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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22 May 2006
TL;DR: This paper proposes an optimal deployment pattern to achieve both full coverage and 2-connectivity, and proves its optimality for all values of rc/rs, where rc is the communication radius, and rs is the sensing radius.
Abstract: It is well-known that placing disks in the triangular lattice pattern is optimal for achieving full coverage on a plane. With the emergence of wireless sensor networks, however, it is now no longer enough to consider coverage alone when deploying a wireless sensor network; connectivity must also be con-sidered. While moderate loss in coverage can be tolerated by applications of wireless sensor networks, loss in connectivity can be fatal. Moreover, since sensors are subject to unanticipated failures after deployment, it is not enough to have a wireless sensor network just connected, it should be k-connected (for k > 1 ). In this paper, we propose an optimal deployment pattern to achieve both full coverage and 2-connectivity, and prove its optimality for all values of rc/rs, where rc is the communication radius, and rs is the sensing radius. We also prove the optimality of a previously proposed deployment pattern for achieving both full coverage and 1-connectivity, when rc/rs
589 citations
"Coverage of Targets in Mobile Senso..." refers methods in this paper
...• For the coverage, the disk Model [17] is adopted i.e., a target tj is said to be covered by a sensor si if the Euclidean distance between them is less than or equal to the sensing radius of sensor si....
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...• For the coverage, the disk Model [17] is adopted i....
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28 Sep 2002
TL;DR: This paper presents an approach for sequential deployment in steps and illustrates that the cost of deployment can be minimized to achieve the desired detection performance by appropriately choosing the number of sensors deployed in each step.
Abstract: In order to monitor a region for traffic traversal, sensors can be deployed to perform collaborative target detection. Such a sensor network achieves a certain level of detection performance with an associated cost of deployment. This paper addresses this problem by proposing path exposure as a measure of the goodness of a deployment and presents an approach for sequential deployment in steps. It illustrates that the cost of deployment can be minimized to achieve the desired detection performance by appropriately choosing the number of sensors deployed in each step.
393 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...Most of the works dealing with sensor deployment [6]–[9] are based on the assumption that the environment is well known and under control....
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08 Jul 2002
TL;DR: A resource-bounded optimization framework for sensor resource management under the constraints of sufficient grid coverage of the sensor field and a unique "minimalistic" view of distributed sensor networks in which sensors transmit/report a minimum amount of sensed data are presented.
Abstract: We present a resource-bounded optimization framework for sensor resource management under the constraints of sufficient grid coverage of the sensor field We offer a unique "minimalistic" view of distributed sensor networks in which sensors transmit/report a minimum amount of sensed data The proposed theory is aimed at optimizing the number of sensors and determine their placement to support such minimalistic sensor networks We represent the sensor field as a grid (two- or three-dimensional) of points The optimization framework is inherently probabilistic due to the uncertainty associated with sensor detections The proposed algorithm addresses coverage optimization under constraints of imprecise detections and terrain properties The issue of preferential coverage of grid points (based on relative measures of security and tactical importance) is also modeled Experimental results for an example sensor field with obstacles demonstrate the application of our approach
342 citations
TL;DR: A modeling, simulation, and emulation framework for WSNs in J-Sim - an open source, component-based compositional network simulation environment developed entirely in Java that provides an object-oriented definition of target, sensor, and sink nodes, sensor and wireless communication channels, and physical media such as seismic channels, mobility models, and power models.
Abstract: Wireless sensor networks have gained considerable attention in the past few years. They have found application domains in battlefield communication, homeland security, pollution sensing, and traffic monitoring. As such, there has been an increasing need to define and develop simulation frameworks for carrying out high-fidelity WSN simulation. In this article we present a modeling, simulation, and emulation framework for WSNs in J-Sim - an open source, component-based compositional network simulation environment developed entirely in Java. This framework is built on the autonomous component architecture and extensible internetworking framework of J-Sim, and provides an object-oriented definition of target, sensor, and sink nodes, sensor and wireless communication channels, and physical media such as seismic channels, mobility models, and power models (both energy-producing and energy-consuming components). Application-specific models can be defined by subclassing classes in the simulation framework and customizing their behaviors. We also include in J-Sim a set of classes and mechanisms to realize network emulation. We demonstrate the use of the proposed WSN simulation framework by implementing several well-known localization, geographic routing, and directed diffusion protocols, and perform performance comparisons (in terms of the execution time incurred and memory used) in simulating WSN scenarios in J-Sim and ns-2. The simulation study indicates the WSN framework in J-Sim is much more scalable than ns-2 (especially in memory usage). We also demonstrate the use of the WSN framework in carrying out real-life full-fledged Future Combat System (FCS) simulation and emulation
301 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...INTRODUCTION Recent technological advances have led to the improvement of sensor frameworks which open new vistas for some potential applications like rural control, catastrophe help, biomedical and so on [1], [2]....
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TL;DR: This paper model the CTC problem as a maximum cover tree (MCT) problem, determines an upper bound on the network lifetime for the MCT problem and develops a (1+w)H(M circ) approximation algorithm to solve it, which shows that the lifetime obtained is close to the upper bound.
Abstract: In this paper, we consider the connected target coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity among all the active sensors and the sink. We model the CTC problem as a maximum cover tree (MCT) problem and prove that the MCT problem is NP-Complete. We determine an upper bound on the network lifetime for the MCT problem and then develop a (1+w)H(M circ) approximation algorithm to solve it, where w is an arbitrarily small number, H(M circ)=1 lesilesM circ(1/i) and M circ is the maximum number of targets in the sensing area of any sensor. As the protocol cost of the approximation algorithm may be high in practice, we develop a faster heuristic algorithm based on the approximation algorithm called Communication Weighted Greedy Cover (CWGC) algorithm and present a distributed implementation of the heuristic algorithm. We study the performance of the approximation algorithm and CWGC algorithm by comparing them with the lifetime upper bound and other basic algorithms that consider the coverage and connectivity problems independently. Simulation results show that the approximation algorithm and CWGC algorithm perform much better than others in terms of the network lifetime and the performance improvement can be up to 45% than the best-known basic algorithm. The lifetime obtained by our algorithms is close to the upper bound. Compared with the approximation algorithm, the CWGC algorithm can achieve a similar performance in terms of the network lifetime with a lower protocol cost.
213 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...The other, called target coverage [5] aims to ensure specific targets within the surveillance region are covered....
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