Versatile low power media access for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
A preliminary investigation of worm infections in a bluetooth environment
Proceedings of the 4th ACM workshop on Recurring malcode
Ultra-low duty cycle MAC with scheduled channel polling
Proceedings of the 4th international conference on Embedded networked sensor systems
A node discovery service for partially mobile sensor networks
Proceedings of the 2nd international workshop on Middleware for sensor networks
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Reliable neighbor discovery for mobile ad hoc networks
Proceedings of the 6th International Workshop on Foundations of Mobile Computing
Evolution and sustainability of a wildlife monitoring sensor network
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
The announcement layer: beacon coordination for the sensornet stack
EWSN'11 Proceedings of the 8th European conference on Wireless sensor networks
Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey
ACM Transactions on Sensor Networks (TOSN)
A framework for Resource-Aware Data Accumulation in sparse wireless sensor networks
Computer Communications
Selective reprogramming of mobile sensor networks through social community detection
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
An adaptive strategy for energy-efficient data collection in sparse wireless sensor networks
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
WILDSENSING: Design and deployment of a sustainable sensor network for wildlife monitoring
ACM Transactions on Sensor Networks (TOSN)
Reliable neighbor discovery for mobile ad hoc networks
Ad Hoc Networks
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Energy is one of the most crucial aspects in real deployments of mobile sensor networks. As a result of scarce resources, the duration of most real deployments can be limited to just several days, or demands considerable maintenance efforts (e.g., in terms of battery substitution). A large portion of the energy of sensor applications is spent in node discovery as nodes need to periodically advertise their presence and be awake to discover other nodes for data exchange. The optimization of energy consumption, which is generally a hard task in fixed sensor networks, is even harder in mobile sensor networks, where the neighbouring nodes change over time.In this paper we propose an algorithm for energy efficient node discovery in sparsely connected mobile wireless sensor networks. The work takes advantage of the fact that nodes have temporal patterns of encounters and exploits these patterns to drive the duty cycling. Duty cycling is seen as a sampling process and is formulated as an optimization problem. We have used reinforcement learning techniques to detect and dynamically change the times at which a node should be awake as it is likely to encounter other nodes. We have evaluated our work using real human mobility traces, and the paper presents the performance of the protocol in this context.