Cellular M2M Research Articles

Priority‐based learning automata in Q‐learning random access scheme for cellular M2M communications

This paper applies learning automata to improve the performance of a Q-learning based random access channel (QL-RACH) scheme in a cellular machine-to-machine (M2M) communication system. A prioritized learning automata QL-RACH (PLA-QL-RACH) access scheme is proposed. The scheme employs a prioritized learning automata technique to improve the throughput performance by minimizing the level of interaction and collision of M2M devices with human-to-human devices sharing the RACH of a cellular system. In addition, this scheme eliminates the excessive punishment suffered by the M2M devices by controlling the administration of a penalty. Simulation results show that the proposed PLA-QL-RACH scheme improves the RACH throughput by approximately 82% and reduces access delay by 79% with faster learning convergence when compared with QL-RACH.

An Efficient Resource Allocation for Massive MTC in NOMA-OFDMA Based Cellular Networks

To alleviate random access congestion and support massive-connections with less energy consumption for machine-type communications (MTC) in the 5G cellular network, we propose an efficient resource allocation for massive MTC (mMTC) with hybrid non-orthogonal multiple access (NOMA)-orthogonal frequency division multiple access (OFDMA). First, a hybrid multiple access scheme, including the NOMA-based congestion-alleviating access scheme (NCAS) and OFDMA-based congestion-alleviating access scheme (OCAS), is proposed, in which the NOMA based devices coexist with OFDMA based ones. Then, aiming at maximizing the system access capacity, a traffic-aware resource blocks (RBs) allocation is investigated to optimize RBs allocation for preamble transmission and data packets transmission, as well as to optimize the RBs allocation between NCAS and OCAS for the RBs usage efficiency improvement. Next, aiming at the problem of high computational complexity and improving energy efficiency in hybrid NOMA-OFDMA based cellular M2M communications, this paper proposes an improved low complexity power allocation algorithm. The feasibility conditions of power allocation solution under the maximum transmit power constraints and quality of service (QoS) requirements of the devices is investigated. The original non-convex optimization problem is solved under the feasibility conditions by two iterative algorithms. Finally, a device clustering scheme is proposed based on the channel gain difference and feasible condition of power allocation solution, by which NOMA based devices and OFDMA based devices can be determined. Simulation results show that compared with non-orthogonal random access and transmission (NORA-DT), the proposed resource allocation scheme for hybrid NOMA-OFDMA systems can efficiently improve the performance of access capacity and energy efficiency.

Dynamic Backoff Collision Resolution for Massive M2M Random Access in Cellular IoT Networks

The deployment of machine-to-machine (M2M) communications on cellular networks provides ubiquitous services to Internet-of-Things (IoT) systems. Cellular networks have been chosen as the best infrastructure for M2M communications due to the wide coverage and spectral efficiency. However, with the increased number of devices connecting to the network, massive number of devices are expected to simultaneously access the network resources. This massive access results in excessive congestion and collisions in the random access channel (RACH) which causes major degradation in systems performance. This article focuses on resolving the RACH collisions during the massive access scenarios for cellular M2M communications. We propose a collision resolution scheme using the backoff procedure which dynamically adjusts the backoff indicator (BI) based on the number of backlog devices and the available resources. The proposed scheme is integrated with three well-known random access schemes; standard random access (SRA), static access class barring (ACB) and dynamic access class barring (DAB). Furthermore, the paper presents an analysis for access success probability based on the dynamic backoff procedure. The optimal value of BI that achieves the highest access success probability is derived for the three different schemes. The analysis and simulation results indicate that the dynamic value of BI achieves approximately 99.9% access success rate with a slight increase in access delay of around 10%, which is considered a reasonable increment for delay-tolerant applications during the massive arrivals scenarios.

