Coverage area maximization by heterogeneous sensor nodes with minimum displacement in mobile networks
01 Dec 2015-pp 1-6
TL;DR: In this article, the authors propose an energy-efficient and light-weight self-organized distributed greedy heuristic to maximize area coverage such that the amount of computation, rounds of communication, and the distance traversed by a node, can be reduced utilizing minimal number of nodes.
Abstract: Given a random deployment of heterogeneous mobile nodes having different sensing ranges, this paper addresses the problem of covering a region using minimum number of nodes with minimum displacement. We propose an energy-efficient and light-weight self-organized distributed greedy heuristic to maximize area coverage such that the amount of computation, rounds of communication, and the distance traversed by a node, can be reduced utilizing minimal number of nodes. Extensive simulation studies on random deployment of nodes with sufficient node density over a 2-D area, show that our proposed technique results hole free area coverage with small number of nodes with minimum possible displacement, both in turn help to prolong the network lifetime.
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01 Aug 2018
TL;DR: A self-organized reciprocal control method is proposed, which is directly optimized in velocity space, different from the traditional method modeled in configuration space, which considers the reciprocal of neighboring agents that is ignored by most other methods.
Abstract: Multi-agent coverage problem is an active issue of how to cover an accessible region of interest by agents. In this paper, a self-organized reciprocal control method is proposed, which is directly optimized in velocity space, different from the traditional method modeled in configuration space. The method considers the reciprocal of neighboring agents that is ignored by most other methods. And the motion of each agent is collision-free motivated by this method. The simulation results corroborate that the proposed method has higher coverage rate, faster convergence rate and less deadweight loss than other traditional methods.
1 citations
TL;DR: A flow coverage scheme is proposed to cover a specific street based on using a modified localisation method that uses a minimal of GPS sensors and utilises the Zigbee technology to communicate and estimate the distance between nodes.
Abstract: With the rapid growth of sensor technology, smartphone sensing has become an effective approach to improve the quality of applications in smartphones. Mobile crowd sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data in the urban streets. To achieve a good service quality for a MCS application, coverage mechanisms are necessary to achieve the sensing task requirements. The main problem is how to cover all segments in the street sides and select a minimal number of participants in each street segment. To solve this problem, a flow coverage scheme is proposed to cover a specific street. The proposed scheme is based on using a modified localisation method that uses a minimal of GPS sensors and utilises the Zigbee technology to communicate and estimate the distance between nodes. Extensive simulation results well justify the effectiveness and robustness of the proposed scheme.
22 Sep 2022
TL;DR: A fast and light-weight algorithm has been proposed in this paper to produce a random connected graph for a real-time multi-hop wireless sensor networks (WSNs), which has better performance than other existing methods.
Abstract: For analysing networks like social media networks, wireless sensor networks, etc. in many applications, generating random connected graph is very important. As it is time consuming to generate the random connected graph consisting of large nodes it is necessary to generate it in minimum time. Characteristics like dependent edges and non-binomial degree distribution that are absent in many classical random graph models such as the Erdos-Renyi graph model can be captured by random graphs with a given degree range. The problem of random connected graph generation having a prescribed degree range has been addressed here. Random graphs are used to model wireless sensor networks (WSNs) or IoT comprising of sensor nodes with limited power resources. A fast and light-weight algorithm has been proposed in this paper to produce a random connected graph for a real-time multi-hop wireless sensor networks (WSNs). Results show that our method has better performance than other existing methods.
04 Jan 2020
TL;DR: This paper focuses on developing distributed algorithms for the reorganization of drones from the initial deployment to maximize the covered area without holes and proposes two algorithms based on Local Voronoi and Virtual Force that result in hole-free maximal compact coverage with limited displacement.
Abstract: Unmanned Aerial Vehicles (UAVs) or Drones are of massive interest in monitoring large areas with uneven surfaces or managing large events such as protests while ensuring civil security and public safety. A common requirement for this needs the UAVs to cover each point within the area of interest. In general, the region of interest is modeled as a surface, and thus we focus on surveillance of a surface with a random connected distribution of drones. This paper focuses on developing distributed algorithms for the reorganization of drones from the initial deployment to maximize the covered area without holes. In addition, the target is to provide a compact coverage to minimize the diameter of the network formed by the drones, which helps in faster communications. We propose two algorithms based on Local Voronoi and Virtual Force. Extensive simulation studies show that our proposed techniques result in hole-free maximal compact coverage with limited displacement.
References
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TL;DR: An overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements are provided and a detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented.
1,870 citations
26 Sep 2004
TL;DR: This paper introduces the sequential Monte Carlo Localization method and argues that it can exploit mobility to improve the accuracy and precision of localization.
Abstract: Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
1,114 citations
19 May 2003
TL;DR: A robust energy-conserving protocol that can build long-lived, resilient sensor networks using a very large number of small sensors with short battery lifetime, PEAS can extend a sensor network's functioning time in linear proportion to the deployed sensor population.
Abstract: In this paper we present PEAS, a robust energy-conserving protocol that can build long-lived, resilient sensor networks using a very large number of small sensors with short battery lifetime. PEAS extends the network lifetime by maintaining a necessary set of working nodes and turning off redundant ones. PEAS operations are based on individual node's observation of the local environment and do not require any node to maintain per neighbor node state. PEAS performance possesses a high degree of robustness in the presence of both node power depletions and unexpected failures. Our simulations and analysis show that PEAS can maintain an adequate working node density in the face of up to 38% node failures, and it can maintain roughly a constant overhead level under various deployment conditions ranging from sparse to very dense node deployment by using less than 1% of total energy consumption. As a result, PEAS can extend a sensor network's functioning time in linear proportion to the deployed sensor population.
956 citations
01 Jun 2008
TL;DR: This paper reports on the current state of the research on optimized node placement in WSNs, and categorizes the placement strategies into static and dynamic depending on whether the optimization is performed at the time of deployment or while the network is operational, respectively.
Abstract: The major challenge in designing wireless sensor networks (WSNs) is the support of the functional, such as data latency, and the non-functional, such as data integrity, requirements while coping with the computation, energy and communication constraints. Careful node placement can be a very effective optimization means for achieving the desired design goals. In this paper, we report on the current state of the research on optimized node placement in WSNs. We highlight the issues, identify the various objectives and enumerate the different models and formulations. We categorize the placement strategies into static and dynamic depending on whether the optimization is performed at the time of deployment or while the network is operational, respectively. We further classify the published techniques based on the role that the node plays in the network and the primary performance objective considered. The paper also highlights open problems in this area of research.
924 citations
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: -Adequate coverage is very important for sensor networks to fulfill the issued sensing tasks. In many working environments, it is necessary to make use of mobile sensors, which can move to the correct places to provide the required coverage. In this paper, we study the problem of placing mobile sensors to get high coverage. Based on Voronoi diagrams, we design two sets of distributed protocols for controlling the movement of sensors, one favoring communication and one favoring movement. In each set of protocols, we use Voronoi diagrams to detect coverage holes and use one of three algorithms to calculate the target locations of sensors it holes exist. Simulation results show the effectiveness of our protocols and give insight on choosing protocols and calculation algorithms under different application requirements and working conditions.
817 citations