M2M Networks Research Articles

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.

Fast node cardinality estimation and cognitive MAC protocol design for heterogeneous machine-to-machine networks

Machine-to-Machine (M2M) networks are an emerging technology with applications in numerous areas including smart grids, smart cities, vehicular telematics, and healthcare. In this paper, we design two estimation protocols for rapidly obtaining separate estimates of the number of active nodes of each traffic type in a heterogeneous M2M network with $T$ types of M2M nodes (e.g., those that send emergency, periodic, normal type data etc), where $T \geq 2$ is an arbitrary integer. One of these protocols, Method I, is a simple scheme, and the other, Method II, is more sophisticated and performs better than Method I. Also, we design a medium access control (MAC) protocol that supports multi-channel operation for a heterogeneous M2M network with an arbitrary number of types of M2M nodes, operating as a secondary network using Cognitive Radio technology. Our Cognitive MAC protocol uses the proposed node cardinality estimation protocols to rapidly estimate the number of active nodes of each type in every time frame; these estimates are used to find the optimal contention probabilities to be used in the MAC protocol. We compute a closed form expression for the expected number of time slots required by Method I to execute as well as a simple upper bound on it. Also, we mathematically analyze the performance of the Cognitive MAC protocol and obtain expressions for the expected number of successful contentions per frame and the expected amount of energy consumed. Finally, we evaluate the performances of our proposed estimation protocols and Cognitive MAC protocol using simulations.

Development of IoT Technologies for Air Pollution Prevention and Improvement

ABSTRACT In order to mitigate the challenges of air pollution, a large number of Internet of Things (IoT)-related technologies have been developed to evaluate and monitor various parameters of air quality. Hence, this paper reviews the fundamental characteristics of IoT; compares and analyzes radio frequency identification (RFID), M2M, and sensor networks; and accordingly proposes an intelligent and multifunctional monitoring platform for reducing air pollution. Our results indicate that establishing systems for comprehensive network communication, cloud-based decision making, information tracking, and online management based on these technologies will improve the ambient air quality more efficiently. Furthermore, we examine several cases verifying the availability and performance of IoT ambient air quality management platforms.

Media Access Control in Large-Scale Internet of Things: A Review

The Media Access Control (MAC) serves as an imperative part of wireless communication networks which enables efficient provisioning of communication resources for device interconnectivity and ensures Quality of Service (QoS). The emergence of Large-Scale Internet of Things (LS-IoT) networks is characterised by a multi-domain distributed wireless communication network that provides end-to-end connectivity for a multitude of active heterogeneous Machine-to-Machine (M2M) devices. The nature of LS-IoT networks requires robust and scalable MAC protocols to manage concurrent, dynamic and massive media access and resource allocation. Several reviews have been conducted on MAC protocols with a handful of them focused on LS-IoT networks or massive M2M networks. In this paper, the characterisation of LS-IoT networks and the MAC component are extensively discussed delineating the impact of LS-IoT on the MAC. Emerging research issues on the MAC for LS-IoT networks including high collision probability, high control overheads, spectrum constraints, timing constraints, hardware constraints, energy consumption and hidden terminals are discussed. Some recently proposed solutions in literature for enhancing the MAC in LS-IoT networks are discussed under LS-IoT specific MAC protocol enhancement classifications. The contributions and drawbacks of the recent solutions in literature are discussed and summarised. The Future direction that is oriented towards the use of virtualisation within the framework of Network Function Virtualisation (NFV) and Software-Defined Networking (SDN) approaches is proposed. This is aimed at providing a dynamic distributed multidimensional MAC resource scaling approach for LS-IoT access network devices to ensure a robust and effective MAC for massive M2M devices in LS-IoT networks.

Steady-State Analysis of the Distributed Queueing Algorithm in a Single-Channel M2M Network

The Distributed Queuing (DQ) algorithm is predicted as one of the solutions to the issues currently found in IoT networks over the use of Aloha based algorithms. Since recently, the algorithm has been of interest to many IoT researchers as a replacement of those Aloha variants for channel access. However, previous works analyzed and evaluated the DQ algorithm without any consideration of the stability of its queues, assuming it is stable for any given number of nodes in the network. In this paper, we define the DQ stability condition in a single-channel M2M environment considering a traffic model of periodic and urgent frames from each node in the network. Besides, a steady-state evaluation of the algorithm’s performance metrics is also presented. In general, the DQ algorithm, when it is stable, was observed not to efficiently use the contention slots for the collision resolution. In a single-channel environment, the DQ algorithm is found to outperform the Aloha based algorithms only in an idle-to-saturation scenario.

