This thesis investigates the application of Reinforcement Learning (RL) on Medium Access Control (MAC) for Wireless Sensor Networks (WSNs). RL is applied as an intelligent slot selection strategy to Framed ALOHA, along with analytical and experimental performance evaluation. Informed Receiving (IR) and ping packets are applied to multi-hop WSNs to avoid idle listening and overhearing, thereby further improving the energy efficiency.The low computational complexity and signalling overheads of the ALOHA schemes meet the design requirement of energy constraint WSNs, but suffer collisions from the random access strategy. RL is applied to solve this problem and to achieve perfect scheduling. Results show that the RL scheme achieves over 0.9 Erlangs maximum throughput in single-hop networks. For multi-hop WSNs, IR and ping packets are applied to appropriately switch the relay nodes between active and sleep state, to reserve as much energy as possible while ensuring no information loss.The RL algorithms require certain time to converge to steady state to achieve the optimum performance. The convergence behaviour is investigated in this thesis. A Markov model is proposed to describe a learning process, and the model produces the proof of the convergence of the learning process and the estimated convergence time. The channel performance before convergence is also evaluated.Contents
Energy harvesting (EH) technology in the field of wireless sensor networks (WSNs) is gaining increasing popularity through removing the burden of having to replace/recharge depleted energy sources by energy harvester devices. EH provides an alternative source of energy from the surrounding environment; therefore, by exploiting the EH process, WSNs can achieve a perpetual lifetime. In view of this, emphasis is being placed on the design of new medium access control (MAC) protocols that aim to maximize the lifetime of WSNs by using the maximum possible amount of harvested energy instead of saving any residual energy, given that the rate of energy harvested is greater than that which is consumed. Various MAC protocols with the objective of exploiting ambient energy have been proposed for energy‐harvesting WSNs (EH‐WSNs). In this paper, first, the fundamental properties of EH‐WSN architecture are outlined. Then, several MAC protocols proposed for EH‐WSNs are presented, describing their operating principles and underlying features. To give an insight into future research directions, open research issues (key ideas) with respect to design trade‐offs are discussed at the end of this paper.
This paper studies the potential of a novel approach to ensure more efficient and intelligent assignment of capacity through medium access control (MAC) in practical wireless sensor networks. Q-Learning is employed as an intelligent transmission strategy. We review the existing MAC protocols in the context of Q-learning. A recently-proposed, ALOHA and Q-Learning based MAC scheme, ALOHA-Q, is considered which improves the channel performance significantly with a key benefit of simplicity. Practical implementation issues of ALOHA-Q are studied. We demonstrate the performance of the ALOHA-Q through extensive simulations and evaluations in various testbeds. A new exploration/exploitation method is proposed to strengthen the merits of the ALOHA-Q against dynamic the channel and environment conditions.
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