Adaptive and reliable transmission scheduling with low-cost estimation of channel states

An adaptive and reliable transmission scheduling algorithm for wireless sensor networks based on the low-cost estimation of channel states is proposed to jointly optimize the superframe length and reliability. We establish a hierarchical scheduling framework, which includes a global centralized timeslot scheduling (GCTS) and a local distributed channel scheduling (LDCS). On one hand, GCTS aims to guarantee the global optimality of timeslot allocation, during which a mathematical reliability model is built to avoid the resource waste and to improve the transmission reliability. On the other hand, LDCS allocates channel resource according to actual electromagnetism environment. During LDCS, the channel model is established by the dynamic programming method and takes both probing cost and channel quality into consideration, which alleviates the uncertain and time-varying interference and overcomes the blindness of traditional methods. In contrast with previous works that do not consider link reliability and channel probing cost and often assume two channel states, our scheduling algorithm performs reliably for arbitrary number of channels and arbitrary number of channel states. Extensive simulations under a variety of network environments have been conducted to validate our theoretical claims.