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.
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01 Dec 2018
TL;DR: It is shown that, those participants which selects themselves as active suffice to cover all the desired targets and the close proximity of DPSA compared to the optimal one providing the same coverage is revealed.
Abstract: Recent developments in the areas of smart phones and wearable devices have paved the way to many emerging applications in Mobile Crowd Sensing (MCS) domain. There has been a phenomenal increase in the number of people using these devices. In this work we consider an MCS application to cover a set of points of interest (targets) with the objective of selecting a minimum set of participants among the mobile devices. This problem, coined as Participant Selection Problem (PSP) to provide desired coverage can be solved optimally using an Integer Linear Programming (ILP). But for large problem size, solving the ILP becomes impractical from the point of execution time as well as the requirement of having the necessary information about all the participants in one place. In this paper, we have proposed a distributed participants selection algorithm (DPSA) to be run at each participants where, the participants after exchanging messages with its neighbors, decides to remain active or idle. We have shown that, those participants which selects themselves as active suffice to cover all the desired targets. We have run a series of experiments to measure the performance of the proposed algorithm where we measure along with number of participants selected also the total energy consumption. Simulation results reveals the close proximity of DPSA compared to the optimal one providing the same coverage.
2 citations
Cites methods from "Coverage of Targets in Mobile Senso..."
...To overcome the problems associated with static WSN the concept of mobile WSN (M-WSN) was introduced in [12], [13] where all or a subset of the deployed nodes have mobility....
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TL;DR: In this article , a new optimization problem named opportunistic sensing is introduced to find (1) optimal buses to place the sensors and (2) the optimal monitoring timing to maximize the number of monitored critical regions.
Abstract: Monitoring air quality plays a critical role in the sustainable development of developing regions where the air is severely polluted. Air quality monitoring systems based on static monitors often do not provide information about the area each monitor represents or represent only small areas. In addition, they have high deployment costs that reflect the efforts needed to ensure sufficient quality of measurements. Meanwhile, the mobile air quality monitoring system, such as the one in this work, shows the feasibility of solving those challenges. The system includes environmental sensors mounted on buses that move along their routes, broadening the monitoring areas. In such a system, we introduce a new optimization problem named opportunistic sensing that aims to find (1) optimal buses to place the sensors and (2) the optimal monitoring timing to maximize the number of monitored critical regions. We investigate the optimization problem in two scenarios: simplified and general bus routes. Initially, we mathematically formulate the targeted problem and prove its NP-hardness. Then, we propose a polynomial-time 1 2 -, e − 1 2 e − 1 -approximation algorithm for the problem with the simplified, general routes, respectively. To show the proposed algorithms’ effectiveness, we have evaluated it on the real data of real bus routes in Hanoi, Vietnam. The evaluation results show that the former algorithm guarantees an average performance ratio of 72.68%, while the latter algorithm achieves the ratio of 63.87%. Notably, when the sensors can be on (e.g., enough energy) during the whole route, the e − 1 2 e − 1 -approximation algorithm achieves the approximation ratio of ( 1 − 1 e ) . Such ratio, which is almost twice as e − 1 2 e − 1 , enlarges the average performance ratio to 78.42%.
1 citations
03 Jan 2021
TL;DR: This paper proposed a vertex coloring based optimal sensor placement to determine the minimal sensor requirement for an efficient network.
Abstract: Industrial Wireless Sensor Network (IWSN) is the recent emergence in wireless technologies that facilitate industrial applications. IWSN constructs a reliable and self-responding industrial system using interconnected intelligent sensors. These sensors continuously monitor and analyze the industrial process to evoke its best performance. Since the sensors are resource-constrained and communicate wirelessly, the excess sensor placement utilizes more energy and also affects the environment. Thus, sensors need to use efficiently to minimize their network traffic and energy utilization. In this paper, we proposed a vertex coloring based optimal sensor placement to determine the minimal sensor requirement for an efficient network.
Cites background from "Coverage of Targets in Mobile Senso..."
...Authors in [2] proposed a heuristic algorithm with two phases; the first phase ensures target coverage, and the second phase provides sensor connectivity with mobility....
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01 Dec 2022
TL;DR: In this paper , the authors consider two related problems with a limited mobility model where no sensor can move beyond a certain distance and solve the first problem by relaxing the equivalent Integer Linear Program (ILP) where the maximum allowable distance is a parameter.
