Machine Type Communication Devices Research Articles

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.

Outage performance analysis of millimeter-wave NOMA transmission for line of sight and non-line of sight propagations based on different clustering schemes

Internet of Things (IoT), as a novel technology, has many challenges that massive connectivity and low latency are two most important of them. Machine to machine (M2M) communication is regarded as a new technology to realize IoT for the 5G wireless networks. In this paper, we are designed a new clustering scheme in a millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) transmission to reduce the system overhead for massive connectivity, which depends on the distance between machine type communication (MTC) device and the base station (BS). The clustering scheme includes a near, a far and a mid-cell MTC device per cluster with a non-overlapping frequency band for both far and mid-cell MTC devices to reduce inter-cluster interference. The proposed three clustering schemes in different conditions are as 1) the random near, the random mid-cell and the random far MTC devices (RNRMRF); 2) the nearest near, the nearest mid-cell and the nearest far MTC devices (NNNMNF); and 3) the nearest near, the farthest mid-cell and the farthest far MTC devices (NNFMFF). The outage probabilities of the clustering schemes are compared analytically for the line of sight paths. Also, the non-line of sight paths for channel gain and the impact of these paths over outage probability are analyzed. The computer numerical and simulation results show the accuracy of the developed analytical and approximation results in high signal-to-noise ratio cases. As a result, our clustering schemes are offered to settle a trade-off between system overhead and outage performance. Besides as an asymptotic computational algorithm, we provided the order of complexity to assess the difficulty of the system.

Precoder design and impulsive noise mitigation scheme for industrial wireless communications

In industrial wireless scenarios, the impulsive noise (IN) incurred by machine running or operation causes a serious influence on the power-limited industrial wireless communications. It is challenging to ensure efficient and reliable transmission with quality of service (QoS) guarantee for machinetype communication devices (MTCDs). Considering the IN in the industrial process, this paper establishes the multiuser multiple-input single-output (MU-MISO) orthogonal frequency division multiplexing (OFDM) system model, which combines transmitter and receiver design. Two precoding schemes are designed to improve communication effectiveness at the transmitter. More specifically, the precoder design scheme which combines semi-definite relaxation (SDR) with difference-of-two-convex-function (D.C.) iterative algorithm, is developed by utilizing the Dinkelbach method to improve the system effectiveness. To decrease the computational complexity, we devise the quadratic-based fractional programming (QFP) algorithm, which decouples the variables by using a quadratic transform method. On this basis, the IN mitigation scheme is studied to reduce the system error rate (SER) at the receiver. With the goal of improving the reliability of industrial wireless communications, we propose a hybrid nonlinear IN mitigation (HNINM) scheme and then derive its closed-form expression of SER. The simulation results show that the proposed QFP algorithm achieves superior performance while the HNINM scheme decreases the SER of industrial wireless communications.

Group Handover for Drone Base Stations

The widespread use of new technologies such as the Internet of things (IoT) and machine type communication(MTC) forces an increase on the number of user equipments(UEs) and MTC devices that are connecting to mobile networks. Inherently, as the number of UEs inside a base station's (BS) coverage area surges, the quality of service (QoS) tends to decline. The use of drone-mounted BS (UxNB) is a solution in places where UEs are densely populated, such as stadiums. UxNB emerges as a promising technology that can be used for capacity injection purposes in the future due to its fast deployment. However, this emerging technology introduces a new security issue. Mutual authentication, creating a communication channel between terrestrial BS and UxNB, and fast handover operations may cause security issues in the use of UxNB for capacity injection. This new protocol also suggests performing UE handover from terrestrial to UxNB as a group. To the best of the authors' knowledge, there is no authentication solution between BSs according to LTE and 5G standards. The proposed scheme provides a solution for the authentication of UxNB by the terrestrial BS. Additionally, a credential sharing phase for each UE in handover is not required in the proposed method. The absence of a credential sharing step saves resources by reducing the number of communications between BSs. Moreover, many UE handover operations are completed in concise time within the proposed group handover method.

