A Resource Intensive Traffic-Aware Scheme for Cluster-based Energy Conservation in Wireless Devices

Wireless traffic that is destined for a certain device in a network, can be exploited in order to minimize the availability and delay trade-offs, and mitigate the Energy consumption. The Energy Conservation (EC) mechanism can be node-centric by considering the traversed nodal traffic in order to prolong the network lifetime. This work describes a quantitative traffic-based approach where a clustered Sleep-Proxy mechanism takes place in order to enable each node to sleep according to the time duration of the active traffic that each node expects and experiences. Sleep-proxies within the clusters are created according to pairwise active-time comparison, where each node expects during the active periods, a requested traffic. For resource availability and recovery purposes, the caching mechanism takes place in case where the node for which the traffic is destined is not available. The proposed scheme uses Role-based nodes which are assigned to manipulate the traffic in a cluster, through the time-oriented backward difference traffic evaluation scheme. Simulation study is carried out for the proposed backward estimation scheme and the effectiveness of the end-to-end EC mechanism taking into account a number of metrics and measures for the effects while incrementing the sleep time duration under the proposed framework. Comparative simulation results show that the proposed scheme could be applied to infrastructure-less systems, providing energy-efficient resource exchange with significant minimization in the power consumption of each device.Comment: 6 pages, 8 figures, To appear in the proceedings of IEEE 14th International Conference on High Performance Computing and Communications (HPCC-2012) of the Third International Workshop on Wireless Networks and Multimedia (WNM-2012), 25-27 June 2012, Liverpool, U

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Effect of steel fibre volume fraction on thermal performance of lightweight foamed mortar (LFM) at ambient temperature

Lightweight foamed mortar (LFM) has grow into utmost commercial building material in the construction industry for non-structural and semi-structural applications owing to its reduced self-weight, flowability, stability and excellent thermal insulation properties. Hence, this study was conducted with the aims to develop an alternative for conventional concrete bricks and blocks for non-structural and semi-structural applications of masonry. Lightweight foamed mortar (LFM) is either a cement paste or mortar, relegated as lightweight concrete, in which suitable foaming agent entraps the air-voids in mortar. It therefore has a wide range of applications such as material for wall blocks or panels, floor & roof screeds, trench reinstatement, road foundations and voids filling. This research focuses on experimental investigation of thermal properties of LFM with inclusion of relatively low volume fraction (0.2% and 0.4%) of steel fibre at ambient temperature. There are three parameters will be scrutinized such as thermal conductivity, thermal diffusivity as well as the specific heat capacity. There are two densities of 600kg/m3 and 1200kg/m3 had been cast and tested. The mix design proportion of LFM used for cement, aggregate and water ratio was 1: 1.5:0.45. The experimental results had indicated that the thermal conductivity, thermal diffusivity and specific heat value slightly higher than control mix due to the addition of steel fibres. For instance, thermal conductivity, diffusivity and specific heat of 600 kg/m3 density control mix were 0.212W/mK, 0.477mm2/s and 545 J/kg◦C respectively. When 0.2% volume fraction of steel fiber was added in the mix of 600 kg/m3 density, the value of thermal conductivity, diffusivity and specific heat were increased to 0.235W/mK, 0.583mm2/s and 578 J/kg◦C correspondingly. This is due to the characteristic of the steel fibre application in which steel fibre is good as heat conductor and excellent in absorbing heat. Therefore there is a potential of utilizing steel fiber in cement based material like LFM for components that needs excellent heat absorption capacity