Coverage Problem of sensor network in Continuous Region
TL;DR: A new strategy for deployment of extra sensors is developed and the uncovered areas are compared for these two strategies and for different number ofextra sensors, using simulation.
Abstract: In this paper, we consider well known ‘coverage problem’ in Wireless Sensor Networks (WSNs) in continuous domain. Here, we discuss optimal placement of sensors and coverage criteria in Rn with special emphasis on R2. Coverage is essential in WSNs, which are two- or three-dimensional systems. When sensors are deployed from air on some previously fixed points (vertices) in the Region of Interest (ROI), they may not fall on the target vertices. So, some part of the ROI may be uncovered by the sensors. In this paper, we consider the problem, how one reduced the uncovered area? To reduce the uncovered area, extra sensors are usually deployed on some randomly chosen vertices. We develop a new strategy for deployment of extra sensors and compare the uncovered areas for these two strategies and for different number of extra sensors, using simulation.
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TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.
Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.
2,198 citations
1,584 citations
TL;DR: This study concludes by proposing the use of STEH, which is an onsite green energy harvesting scheme that is self-sustaining; ubiquitous; and long lasting; as preferable source to other categories of turbine grid system.
Abstract: In the digital transition era of scaling down from macro through micro turbine; to the setting up of Smart Turbine Energy Harvesters (STEH), this paper presents Project Management (PM) principles applicable and best practices to meet the increasing energy demand of digitised technology. The massive deployment of autonomous devices such as those for Internet of Things (IoT), for the support of smart cities, has necessitated more research about their energy demands. With the use of ‘waterfall’ Project Management Methodology (PMM), turbine grid-connected energy are classified into different categories, and comparative study is made between scaling down of turbine grid from macro to micro, to the economic impact of setting up of STEH. This study concludes by proposing the use of STEH, which is an onsite green energy harvesting scheme that is self-sustaining; ubiquitous; and long lasting; as preferable source to other categories of turbine grid system. Additionally, it is an improvement on energy harvesting (EH) mechanisms using battery; whose replacement and disposal are not economical. STEH is considered economical and time saving with little or no physical and investment risk attached, the Return on Investment (RI) is considered favourable. Also, the energy delivery is sufficient for the requirement of IoT and Wireless Sensor Networks (WSN).
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TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
17,936 citations
"Coverage Problem of sensor network ..." refers background in this paper
...Sensor networks aim at monitoring their surroundings for event detection and tracking an object [2, 25]....
[...]
Book•
01 Dec 1987
TL;DR: The second edition of this book continues to pursue the question: what is the most efficient way to pack a large number of equal spheres in n-dimensional Euclidean space?
Abstract: The second edition of this book continues to pursue the question: what is the most efficient way to pack a large number of equal spheres in n-dimensional Euclidean space? The authors also continue to examine related problems such as the kissing number problem, the covering problem, the quantizing problem, and the classification of lattices and quadratic forms. Like the first edition, the second edition describes the applications of these questions to other areas of mathematics and science such as number theory, coding theory, group theory, analog-to-digital conversion and data compression, n-dimensional crystallography, and dual theory and superstring theory in physics.
4,564 citations
07 Aug 2002
TL;DR: In this paper, the authors describe decentralized control laws for the coordination of multiple vehicles performing spatially distributed tasks, which are based on a gradient descent scheme applied to a class of decentralized utility functions that encode optimal coverage and sensing policies.
Abstract: This paper describes decentralized control laws for the coordination of multiple vehicles performing spatially distributed tasks. The control laws are based on a gradient descent scheme applied to a class of decentralized utility functions that encode optimal coverage and sensing policies. These utility functions are studied in geographical optimization problems and they arise naturally in vector quantization and in sensor allocation tasks. The approach exploits the computational geometry of spatial structures such as Voronoi diagrams.
2,445 citations
Posted Content•
TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.
Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.
2,198 citations
"Coverage Problem of sensor network ..." refers background in this paper
...In order to fulfil its designated tasks, a sensor network must cover the region where the event may occur, without leaving any internal sensing hole [3, 5, 8, 13]....
[...]
1,584 citations