Cognitive M2M Network Research Articles

Deep Reinforcement Learning-based resource allocation strategy for Energy Harvesting-Powered Cognitive Machine-to-Machine Networks

Machine-to-Machine (M2M) communication is a promising technology that may realize the Internet of Things (IoTs) in future networks. However, due to the features of massive devices and concurrent access requirement, it will cause performance degradation and enormous energy consumption. Energy Harvesting-Powered Cognitive M2M Networks (EH-CMNs) as an attractive solution is capable of alleviating the escalating spectrum deficient to guarantee the Quality of Service (QoS) meanwhile decreasing the energy consumption to achieve Green Communication (GC) became an important research topic. In this paper, we investigate the resource allocation problem for EH-CMNs underlaying cellular uplinks. We aim to maximize the energy efficiency of EH-CMNs with consideration of the QoS of Human-to-Human (H2H) networks and the available energy in EH-devices. In view of the characteristic of EH-CMNs, we formulate the problem to be a decentralized Discrete-time and Finite-state Markov Decision Process (DFMDP), in which each device acts as agent and effectively learns from the environment to make allocation decision without the complete and global network information. Owing to the complexity of the problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm to solve the problem. Numerical results validate that the proposed scheme outperforms other schemes in terms of average energy efficiency with an acceptable convergence speed.

Throughput enhancement of cognitive M2M networks based on NOMA for 5G communication systems

SummaryOne of the promising technologies for 5G cellular networks is machine‐to‐machine (M2M) communications. We propose a cognitive radio network (CR) that includes a primary cellular system and a secondary cognitive system. The primary cellular system has a primary client (PC) and the secondary cognitive system has M2M clients which are called secondary clients (SCs). In a conventional system of CR network based on orthogonal frequency division multiple access (CR‐OFDMA), when the primary client (PC) is absent, only one SC can obtain the idle spectrum. But, the SC must leave the spectrum when detecting the existence of the PC. So, the spectrum usage of this system is very low. This paper proposes a cooperative CR network based on nonorthogonal multiple access (CCR‐NOMA) for spectrum sensing using energy detection (ED) to allow multiple SCs to share the same frequency at the same time, but are differentiated according to the power domain or code domain, to improve spectrum efficiency of 5G communications and the transmission performance of CR network at the absence and presence of the PC. To evaluate the channel sensing performance of the ED technique in CCR‐NOMA, we derived a closed‐form expression between the achievable throughput and sensing time for the CCR‐NOMA system. The same analysis for the case of CR‐OFDMA is reproduced for the sake of comparison. The analysis showed that the CR‐NOMA system for M2M communication outperforms the CR‐OFDMA system for M2M communication for the same noncooperative and cooperative spectrum sensing and physical layer parameters.

Statistical QoS Control of Network Coded Multipath Routing in Large Cognitive Machine-to-Machine Networks

Machine-to-machine (M2M) communication enables many applications such as smart grid, vehicular safety, and health care among many others. To achieve ubiquitous data transportation among objects and the surrounding environment, deploying spectrum sharing M2M communications with existing wireless networks is a must. A general large-scale cognitive M2M network (CM2MN), adopting cognitive radio technology, consists of multiradio systems, the primary system (PS), and secondary system(s) with tremendous cooperative cognitive machines, under heterogeneous wireless architecture. For these CM2MNs, due to dynamic spectrum access (DSA) nature, there exists possibly unidirectional opportunistic wireless fading links and thus traditional flow control mechanisms at link level do not fit anymore. Furthermore, effective end-to-end quality-of-service (QoS) control is still required to provide a reliable transportation for such multihop CM2M communications. Facing the above challenges, we propose a novel statistical QoS control mechanism through cooperative relaying, realizing virtual multiple-input and multiple-output (MIMO) communications at session level . In particular, a probabilistic network coded routing algorithm and the statistical QoS guarantee are first proposed to coordinate and cooperate tremendous machines. Next, based on the proposed guarantee and routing algorithm, the statistical QoS control mechanism is designed to enable MIMO communications for the session traffic. Specifically, the diversity mode is used to deal with PS’s opportunistic nature and wireless fading, and the spatial multiplexing mode is employed to obtain the maximum end-to-end throughput. Simulation results confirm that under our control solution, the great improvements of end-to-end delay violation probability are obtained, thus practically facilitating network coded multipath routing in large CM2MNs.