Hybrid Neural Network Model for Protection of Dynamic Cyber Infrastructure

The paper considers a combination of modern artificial neural networks (ANN) that solves the security relative task of intrusion prevention and vulnerabilities detection in cybernetic infrastructure with dynamic network topology. Self-organizing networks, WSN, m2m networks, IIoT, mesh networks are faced with the cyberthreats of specific character: dynamic routing failures, node isolation, DDoS attacks, traffic lack, etc. Most of them are caused by cybersecurity weaknesses: the software vulnerabilities and architectural features of dynamically reconfigured network. The existing methods of binary code analysis and intrusion detection can work with a small number of data sets, are designed for either code inspection or network checking, and are targeted for static networks with regular topology. The proposed neural model demonstrates an universal approach that deals with the cybersecurity weakness as a systems genuine property and attempts to approximate it using a hybrid deep ANN. The new ANN detects both the network security defects and binary code vulnerabilities at once with high accuracy (more than 0.97). It also shows good performance capacity processing big data of the undercontrolled network.

GSMAC: Group-Scheduled MAC Protocol with Energy Beamforming in M2M Networks

In Machine-to-Machine (M2M) networks, an enormous number of stations with low energy budgets contend for the channel access in order to get the transmission opportunity, leading to huge amount of congestion. Energy beamforming technology enables wireless devices to replenish their energy much faster than the traditional energy harvesting strategies. In this paper, a group-scheduled Medium Access Control (GSMAC) protocol is proposed as a solution of managing the access of stations that transmit bursts of data packets while taking into account the benefit of energy beamforming technology in M2M networks. In the proposed protocol, nodes are grouped by multiple Power Beacons in order to reduce the high collision probability. Nodes first contend the channel access in each group for reservation opportunities so that the reserved nodes can transmit data and harvest energy simultaneously by Access Point scheduling the data and energy transfer order. Simulation and analytical results show that GSMAC has significant improvements compared with the traditional distributed coordination function in terms of saturation throughput, delay, and energy-related performances.

IoMT Platform for Pervasive Healthcare Data Aggregation, Processing, and Sharing Based on OneM2M and OpenEHR.

Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).

Probability-based opportunity dynamic adaptation (PODA) of contention window for home M2M networks

With the emergence of the Internet of Things (IoT), the growing use of autonomous sensing and actuating devices in areas such as smart grid, e-healthcare, home networking, and machine-to-machine (M2M) communication has become an important communication paradigm. Nonetheless, to fully exploit the applications facilitated by M2M communication, service requirements such as data throughput, scalability and reliability must be in place. This paper proposes a new backoff adaptation mechanism known as probability-based opportunity dynamic adaptation (PODA) for M2M communication using the IEEE 802.11ah protocol. The proposed PODA is an enhanced version of the binary exponential backoff (BEB) where a station estimates the number of contending stations in a distributed manner and adaptively tunes its minimum contention window (CW) prior to the contention process for better network throughput and packet delivery ratio. Owing to its great flexibility and ease of implementation, BEB has been extended to home M2M communication such as wireless sensor networks and smart grid technologies without relying on wide area communication. However, the current form of BEB has its shortcomings in the emerging M2M paradigm. The adaptation of CW in PODA is based on the optimal station's access opportunity to improve network performance instead of direct CW scaling. Using the proposed adaptation method, the network throughput can be improved by as much as 18 percent in the home M2M network studied, while enhancing network reliability and fairness.

Performance evaluation of framed slotted ALOHA with reservation packets and succesive interference cancelation for M2M networks

Random access protocols like ALOHA have been considered for machine-to-machine (M2M) communication in future networks for their simplicity of operation. This paper evaluates the performance of a Frame Slotted-ALOHA protocol that uses reservation and data packets (FSA-RDP), in a scenario where a controller collects data packets transmitted by a finite number of M2M devices. In FSA-RDP, frames of variable duration are divided in two parts, the reservation and data subframes. During the reservation subframe, active devices send short reservation packets to the controller. The controller assigns reserved slots in the data subframe to those devices that succeeded with the reservation. At devices, the FIFO service discipline and two queue management schemes, tail drop and push-out, have been considered. When the queue size is of one packet, we develop a discrete-time Markov chain to evaluate the protocol performance, including the cumulative distribution function of the delay of data packets that are successfully transmitted. Analytical results are validated by extensive simulations. The simulation model is also used to evaluate the system performance when larger queues are used. In addition, we study the impact that implementing Successive Interference Cancellation (SIC) at the controller has on the system performance. We also evaluate the performance of implementing SIC at the controller together with Irregular Repetition Slotted ALOHA (IRSA) to send the reservation packets. Numerical results show that the protocol efficiency of FSA-RDP is between one and two orders of magnitude larger than the efficiency of conventional Frame Slotted ALOHA, when a perfect channel is assumed. In more realistic channel environments, the use of SIC brings an important performance boost.

