Parallel and distributed computing handbook
Parallel and distributed computing handbook
Proceedings of the 6th international workshop on Hardware/software codesign
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Deadline Assignment in Distributed Hard Real-Time Systems with Relaxed Locality Constraints
ICDCS '97 Proceedings of the 17th International Conference on Distributed Computing Systems (ICDCS '97)
Energy-balanced task allocation for collaborative processing in wireless sensor networks
Mobile Networks and Applications
An efficient heuristic for selecting active nodes in wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cross-Layer Collaborative In-Network Processing in Multihop Wireless Sensor Networks
IEEE Transactions on Mobile Computing
IEEE Transactions on Computers
On the lifetime of wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
QBETS: queue bounds estimation from time series
JSSPP'07 Proceedings of the 13th international conference on Job scheduling strategies for parallel processing
The Internet of Things: A survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Embedded System Design: Embedded Systems Foundations of Cyber-Physical Systems
Embedded System Design: Embedded Systems Foundations of Cyber-Physical Systems
Modeling Interference in Wireless Ad Hoc Networks
IEEE Communications Surveys & Tutorials
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Cyber physical systems (CPS) have recently emerged as a promising approach to improve the synergy between physical and virtual worlds. CPS applications can be built on top of Wireless Sensor Networks (WSNs) exploiting the physical information collected by them to bridge real and cyber realms. In this paper, we investigated the emerging problem of how to schedule multiple applications onto cyber-physical systems encompassed of multiple WSNs, while meeting demands of these applications. Our goal is to find out an optimal scheduling scheme for each application so that: (i) it minimizes the overall energy consumption, (ii) it meets the application's deadline, (iii) it provides the required data accuracy to applications, and (iv) it balances the workload of the system. To achieve such goals, we developed a polynomial-time three phase task scheduling heuristic, named HTPTS. Experimental results show that the time and energy performance of HTPTS are close to the time and energy of the benchmark in most of the cases, while load balance is satisfied.