Developing media access technique in wireless sensor networks with the internet of things

Intelligent medium access protocols (MAC) have been developed to optimize the performance of wireless sensor networks (WSN) such as channel efficiency and latency. ALOHA, as the first MAC approach, inspired the development of several MAC schemes in network domain with primary advantage of simplicity. In this thesis, we present design, implementation, and performance evaluations of ALOHA approach through significant improvements to attain high channel utilization as the most important performance metric. A critical emphasis is currently focused on removing the burden of packet collisions while satisfying requirements of energy and delay criterions. We first implement ALOHA protocol to practically explore its performance behaviors in comparison to analytical models. Then, the transmission power of nodes is systematically increased to observe the performance metrics. We then introduce the concept of dynamic payload instead of fixed-length packets, whereby a dynamic selection of length of each transmitted packet is employed. A throughput performance of 0.47 is achieved when the payload length is ranged from 15 to 32 bytes. One of the main concerns to begin the practical implementations as an underlying contribution of this thesis is to observe the throughput performance of ALOHA without acknowledgement (ACK) mechanism. In this scenario, all generated packets are transmitted once without ACK and retransmission policy. Later, a critical part of the performance evaluations is dedicated to the throughput and delay performances of ALOHA under the presence of ACK mechanism. To solve the problem arising from the placement of the battery with limited capacity in the WSNs, continuous energy support was provided to the battery by using solar energy, which is one of the various ambient energies. Another specific contribution of this paper is to integrate the transmission policy of ALOHA with the potential of Internet of Things (IoT) opportunities. The proposed policy utilizes a state-less Q-learning strategy to achieve the maximum performance efficiency. Performance outputs prove that the proposed idea ensures a maximum throughput of approximately 58% while ALOHA is limited to nearly 18% over a single-hop scenario.