User Association for Load-Balance in Heterogeneous M2M Networks

User Association for Load-Balance in Heterogeneous M2M Networks

Design of a Secure Password-Based Authentication Scheme for M2M Networks in IoT Enabled Cyber-Physical Systems

The Internet of Things (IoT) forms a foundation for cyber-physical systems. We propose an efficient and secure authentication scheme for machine-to-machine (M2M) networks in IoT enabled cyber-physical systems. Smart objects and smart devices over CPS are capable of capturing a variety of multimedia contents; interact with each other and also with the physical world in a fully automatic manner without human interference. The proposed scheme allows any pair of entities in an M2M network to mutually authenticate each other and agree on a session key for communicating data in a secure and efficient way. The authentication process does not incorporate the M2M service provider, and hence eliminates the burden of managing the authentication of massive scale devices at the edge of the network. The burden of the authentication process is offloaded and distributed on the gateways under the authority of this M2M service provider. The proposed scheme requires the mobile user to hold only one secret key provided by the M2M service provider, by which, he can roam randomly in the M2M network and authenticate to any of the gateways in the domain. Then, this authenticated gateway allows the mobile user to authenticate with any sensor node in the domain. In the proposed scheme, the authentication process does not rely on any public key cryptographic operations. Authentication is achieved using very few hash invocations and symmetric key encryptions. Therefore, the scheme is suitable for environmental sensors which are limited in resources (computation, storage, and energy). We analyze the security of the proposed scheme using BAN logic, which is widely accepted as a framework for the assessment of authentication protocols and also using ProVerif. We assess the efficiency of the proposed scheme and compare with some recently proposed schemes.

Performance Analysis of Cognitive Clustered M2M Random Networks With Joint User and Machine Device Selection

In this paper, a machine-to-machine (M2M) communication system is proposed with joint M2M and cellular user equipment (CUE) device selection schemes to decrease the outage probability of the system. The machine devices and CUEs are positioned randomly according to a binomial point process (BPP), and two novel ordering metrics are proposed for the joint selection scheme: one based on the locations of the M2M devices and the other based on instantaneous channel gains. The simulation results confirm that the proposed selection scheme attains a significant reduction in the outage probability for M2M networks while limiting the interference to the base station (BS) by a delimited threshold. A hybrid-duplex BS is employed to switch between a half-duplex (HD) and a full-duplex (FD) to attain the best performance corresponding to various levels of residual self-interference. The closed-form formulas of the outage probability are derived for each of these ordering policies corresponding to different path loss exponents, and the analytical results are verified through Monte Carlo simulations. The proposed model and its related analysis is given in this paper lead the way for further work in the 5G Internet of Things (IoT) area.

A NOMA-Enhanced Reconfigurable Access Scheme With Device Pairing for M2M Networks

This paper aims to address the distinct requirements of machine-to-machine networks, particularly heterogeneity and massive transmissions. To this end, a reconfigurable medium access control with the ability to choose a proper access scheme with the optimal configuration for devices based on the network status is proposed. In this scheme, in each frame, a separate time duration is allocated for each of the non-orthogonal multiple access (NOMA)-based, orthogonal multiple access (OMA)-based, and random access-based segments, where the length of each segment can be optimized. To solve this optimization problem, an iterative algorithm consisting of two sub-problems is proposed. The first sub-problem deals with selecting devices for the NOMA-/OMA-based transmissions, while the second one optimizes the parameter of the random access scheme. To show the efficacy of the proposed scheme, the results are compared with the reconfigurable scheme which does not support NOMA. The results demonstrate that by using a proper device pairing scheme for the NOMA-based transmissions, the proposed reconfigurable scheme achieves better performance when NOMA is adopted.

