Number Of Machine-type Communication Devices Research Articles

A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks.

In the realm of the fifth-generation (5G) wireless cellular networks, renowned for their dense connectivity, there lies a substantial facilitation of a myriad of Internet of Things (IoT) applications, which can be supported by the massive machine-type communication (MTC) technique, a fundamental communication framework. In some scenarios, a large number of machine-type communication devices (MTCD) may simultaneously enter the communication coverage of a target base station. However, the current handover mechanism specified by the 3rd Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead within the access and core networks, which may have negative impacts on network efficiency. Additionally, other existing solutions are vulnerable to malicious attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS) attacks, and the failure of Key Forward Secrecy (KFS). To address this challenge, this paper proposes an efficient and secure handover authentication protocol for a group of MTCDs supported by blockchain technology. This protocol leverages the decentralized nature of blockchain technology and combines it with certificateless aggregate signatures to mutually authenticate the identity of a base station and a group of MTCDs. This approach can reduce signaling overhead and avoid key escrow while significantly lowering the risk associated with single points of failure. Additionally, the protocol protects device anonymity by encrypting device identities with temporary anonymous identity markers with the Elliptic Curve Diffie-Hellman (ECDH) to abandon serial numbers to prevent linkage attacks. The resilience of the proposed protocol against predominant malicious attacks has been rigorously validated through the application of the BAN logic and Scyther tool, underscoring its robust security attributes. Furthermore, compared to the existing solutions, the proposed protocol significantly reduces the authentication cost for a group of MTCDs during handover, while ensuring security, demonstrating commendable efficiency.

Joint Resource and Power Allocation for Clustered Cognitive M2M Communications Underlaying Cellular Networks

The massive number of Machine-Type Communication Devices (MTCDs) coexisting with Cellular User Equipment (CUE), in addition to the diverse Quality-of-Service (QoS) requirements of M2M communications and cellular communications, present significant implementation challenges due to interference, congestion, and spectrum scarcity. This makes resource allocation an important but challenging problem. In this article, clustered Cognitive Machine-to-Machine (CM2M) communications underlaying cellular networks is proposed to solve this problem. In this system, MTCDs are grouped in clusters based on their spatial locations and communicate with the Base Station (BS) via a Machine-Type Communication Gateway (MTCG). Underlay Cognitive Radio (CR) is employed so that MTCDs within a cluster can share the spectrum of neighbouring CUE. A joint resource-power allocation problem is formulated and solved using a two-phase resource and power allocation scheme. The goal is to maximize the uplink sum-rate of the neighbouring CUE and clustered MTCDs while satisfying interference, power, and minimum data rate constraints. Simulation results are presented which show that the proposed scheme significantly improves the sum-rate of the network compared to other schemes while satisfying the constraints.

Impact of Compression and Small Cell Deployment on NB-IoT Devices Coverage and Energy Consumption with a Realistic Simulation Model

In the last few years, Low-Power Wide-Area Network (LPWAN) technologies have been proposed for Machine-Type Communications (MTC). In this paper, we evaluate wireless relay technologies that can improve LPWAN coverage for smart meter communication applications. We provide a realistic coverage analysis using a realistic correlated shadow-fading map and path-loss calculation for the environment. Our analysis shows significant reductions in the number of MTC devices in outage by deploying either small cells or Device-to-Device (D2D) communications. In addition, we analyzed the energy consumption of the MTC devices for different data packet sizes and Maximum Coupling Loss (MCL) values. Finally, we study how compression techniques can extend the battery lifetime of MTC devices.

Efficient of Floading Approach for Resource Allocation and Optimization in Mobile Edge Computing Environment

Cloud computing provides a platform for services and resources over the internet for users. The large pool of data resources and services has enabled the emergence of several novel applications such as smart grids, smart environments, and virtual reality. However, the state-of-the-art of cloud computing faces a delay constraint, which becomes a major barrier for reliable cloud services. This constraint is mostly highlighted in the case of smart cities (SC) and the Internet of Things (IoT). Therefore, the recent cloud computing paradigm has poor performance and cannot meet the low delay, navigation, and mobility support requirements.Machine-to-machine (M2M) connectivity has drawn considerable interest from both academia and industry with a growing number of machine-type communication devices (MTCDs). The data links with M2M communications are usually small but high bandwidth, unlike conventional networking networks, demanding performance management of both energy consumption and computing. The main challenges faced in mobile edge computing are task offloading, congestion control, Resource allocation, security and privacy issue, mobility and standardization .Our work mainly focus on offloading based resource allocation and security issues by analyzing the network parameters like reduction of latency and improvisation of bandwidth involved in cloud environment. The cloudsim simulation tool has been utilized to implement the offload balancing mechanism to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability.

