Adaptive Power Control and Fuzzy Based Duty Cycle Scheduling in Under Water Sensor Network (UWSN)

In Under Water Sensor Network (UWSN), power control algorithm should consider channel condition and frame loss probability. Also the queue length variations or traffic load should be considered for fixing the sleep duty cycle. In this paper, we propose to design an Adaptive Power Control and Fuzzy based Duty Cycle Scheduling (APC-FDS) algorithm for clustered UWSN. In the adaptive power control algorithm, the sender node will fix the minimum transmit power such that the energy consumption and frame loss probability are minimized. In adaptive duty cycle scheduling, the duty cycle is adaptively adjusted by considering the connection value and traffic load using fuzzy logic technique. The connectivity between a cluster and its neighboring cluster is estimated using the connection value. The cluster head collects the traffic information from its members. Experimental results show that the APS-FDS algorithm reduces the average energy consumption and frame loss probability.

DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks

Since sensor nodes usually have a large duty cycle interval to prolong network lifetime, duty-cycled wireless sensor networks (WSNs) can suffer from a long end-to-end (E2E) delay. Because delay-sensitive applications have a certain E2E delay requirement, a lot of studies have tried to tackle the long E2E delay problem. However, most existing studies focused on simply reducing the E2E delay rather than considering the delay bound requirement, which makes it hard to achieve balanced performance between E2E delay and energy efficiency. Although a few studies took into consideration both the delay bound requirement and energy consumption, they required specific node deployment or strict time synchronization between nodes in the network. In order to address the limitations of the existing studies, we propose a delay-constrained duty-cycle scheduling (DDS) algorithm. The objective of DDS is to achieve low energy consumption while satisfying the delay bound requirement in various node deployment scenarios depending on user demands. First, based on network topology information collected by the sink, one-hop delay distribution is derived as a function of the duty cycle interval. Then, the E2E delay distribution is estimated using the Lyapunov central limit theorem, which allows each node group to have a different delay distribution. Finally, the duty cycle interval is determined using the estimated E2E delay distribution such that energy consumption is minimized while meeting the delay bound requirement. Practical WSN deployment scenarios are considered to evaluate the proposed algorithm. The simulation results show that DDS can guarantee the given delay bound requirement and outperform existing algorithms in terms of energy efficiency.