Coverage improvement using Voronoi diagrams in directional sensor networks

Zarei, Zahra; Bag-Mohammadi, Mozafar

doi:10.1049/wss2.12015

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…( Chawla and Duhan, 2018 ; Sutantyo et al, 2011 ). There are other forms of hybrid methods that use geometric processes, e.g., Voronoi tessellation in pure form or augmented with order processes, e.g., buffering ( Arul and Manocha, 2021 ), k-means algorithm ( Chowdhury and De, 2021 ), gradient descent algorithm ( Inoue et al, 2021 ), area prioritization ( Zarei and Mozafar, 2021 ), and particle swarm optimization ( Zaimen et al, 2021 ). In these methods, the plan generation is controlled by theorems, propositions, lemmas, and protocols of the geometric process.…”

Section: Related Workmentioning

confidence: 99%

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Yusuf

Baber²

2022

Front. Robot. AI

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This article describes an approach for multiagent search planning for a team of agents. A team of UAVs tasked to conduct a forest fire search was selected as the use case, although solutions are applicable to other domains. Fixed-path (e.g., parallel track) methods for multiagent search can produce predictable and structured paths, with the main limitation being poor management of agents’ resources and limited adaptability (i.e., based on predefined geometric paths, e.g., parallel track, expanding square, etc.). On the other hand, pseudorandom methods allow agents to generate well-separated paths; but methods can be computationally expensive and can result in a lack of coordination of agents’ activities. We present a hybrid solution that exploits the complementary strengths of fixed-pattern and pseudorandom methods, i.e., an approach that is resource-efficient, predictable, adaptable, and scalable. Our approach evolved from the Delaunay triangulation of systematically selected waypoints to allocate agents to explore a specific region while optimizing a given set of mission constraints. We implement our approach in a simulation environment, comparing the performance of the proposed algorithm with fixed-path and pseudorandom baselines. Results proved agents’ resource utilization, predictability, scalability, and adaptability of the developed path. We also demonstrate the proposed algorithm’s application on real UAVs.

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Section: Related Workmentioning

confidence: 99%

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Yusuf

Baber²

2022

Front. Robot. AI

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“…The aim of the target coverage is to determine a subset of directional sensors to cover a set of targets [4,5]. The area coverage is related to the field coverage [6][7][8], where directional sensors are activated in such a way to ensure a full region coverage. In the barrier coverage, the main objective is to have the ability to detect intrusion crossing a barrier of directional sensors [9].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Barrier Coverage in Surveillance Applications Through Hybrid Directional Sensor Networks

Bendimerad,

Boumedjout,

Bot

2024

IJSSE

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Directional sensor networks (DSNs) have possibility to provide more precise surveillance than conventional omni-directional sensor networks since the captured data can be of a visual nature. Therefore, the coverage problem in DSNs is completely different from that of traditional scalar sensors. Barrier coverage is a specific type of coverage that is emerged in DSNs to ensure a high-level monitoring application. In this paper, we focus on solving the barrier coverage problem in hybrid DSNs using stationary and mobile directional sensors. Our aim is to ensure a high barrier coverage specially developed for surveillance applications by proposing two approaches. In the first one, we implement a distributed algorithm based on a geometric mathematical model to calculate the new orientation of directional sensors to maximize barrier coverage. The purpose of using a geometric mathematical model is to compute, after a random deployment, the most appropriate angle of rotation for each directional sensor so that the novel DSN configuration ensures a high level of intrusion detection across the barrier. In the second one, we enhance the first approach by extending its algorithm to further improve barrier coverage using mobile directional sensors deployed far from the barrier. This solution allows us to achieve a strong barrier coverage with a minimum number of active directional sensors. Extensive simulation experiments with different scenarios are conducted to show the effectiveness of the proposed algorithms in hybrid DSNs, such as those varying the number of stationary and mobile directional sensors with different angles of view.

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“…17,18 The partitions of a network field based on the position of sensors are devised in the Voronoi diagram. 19 If the source and destination points are placed on the edge of the Voronoi diagram, the methods based on the Voronoi diagram will not provide optimal solutions. Recently, researchers adopted Artificial intelligence (AI) methods in the routing protocols of WMSN.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, the Voronoi diagram‐based algorithm is utilized to find the routing path between the sensors in a specified region of interest 17,18 . The partitions of a network field based on the position of sensors are devised in the Voronoi diagram 19 . If the source and destination points are placed on the edge of the Voronoi diagram, the methods based on the Voronoi diagram will not provide optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Energy aware optimal routing model for wireless multimedia sensor networks using modified Voronoi assisted prioritized double deep Q‐learning

Suseela,

Krithiga,

Revathi

et al. 2023

Concurrency and Computation

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SummaryThe energy hole problem is critical in Wireless Multimedia Sensor Network (WMSN), wherein the nodes closer to the sink node will expire sooner than the outer sub‐regions. Because they transmit their packet and forward the outer sub‐regions packet to the sink. Hence, a hole arises near the sink node after a very short time. Thus, an energy‐aware optimal routing model is introduced in this research using Modified Voronoi‐assisted prioritized double deep Q‐learning (PDDQL). Initially, the sensor nodes are deployed using the Voronoi cell structure that splits the entire region of interest into different Voronoi polygons. However, the Voronoi diagram will not always predict an efficient route because of the nodes at a Voronoi edge or vertex outside the transmission range. To tackle this issue, a new Voronoi diagram is proposed that depends on the maximum horizontal transmission range to remove the portion of Voronoi edges uncovered by the sensor node. After the deployment, a PDDQL algorithm is used to select the Relay Nodes based on their remaining energy. The proposed PDDQL determines an alternate route to transmit the packet if an energy hole problem has occurred in WMSN. The analysis of a proposed routing protocol based on the assessment measures like packet delivery ratio, average residual energy, number of alive nodes, and average end‐to‐end delay accomplished the value of 198.33 (ms), 2.05 (J), 100, and 0.85, respectively.

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Coverage improvement using Voronoi diagrams in directional sensor networks

Cited by 9 publications

References 20 publications

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

Enhancing Barrier Coverage in Surveillance Applications Through Hybrid Directional Sensor Networks

Energy aware optimal routing model for wireless multimedia sensor networks using modified Voronoi assisted prioritized double deep Q‐learning

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