A collaborative scheme of multiple ground and aerial robots applying a heterogeneous coverage control approach is presented. It aims to provide a density map of a contaminated area from hazardous material. Compared to...
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A collaborative scheme of multiple ground and aerial robots applying a heterogeneous coverage control approach is presented. It aims to provide a density map of a contaminated area from hazardous material. Compared to a homogeneous scheme, heterogeneity enhances the coverage level by minimizing error and variance due to the estimation process. In this scheme, a weighting formulation based on the different characteristics of ground and aerial robots is formalized. The contaminated area is partitioned unequally according to the number of deployed robots corresponding to the robot's weight and type. It shows better estimation values of the estimated density distribution map than the homogeneous scheme. The operation time needed to provide an estimation map of density distribution over the region is also faster than the homogeneous scheme.
We address "heterogeneous coverage" in visual sensor networks where coverage requirements of some randomly deployed targets vary from target to target. The main objective is to maximize the coverage of all t...
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We address "heterogeneous coverage" in visual sensor networks where coverage requirements of some randomly deployed targets vary from target to target. The main objective is to maximize the coverage of all the targets to achieve their respective coverage requirement by activating minimal sensors. The problem can be viewed as an interesting variation of the classical Max-Min problem (i.e., Maximum coverage with Minimum Sensors (MCMS)). Therefore, we study the existing Integer Linear Programming (ILP) formulation for single and k-coverage MCMS problem in the state-of-the-art and modify them to solve the heterogeneous coverage problem. We also propose a novel Integer Quadratic Programming (IQP) formulation that minimizes the Euclidean distance between the achieved and the required coverage vectors. Both ILP and IQP give exact solution when the problem is solvable but as they are non-scalable due to their computational complexity, we devise a Sensor Oriented Greedy Algorithm (SOGA) that approximates the formulations. For under-provisioned networks where there exist insufficient number of sensors to meet the coverage requirements, we propose prioritized IQP and reduced-variance IQP formulations to capture prioritized and group wise balanced coverage respectively. Moreover, we develop greedy heuristics to tackle under provisioned networks. Extensive evaluations based on simulation illustrate the efficiency and efficacy of the proposed formulations and heuristics under various network settings. Additionally, we compare our methodologies and algorithm with two state-of-the-art algorithms available for target coverage and show that our methodologies and algorithm substantially outperform both the algorithms.
Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage perfo...
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Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and (MOPSO)-O-2) in terms of solution quality and computational robustness.
Multimedia sensor networks (MSNs), which allow capturing acoustic and visual information, provide an unprecedented opportunity for variety of applications. This paper investigates the heterogeneous coverage problem in...
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ISBN:
(纸本)9781612842332
Multimedia sensor networks (MSNs), which allow capturing acoustic and visual information, provide an unprecedented opportunity for variety of applications. This paper investigates the heterogeneous coverage problem in MSNs, i.e., how many multimedia sensors should be deployed to guarantee that each point in the monitored region is covered by multiple types of sensors. It is different from the coverage problem in conventional homogeneous sensor networks, mainly because that it is based on a heterogeneous sensing model, which is a hybrid of the omni-sensing model and the directional-sensing model. We propose a mathematical model to describe the relationships among the number of multimedia sensors, the sensing parameters of multimedia sensors and the heterogeneous coverage rate. Our simulation results show that our proposed model and the theoretical analysis is effective.
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