Cognitive IoT relaying NOMA networks with user clustering and imperfect SIC

To implement machine type communications (MTCs) in next generation mobile networks, Narrowband-Internet of Things (NB-IoT) need be studied. Such NB-IoT has been released by the third generation partnership project (3GPP) as a promising method to exhibit extended coverage and low energy consumption, especially for cheap MTC devices. However, the existing NB-IoT using orthogonal multiple access (OMA) scheme which cannot provide connectivity for a massive MTC devices. In this paper, we consider NB-IoT using non-orthogonal multiple access (NOMA). We find that power allocation factors for devices in clustering users as main enabler to exhibit differences in term of performance of devices. The worse situation of imperfect successive interference cancellation (SIC) is further studied due to main reason regarding degraded system performance. We derive closed-form expressions of outage probability for devices in a cluster to highlight main parameters affecting quality of devices. We verify our mathematical analysis via Monte-Carlo simulations.

A Probabilistic Approach to Model SIC Based RACH Mechanism for Machine Type Communications in Cellular Networks

In a cellular Internet of Things, burst transmissions from millions of machine type communications (MTC) devices can result in channel congestion. The main bottleneck in such scenario is inefficient random access channel (RACH) mechanism that is used to attach MTC devices to a base station (BS). To address this issue of congestion in RACH mechanism, 3GPP has proposed an extended access barring (3GPP-EAB) mechanism. However, several works indicate that the performance of the 3GPP-EAB mechanism can be further improved. In this work, a successive interference cancellation (SIC) based RACH mechanism is considered to significantly increase the success rate and reduce congestion. In the proposed mechanism, the devices are allowed to transmit repeatedly for a finite number of times in a given cycle, and thereafter, the success rate is improved by applying back-and-forth SIC at the BS. A novel probabilistic approach of the proposed mechanism is presented with all transition and steady-state probabilities. Further, the probability of SIC for a given slot is derived. Through extensive numerical results, it is shown that the proposed mechanism significantly outperforms the existing ones in terms of the success rate. Moreover, to obtain the maximum success rate, the optimum number of devices to be entered in a cycle is also calculated.

QoS-Based Data Aggregation and Resource Allocation Algorithm for Machine Type Communication Devices in Next-Generation Networks

Machine Type Communication (MTC) becomes one of enablers of the internet of things, it faces many challenges in its integration with human-to-human (H2H) communication methods. To this aim, the Long Term Evolution (LTE) needs some adaptation in the scheduling algorithms that assign resources efficiently to both MTC devices (MTCDs) and H2H users. The minimum amount of LTE resources that can be assigned to one user is much larger than the requirements of a single MTCD. In this paper, a QoS-enabled algorithm is proposed to aggregate MTCD traffic coming from many sources at the Relay Node (RN) that classifies and aggregates the MTCD traffic based on the source type and delay requirements. In this study, three types of MTCD and one H2H sources will be considered. Each type of MTCD traffic will be grouped into a separate queue, and will be served with the appropriate priority. Resources are then assigned to the aggregated MTC traffic instead of an individual assignment for each MTCD, while the H2H users will be directly connected to the LTE. Two schemes of resource partitioning and sharing between the MTCDs and the H2H users will be considered: one proportional and the other moving-boundary. Simulation models will be built to evaluate the proposed algorithms. While the obtained results for the first scheme showed a clear improvement in LTE resource utilization for the MTCDs, a negative effect was noticed in the performance of the H2H users. The second scheme achieved a positive improvement for both MTCDs and H2H users.

Adaptive DRX Method for MTC Device Energy Saving by Using a Machine Learning Algorithm in an MEC Framework

Machine-type communication (MTC) is recognized as an enabling building block for constructing the Internet of Things (IoT) through cellular networks. Since MTC devices (MTCDs) are usually powered by batteries, reducing the energy consumption of an MTCD is an important requirement in the design of an IoT system. The third-generation partnership project (3GPP) specified discontinuous reception (DRX) to save energy in user equipment (UE) by making a UE device sleep whenever it is not involved in data transmission or reception. However, UE is used for human-type communications (HTCs), and MTCs are different from HTCs in many ways, namely, in terms of traffic arrival patterns, mobility, and information priority. Thus, using the DRX developed for UE to save the energy of an MTCD leads to inefficiency in terms of the amount of power saved and the data transmission delay. In the literature, many efforts have been made to dynamically vary the DRX cycle based on traffic activities to reduce the energy consumption of an MTCD. These assume a specific distribution for the traffic arrival process and predict the next packet arrival time to control the DRX cycle. However, since the characteristics of the MTC traffic arrival pattern vary widely according to the types of applications and the network status, previous approaches are limited in the achievable energy efficiency and the range of MTC applications that they can support. To address these issues, we devise adaptive DRX (ADRX) by adopting a machine learning algorithm called bank-of-experts. Without making any assumption regarding the underlying traffic process, ADRX predicts the next packet arrival time using only the observed packet arrival times. Using the predicted next packet arrival time, ADRX dynamically adjusts the sleep interval. In terms of implementation, ADRX is executed in an eNB by following the Multi-access Edge Computing (MEC) framework. Thus, an MTCD avoids consuming the energy required to execute a sophisticated energy-saving algorithm while obtaining the benefits provided by ADRX. Since different IoT services have different requirements for the amount of saved energy and data transmission delay, we design a loss function by combining these two performance metrics so that ADRX can support a wide range of application services. We first analyze the bound of the cumulative regrets of ADRX, and then through simulations, we compare the performance of ADRX with the standard DRX with optimal sleep duration (OSDRX) and a generalized and autonomous DRX (GDRX) scheme. The results show that ADRX outperforms GDRX in terms of the loss function and has almost the same performance as OSDRX.

