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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TL;DR: The combination of the solutions to TCOV and NCON offers a promising solution to the original MSD problem that balances the load of different sensors and prolongs the network lifetime consequently.
Abstract: Coverage of interest points and network connectivity are two main challenging and practically important issues of Wireless Sensor Networks (WSNs). Although many studies have exploited the mobility of sensors to improve the quality of coverage andconnectivity, little attention has been paid to the minimization of sensors’ movement, which often consumes the majority of the limited energy of sensors and thus shortens the network lifetime significantly. To fill in this gap, this paper addresses the challenges of the Mobile Sensor Deployment (MSD) problem and investigates how to deploy mobile sensors with minimum movement to form a WSN that provides both target coverage and network connectivity. To this end, the MSD problem is decomposed into two sub-problems: the Target COVerage (TCOV) problem and the Network CONnectivity (NCON) problem. We then solve TCOV and NCON one by one and combine their solutions to address the MSD problem. The NP-hardness of TCOV is proved. For a special case of TCOV where targets disperse from each other farther than double of the coverage radius, an exact algorithm based on the Hungarian method is proposed to find the optimal solution. For general cases of TCOV, two heuristic algorithms, i.e., the Basic algorithm based on clique partition and the TV-Greedy algorithm based on Voronoi partition of the deployment region, are proposed to reduce the total movement distance ofsensors. For NCON, an efficient solution based on the Steiner minimum tree with constrained edge length is proposed. Thecombination of the solutions to TCOV and NCON, as demonstrated by extensive simulation experiments, offers a promising solutionto the original MSD problem that balances the load of different sensors and prolongs the network lifetime consequently.
167 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...As pointed out in [13], the coverage problem is reduced to the Linear Sum Assignment Problem [14] (LSAP), if the distance between every pair of targets is greater than 2rs....
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...The works in [13] has also assumed the disk model....
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...In [13], this work is extended to ensure connectivity....
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TL;DR: A sensor movement scheduling algorithm is developed that achieves near-optimal system detection performance under a given detection delay bound and is validated by extensive simulations using the real data traces collected by 23 sensor nodes.
Abstract: Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality-of-Service requirements including high detection probability, low false alarm rate, and bounded detection delay. Although a dense all-static network may initially meet these Quality-of-Service requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher Signal-to-Noise Ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance under a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes.
92 citations
"Coverage of Targets in Mobile Senso..." refers methods in this paper
...The existing sensor relocation algorithms for target coverage and connectivity are based on the assumption of the free mobility model [16]....
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TL;DR: A polynomial-time distributed algorithm for maximizing the lifetime of the network is designed and it is proved that its lifetime is at most a factor O(logn * lognB) lower than the maximum possible lifetime.
Abstract: In wireless sensor networks (WSNs), a large number of sensors perform distributed sensing of a target field. A sensor cover is a subset of the set of all sensors that covers the target field. The lifetime of the network is the time from the point the network starts operation until the set of all sensors with nonzero remaining energy does not constitute a sensor cover any more. An important goal in sensor networks is to design a schedule--that is, a sequence of sensor covers to activate in every time slot--so as to maximize the lifetime of the network. In this paper, we design a polynomial-time distributed algorithm for maximizing the lifetime of the network and prove that its lifetime is at most a factor O(log n * log nB) lower than the maximum possible lifetime, where n is the number of sensors and B is an upper bound on the initial energy of each sensor. Our algorithm does not require knowledge of the locations of nodes or directional information, which is difficult to obtain in sensor networks. Each sensor only needs to know the distances between adjacent nodes in its transmission range and their sensing radii. In every slot, the algorithm first assigns a weight to each node that is exponential in the fraction of its initial energy that has been used up so far. Then, in a distributed manner, it finds an O(log n) approximate minimum weight sensor cover, which it activates in the slot.
88 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...This is termed as area coverage [3], [4]....
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10 Apr 2016
TL;DR: This paper study the target coverage problem in mobile sensor networks, and proposes a PTAS, named Energy Effective Movement Algorithm (EEMA), which proves that the approximation ratio of EEMA is 1 + ε and the time complexity is ηO(1/ε2).
