Technical Note: \cal Q-Learning
Machine Learning
Impala: a middleware system for managing autonomic, parallel sensor systems
Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming
Learning-Enforced Time Domain Routing to Mobile Sinks in Wireless Sensor Fields
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
Middleware: Middleware Challenges and Approaches for Wireless Sensor Networks
IEEE Distributed Systems Online
Exploiting mobility for energy efficient data collection in wireless sensor networks
Mobile Networks and Applications
Decentralized, adaptive resource allocation for sensor networks
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Efficient Node Discovery in Mobile Wireless Sensor Networks
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
Energy conservation in wireless sensor networks: A survey
Ad Hoc Networks
Computer Networking: A Top-Down Approach
Computer Networking: A Top-Down Approach
Reliable and energy-efficient data collection in sparse sensor networks with mobile elements
Performance Evaluation
Mobile data collection in sensor networks: The TinyLime middleware
Pervasive and Mobile Computing
Using predictable observer mobility for power efficient design of sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Middleware to support sensor network applications
IEEE Network: The Magazine of Global Internetworking
Query-driven data collection and data forwarding in intermittently connected mobile sensor networks
Proceedings of the Seventh International Workshop on Data Management for Sensor Networks
Processing continuous top-k data collection queries in lifetime-constrained wireless sensor networks
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
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
Scheduling sensors for monitoring sentient spaces using an approximate POMDP policy
Pervasive and Mobile Computing
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Sparse wireless sensor networks (WSNs) are being effectively used in several applications, which include transportation, urban safety, environment monitoring, and many others. Sensor nodes typically transfer acquired data to other nodes and base stations. Such data transfer operations are critical, especially in sparse WSNs with mobile elements. In this paper, we investigate data collection in sparse WSNs by means of special nodes called Mobile Data Collectors (MDCs), which visit sensor nodes opportunistically to gather data. As contact times and other information are not known a priori, the discovery of an incoming MDC by the static sensor node becomes a critical task. Ideally, the discovery strategy should be able to correctly detect contacts while keeping a low energy consumption. In this paper, we propose an adaptive discovery strategy that exploits distributed independent reinforcement learning to meet these two necessary requirements. We carry out an extensive simulation analysis to demonstrate the energy efficiency and effectiveness of the proposed strategy. The obtained results show that our solution provides superior performance in terms of both discovery efficiency and energy conservation.