Directional-Sensor Network Deployment Planning for Mobile-Target Search

Wasim, Shiraz; Kashino, Zendai; Nejat, Goldie; Benhabib, B.

doi:10.3390/robotics9040082

Cited by 3 publications

(1 citation statement)

References 79 publications

(97 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm is based on connected coverage methodology. In [25], authors have suggested a method for coverage of a number of discrete points through sectorial sensing. They have formulated maximum coverage with a minimum sensors problem (MCMS), and they solved MCMS applying exact integer linear programming.…”

Section: Related Workmentioning

confidence: 99%

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Kumar

AlShboul

et al. 2021

Sensors

View full text Add to dashboard Cite

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network.

show abstract

Section: Related Workmentioning

confidence: 99%

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Kumar

AlShboul

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

Distributed Markov Chain Redesign for Multiagent Decision-Making Problems

Oliva

Setola

Gasparri

2023

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team

Nejat

Benhabib

2022

Robotics

View full text Add to dashboard Cite

Mobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable of switching between aerial and terrestrial modes of locomotion for wilderness search and rescue (WiSAR) scenarios. It presents a novel search planning method that coordinates the members of the robotic team to maximize the probability of locating a mobile target in the wilderness, potentially, last seen on an a priori known trail. It is assumed that the search area expands over time and, thus, an exhaustive search is not feasible. Earlier research on search planning methods for heterogeneous though unimodal search teams have exploited synergies between robots with different locomotive abilities through coordination and/or cooperation. Work on multimodal robots, on the other hand, has primarily focused on their mechanical design and low-level control. In contrast, our recent work, presented herein, has two major components: (i) target-motion prediction in the presence of a priori known trails in the wilderness, and (ii) probability-guided multimodal robot search-trajectory generation. For the former sub-problem, the novelty of our work lies in the formulation and use of 3D probability curves to capture target distributions under the influence of a priori known walking/hiking trails. For the latter, the novelty lies in the use of a tree structure to represent the decisions involved in multimodal probability-curve-guided search planning, which enables trajectory generation and mode selection to be optimized simultaneously, for example, via a Monte Carlo tree search technique. Extensive simulations, some of which are included herein, have shown that multimodal robotic search teams, coordinated via the trajectory planning method proposed in this paper, clearly outperform their unimodal counterparts in terms of search success rates.

show abstract

Directional-Sensor Network Deployment Planning for Mobile-Target Search

Cited by 3 publications

References 79 publications

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Distributed Markov Chain Redesign for Multiagent Decision-Making Problems

Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team

Contact Info

Product

Resources

About