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Market-Based Sensor Relocation by a Team of Robots in Wireless Sensor NetworksLi, Haotian 25 March 2014 (has links)
Randomly scattered sensors may cause sensing holes and redundant sensors. In carrier-based sensor relocation, mobile robots (with limited capacity to carry sensors) pick up additional or redundant sensors and relocate them at sensing holes. In the only known localized algorithm, robots randomly traverse field and act based on identified pair of spare sensor and coverage hole. We propose a Market-based Sensor Relocation (MSR) algorithm, which optimizes sensor deployment location, and introduces bidding and coordinating among neighboring robots. Sensors along the boundary of each hole elect one of them as the representative, which bids to neighboring robots for hole filling service. Robot randomly explores by applying Least Recently Visited policy. It chooses the best bid according to Cost over Progress ratio and fetches a spare sensor nearby to cover the corresponding sensing hole. Robots within communication range share their tasks to search for better possible solutions. Simulation shows that MSR outperforms the existing competing algorithm G-R3S2 significantly on total robot traversed path and energy, and time to cover holes, slightly on number of sensors needed to cover the hole, and the cost of additional messages for bidding and deployment location sharing.
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Market-Based Sensor Relocation by a Team of Robots in Wireless Sensor NetworksLi, Haotian January 2014 (has links)
Randomly scattered sensors may cause sensing holes and redundant sensors. In carrier-based sensor relocation, mobile robots (with limited capacity to carry sensors) pick up additional or redundant sensors and relocate them at sensing holes. In the only known localized algorithm, robots randomly traverse field and act based on identified pair of spare sensor and coverage hole. We propose a Market-based Sensor Relocation (MSR) algorithm, which optimizes sensor deployment location, and introduces bidding and coordinating among neighboring robots. Sensors along the boundary of each hole elect one of them as the representative, which bids to neighboring robots for hole filling service. Robot randomly explores by applying Least Recently Visited policy. It chooses the best bid according to Cost over Progress ratio and fetches a spare sensor nearby to cover the corresponding sensing hole. Robots within communication range share their tasks to search for better possible solutions. Simulation shows that MSR outperforms the existing competing algorithm G-R3S2 significantly on total robot traversed path and energy, and time to cover holes, slightly on number of sensors needed to cover the hole, and the cost of additional messages for bidding and deployment location sharing.
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Localized Ant Colony of Robots for Redeployment in Wireless Sensor NetworksWang, Yuan 25 March 2014 (has links)
Sensor failures or oversupply in wireless sensor networks (WSNs), especially initial
random deployment, create both spare sensors (whose area is fully covered by other
sensors) and sensing holes. We envision a team of robots to relocate sensors and
improve their area coverage. Existing algorithms, including centralized ones and the
only localized G-R3S2, move only spare sensors and have limited improvement because
non-spare sensors, with area coverage mostly overlapped by neighbour sensors,
are not moved, and additional sensors are deployed to fill existing holes. We propose
a localized algorithm, called Localized Ant-based Sensor Relocation Algorithm with
Greedy Walk (LASR-G), where each robot may carry at most one sensor and makes
decision that depends only on locally detected information. In LASR-G, each robot
calculates corresponding pickup or dropping probability, and relocates sensor with
currently low coverage contribution to another location where sensing hole would be
significantly reduced. The basic algorithm optimizes only area coverage, while modified algorithm includes also the cost of robot movement. We compare LASR-G with
G-R3S2, and examine both single robot and multi robots scenarios. The simulation
results show the advantages of LASR-G over G-R3S2.
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Localized Ant Colony of Robots for Redeployment in Wireless Sensor NetworksWang, Yuan January 2014 (has links)
Sensor failures or oversupply in wireless sensor networks (WSNs), especially initial
random deployment, create both spare sensors (whose area is fully covered by other
sensors) and sensing holes. We envision a team of robots to relocate sensors and
improve their area coverage. Existing algorithms, including centralized ones and the
only localized G-R3S2, move only spare sensors and have limited improvement because
non-spare sensors, with area coverage mostly overlapped by neighbour sensors,
are not moved, and additional sensors are deployed to fill existing holes. We propose
a localized algorithm, called Localized Ant-based Sensor Relocation Algorithm with
Greedy Walk (LASR-G), where each robot may carry at most one sensor and makes
decision that depends only on locally detected information. In LASR-G, each robot
calculates corresponding pickup or dropping probability, and relocates sensor with
currently low coverage contribution to another location where sensing hole would be
significantly reduced. The basic algorithm optimizes only area coverage, while modified algorithm includes also the cost of robot movement. We compare LASR-G with
G-R3S2, and examine both single robot and multi robots scenarios. The simulation
results show the advantages of LASR-G over G-R3S2.
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