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

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

Ieee access special section editorial: Cloud and big data-based next-generation cognitive radio networks

In cognitive radio networks (CRN), secondary users (SUs) are required to detect the presence of the licensed users, known as primary users (PUs), and to find spectrum holes for opportunistic spectrum access without causing harmful interference to PUs. However, due to complicated data processing, non-real-Time information exchange and limited memory, SUs often suffer from imperfect sensing and unreliable spectrum access. Cloud computing can solve this problem by allowing the data to be stored and processed in a shared environment. Furthermore, the information from a massive number of SUs allows for more comprehensive information exchanges to assist the

Greening and Optimizing Energy Consumption of Sensor Nodes in the Internet of Things through Energy Harvesting: Challenges and Approaches

This paper presents a survey of current energy efficient technologies that could drive the IoT revolution while examining critical areas for energy improvements in IoT sensor nodes. The paper reviews improvements in emerging energy techniques which promise to revolutionize the IoT landscape. Moreover, the current work also studies the sources of energy consumption by the IoT sensor nodes in a network and the metrics adopted by various researchers in optimizing the energy consumption of these nodes. Increasingly, researchers are exploring better ways of sourcing sufficient energy along with optimizing the energy consumption of IoT sensor nodes and making these energy sources green. Energy harvesting is the basis of this new energy source. The harvested energy could serve both as the principal and alternative energy source of power and thus increase the energy constancy of the IoT systems by providing a green, sufficient and optimal power source among IoT devices. Communication of IoT nodes in a heterogeneous IoT network consumes a lot of energy and the energy level in the nodes depletes with time. There is the need to optimize the energy consumption of such nodes and the current study discusses this as well

Energy efficient multi channel packet forwarding mechanism for wireless sensor networks in smart grid applications

Multichannel Wireless Sensor Networks (MWSNs) paradigm provides an opportunity for the Power Grid (PG) to be upgraded into an intelligent power grid known as the Smart Grid (SG) for efficiently managing the continuously growing energy demand of the 21st century. However, the nature of the intelligent grid environments is affected by the equipment noise, electromagnetic interference, and multipath effects, which pose significant challenges in existing schemes to find optimal vacant channels for MWSNs-based SG applications. This research proposed three schemes to address these issues. The first scheme was an Energy Efficient Routing (ERM) scheme to select the best-optimized route to increase the network performance between the source and the sink in the MWSNs. Secondly, an Efficient Channel Detection (ECD) scheme to detect vacant channels for the Primary Users (PUs) with improved channel detection probability and low probability of missed detection and false alarms in the MWSNs. Finally, a Dynamic Channel Assignment (DCA) scheme that dealt with channel scarcities by dynamically switching between different channels that provided higher data rate channels with longer idle probability to Secondary Users (SUs) at extremely low interference in the MWSNs. These three schemes were integrated as the Energy Efficient Multichannel Packet Forwarding Mechanism (CARP) for Wireless Sensor Networks in Smart Grid Applications. The extensive simulation studies were carried through an EstiNet software version 9.0. The obtained experimental simulation facts exhibited that the proposed schemes in the CARP mechanism achieved improved network performance in terms of packets delivery ratio (26%), congestion management (15%), throughput (23%), probability of channel detection (21%), reduces packet error rate (22%), end-to-end delay (25%), probability of channel missed-detection (25%), probability of false alarms (23.3%), and energy consumption (17%); as compared to the relevant schemes in both EQSHC and G-RPL mechanisms. To conclude, the proposed mechanism significantly improves the Quality of Service (QoS) data delivery performance for MWSNs in SG

Optimizing performance and energy efficiency of group communication and internet of things in cognitive radio networks

Data traffic in the wireless networks has grown at an unprecedented rate. While traditional wireless networks follow fixed spectrum assignment, spectrum scarcity problem becomes a major challenge in the next generations of wireless networks. Cognitive radio is a promising candidate technology that can mitigate this critical challenge by allowing dynamic spectrum access and increasing the spectrum utilization. As users and data traffic demands increases, more efficient communication methods to support communication in general, and group communication in particular, are needed. On the other hand, limited battery for the wireless network device in general makes it a bottleneck for enhancing the performance of wireless networks. In this thesis, the problem of optimizing the performance of group communication in CRNs is studied. Moreover, energy efficient and wireless-powered group communication in CRNs are considered. Additionally, a cognitive mobile base station and a cognitive UAV are proposed for the purpose of optimizing energy transfer and data dissemination, respectively. First, a multi-objective optimization for many-to-many communication in CRNs is considered. Given a many-to-many communication request, the goal is to support message routing from each user in the many-to-many group to each other. The objectives are minimizing the delay and the number of used links and maximizing data rate. The network is modeled using a multi-layer hyper graph, and the secondary users\u27 transmission is scheduled after establishing the conflict graph. Due to the difficulty of solving the problem optimally, a modified version of an Ant Colony meta-heuristic algorithm is employed to solve the problem. Additionally, energy efficient multicast communication in CRNs is introduced while considering directional and omnidirectional antennas. The multicast service is supported such that the total energy consumption of data transmission and channel switching is minimized. The optimization problem is formulated as a Mixed Integer Linear Program (MILP), and a heuristic algorithm is proposed to solve the problem in polynomial time. Second, wireless-powered machine-to-machine multicast communication in cellular networks is studied. To incentivize Internet of Things (IoT) devices to participate in forwarding the multicast messages, each IoT device participates in messages forwarding receives Radio Frequency (RF) energy form Energy Transmitters (ET) not less than the amount of energy used for messages forwarding. The objective is to minimize total transferred energy by the ETs. The problem is formulated mathematically as a Mixed Integer Nonlinear Program (MINLP), and a Generalized Bender Decomposition with Successive Convex Programming (GBD-SCP) algorithm is introduced to get an approximate solution since there is no efficient way in general to solve the problem optimally. Moreover, another algorithm, Constraints Decomposition with SCP and Binary Variable Relaxation (CDR), is proposed to get an approximate solution in a more efficient way. On the other hand, a cognitive mobile station base is proposed to transfer data and energy to a group of IoT devices underlying a primary network. Total energy consumed by the cognitive base station in its mobility, data transmission and energy transfer is minimized. Moreover, the cognitive base station adjusts its location and transmission power and transmission schedule such that data and energy demands are supported within a certain tolerable time and the primary users are protected from harmful interference. Finally, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to IoT devices. The UAV senses the spectrum and finds an idle channel, then it predicts when the corresponding primary user of the selected channel becomes active based on the elapsed time of the off period. Accordingly, it starts its transmission at the beginning of the next frame right after finding the channel is idle. Moreover, it decides the number of the consecutive transmission slots that it will use such that the number of interfering slots to the corresponding primary user does not exceed a certain threshold. A mathematical problem is formulated to maximize the minimum number of bits received by the IoT devices. A successive convex programming-based algorithm is used to get a solution for the problem in an efficiency way. It is shown that the used algorithm converges to a Kuhn Tucker point