Energy Efficient Cognitive M2M Communications

The number of Machine-to-Machine (M2M) devices has increased massively in the last few years and will continue to increase in the years to come. Spectrum utilisation efficiency and energy efficiency are the main challenges and design goals for M2M networks. Cognitive radio (CR) is a promising technology that can address these challenges. In this paper, the authors have proposed and developed an energy efficient mechanism to reduce energy consumption in Cognitive M2M (CM2M) networks. Their solution guarantees the throughput and reliability constraints for CM2M Devices (CM2MDs). The proposed mechanism can reduce energy consumption in CM2M networks by exploiting efficient sensing and accessing schemes for CM2MDs. The authors further develop sleep-mode/switching and accessing techniques to work efficiently with the proposed mechanism. The simulation results show that the proposed mechanism guarantees a desirable throughput and reduces overall energy consumption in the network.

PRMA-Based Cognitive Machine-to-Machine Communications in Smart Grid Networks

It is expected that cognitive machine-to-machine (M2M) communications in smart grid networks will be indispensable in the near future. In this paper, after discussing the motivation of cognitive M2M communications in a smart grid network, we design a cognitive medium access control (MAC) protocol, keeping in view the unique features of M2M devices and smart grid communication requirements. We propose the use of packet reservation multiple access (PRMA), carry out its feasibility study, and adapt and significantly enhance it with modifications particularly tailored for M2M environments. The proposed protocol is centralized in nature and utilizes a specialized frame structure for supporting the coexistence of the cognitive M2M network with the primary network. This frame structure is further optimized, considering different tradeoffs pertaining to primary network protection and the utility of a secondary M2M network. Performance evaluation is carried out by analytical modeling and simulation studies. We also present a case study for application of the proposed protocol in advanced metering infrastructure (AMI) networks under the dynamics of power systems.

Cognitive Machine-to-Machine Communications for Internet-of-Things: A Protocol Stack Perspective

Machine-to-machine (M2M) communications enables networked devices to exchange information among each other as well as with business application servers and therefore creates what is known as the Internet-of-Things (IoT). The research community has a consensus for the need of a standardized protocol stack for M2M communications. On the other hand, cognitive radio technology is very promising for M2M communications due to a number of factors. It is expected that cognitive M2M communications will be indispensable in order to realize the vision of IoT. However cognitive M2M communications requires a cognitive radio-enabled protocol stack in addition to the fundamental requirements of energy efficiency, reliability, and Internet connectivity. The main objective of this paper is to provide the state of the art in cognitive M2M communications from a protocol stack perspective. This paper covers the emerging standardization efforts and the latest developments on protocols for cognitive M2M networks. In addition, this paper also presents the authors' recent work in this area, which includes a centralized cognitive medium access control (MAC) protocol, a distributed cognitive MAC protocol, and a specially designed routing protocol for cognitive M2M networks. These protocols explicitly account for the peculiarities of cognitive radio environments. Performance evaluation demonstrates that the proposed protocols not only ensure protection to the primary users (PUs) but also fulfil the utility requirements of the secondary M2M networks.

Cognitive machine-to-machine communications: visions and potentials for the smart grid

Based upon cognitive radio technology, we propose a new Machine-to-Machine (M2M) communications paradigm, namely Cognitive M2M (CM2M) communication. We first motivate the use of cognitive radio technology in M2M communications from different point of views, including technical, applications, industry support, and standardization perspectives. Then, our CM2M network architecture and cognitive machine model are presented and the CM2M systems coexistence in TV white spaces is discussed. After that, a CM2M communications architecture for the smart grid is presented, for which we also propose an energy-efficiency driven spectrum discovery scheme. Numerical results demonstrate significant energy saving and the reliability in supporting data transmissions in the smart grid.