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

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

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