272 research outputs found
Optimising WLANs Power Saving: Context-Aware Listen Interval
Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, a number of power saving approaches have been devised including Static Power Save Mode (SPSM), Adaptive PSM (APSM), and Smart Adaptive PSM (SAPSM). However, the existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements.
This thesis proposes a novel Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. We focused on the network activity of a single smartphone application while ignoring the network activity of applications running simultaneously.
We introduced a context-aware network traffic classification approach based on ML classifiers to classify the network traffic of wireless devices in WLANs. Smartphone applications’ network traffic reflecting a diverse array of network behaviour and interactions were used as contextual inputs for training ML classifiers of output traffic, constructing an ML classification model. A real-world dataset is constructed, based on nine smartphone applications’ network traffic, this is used firstly to evaluate the performance of five ML classifiers using cross-validation, followed by conducting extensive experimentation to assess the generalisation capacity of the selected classifiers on unseen testing data. The experimental results further validated the practical application of the selected ML classifiers and indicated that ML classifiers can be usefully employed for classifying the network traffic of smartphone applications based on different levels of behaviour and interaction.
Furthermore, to optimise the sleep and awake cycles of the WNIC in accordance with the smartphone applications’ network activity. Four CALI power saving modes were developed based on the classified output traffic. Hence, the ML classification model classifies the new unseen samples into one of the classes, and the WNIC will be adjusted to operate into one of CALI power saving modes. In addition, the performance of CALI’s power saving modes were evaluated by comparing the levels of energy consumption with existing benchmark power saving approaches using three varied sets of energy parameters. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to SAPSM power saving approach, which also employs an ML classifier
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Computational Intelligence for Cooperative Swarm Control
Over the last few decades, swarm intelligence (SI) has shown significant benefits in many practical applications. Real-world applications of swarm intelligence include disaster response and wildlife conservation. Swarm robots can collaborate to search for survivors, locate victims, and assess damage in hazardous environments during an earthquake or natural disaster. They can coordinate their movements and share data in real-time to increase their efficiency and effectiveness while guiding the survivors. In addition to tracking animal movements and behaviour, robots can guide animals to or away from specific areas. Sheep herding is a significant source of income in Australia that could be significantly enhanced if the human shepherd could be supported by single or multiple robots.
Although the shepherding framework has become a popular SI mechanism, where a leading agent (sheepdog) controls a swarm of agents (sheep) to complete a task, controlling a swarm of agents is still not a trivial task, especially in the presence of some practical constraints. For example, most of the existing shepherding literature assumes that each swarm member has an unlimited sensing range to recognise all other members’ locations. However, this is not practical for physical systems. In addition, current approaches do not consider shepherding as a distributed system where an agent, namely a central unit, may observe the environment and commu- nicate with the shepherd to guide the swarm. However, this brings another hurdle when noisy communication channels between the central unit and the shepherd af- fect the success of the mission. Also, the literature lacks shepherding models that can cope with dynamic communication systems. Therefore, this thesis aims to design a multi-agent learning system for effective shepherding control systems in a partially observable environment under communication constraints.
To achieve this goal, the thesis first introduces a new methodology to guide agents whose sensing range is limited. In this thesis, the sheep are modelled as an induced network to represent the sheep’s sensing range and propose a geometric method for finding a shepherd-impacted subset of sheep. The proposed swarm optimal herding point uses a particle swarm optimiser and a clustering mechanism to find the sheepdog’s near-optimal herding location while considering flock cohesion. Then, an improved version of the algorithm (named swarm optimal modified centroid push) is proposed to estimate the sheepdog’s intermediate waypoints to the herding point considering the sheep cohesion. The approaches outperform existing shepherding methods in reducing task time and increasing the success rate for herding.
Next, to improve shepherding in noisy communication channels, this thesis pro- poses a collaborative learning-based method to enhance communication between the central unit and the herding agent. The proposed independent pre-training collab- orative learning technique decreases the transmission mean square error by half in 10% of the training time compared to existing approaches. The algorithm is then ex- tended so that the sheepdog can read the modulated herding points from the central unit. The results demonstrate the efficiency of the new technique in time-varying noisy channels.
Finally, the central unit is modelled as a mobile agent to lower the time-varying noise caused by the sheepdog’s motion during the task. So, I propose a Q-learning- based incremental search to increase transmission success between the shepherd and the central unit. In addition, two unique reward functions are presented to ensure swarm guidance success with minimal energy consumption. The results demonstrate an increase in the success rate for shepherding
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