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

Machine-to-Machine (M2M) communication refers to autonomous communication among devices that aims for a massive number of connected devices. M2M communication can support ubiquitous communication and full mechanical automation, and it will change everything from industry to ourselves. Recent developments in communication technology make Long Term Evolution (LTE)/Long Term Evolution-Advance (LTE-A) a promising technology for supporting M2M communication. LTE can support the diverse characteristic of M2M communication due to its IP connectivity, coverage area, and scalability. Therefore, the LTE schedulers should satisfy the need for M2M communication. Motivated by these facts, in this paper, we present a survey on the classification of LTE / LTE-A scheduling methodologies from the perspective of M2M communication. We classify the schedulers based on their objectives, such as energy efficiency, spectrum efficiency, group-based, and Quality-of-Service (QoS) support for Machine Type Communication Devices (MTCDs). We also highlight the scope of future research direction for the scheduling work.

Binary Golay Spreading Sequences and Reed-Muller Codes for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) is an emerging technology for massive connectivity in machine-type communications (MTC). In code-domain NOMA, non-orthogonal spreading sequences are uniquely assigned to all devices, where active ones attempt a grant-free access to a system. In this paper, we study a set of user-specific, non-orthogonal, binary spreading sequences for uplink grant-free NOMA. Based on binary Golay complementary sequences, each spreading sequence provides the peak-to-average power ratio (PAPR) of at most 3 dB for multicarrier transmission. Exploiting the theoretical connection to Reed-Muller codes, we conduct a probabilistic analysis to search for a permutation set for Golay sequences, which presents theoretically bounded low coherence for the spreading matrix. Simulation results confirm that the maximum PAPR of transmitted multicarrier signals via the spreading sequences is significantly lower than those for random bipolar, Gaussian, and pseudo-random sequences. Thanks to the low coherence, the performance of compressed sensing (CS) based joint channel estimation and multiuser detection using the spreading sequences turns out to be superior or similar to those for random bipolar, Gaussian, pseudo-random, and Zadoff-Chu sequences. The binary Golay spreading sequences have only two phases regardless of the sequence length, which can make them suitable for low cost MTC devices.

Load Balancing Relay for Two-Tier MTC Uplink

A Load Balancing Relay Algorithm (LBRA) is proposed in this letter to solve the unfair spectrum resource allocation problem in two-tier machine-type communications (MTC) uplink transmission. By adopting MTC devices (MTCDs)'s distribution, spectrum resources are dynamically allocated and reclustered for the link between MTCDs to cluster-head. Moreover, the system outage probability and transmission capacity are derived for the LBRA in this letter. The numerical results provide some insights on potential use of the LBRA for the scenario with high MTCD density.

A New Contention-Based PUSCH Resource Allocation in 5G NR for mMTC Scenarios

This letter addresses the uplink radio resource allocation problem in a 5G massive Machine Type Communication (mMTC) scenario, where a large number of MTC devices generate small-sized packets. The current cellular networks are unsuitable for this scenario, due to the limited resources allocated to the Physical Random Access Channel (PRACH) and to the Physical Uplink Shared Channel (PUSCH). To overcome this issue, instead of improving the succeeded access attempts in the PRACH, as widely addressed in literature, we focus on the PUSCH. We present the innovative idea to exploit the unused PUSCH resources to serve also the MTC devices that have failed their access attempt, by assigning them a pool of resources, in a contention-based mode. The simulation results show that the proposed PUSCH Resource Reallocation Algorithm improves significantly any static and dynamic system performance in terms of number of succeeded communications, at the cost a negligible increment of energy consumption.