Abstract: Energy consumption is a fundamental and critical issue in wireless sensor networks. Mobile sensors consume much more energy during the movement than that during the communication or sensing process. Thus how to schedule mobile sensors and minimize their moving distance has great significance to researchers. In this paper, we study the target coverage problem in mobile sensor networks. Our goal is to minimize the moving distance of sensors to cover all targets in the surveillance region. Here initially all the sensors are located at k base stations. Thus we define this problem as k-Sink Minimum Movement Target Coverage. To solve this problem, we propose a PTAS, named Energy Effective Movement Algorithm (EEMA). We can divide EEMA into two phases. In the first phase, we partition the surveillance region into some subareas. In the second phase, we select subareas and schedule sensors to the selected subareas. We also prove that the approximation ratio of EEMA is 1 + e and the time complexity is ηO(1/e2 Finally, we conduct experiments to validate the efficiency and effectiveness of EEMA.
25 citations
"Coverage of Targets in Mobile Senso..." refers background in this paper
...[15] have considered k sink Minimum Movement Target Coverage Problem (k-MMTC), where all the sensors are located in k sinks and each sink can supply the unlimited number of sensors....
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18 Jun 2012
TL;DR: This paper forms the problem of minimizing sensor's movement to achieve target coverage (MMTC), and proves it is at least NP-complete, and proposes a target based Voronoi greedy algorithm (TV-Greedy) to find approximate optimal solutions.
Abstract: Target coverage is an important problem in wireless sensor networks (WSNs), whose goal is to cover points of interest to collect data for further processing. With the emergence of mobile sensors, many researchers have exploited the mobility of sensors to improve coverage quality. However, little attention has been paid to minimize the sensor's movement, which consumes the majority of a sensor's limited energy. Since sensors have limited energy supply and their operation continues until their energy drains, movement of mobile sensors should be minimized to extend the lifetime of sensors. In this paper, we first formulate the problem of minimizing sensor's movement to achieve target coverage (MMTC), and prove it is at least NP-complete. Then for a special case of MMTC when targets are spacing greater than twice of the coverage radius, we transform MMTC into a typical assignment problem, and get the optimal solution by an extended Hungarian method. For general cases of MMTC, we further propose a target based Voronoi greedy algorithm (TV-Greedy) to find approximate optimal solutions. Analysis results show that TV-Greedy has a low complexity, can enhance the robustness of WSNs to sensor failure. Extensive simulations are conducted to evaluate our solutions, which exhibit good performance in large scale WSNs.
25 citations
"Coverage of Targets in Mobile Senso..." refers background or methods in this paper
...[14] H. W. Kuhn, ‘‘The Hungarian method for the assignment problem,’’ Naval Res....
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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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...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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...The complexity of extended Hungarian [12] method to solve the assignment problem is O(|Sf |3) where |Sf | is definitely less than n....
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...Algorithm 4 Algorithm MinCon Require: 1: set S = {s1, s2, . . . , sn,BS}, with the revised position of sensors after algorithm 2; 2: Sa : set of assigned sensors and BS; 3: E : set of edges between nodes of S based on rc; 4: Graph G(S,E) Ensure: Set of sensors and their movements needed to ensure connectivity 5: K ← DFS(G) 6: #K is the set of components returned by DFS 7: if Sa belongs to a single component then 8: no treatment required 9: else 10: K1← {κ|κ ∈ K , (κ ∩ Sa) 6= φ} 11: K2 = K − K1 12: Sf ← ∪κ∈K2 (s : s ∈ κ); 13: # Sf :the set of sensors free to move 14: for each pair κi, κj ∈ K1 do 15: dist(κi, κj)← minni∈κi,nj∈κj d(ni, nj) 16: Pa(κi, κj) ← {ni, nj} where ni ∈ κi, nj ∈ κj and d(ni, nj) = dist(κi, κj) 17: end for 18: Construct a graph G1(V1,E1) where node set V1 = {κ|κ ∈ K1} and edge set E1 = {(u, v)|u, v ∈ V1} with weights w(u, v) according to Definition 8 19: T ← MST (G1); 20: # T is the minimum spanning tree of G1 with BS as root 21: Divide each edge e ∈ T into w(e) equal parts 22: Put separating points into P 23: for each edge e(κi, κj) ∈ T do 24: Sa← Sa ∪ Pa(κi, κj) 25: # End points of the edges of MST cannot be moved 26: end for 27: for each κ ∈ K1 do 28: Sf ← Sf ∪ ExtractFree(κ, Sa) 29: end for 30: H ← Extended Hungarian (P, Sf ) 31: # assign free nodes to separating points 32: for each x ∈ P do 33: moveH (x) = dM (x,H (x)) 34: end for 35: end if For breaking into segments we find amiddle pont pj andmove sensorH (pj) to pj and successively break segments p0, pj and pj, pl+1, if required....
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