Self-Sustainable Dense Cellular M2M System With Hybrid Energy Harvesting and High Sensitivity Rectenna

The Internet of Things (IoT) is envisioned as an essential enabler of the smart city, which 5G small cell is going to cover. In this paper, we model the dense cellular M2M communication system using a Ginibre determinant point process and study the corresponding green communication metrics, including grid energy consumption and grid energy efficiency (EE). A dedicated millimeter-wave-based hybrid energy harvesting (EH) mechanism is proposed to maintain self-sustainable communication. Two EH modes are discussed, of which Mode 1 is suitable for long-term operation, while Mode 2 is preferred in dealing with a temporary energy shortage. We first minimize the average grid energy consumption by solving a large-scale linear programming problem. When the battery at machine device is charged with sufficient energy at the initial time, closed-form formula describing the average grid energy consumption of both the modes can be derived under moderate throughput constraints, which is proved to have clear relationship with 1) initial battery energy level at machine gateway and machine device; 2) specific hybrid EH time-division strategy; 3) system operation time; and 4) static power consumption at machine gateway and machine device. The numerical solutions are obtained using the interior-point method, and the results are illustrated and discussed. When dealing with the originally non-convex EE maximization problem, we first transform it into a series of sub-problems using the successive convex approximation method and then iteratively solve them with a trick combining Taylor expansion and square error minimization. Non-orthogonal multi-access (NOMA) transmission is investigated in our model to further improve the EE performance. The experiments show that NOMA achieves nearly three times throughput promotion and two times EE improvement.

Millimeter-Wave NOMA Transmission in Cellular M2M Communications for Internet of Things

Massive connectivity and low latency are two important challenges for the Internet of Things (IoT) to achieve the quality of service provisions required by the numerous devices it is designed to service. Motivated by these challenges, in this paper we introduce a new millimeter-wave nonorthogonal multiple access (mmWave-NOMA) transmission scheme designed for cellular machine-to-machine (M2M) communication systems for IoT applications. It consists of one base station (BS) and numerous multiple machine type communication (MTC) devices operating in a cellular communication environment. We consider its down-link performance and assume that multiple MTC devices share the same communication resources offered by the proposed mmWave-NOMA transmission scheme, which can support massive connectivity. For this system, a novel MTC pairing scheme is introduced the design of which is based upon the distance between the BS and the MTC devices aiming at reducing the system overall overhead for massive connectivity and latency. In particular, we consider three different MTC device pairing schemes, namely: 1) random near and the random far MTC devices; 2) nearest near and the nearest far MTC devices (NNNF); and 3) nearest near and the farthest far MTC device. For all three pairing schemes, their performance is analyzed by deriving closed-form expressions of the outage probability and the sum rate. Furthermore, performance comparison studies of the three MTC device pairing schemes have been carried out. The validity of the analytical approach has been verified by means of extensive computer simulations. The obtained performance evaluation results have demonstrated that the proposed cellular M2M communication system employing the mmWave-NOMA transmission scheme improves outage probability as compared to equivalent systems using mmWave with orthogonal multiple access schemes.

A Group-Based Massive Multiple Access Scheme in Cellular M2M Networks

With the rapid development of machine-to-machine (M2M) applications, there is an explosion in the number of M2M devices emerging in the network. It poses a significant challenge for the network to support random access requests of a huge number of M2M devices. In this paper, we propose a novel group-based random access scheme for cellular M2M communications, where the beamforming technology enhanced by massive multiple-input multiple-output (MIMO) is applied. The key idea of the proposed scheme is that the evolved Node-B (eNB) makes use of multiple beams to divide M2M devices into different groups. Benefited from the spatial selectivity of beams, the interference among different groups is limited, which can efficiently reduce collisions during the random access procedure. Numerical results demonstrate that our proposed group-based random access scheme has the higher access success probability of devices compared with the legacy random access scheme.

Scalable Coding and Prioritized Transmission of ECG for Low-Latency Cardiac Monitoring Over Cellular M2M Networks

This paper reduces the delay in wireless transmission of electrocardiogram (ECG) data while protecting their essential elements to produce a clinically usable trace. The ECG data are divided into nine layers according to the signal amplitude resolution (6, 8, and 11 bits per sample) and sampling frequency (75, 300, and 2400 Hz). To avoid retransmission, each layer is delivered with a dynamic link adaptation scheme that provides the different levels of error protection in a forward error correction process according to layer importance. This scheme is built on the conservative modulation and coding, as well as on the machine-type communication (MTC) protocol, from LTE-Advanced. Simulations show that our proposed system delivers ECG data that sufficiently support a clinical diagnosis with significantly low latencies (less than 10 ms) under severely erroneous channel conditions.

