Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Proceedings of the 3rd ACM international workshop on Performance evaluation of wireless ad hoc, sensor and ubiquitous networks
Adaptive design optimization of wireless sensor networks using genetic algorithms
Computer Networks: The International Journal of Computer and Telecommunications Networking
A mathematical model for performance of IEEE 802.15.4 beacon-enabled mode
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
An MDP-based application oriented optimal policy for wireless sensor networks
CODES+ISSS '09 Proceedings of the 7th IEEE/ACM international conference on Hardware/software codesign and system synthesis
Proceedings of the 4th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
An accurate and scalable analytical model for IEEE 802.15.4 slotted CSMA/CA networks
IEEE Transactions on Wireless Communications
Performance analysis of GTS allocation in beacon enabled IEEE 802.15.4
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Exploring simulated annealing and graphical models for optimization in cognitive wireless networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Minimizing Energy Consumption in Body Sensor Networks via Convex Optimization
BSN '10 Proceedings of the 2010 International Conference on Body Sensor Networks
Decision-theoretic design space exploration of multiprocessor platforms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
System-Level Synthesis for Wireless Sensor Node Controllers: A Complete Design Flow
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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The complexity of Wireless Sensor Networks (WSNs) has been constantly increasing over the last decade, and the necessity of efficient CAD tools has been growing accordingly. In fact, the size of the design space of a WSN has become large, and an exploration conducted by using semi-random algorithms (such as the popular genetic or simulated annealing algorithms) requires an unacceptable amount of time to converge due to the high number of parameters involved. To address this issue, in this paper we introduce a knowledge-based design space exploration algorithm for the WSN domain, which is based on a discrete-space Markov decision process (MDP). In order to enhance the performance of the proposed algorithm and to increase its scalability, we tailor the classical MDP approach to the specific aspects that characterize the WSN domain. We exploit domain-specific knowledge to choose the best node-level configuration in WSNs using slotted star topology in order to reduce the exploration time. The proposed approach has been tested on IEEE 802.15.4 star networks with various configurations of the number of nodes and their packet rates. Experimental results show that the proposed algorithm reduces the number of simulations required to converge, with respect to state-of-the-art algorithms (e.g., NSGA-II, PMA and MOSA), from 60 to 87%