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.

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.