Knowledge-based design space exploration of wireless sensor networks

Abstract

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 knowledgebased 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%.

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“…Por lo anterior, el manejo eficiente de las variables que determinan el tamaño de la supertrama en una transmisión de datos es fundamental y entre menos retransmisiones se hagan, menor será el consumo de energía. cadenas de Markov, se pueden encontrar en [9] y [13][14][15]. Una conclusión similar presenta [6]; mediante simulaciones encuentra que entre mayor sea el valor de BO con respecto a un valor de SO=0, la latencia es mayor.…”

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

“…3) Dynamic Configuration and Lifetime Modeling: In [92], Grassi et al considered the computer-aided design (CAD) of WSNs that helps in selecting the optimized configuration of hardware components. The node's components, such as the central processing unit (CPU), memories, and radio transceiver, are designed to fit the deployment scenario and requirements.…”

A framework to support dependability evaluation of WSNs from AADL models

“…The behaviour of a complex system is often structured using the notion of operational modes and mode transitions. AADL can also describe the dynamic behavior of the runtime architecture [19], [2]. It can also be extended by several annexes.…”