Energy-Efficient Machine-to-Machine (M2M) Communications in Virtualized Cellular Networks with Mobile Edge Computing (MEC)

With an increasing number of machine-type communication devices (MTCDs), machine-to-machine (M2M) communications have attracted great attentions from both academia and industry. Different from traditional communication networks, the data connections with M2M communications are typically small-sized but with high frequency, necessitating the efficiency optimization of both energy consumption and computation. In this paper, we introduce mobile edge computing (MEC) into virtualized cellular networks with M2M communications, to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability. Moreover, based on different functions and quality of service (QoS) requirements, the physical network can be virtualized into several virtual networks, and then each MTCD selects the corresponding virtual network to access through the embedded-SIM (eSIM) technology. Meanwhile, the random access process of MTCDs is formulated as a partially observable Markov decision process (POMDP) to minimize the system cost, which consists of both the energy consumption and execution time of computing tasks. Furthermore, to facilitate the network architecture integration, software-defined networking (SDN) is introduced to deal with the diverse protocols and standards in the networks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

Low Latency Random Access for Sporadic MTC Devices in Internet of Things

This paper proposes a compressed sensing-based random access protocol (CS-RACH), which is suitable for servicing a large number of machine-type communication devices in Internet of Things (IoT) network. In CS-RACH, we utilize a larger number of unique preambles compared to conventional LTE-RACH, however, the compressed sensing technique makes it possible to simultaneously detect the users with high accuracy. Compared to the user detection in conventional LTE-RACH, the proposed user detection can get rid of preamble collisions and decrease the collision probability, thereby the overall access latency is significantly reduced. To prove the benefits of the proposed CS-RACH, we mathematically analyze and compare access latency performance of LTE-RACH and CS-RACH. In particular, based on the least absolute shrinkage and selection operator approach, we derive a normalized throughput, access success probability, and average access latency. Our simulation results also exhibit that the proposed CS-RACH considerably reduces the access latency under reasonable conditions in IoT environments.

MCMC-Based Detection for Random Access With Preambles in MTC

Random access has been considered for machine-type communication (MTC). In particular, a random access scheme with a pool of preambles is widely studied, which is similar to multichannel ALOHA, to support a number of MTC devices. In this paper, we study optimal approaches for user activity detection in random access with preambles over fading channels. Since the computational complexity grows exponentially with the number of preambles, a low-complexity detector is derived using Markov chain Monte Carlo (MCMC) approaches that can approximately solve an optimal maximum a posteriori detection problem. The resulting MCMC detector can enjoy a trade-off between performance and complexity, while its complexity to obtain a sample is linearly proportional to the number of preambles. A performance analysis for optimal detection is also studied to see the optimal performance. Simulation results confirm that the MCMC detector performs better than compressive sensing-based approaches and can provide a near optimal performance under certain conditions with a reasonable computational complexity.

MIMO receivers considering preamble collisions for grant-free random access in machine type communication systems

One of the main requirements for next generation mobile or wireless communication systems is to effectively support a large number of machine type communication (MTC) devices for internet of things applications. In this paper, we consider grant-free random accesses from MTC devices, where a small-sized data can be transmitted concurrently with a preamble randomly selected from a pool of preambles. In order to support a huge number of MTC devices, large-scale multiple-input multiple-output (MIMO) techniques can be used. Concurrent transmissions from multiple devices to a single time–frequency resource can be handled by a large number of antennas at a base station if the number of preambles is considerably large compared to the number of the concurrent accesses. However, in practice, the number of preambles is limited and preamble collisions can deteriorate the reception performance. In this paper, we propose a MIMO receiver scheme for grant-free random access with taking care of preamble collisions. The proposed scheme attempts to decode all received packets even with preamble collisions, by exploiting the capture effect and interference cancellation techniques.

