Data harvesting with mobile elements in wireless sensor networks

Gu, Yaoyao; Bozdağ, Doruk; Brewer, Robert W.; Ekici, Eylem

doi:10.1016/j.comnet.2006.01.008

Cited by 88 publications

(66 citation statements)

References 12 publications

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“…An extensive review of research related to the bio-inspired techniques and the most behaviour of the robots can be found in [9,27]. Regarding the energy consumption problem, researchers have approached this problem in different ways, including minimizing the weight of robots, pre-positioning energy sources into the environments, minimizing communication ranges of robots, sending data in a simple form [28], reducing the direct communication and the use of multi-hop communication links between robots [29], minimizing the distrance of the traveling path [30,31]. For example, Barca et al addressed the problems related to energy consumption in [32].…”

Section: Related Workmentioning

confidence: 99%

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Palmieri

Yang

Rango

et al. 2017

Neural Comput & Applic

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Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two subproblems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete

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Section: Related Workmentioning

confidence: 99%

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Palmieri

Yang

Rango

et al. 2017

Neural Comput & Applic

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“…Gu, Bozdag and Brewer [13] use a partitioning-based algorithm to schedule the movements of mobile sinks in order to reduce data loss due to buffer overflow while waiting for a sink to arrive. This aspect is ignored in our proposed solution.…”

Section: Related Workmentioning

confidence: 99%

Calculation of an Optimum Mobile Sink Path in a Wireless Sensor Network

Chinnappen-Rimer¹,

Hancke²

2012

Wireless Sensor Networks - Technology and Protocols

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“…For this reason, prior work has used various simplified radio models, all of which assume that sensor locations are known. In Visit models, the ferry automatically exchanges all data upon visiting a node [5][6][7][8]. Communication radius models assume a mechanism for complete data transfer below a threshold distance, possibly by permitting the ferry to hover [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Model-Free Trajectory Optimisation for Unmanned Aircraft Serving as Data Ferries for Widespread Sensors

Pearre

Brown

2012

Remote Sensing

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Given multiple widespread stationary data sources such as ground-based sensors, an unmanned aircraft can fly over the sensors and gather the data via a wireless link. Performance criteria for such a network may incorporate costs such as trajectory length for the aircraft or the energy required by the sensors for radio transmission. Planning is hampered by the complex vehicle and communication dynamics and by uncertainty in the locations of sensors, so we develop a technique based on model-free learning. We present a stochastic optimisation method that allows the data-ferrying aircraft to optimise data collection trajectories through an unknown environment in situ, obviating the need for system identification. We compare two trajectory representations, one that learns near-optimal trajectories at low data requirements but that fails at high requirements, and one that gives up some performance in exchange for a data collection guarantee. With either encoding the ferry is able to learn significantly improved trajectories compared with alternative heuristics. To demonstrate the versatility of the model-free learning approach, we also learn a policy to minimise the radio transmission energy required by the sensor nodes, allowing prolonged network lifetime.

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Data harvesting with mobile elements in wireless sensor networks

Cited by 88 publications

References 12 publications

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Calculation of an Optimum Mobile Sink Path in a Wireless Sensor Network

Model-Free Trajectory Optimisation for Unmanned Aircraft Serving as Data Ferries for Widespread Sensors

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