Abstract: In Mobile Sensor Networks (MSN), covering targets with minimum movement is an important issue. We consider two related problems but with a limited mobility model where no sensor can move beyond a certain distance. In the first problem, we minimize the sum of the movements of all sensors. And in the other, we minimize their maximum. We solve the first problem by relaxing the equivalent Integer Linear Program (ILP) where the maximum allowable distance is a parameter. Experimental results show that our algorithm gives the solution very close to the optimal. For the second problem, we apply binary search and repeatedly execute the relaxed LP until we find the smallest value of the maximum distance that gives a feasible solution. We could find movements of sensors that satisfy the above limit in all our experiments with different random placements of sensors and targets.
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TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
Abstract: This paper has always been one of my favorite “children,” combining as it does elements of the duality of linear programming and combinatorial tools from graph theory. It may be of some interest to tell the story of its origin.
11,096 citations
"Coverage of Targets in Mobile Senso..." refers background or methods in this paper
...Here, we see that LSAP require total movement of 450m approximately and the corresponding value for Min_cov is 350m (approx)....
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...We use LSAP to find the solution to coverage problem and compare it with that obtained by the algorithm Min_cov....
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...Here the cost matrix of dimension |Sf |×|P| has the (i, j)th entry as the Manhattan distance between the ith element of Sf and jth element of P. LSAP can easily be solved by a polynomial time algorithm employing Extended Hungarian method [14]....
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...We observe that the lines corresponding to LSAP and Min_cov are quite close....
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...However, if a restriction is imposed on the placement of targets such that distance between any two of them is greater than the sensing radius, MMTC is reduced to the Linear Sum Assignment Problem (LSAP) and can be solved in polynomial time by Extended Hungarian Method [12]....
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01 May 2005
TL;DR: The three main categories explored in this paper are data-centric, hierarchical and location-based; each routing protocol is described and discussed under the appropriate category.
Abstract: Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. This paper surveys recent routing protocols for sensor networks and presents a classification for the various approaches pursued. The three main categories explored in this paper are data-centric, hierarchical and location-based. Each routing protocol is described and discussed under the appropriate category. Moreover, protocols using contemporary methodologies such as network flow and quality of service modeling are also discussed. The paper concludes with open research issues. � 2003 Elsevier B.V. All rights reserved.
3,573 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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22 Apr 2001
TL;DR: This work establishes the main highlight of the paper-optimal polynomial time worst and average case algorithm for coverage calculation, which answers the questions about quality of service (surveillance) that can be provided by a particular sensor network.
Abstract: Wireless ad-hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential, ability to transform our lives, and pose many new system-building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. We address one of the fundamental problems, namely coverage. Coverage in general, answers the questions about quality of service (surveillance) that can be provided by a particular sensor network. We first define the coverage problem from several points of view including deterministic, statistical, worst and best case, and present examples in each domain. By combining the computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, we establish the main highlight of the paper-optimal polynomial time worst and average case algorithm for coverage calculation. We also present comprehensive experimental results and discuss future research directions related to coverage in sensor networks.
1,837 citations
07 Mar 2004
TL;DR: This paper designs two sets of distributed protocols for controlling the movement of sensors, one favoring communication and one favoring movement, and uses Voronoi diagrams to detect coverage holes and use one of three algorithms to calculate the target locations of sensors it holes exist.
Abstract: Sensor deployment is an important issue in designing sensor networks. We design and evaluate distributed self-deployment protocols for mobile sensors. After discovering a coverage hole, the proposed protocols calculate the target positions of the sensors where they should move. We use Voronoi diagrams to discover the coverage holes and design three movement-assisted sensor deployment protocols, VEC (vector-based), VOR (Voronoi-based), and minimax based on the principle of moving sensors from densely deployed areas to sparsely deployed areas. Simulation results show that our protocols can provide high coverage within a short deploying time and limited movement.
946 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...In [11], a set of algorithms are proposed which are intended to ensure complete area coverage with minimum delay and movement....
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16 Jul 2001
TL;DR: This work has developed an efficient and effective algorithm for exposure calculation in sensor networks, specifically for finding minimal exposure paths and provides an unbounded level of accuracy as a function of run time and storage.
Abstract: Wireless ad-hoc sensor networks will provide one of the missing connections between the Internet and the physical world One of the fundamental problems in sensor networks is the calculation of coverage Exposure is directly related to coverage in that it is a measure of how well an object, moving on an arbitrary path, can be observed by the sensor network over a period of timeIn addition to the informal definition, we formally define exposure and study its properties We have developed an efficient and effective algorithm for exposure calculation in sensor networks, specifically for finding minimal exposure paths The minimal exposure path provides valuable information about the worst case exposure-based coverage in sensor networks The algorithm works for any given distribution of sensors, sensor and intensity models, and characteristics of the network It provides an unbounded level of accuracy as a function of run time and storage We provide an extensive collection of experimental results and study the scaling behavior of exposure and the proposed algorithm for its calculation
718 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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