Distributed Algorithm for Base Station Assignment in 4G/5G Machine-Type Communication Scenarios with Backhaul Limited Conditions.

A progressive paradigm shift from centralized to distributed network architectures has been consolidated since the 4G communication standard, calling for novel decision-making mechanisms with distributed control to operate at the network edge. This situation implies that each base station (BS) must manage resources independently to meet the quality of service (QoS) of existing human-type communication devices (HTC), as well as the emerging machine type communication (MTC) devices from the internet of things (IoT). In this paper, we address the BS assignment problem, whose aim is to determine the most appropriate serving BS to each mobile device. This problem is formulated as an optimization problem for maximizing the system throughput and imposing constraints on the air interface and backhaul resources. The assignment problem is challenging to solve, so we present a simple yet valid reformulation of the original problem while using dual decomposition theory. Subsequently, we propose a distributed price-based BS assignment algorithm that performs at each BS the assignment process, where a novel pricing update scheme is presented. The simulation results show that our proposed solution outperforms traditional maximum signal to interference plus noise ratio (Max-SINR) and minimum path-loss (Min-PL) approaches in terms of system throughput.

The Wireless Solution to Realize Green IoT: Cellular Networks with Energy Efficient and Energy Harvesting Schemes

With the tremendous increase of heterogeneous Internet of Things (IoT) devices and the different service requirements of these IoT applications, machine-type communication (MTC) has attracted considerable attention from both industry and academia. Owing to the prominent advantages of supporting pervasive connectivity and wide area coverage, the cellular network is advocated as the potential wireless solution to realize IoT deployment for MTC, and this creative network paradigm is called the cellular IoT (C-IoT). In this paper, we propose the three-layer structured C-IoT architecture for MTC and review the challenges for deploying green C-IoT. Then, effective strategies for realizing green C-IoT are presented, including the energy efficient and energy harvesting schemes. We put forward several strategies to make the C-IoT run in an energy-saving manner, such as efficient random access and barring mechanisms, self-adapting machine learning predictions, scheduling optimization, resource allocation, fog computing, and group-oriented transmission. As for the energy harvesting schemes, the ambient and dedicated energy harvesting strategies are investigated. Afterwards, we give a detailed case study, which shows the effectiveness of reducing power consumption for the proposed layered C-IoT architecture. Additionally, for real-time and non-real-time applications, the power consumption of different on-off states for MTC devices is discussed.

Optimized neural network model for attack detection in LTE network

Machine-type communication (MTC) senses our environment througfh connecting millions of devices to one another, and becomes an enabler for context-aware and ubiquitous computing services. Still, there has been no research managing the network lifetime for these MTC devices due to a lack of authentication requirements and attack detection. Thus, this research work intends to develop an efficient attack detection model for MTC. Since the previous work on MTC based Long Term Evaluation(LTE) model had only authenticated the MTC devices via clustering, security is a critical issue that is focused on the current research work. Here, the attack detection is undergone by introducing an enhanced Neural Network (NN) model, where the authenticated nodes, as well as cluster Head (CH) from elliptic curve cryptography (ECC), are detected for attacks. In case of the presence of attack, the enhanced NN introduces a penalty as one and prohibits those nodes from taking part in MTC communication. As a novelty, here the training of enhanced NN is accomplished via a new optimization algorithm referred to Whale with Tri-level Update (WTU). Moreover, the security requirement is fulfilled by fixing the objectives like confidentiality and repudiation on data transmission. The efficiency of the proposed attack detection model is proved and a comparative evaluation will be accomplished in terms of certain security analysis.

Socially Aware Joint Resource Allocation and Computation Offloading in NOMA-Aided Energy-Harvesting Massive IoT

As a typical usage scenario for the next-generation mobile communication network, massive Internet of Things (mIoT) is requested to provide high machine-type communication device (MTCD) density service. Nonorthogonal multiple access (NOMA) and mobile-edge computing (MEC) can further enhance the performance of mIoT. Furthermore, to cope with the energy consumption constraint of MTCD, energy harvesting (EH) can be leveraged. In this article, considering the social trusts of MTCDs, we propose an MEC offloading scheme for cellular Internet of Things networks with massive NOMA-aided EH MTCD and several road side units with edge servers randomly distributed in a macrocell. We aim to maximize the total sum rate of the network by jointly considering the processing mode selection, device clustering, subchannel allocation, and power allocation while satisfying the power, energy, latency, and quality of service requirements. To this end, we prove the NP-hardness of the considered optimization problem and decompose it into three subproblems, which can be solved by an iterative algorithm. Numerical results demonstrate the superior performance of the proposed scheme.

