area. Specifically, we investigate the optimal buses to place the sensors as well as the optimal monitoring timings to maximize the number of critical regions that are monitored. We mathematically formulate the targeted problem and prove its NP-hardness. Then, we exploit the greedy and dynamic programming approaches to propose a polynomial-time 1/2-approximation algorithm. We use the data of real bus routes in Hanoi, Vietnam, for the experimentation and show that the proposed algorithm guarantees an average performance ratio of 72.68%.
4 citations
TL;DR: The deployment of mobile and adaptive virtual force barrier coverage (MA-VFBC) classification scheme using a mobile emergency response and command interface (MERCI) platform that is functionally implemented to track and report incidences and consequent collateral damages to infrastructures within a region of interest (ROI) is proposed.
Abstract: The deployment of mobile and adaptive virtual force barrier coverage (MA-VFBC) classification scheme using a mobile emergency response and command interface (MERCI) platform that is functionally implemented to track and report incidences and consequent collateral damages to infrastructures within a region of interest (ROI) is proposed. Considering the enormous use of the global positioning system (GPS) devices for location data-gathering and processing, and its inherent limitations, the proposed GUI-based MA-VFBC platform is implemented using self-deploying and obstacle-avoiding scattered mobile sensor nodes. The GPS service is kept as alternatives, since only initial co-ordinates from where the deployed sensor starts to move and the maximum boundary location of the target location is considered. The practical experimentation work appraises the use and feel of the (MERCI) platform when integrated with the proposed novel MA-VFBC path-tracking classification schemes, while the simulation work investigates evident real-time system reliability issues as direction of node deployment with path distances, system computation time and system overheads in the presence of dissimilar multiple obstacles.
4 citations
Cites background from "Coverage of Targets in Mobile Senso..."
...considered by authors in [19], [20], while a holistic approach as to the compatibility of the mobile deployment in a 3D envi-...
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01 Jun 2018
TL;DR: The proposal adopts a Quality of Context (QoC) paradigm which was conceived to improve an e-health IoT environment and was characterized by supporting the care of people with special needs thus improving their quality of life.
Abstract: Nowadays, it is a common ground to find an ehealth environment flooded by a large amount of data, which comes from several mobile devices/sensors, and could not represent hundred percent of useful information. In other words, a process to enhance this data scenario is an essential effort. Therefore, in this paper, we present an approach oriented to the context which targets to provide more dynamic and personalized services in an e-health environment. The proposal adopts a Quality of Context (QoC) paradigm which was conceived to improve an e-health IoT environment. The scenario was characterized by supporting the care of people with special needs (elderly or with health problems) thus improving their quality of life. Thereby, the objective was to demonstrate the use of the proposed QoC evaluation, appraising some parameters. Experiments considered the use of diverse types of sensors, such as pulse and oxygen in the blood, body temperature, blood pressure, patient’s position and falls, environment temperature and humidity. Results indicate the success of the proposal.
3 citations
Cites background from "Coverage of Targets in Mobile Senso..."
...The availability of several types of sensors and the coverage, as highlighted in [1], is creating an unpreceded amount of data never seen before and which could not be translated to useful information....
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References
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01 Oct 2007
TL;DR: A dynamic programming solution is developed for the mouse's optimal strategy and how the cats' chosen movement model will affect its presence matrix in the network, and hence its payoff in the game is discussed.
Abstract: We study the problem of a mobile target (the mouse) trying to evade detection by one or more mobile sensors (we call such a sensor a cat) in a closed network area. We view our problem as a game between two players: the mouse, and the collection of cats forming a single (meta-)player. The game ends when the mouse falls within the sensing range of one or more cats. A cat tries to determine its optimal strategy to minimize the worst case expected detection time of the mouse. The mouse tries to determine an optimal counter movement strategy to maximize the expected detection time. We divide the problem into two cases based on the relative sensing capabilities of the cats and the mouse. When the mouse has a larger sensing range than the cats, we show how the mouse can determine its optimal movement strategy based on local observations of the cats' movements. When the mouse has a sensing range smaller than or equal to the cats', we develop a dynamic programming solution for the mouse's optimal strategy, assuming high level information about the cats' movement model. We discuss how the cats' chosen movement model will affect its presence matrix in the network, and hence its payoff in the game. Extensive experimental results verify and illustrate the analytical results, and evaluate the game's payoffs as a function of several important system parameters.
16 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...Other than improving coverage, sensor mobility may also be useful in detecting mobile intruders in a specified area as done in [10]....
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01 Dec 2015
TL;DR: This paper reduces the problem of area coverage to target coverage and it is possible to compute an upper bound on the lifetime achievable for target coverage, and an Integer Programming Problem (IPP) formulation of the problem is given.
Abstract: One important and challenging problem for sensor network is how to ensure coverage of the target area and maximize the lifetime of the network at the same time This problem is known to be a NP-complete and there are many attempts to find an approximate solution In this paper, we reduce the problem of area coverage to target coverage and it is possible to compute an upper bound on the lifetime achievable for target coverage We have given an Integer Programming Problem (IPP) formulation of the problem using this upper bound We do some approximation on the problem before applying IPP, so that solution could be obtained in reasonable time In the experiments with number of sensors ranging from 50 to 150 we have been able to achieve the upper bound in all the cases
1 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...This is termed as area coverage [3], [4]....
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