Achieving massive connectivity with non-orthogonal pilots and dirty-RFs

Emerging 5G wireless networks need to support sufficient connectivity for a large number of machine-type communication devices, which may have poor radio-frequency (RF) circuit quality due to cost- and energy-efficient design. In this paper, the connectivity is analyzed for a training-based large-scale antenna system employing both non-orthogonal pilots and dirty-RFs on both the transmitter and receiver sides. By considering the effects of imperfect hardware and interference caused by non-orthogonal pilots, the performance of the linear minimum mean-square error (MMSE) channel estimator is derived, and the corresponding average rate of the maximum ratio combiner (MRC) is obtained. From these results, the connectivity maximization problem is formulated and the closed-form solutions for the optimal training length and the optimal number of simultaneously served users are provided. Asymptotic analysis further reveals that allowing only orthogonal pilots limits both the connectivity even when a large number of antenna is employed. However, by allowing non-orthogonal pilots, both the connectivity and the energy efficiency can be improved significantly, even when dirty-RFs are taken into account.

Efficient Random-Access Scheme for Massive Connectivity in 3GPP Low-Cost Machine-Type Communications

In order to facilitate low-cost network connection of many devices, machine-type communication (MTC) has evolved to low-cost MTC (LC-MTC) in the third-generation partnership project standard. LC-MTC should be able to effectively handle intensive accesses through multiple narrowband random-access channels (RACHs) assigned within the bandwidth of a long-term evolution system. As the number of MTC devices and their congestion rapidly increase, the random-access scheme for the LC-MTC RACH needs to be improved. This paper presents a novel random-access scheme that introduces virtual preambles of LC-MTC devices and associates them with RACH indexes to effectively discern LC-MTC devices. In comparison with the sole use of preambles, our scheme allows an LC-MTC device to better choose a unique virtual preamble. Thereby, the probability of successful accesses of LC-MTC devices increases in contention-based random-access environments. We experimentally assessed our scheme, and the results show that our scheme performs better than the existing preamble-based scheme in terms of collision probability, access delay, and access blocking probability.

Efficient Data Forwarding for Machine Type Communications in Internet of Things Networks

The MTC (Machine Type Communications) system is one of the most promising technologies to provide IoT (Internet of Things) applications. The MTC system suffers from congestion due to limited data transmission capacity and burst traffic. The congestion disturbs data delivery, results in an increase of energy consumption due to data retransmission, and finally poses a threat to the providers of IoT applications. In this paper, we focus on the congestion problem and present an efficient data forwarding mechanism to regulate the burst channel access from a large number of MTC devices. To regulate the communication channel access from the devices, we predict the number of devices by separating traffic load into the prediction of the number of devices that newly attempt to have access and the prediction of the number of devices that retry to access the channel. Through simulations, we show that our mechanism improves access success probability and reduces collision probability and access delay.

System-Level Analysis of IEEE 802.11ah Technology for Unsaturated MTC Traffic

Enabling the Internet of Things, machine-type communications (MTC) is a next big thing in wireless innovation. In this work, we concentrate on the attractive benefits offered by the emerging IEEE 802.11ah technology to support a large number of MTC devices with extended communication ranges. We begin with a comprehensive overview of the novel features introduced by the latest IEEE 802.11ah specifications followed by the development of a powerful mathematical framework capturing the essential properties of a massive MTC deployment with unsaturated traffic patterns. Further, we compare our analytical findings for a characteristic MTC scenario against respective system-level simulations across a number of important performance indicators. Our analytical results provide adequate performance predictions even when simulations are challenged by the excessive computational complexity. In addition, we study the novel IEEE 802.11ah mechanisms offering improved support for massive device populations and conclude on their expected performance.

Gateway-assisted two-stage radio access for machine type communication in LTE-Advanced network

In Long Term Evolution-Advanced (LTE-A) network, Machine Type Communication (MTC) provides random access-based communication for automation applications. One of the most critical issues in MTC is the emergence of large number of MTC devices which may send requests to the base station simultaneously, and incur severe collision at the base station. When collision occurs, the data delivery fails, and the message delay accumulates.In this paper, we propose a gateway-assisted two-stage (GATS) radio access scheme to alleviate collision for delay-tolerant MTC applications. In GATS, MTC devices are divided into several groups, where random access in each group (as the first stage) is controlled by a device called MTC gateway. Collision of random access to the base station (as the second stage) is then effectively alleviated. Then we propose analytical model and simulation model to investigate the performance of GATS in terms of utilization of random access slots, access success probability and average message delay. Simulation results show that, by sacrificing a little message delay, GATS scheme significantly improves access success probability. Besides, when the group number increases, the delay becomes smaller.

