Multi-channel Communication in Wireless Networks

Multi-channel communication has been developed to overcome some limitations related to the throughput and delivery rate which become necessary for many applications that require sufficient bandwidth to transmit a large amount of data in Wireless Networks (WNs) such as multimedia communication. However, the requirement of frequent negotiation for the channels assignment process incurs extra-large communication overhead and collisions, which results in the reduction of both communication quality and network lifetime. This effect can play an important role in the performance deterioration of certain WNs types, especially the Wireless Sensor Networks (WSNs) since they are characterized by their limited resources. This work addresses the improvement of communication in multi-channel WSNs. Consequently, four protocols are proposed. The first one is the Multi-Channel Scheduling Protocol (MCSP) for wireless personal networks IEEE802.15.4, which focuses on overcoming the collisions problem through a multi-channel scheduling scheme. The second protocol is the Energy-efficient Reinforcement Learning (RL) Multi-channel MAC (ERL MMAC) for WSNs, which bases on the enhancement of the energy consumption in WSNs by reducing collisions and balancing the remaining energy between the nodes using a singleagent RL. The third work is the proposition of a new heuristically accelerated RL protocol named Heuristically Accelerated Reinforcement Learning approach for Channel Assignment (HARL CA) for WSNs to reduce the number of learning iterations in an energy-efficient way taking into account the bandwidth aspect in the scheduling process. Finally, the fourth contribution represents a proposition of a new cooperative multi-agent RL approach for Channel Assignment (CRLCA) in WSNs, which improves cooperative learning using an accelerated learning model, and overcomes the extra communication overhead problem of the cooperative RL using a new method for self-scheduling and energy balancing. The proposed approach is performed through two algorithms SCRLCA and DCRLCA for Static and Dynamic performance respectively. The proposed protocols and techniques have been successfully evaluated and show outperformed results in different cases through several experiments

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

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

This paper investigates the use of deep reinforcement learning (DRL) in a MAC protocol for heterogeneous wireless networking referred to as Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". Specifically, this paper considers the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the resulting rewards, a DLMA node can learn an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes according to a specified objective (e.g., the objective could be the sum throughput of all networks, or a general alpha-fairness objective)