Machine-to-Machine Communication and Research Challenges: A Survey

In recent years, with the proliferation of machine-to-machine (M2M) applications into industries, M2M communications has attracted researchers in this prominent field of research. M2M communications has emerged to achieve ubiquitous communication among intelligent devices to monitor applications with little or no human intervention. The autonomous characteristics of M2M communications, which decrease the cost for human resource significantly, give impetus for the research of M2M communications in both industry and academic areas. This paper focuses on state-of-the-art M2M technologies, future challenges and envisioned opportunities. We have divided M2M communications into two categories, namely, capillary M2M and cellular M2M. In this paper, we provide a comprehensive study of M2M communications, including different categories and their challenges. This paper also investigates into the standards defined by the standardization organizations, such as IEEE, IETF, 3GPP for the multiplicity of M2M communications. At last, we analyze and discuss the key research challenges in M2M application designs.

An Early Preamble Collision Detection Scheme Based on Tagged Preambles for Cellular M2M Random Access

In this paper, we propose an early preamble collision detection (e-PACD) scheme at the first step of random access (RA) procedure based on tagged preambles (PAs), which consist of both PA and tag Zadoff–Chu sequences using different root numbers, respectively. The proposed e-PACD scheme enables faster PA collision detection and notification, compared with the conventional RA scheme. Accordingly, it can reduce the RA delay and remove resource wastes which occur at the third step of the conventional RA procedure. The reduced RA delay achieved by the proposed e-PACD scheme can contribute to an ultralow latency requirement in fifth-generation (5G) cellular networks. In addition, the proposed e-PACD scheme enables the eNodeB to monitor the number of RA-attempting nodes (RA load) on each physical RA channel slot. The PACD probability and the RA load monitoring accuracy are mathematically analyzed, and the RA performance enhancement of the proposed scheme is evaluated in terms of RA success probability, average RA delay, and RA resource efficiency, compared with the conventional RA scheme.

Pricing-Based Load Control of M2M Traffic for the LTE-A Random Access Channel

Existing cellular infrastructures have to be revisited for emerging machine-to-machine (M2M) traffic as semi-synchronized M2M arrivals may create a significant congestion resulting in a high access delay. In such a case, there is a strong need for service differentiation, since the delay requirements of IoT applications may vary greatly from delay-tolerant metering applications to security applications with stringent requirements. This problem has been receiving significant interest from the research community in the context of the LTE-A random access channel. Most studies, however, consider load control schemes with few fixed service classes, which can provide limited service differentiation. We propose an alternative scheme where the load is controlled by the price announced by the base station. The proposed method controls the load effectively and provides negligible delay for messages with the highest priority. It also enables low-cost wireless access to delay-tolerant messages by generating most of the revenue from high-priority messages. We derive pricing-based load control schemes for throughput and revenue maximization, and present the relationship between delay, revenue, and cost in both schemes. Our results suggest that dynamic pricing is a promising solution for major problems associated with cellular M2M traffic.

A Multi-Service Oriented Multiple Access Scheme for M2M Support in Future LTE

We propose a novel multiple access technique to overcome the shortcomings of the current proposals for the future releases of LTE. We provide a unified radio access system that efficiently and flexibly integrates both traditional cellular services and M2M connections arising from IoT applications. The proposed solution, referred to as MOMA, is based on establishing separate classes of users using relevant criteria that go beyond the simple handheld-IoT device split; service-dependent hierarchical spreading of the data signals; and a mix of multiuser and single-user detection schemes at the receiver. Signal spreading in MOMA allows densely connected devices with different QoS profiles to be handled, and at the same time its flexible receiver structure allows the receiver computational resources to be allocated to the connections that need them most. This yields scalable and efficient use of the available radio resources and better service integration. While providing significant advantages for key future communications scenarios, MOMA can be incorporated into LTE with a limited impact on the protocol structure and the signaling overhead.

