Formal Methods in System Design - Special issue on The First Federated Logic Conference (FLOC'96), part II
Model checking
A survey of design techniques for system-level dynamic power management
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on low-power electronics and design
A survey of rollback-recovery protocols in message-passing systems
ACM Computing Surveys (CSUR)
Dynamic Power Management in Wireless Sensor Networks
IEEE Design & Test
Model Checking of Probabalistic and Nondeterministic Systems
Proceedings of the 15th Conference on Foundations of Software Technology and Theoretical Computer Science
Strong Minimum Energy Topology in Wireless Sensor Networks: NP-Completeness and Heuristics
IEEE Transactions on Mobile Computing
PRISM 2.0: A Tool for Probabilistic Model Checking
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Energy Scavenging for Mobile and Wireless Electronics
IEEE Pervasive Computing
Improving Power Output for Vibration-Based Energy Scavengers
IEEE Pervasive Computing
The low power energy aware processing (LEAP)embedded networked sensor system
Proceedings of the 5th international conference on Information processing in sensor networks
Low-complexity video compression for wireless sensor networks
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Mobile Networks and Applications
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Environmental energy is becoming a feasible alternative to traditional energy sources for ultra low-power devices such as sensor nodes. These devices can run reactive applications that adapt their control flow depending on the sensed data. In order to reduce the energy consumption of the platform and also to meet the timing constraints imposed by the application, we propose to dynamically reconfigure the system through the use of Field Programmable Gate Array (FPGA) fabric such that it executes more efficiently the tasks of the application.In this paper we present a new approach that enables the designer to efficiently explore different reconfiguration strategies for environmentally powered systems. For this we define a stochastic model of a harvesting video sensor node that captures the behavior of the node and of its environment. We use this approach to investigate the impact of different reconfiguration strategies for a video surveillance node on metrics of interest, such as the expected lifetime or downtime of the system.Then, we create a hardware implementation of an energy-aware reconfiguration manager on top of a custom multi-FPGA board.Our results show that the systems improve their processing capabilities if suitable reconfiguration strategies are defined for their respective configuration environments.