Resource Optimization for Delay-Tolerant Data in Blockchain-Enabled IoT With Edge Computing: A Deep Reinforcement Learning Approach

Recently, the development of the Internet of Things (IoT) provides plenty of opportunities and challenges in various fields. As an essential part of IoT, machine-to-machine (M2M) communications open a novel way that the machine-type communication devices (MTCDs) are connected and communicated without any human intervention. Meanwhile, delay-tolerant data play an important role in M2M communications-based IoT, and it puts more emphasis on powerful data caching, computing, and processing, as well as the security and stability of data transmission. To meet these requirements in M2M communications networks, in this article, we introduce some promising technologies, such as edge computing and blockchain, and propose a joint optimization framework about caching, computation, and security for delay-tolerant data in M2M communications networks based on dueling deep $Q$ -network (DQN). According to the dynamic decision process by DQN, the optimal selection and decision of caching servers, computing servers, and blockchain systems can be made to achieve maximum system rewards, which includes higher efficiency of data processing, lower network costs, and better security of data interaction. Extensive simulation results with different system parameters show that our proposed framework can effectively improve the system performance for blockchain-enabled M2M communications compared to the existing schemes.

Spatial Filtering Based Random Access for Machine-Type Communications in Massive MIMO Cellular Networks

Machine-type communication (MTC) is one of the most important enabling technologies for the emerging Internet of Things (IoT). However, the potential massive access will significantly degrade the access performance of MTC. To meet the massive access requirement of MTC, we propose a spatial filtering based random access (SFRA) scheme in massive MIMO cellular networks. Specifically, the angle of arrival (AOA) information provided by massive MIMO is utilized to distinguish collided MTC devices (MTDs). To improve the preamble utilization as possible, we further propose a novel preamble reuse scheme to eliminate the influence of multipath angular spread on preamble detection at the base station (BS). Theoretical analysis and simulation results both show that the proposed SFRA scheme can efficiently reuse preambles and considerably improve the access capacity. In typical urban environments, preambles can be perfectly reused for 9 times at most. Moreover, the average access delay of SFRA is only about one seventh of that of the conventional random access scheme when 10000 MTDs attempt to access the network simultaneously.

Connectivity Performance Evaluation for Grant-Free Narrowband IoT With Widely Linear Receivers

Future wireless cellular communication networks are expected to provide connectivity for massive machine-type communication (mMTC) devices. The main challenge of supporting mMTC traffic for a cellular network lies in the high density of these devices, which individually have relatively little data to transmit. This suggests the use of low overhead, grant-free access scheme for uplink data transmission, which, however, suffers from packet collisions when devices attempt to access the channel. In this article, we suggest the use of real-valued transmission together with widely linear (WL) reception for improving resource access and thus data throughput in the uplink of mMTC traffic scenarios. We show that not surprisingly, the WL scheme can virtually double the number of receive antennas at the base station (BS). We analyze the effect of this on the supported user density and data throughput for grant-free uplink transmission. As a specific example, we consider the narrowband Internet-of-Things (NB-IoT) cellular communication system, which already includes real-valued modulation modes. Our numerical results show that the supported user density and data throughput of grant-free NB-IoT systems can be significantly improved due to the use of WL receivers. For example, considering an NB-IoT system with 1% packet drop probability, we obtain a tenfold (for single-antenna BS) and a sixfold (for dual-antenna BS) increase in the supported user density by using WL receivers instead of their conventional linear counterparts.

Joint Access Control and Resource Allocation for Short-Packet-Based mMTC in Status Update Systems

In this article, we investigate the performance of massive machine type communications (mMTC) in status update systems, where massive machine type communication devices (MTCDs) send status packets to the BS for system monitoring. However, massive MTCDs sending status packets to the BS will cause severe packet collisions, which will have a negative impact on status update performance. In this case, it is necessary to carry out reasonable access control and resource allocation scheme to improve the status update performance for mMTC. In this article, taking the features of mMTC into consideration, we first analyze access control, packet collisions and packet errors in mMTC respectively, and derive the closed-form expression of the average age of information for all MTCDs as the performance metric, and then propose a joint access control, frame division and subchannel allocation scheme to improve the overall status update performance. Simulation and numerical results verify the correctness of theoretical results and show that our proposed scheme can achieve almost the same performance as the exhaustive search method and outperforms benchmark schemes.