Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. This trend motivated several research efforts to employ drones as standalone systems or to assist legacy deployments. However, several fundamental challenges remain unsolved including: 1) Adequate coverage of sizable targets; 2) Target orientation that render coverage effective only from certain view points; 3) Occlusion by elements in the environment, including other targets. In this paper, we present Argus, a system that provides coverage of wide and oriented targets, using camera-mounted drones, taking into account the challenges stated above. Argus relies on a geometric model that captures both target shapes and coverage constraints. With drones being the scarcest resource in Argus, we study the problem of minimizing the number of drones required to cover a set of such targets and derive a best-possible approximation algorithm. Furthermore, we present a sampling heuristic that yields a comparable performance, yet is up to 100x faster than the approximation algorithm. We build a prototype of Argus to demonstrate and evaluate the proposed coverage algorithms as part of a complete autonomous surveillance system. We evaluate the proposed algorithms using simulations to compare their performance at scale under various conditions. Finally, we present extensions and discuss open problems related to the studied problem.
The goal of area coverage algorithms is to detect any activity of interest within a certain area in a sensor network deployment, or to ensure all clients can communicate within an area for wireless communication. Several approaches to area coverage have been studied including static randomly deployed sensors @cite_32 and strategically placed mobile sensors @cite_62 using either isotropic @cite_60 or anisotropic sensors @cite_40 . Full-view coverage algorithms are another example of area coverage algorithms. Target coverage algorithms are used to cover a number of targets within an area, and were studied in randomly deployed sensors @cite_51 @cite_29 @cite_57 , or strategically placed directional sensors or antennas @cite_4 @cite_1 @cite_38 . Target coverage under quality constraints using randomly placed Pan-Tilt-Zoom (PTZ) cameras was shown to be NP-hard and a 2-approximation algorithm was presented @cite_29 . A 3-approximation algorithm was developed for antenna placement to cover targets such that the bandwidth demand on each antenna stays within a certain limit @cite_4 .
@cite_38 @cite_62 @cite_4 @cite_60 @cite_29 @cite_1 @cite_32 @cite_57 @cite_40 @cite_51
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One of the most fundamental tasks of wireless sensor networks is to provide coverage of the deployment region. In this paper, we study the coverage of a line segment with a set of wireless sensors with adjustable coverage ranges. Each coverage range of a sensor is an interval centered at that sensor whose length is decided by the power the sensor chooses. The objective is to find a range assignment with the minimum cost. There are two variants of the optimization problem. In the discrete variant, each sensor can only choose from a finite set of powers while in the continuous variant, each sensor can choose power from a given interval. For the discrete variant of the problem, we present a polynomial-time exact algorithm. For the continuous variant of the problem, we develop constant-approximation algorithms when the cost for all sensors is proportional to rk for some constant k ≥ 1, where r is the covering radius corresponding to the chosen power. Specifically, if k = 1, we give a simple 1.25-approximation algorithm and a fully polynomial-time approximation scheme (FPTAS); if k > 1, we give a simple 2-approximation algorithm. One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness. Sensor nodes may be equipped with a "directional" sensing device (such as a camera) which senses a physical phenomenon in a certain direction depending on the chosen orientation. In this article, we address the problem of selection and orientation of such directional sensors with the objective of maximizing coverage area. Prior works on sensor coverage have largely focused on coverage with sensors that are associated with a unique sensing region. In contrast, directional sensors have multiple sensing regions associated with them, and the orientation of the sensor determines the actual sensing region. Thus, the coverage problems in the context of directional sensors entails selection as well as orientation of sensors needed to activate in order to maximize ensure coverage. In this article, we address the problem of selecting a minimum number of sensors and assigning orientations such that the given area (or set of target points) is k-covered (i.e., each point is covered k times). The above problem is NP-complete, and even NP-hard to approximate. Thus, we design a simple greedy algorithm that delivers a solution that k-covers at least half of the target points using at most M log(k|C|) sensors, where |C| is the maximum number of target points covered by a sensor and M is the minimum number of sensor required to k-cover all the given points. The above result holds for almost arbitrary sensing regions. We design a distributed implementation of the above algorithm, and study its performance through simulations. In addition to the above problem, we also look at other related coverage problems in the context of directional sensors, and design similar approximation algorithms for them. Mobile wireless sensor networks have been extensively deployed for enhancing environmental monitoring and surveillance. The availability of low-cost mobile robots equipped with a variety of sensors makes them promising in target coverage tasks. They are particularly suitable where quick, inexpensive, or nonlasting visual sensing solutions are required. In this paper, we consider the problem of low complexity target tracking to cover and follow moving targets using flying robots. We tackle this problem by clustering targets while estimating the camera location and orientation for each cluster separately through a cover-set coverage method. We also leverage partial knowledge of target mobility to enhance the efficiency of our proposed algorithms. Three computationally efficient approaches are developed: predictive fuzzy , predictive incremental fuzzy , and local incremental fuzzy . The objective is to find a compromise among coverage efficiency, traveled distance, number of drones required, and complexity. The targets move according to one of the following three possible mobility patterns: random waypoint, Manhattan grid, and reference point group mobility patterns. The feasibility of our algorithms and their performance are also tested on a real-world indoor testbed called drone-be-gone , using Parrot AR.Drone quadcopters. The deployment confirms the results obtained with simulations and highlights the suitability of the proposed solutions for real-time applications.