Massive Multiple Access Based on Superposition Raptor Codes for Cellular M2M Communications

Machine-to-machine (M2M) wireless systems aim to provide ubiquitous connectivity between machine-type communication (MTC) devices without any human intervention. Given the exponential growth of MTC traffic, it is of utmost importance to ensure that future wireless standards are capable of handling this traffic. In this paper, we focus on the design of a very efficient massive access strategy for highly dense cellular networks with M2M communications. Several MTC devices are allowed to simultaneously transmit in the same resource block by incorporating Raptor codes and a simple modulation scheme. This significantly reduces the access delay and improves the achievable system throughput. A simple yet efficient random access strategy is proposed to not only detect the selected preambles, but also estimate the number of devices which have chosen them. No device identification is needed in the random access phase which significantly reduces the signaling overhead. The proposed scheme is analyzed and the maximum number of MTC devices that can be supported in a resource block is characterized as a function of the message length, number of available resources, and the number of preambles. Simulation results show that the proposed scheme can effectively support a massive number of MTC devices for a limited number of available resources, when the message size is small.

A Non-Orthogonal Resource Allocation Scheme in Spatial Group Based Random Access for Cellular M2M Communications

Cellular machine-to-machine (M2M) communication can be one of the major candidate technologies to develop an Internet of Things (IoT) platform. A massive number of machine nodes access the cellular networks and typically send/receive small-sized data. In this situation, severe random access (RA) overload and radio resource shortage problems may occur if there is no evolution in the conventional cellular system. Focusing on RA, we need a larger number of preambles, as well as a more efficient resource allocation scheme in order to accommodate a significantly large number of RA requests from machine nodes. In this paper, we propose a non-orthogonal resource allocation (NORA) scheme, combined with our spatial group based RA (SGRA) mechanism, in order to provide a sufficiently large number of preambles at the first step of the RA procedure and non-orthogonally allocate physical uplink shared channel resources at the second step of the RA procedure. As a result, the proposed SGRA-NORA scheme can significantly increase the RA success probability, compared with that of the conventional RA scheme.

AOM: adaptive mobile data traffic offloading for M2M networks

With the increasing application of machine-to machine (M2M) communication through cellular networks, such as telematics, smart metering, point-of-sale terminals, and home security, more data traffice has been produced in the cellular network. Although many schemes have been proposed to reduce data traffic, they are inefficient in practical application due to poor adaption. In this paper, we focus on how to adaptively offload data traffic for cellular M2M networks. To this end, we propose an adaptive mobile data traffic offloading model (AOM). This model can decide whether to adopt opportunistic communications or communicate via cellular networks adaptively. In the AOM, we introduce traffic offloading rate (called TOR) and local resource consumption rate (called LRCR) and analyze them based on continue time Markov chain. Theory proof and extensive simulations demonstrate that our model is accurate and effective, and can adaptively offload data traffic of cellular M2M networks.

Performance of Dynamic Access Class Barring Method in Cellular M2M Networks

Cellular based machine-to-machine (M2M) systems generate massive number of access requests which create congestion in the cellular network. The contention-based random access procedures are designed for cellular networks which cannot accommodate a large number of M2M traffic. In this paper, a contention-based slotted Aloha random access procedure for M2M network is first analyzed using different performance metrics. The impact of massive M2M traffic over cellular traffic is studied based on different arrival rate, random access opportunity and throughput. Then, an analytical model of selecting a base station (eNB) along with load balancing is developed. Finally, a dynamic access class barring method and RAN level congestion control mechanism by selecting the appropriate eNB is presented and evaluated with M2M traffic. Numerical results show that frame transmission rate influences the selection probability of the base stations. Moreover, the dynamic access class barring parameter along with frame transmission rate improve the overall throughput and access success probability among base stations as well as avoid overload situation in a particular base station.

M2M Potentials in logistics and transportation industry

All over the world, road congestion is among the most prevalent transport challenges usually in urban environments which not only increases fuel consumption and emission of harmful gases, but also causes stress for the drivers. Intelligent Transportation System (ITS) enables a better use of the infrastructure by connecting vehicles to other vehicles as well as infrastructure and thus delivers a faster communication opportunity to ensure safe and secure driving. Machine-to-machine (M2M) communication is one of the latest information and communication technologies which offers ubiquitous connectivity among several smart devices. The use of mobile (cellular) M2M communications has emerged due to the wide range, high reliability, increased data rates, decreased costs as well as easy and short-term deployment opportunities. Since the radio spectrum is a scarce resource, M2M traffic can potentially degrade the performance of mobile networks due to the large number of devices sending small-sized data. This paper presents an efficient data multiplexing scheme by using Long-Term Evolution Advanced (LTE-Advanced) Relay Nodes, which aggregates M2M traffic to maximize radio resource utilization. Extensive system-level simulations are performed using an LTE-Advanced-based model developed in the RIVERBED modeler to evaluate the performance of the proposed data multiplexing scheme. Simulation results show that approximately 40 % more smart M2M devices used in ITS and logistics are served per LTE-Advanced cell under the present system settings.