Energy-Efficient Resource Allocation With Hybrid TDMA–NOMA for Cellular-Enabled Machine-to-Machine Communications

Machine-to-machine (M2M) communications, which are considered as an integral part of the Internet of Things (IoT), are being more and more ubiquitous. Meanwhile, due to the advantages of cellular networks (e.g., excellent coverage and mobility/roaming support), cellular-enabled M2M communication is a promising solution for M2M-based applications. However, there are significant challenges in cellular-enabled M2M communications due to the special features of M2M-based applications [e.g., massive concurrent uplink transmissions, small bursty traffic, and the high requirements of energy efficiency (EE)]. On the other hand, non-orthogonal multiple access (NOMA) can simultaneously serve multiple users at the same frequency and time by splitting different users in the power domain and thus increase the number of concurrent connections. In this paper, we propose an energy-efficient resource allocation scheme with hybrid time division multiple access (TDMA)-non-orthogonal multiple access (NOMA) for cellular-enabled M2M networks. First, we configure the user equipments (UEs) as the machine type communication gateways (MTCGs). Then, we propose our time sharing scheme. Next, we formulate the resource allocation problems as a noncooperative game. Finally, in order to obtain the optimal EE of machine type communication devices (MTCDs) and UEs in the game to confirm the power allocation, we transform each non-convex optimization problem into the convex form by using nonlinear fractional programming and solve the transformed problem by Dinkelbach's method and Lagrangian duality theory. The simulation results show that our scheme can dramatically shorten the total transmission time at the cost of a little more total energy when compared to the existing works.

Optimised approach based on hybrid MAC protocol for M2M networks

In machine-to-machine (M2M) networks, the ability to access the radio channel requires a rigid design of a medium access control (MAC) protocol. Here, an optimised hybrid MAC protocol will be proposed which is composed of two main processes, the contending process and the transmission process. During contention process, the devices randomly contend the transmission opportunities with equal probabilities by using the conventional-based slotted ALOHA (S-ALOHA) mechanism. Only successful devices can assign a transmission slot during the transmission process by using the reservation-based time division multiple access mechanism. Additionally, an optimisation problem will be formulated to obtain the maximum aggregated throughput. Moreover, the optimum relation between the contention and transmission periods under different number of devices is calculated. The optimisation problem is solved with the aid of mathematical analysis which is carried out through Monte Carlo simulation. Furthermore, extensive MATLAB programs are executed to study the system performance in terms of: the system throughput, the average packet delay, the success access ratio, and the reservation ratio. The simulation results show that the proposed hybrid technique outperforms other related hybrid when compared.

M2M-REP: Reputation system for machines in the internet of things

In the age of IoT (Internet of Things), Machine-to-Machine (M2M) communication has gained significant popularity over the last few years. M2M communication systems may have a large number of autonomous connected devices that provide services without human involvement. Interacting with compromised, infected and malicious machines can bring damaging consequences in the form of network outage, machine failure, data integrity, and financial loss. Hence, users first need to evaluate the trustworthiness of machines prior to interacting with them. This can be realized by using a reputation system, which evaluates the trustworthiness of machines by utilizing the feedback collected from the users of the machines. The design of a reliable reputation system for the distributed M2M communication network should preserve user privacy and have low computation and communication overheads. To address these challenges, we propose an M2M-REP System (Machine to Machine REPutation), a privacy-preserving reputation system for evaluating the trustworthiness of autonomous machines in the M2M network. The system computes global reputation scores of machines while maintaining privacy of the individual participant score by using secure multi-party computation techniques. The M2M-REP system ensures correctness, security and privacy properties under the malicious adversarial model, and allows public verifiability without relying on a centralized trusted system. We implement a prototype of our system and evaluate the system performance in terms of the computation and bandwidth overhead.

SDN-Enabled Traffic-Aware Load Balancing for M2M Networks

This paper proposes a traffic-aware load balancing scheme for machine-to-machine (M2M) networks using software-defined networking (SDN). Load balancing techniques are essential for M2M networks to relieve the heavy loading caused by bursty traffic. Leveraging the capability of SDN to monitor and control the network, the proposed load balancing scheme can satisfy different quality of service requirements through traffic identification and rerouting. Experimental results show that the proposed scheme can reduce service response time up to 50% compared to the non-SDN load balancing scheme.

Green Machine-to-Machine Communications with Mobile Edge Computing and Wireless Network Virtualization

With the explosive growth of machine-type communication devices (MTCDs), both academia and industry are paying more attention to M2M communications. In this article, we introduce mobile edge computing into virtualized cellular networks with M2M communications to decrease energy consumption and optimize computing resource allocation. Moreover, based on different functions and QoS requirements, the physical network can be virtualized into several virtual networks; then each MTCD selects the corresponding virtual network to access through the embedded subscriber identity module (eSIM) technology. Meanwhile, the random access process of MTCDs is formulated as a partially observable Markov decision process to minimize the system cost, which consists of both energy consumption and execution time of computing tasks. Furthermore, to facilitate the network architecture integration, software-defined networking is introduced to deal with the diverse protocols and standards in M2M networks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

Performance Evolution of M2M Networks with Improved Particle Swarm Optimization Algorithm

Performance Evolution of M2M Networks with Improved Particle Swarm Optimization Algorithm