Timed strategic machine type communications for dual-level future wireless networks

For the next generation wireless networks, machine type communication (MTC) is gaining an enormous interest as a new communication paradigm. MTC is expected to become a cost-effective solution for improving the wireless communication performance. In MTC, one of the most critical issues is to support data transfers among devices without human interaction. In this study, we introduce a new MTC control scheme for the future network infrastructure. To effectively support a large number of MTC devices, we investigate a dual-level interaction mechanism by employing the timed strategy game model. In dynamic wireless network environments, our timed strategic game approach can practically adapt current system conditions while maximizing system performance. Main contribution of this research is to show a way of MTC system modeling based on the competitive and cooperative manner. Finally, numerical simulations illustrate the validity of our game-based approach. Our solution enables a better network resource utilization for heterogeneous traffic services in contrast to existing schemes.

Uplink access control for machine-type communications in LTE-A networks

Internet of things (IoT) has been considered as one of the most promising technologies over the next decade. One of the basic requirements of IoT is the global communication connectivity between smart objects. LTE-A has been considered as the main communication channel for connecting devices. For this reason, the machine-type communication (MTC) has been defined in the 3GPP LTE-A specification. With the rapid growth of the IoT devices, MTC in LTE-A faces many challenges. The primary design of a LTE-A network is to support the human-type communication (HTC). However, MTC and HTC have different characteristics, such as packet size, traffic arrival rate, and delay tolerance. How to accommodate a large amount of MTC traffic without affecting the grade of service of the HTC is challenging. More specifically, before accessing the LTE-A network, a random access (RA) procedure needs to be performed by a device to synchronize in the uplink of an eNB. Since the number of MTC devices is expected to be much greater than the HTC devices, without suitable access control, the success rate of the RA requests from the HTC devices can be significantly degraded. Therefore, this research aims to design an appropriate RA procedure to relieve this problem. The authors propose an Adaptive RACH Resource Allocation (ARRA) which integrates several control schemes, including a Resource Allocation scheme, an Access Class Barring scheme, and a Priority Device Setting scheme. Simulation results show that the proposed ARRA is able to achieve a higher access success rate and a lower latency for HTC devices while providing a different quality of service to different types of MTC devices.

Efficient Cluster-Based Sleep Scheduling for M2M Communication Network

Machine-to-machine (M2M) communication networks comprise a large number of machine-type communication (MTC) devices such as sensors, radio frequency identification readers, and smart meters. Thus, M2M networks are becoming popular in real-time monitoring, surveillance, and security applications. Scheduling the active and idle states of MTC devices is significantly important to achieve a longer network lifetime and reduce collision during data transmission. Existing node sleep scheduling schemes are mainly designed to reduce the energy consumption of MTC devices. These schemes do not support mobility of MTC devices and thus cannot be used in mobility-centric M2M applications. Thus, we propose a cluster-based energy-efficient, mobility-centric node scheduling scheme for M2M (CENM) communication networks. The proposed CENM scheduling scheme provides (i) network coverage by keeping a minimum number of MTC devices in active state and (ii) fault tolerance by selecting alternative cluster heads and member nodes. Simulation results show that the CENM scheduling scheme is more reliable than the existing CCNS scheduling scheme since the first node in CENM scheme fails much later than that in CCNS scheduling scheme. Simulation results also show that the CENM scheduling scheme outperforms the CCNS, LEACH-M and LEACH-ME scheduling schemes in terms of network energy consumptions, network lifetime, and total message transmissions.

Multiple Beam Selection for Combing M2M Communication Networks and Cellular Networks with Limited Feedback

We study the scenario in which a large number of machine-type communication devices (MTCDs) communicate with each other by utilizing the help of the base station (BS) through some MTCD gateways. We consider an overlay mode of an orthogonal frequency division multiple access (OFDMA) based cellular system using orthogonal beamforming to provide broadband wireless access for the MTCD gateways. In order to avoid the interference with mobile users, the beamforming vectors to the MTCD gateways have to be orthogonal to the channel vectors of mobile users, which become a beamforming constraint for the MTCD gateways. However, with limited feedback of channel state information (CSI) at a BS, the orthogonal beamforming constraints may not be achieved. In such a practical case, conventional feedback schemes are feasible but not efficient due to the orthogonality constraints. In this paper, we propose a novel multiple beam selection (MBS) approach with limited feedback for MTCD gateways by taking into account the previous orthogonality constraints. Simulation results show that the performance improvement of the proposed approach over the conventional ones is generally about 10% when a BS is equipped with an array of 6 elements.