Survey of radio resource management issues and proposals for energy-efficient cellular networks that will cover billions of machines

A huge increase of machines attached to wireless networks is expected in the next few years. A large part of these machines will be covered by some wireless wide area networks. The arrival of cellular M2M (machine-to-machine) communication poses new requirements due to its specific characteristics. For most of the cellular M2M applications, the essential requirement is low energy consumption level or high energy efficiency. This survey provides a global view of the network technologies previewed for cellular M2M. In this survey, we study the existing classifications of M2M applications according to different criteria in the literature. The comparison of traffic characteristics between M2M and human-to-human is also proposed. Quality of service (QoS) requirements for typical M2M applications are resumed. The advance of reference M2M network architectures proposed by the Standard Development Organization (SDO) is investigated. We identify two possible effort directions to improve the energy efficiency for cellular M2M. The first one is to evolve the current existing 3rd Generation Partnership Project (3GPP) Consortium cellular networks to effectively support MTC (Machine Type Communication). The other direction is to design M2M-dedicated networks from scratch, which are often called low-power wide-area (LPWA) networks. We review, compare and categorize the proposals related to energy issues of cellular M2M mainly over the period 2011–2015 for the first direction. We introduce the development of LPWA networks for the other research directions. We highlight that the cooperative relaying, the design of energy-efficient signaling and operation, the new radio resource allocation schemes, and the energy-efficient random access procedure are the main points of improvement. It is important to jointly use the aforementioned approaches, for example, joint design of random access control and radio resource allocation, to seek for a trade-off between energy efficiency and other system performances.

An Enhanced Random Access Scheme With Spatial Group Based Reusable Preamble Allocation in Cellular M2M Networks

In the near future, cellular machine-to-machine (M2M) communications are expected to play an important role to realize the Internet of Things (IoT). Due to the exponentially growing number of machine nodes, however, one of challenging issues is to accommodate their random access (RA) requests without severely degrading the performance of human-to-human (H2H) communications. In this letter, we propose an enhanced RA scheme with spatial group based reusable preamble allocation (ERA-SGRPA), which reuses the preamble resources based on a spatial grouping during the RA procedure. By allocating the identical preamble set to the groups which are far apart from each other, the ERA-SGRPA scheme enables the given preamble set to be efficiently reused. The performance evaluation shows that the ERA-SGRPA scheme significantly lowers the collision probability and reduces the access delay and can accommodate a significantly larger number of machine nodes without degrading the performance of H2H communications.

Understanding the IoT connectivity landscape: a contemporary M2M radio technology roadmap

This article addresses the market-changing phenomenon of the Internet of Things (IoT), which relies on the underlying paradigm of machine-to-machine (M2M) communications to integrate a plethora of various sensors, actuators, and smart meters across a wide spectrum of businesses. The M2M landscape features today an extreme diversity of available connectivity solutions which -- due to the enormous economic promise of the IoT -- need to be harmonized across multiple industries. To this end, we comprehensively review the most prominent existing and novel M2M radio technologies, as well as share our first-hand real-world deployment experiences, with the goal to provide a unified insight into enabling M2M architectures, unique technology features, expected performance, and related standardization developments. We pay particular attention to the cellular M2M sector employing 3GPP LTE technology. This work is a systematic recollection of our many recent research, industrial, entrepreneurial, and standardization efforts within the contemporary M2M ecosystem.

Practical aspects of cellular M2M systems design

This paper highlights some crucial design challenges of Machine-to-Machine (M2M) systems. The focus is on the cellular based, wireless wide area network systems. Design of M2M terminals, used as wireless sensor nodes, is covered in detail, including the criteria for selecting appropriate core and hard- ware peripherals. Discussion is extended to modeling and design of terminal?s embedded software. Communication using framework and backend application software architectures are explored. Practical examples of the described design principles are demonstrated.