Introduction to Reinforcement Learning
Introduction to Reinforcement 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
Message Ferrying: Proactive Routing in Highly-Partitioned Wireless Ad Hoc Networks
FTDCS '03 Proceedings of the The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Intelligent fluid infrastructure for embedded networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
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
Exploiting mobility for energy efficient data collection in wireless sensor networks
Mobile Networks and Applications
Controllably Mobile Infrastructure for Low Energy Embedded Networks
IEEE Transactions on Mobile Computing
A survey of middleware for sensor networks: state-of-the-art and future directions
Proceedings of the international workshop on Middleware for sensor networks
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
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor 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
Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey
ACM Transactions on Sensor Networks (TOSN)
Multiple controlled mobile elements (data mules) for data collection in sensor networks
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
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
Middleware to support sensor network applications
IEEE Network: The Magazine of Global Internetworking
Issues in designing middleware for wireless sensor networks
IEEE Network: The Magazine of Global Internetworking
Energy efficient and reliable data delivery in urban sensing applications: A performance analysis
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Wireless sensor networks (WSNs) have become an enabling technology for a wide range of applications. In contrast with traditional scenarios where static sensor nodes are densely deployed, a sparse WSN architecture can also be used in many cases. In a sparse WSN, special mobile data collectors (MDCs) are used to gather data from ordinary sensor nodes. In general, sensor nodes do not know when they will be in contact with the MDC, hence they need to discover its presence in their communication range. To this end, discovery mechanisms based on periodic listening and a duty-cycle have shown to be effective in reducing the energy consumption of sensor nodes. However, if not properly tuned, such mechanisms can hinder the data collection process. In this paper, we introduce a Resource-Aware Data Accumulation (RADA), a novel and lightweight framework which allows nodes to learn the MDC arrival pattern, and tune the discovery duty-cycle accordingly. Furthermore, RADA is able to adapt to changes in the operating conditions, and is capable of effectively supporting a number of different MDC mobility patterns. Simulation results show that our solution obtains a higher discovery efficiency and a lower energy consumption than comparable schemes.