The shared-medium nature and complex wireless environment of wireless sensor networks (WSNs) poses fundamental challenges to the design of effective transmission scheduling algorithms that are optimised with respect to superframe length and reliability. In this study, the authors propose an adaptive and reliable transmission scheduling algorithm for WSNs based on lowcost estimation of channel states. The authors establish a hierarchical scheduling framework on global centralised timeslot scheduling and local distributed channel scheduling. On the one hand, global centralised timeslot scheduling aims to guarantee global optimality of resource allocation, during which a mathematical reliability model is built to avoid resource waste by the stationary allocation method and improve the reliability of packet transmission. On the other hand, local distributed channel scheduling shares the responsibility of resource allocation. During channel scheduling, the channel model is constructed 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, the scheduling algorithm performs reliably for an arbitrary number of channels and arbitrary number of channel states. Extensive simulations and experiments under a variety of network environments have been conducted to validate our theoretical claims. † channel encoding schemes that reduce packet errors at physical layer; † retransmission at MAC layer; † multi-path routing by forwarding data via several paths at the network layer; † end-to-end transport protocols at transport layer, such as transmission control protocol.
Zhang et al. REF propose an adaptive and reliable transmission scheduling algorithm for wireless sensor networks based on low-cost estimation of channel states.
Reliable transmission scheduling for multi-channel wireless sensor networks with low-cost channel estimation
IET Communications
IET Communications
Mathematics Computer Science