Massive Devices Research Articles

Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks

The advent of the fifth-generation (5 G) network integrates a spectrum of powerful technologies. Among these, Device-to-Device (D2D) communication stands as a focal point in research due to its expansive array of applications. Within the realm of 5 G, D2D communication offers support for high data rates and ultra-reliable, low-latency communications. While machine learning-based approaches have been devised to facilitate D2D communication in 5 G, ensuring robust connectivity and optimal network throughput remains a persisting challenge. Therefore, the bagging ensemble mean-shift Gaussian kernelized clustering-based D2D Connectivity (BEMSGKC-DC) algorithm is designed to enhance device-to-device communication in 5 G Cellular Networks with better packet delivery ratio and throughput. The proposed BEMSGKC-DC algorithm is proposed to enable massive device connectivity while improving the overall network performance. This can be achieved by employing bootstrap aggregating clustering where it clusters the devices via forming a set of base clusters i.e., Mean-Shift Gaussian kernelized clustering algorithm. To further enhance the performance of the algorithm, this paper also computes the resources of the devices in the clustering process using the Gaussian kernel function. All the weak cluster outcomes are merged to acquire the final clustering output with lower error. Through the resultant clustering of the device, the connectivity between D2D is ensured and thereby improves the data communication in the 5 G network. The result demonstrates that the BEMSGKC-DC algorithm optimizes the connectivity with lower energy, latency, and higher data delivery ratio and packet loss rate than the conventional algorithms.

Power-Efficient and Trustworthy Data Dissemination for Social Vehicle Associations in the Internet of Vehicles

<div>In modern era, with the global spread of massive devices, connecting, controlling, and managing a significant amount of data in the IoT environment, especially in the Internet of vehicles (IoV) is a great challenge. There is a big problem of high-energy consumption due to overhead-unwanted data communication to the non-participatory vehicles, at high enduring connection rate. Therefore, this article proposed a social vehicle association-based data dissemination approach, which was segregated into three parts: <i>First</i>, develop an improved power evaluation approach for discovering power-efficient vehicles. <i>Second</i>, using the Fokker–Planck equation, the connection likelihood of these vehicles is calculated in the second phase to find trustworthy and steady connections. <i>Last</i>, develop an evaluation approach for vehicles community association using convolutional neural network (CNN). It filtered most likely vehicles to form a community for data dissemination by considering temporal, spatial, and social attributes of vehicles. The proposed approach has evaluated using widespread simulation tests in a highway environment. It verified the efficacy of proposed approach regarding power, linking, and community score of vehicles. The finding of experiment shows that, with advancement of power, connectivity, and community score of vehicles, data dissemination also enhanced. Furthermore; it guarantees that data will be shared efficiently with great reliability.</div>

Channel reconstruction for mmWave Massive MIMO systems based on Channel Path Map

For the needs of high data rate and massive device access in current fifth-generation (5G) mobile communication networks and future sixth-generation (6G) mobile communication networks, Millimeter-Wave (mmWave) Massive Multiple-Input Multiple-Output (MIMO) is a crucial technical support. However, the conventional pilot based channel estimation methods are exceedingly time-consuming and substantially degrade system performance when the number of users or antennas is high. This paper proposes a Path Clustering based Channel Reconstruction (PCCR) scheme for channel reconstruction in order to solve this issue. By leveraging channel information (such as angle of arrival, angle of departure, and path gain) at a number of geographical locations that are recorded in the Channel Path Map (CPM), we aim to reconstruct the mmWave channel of any locations of interests. To be specific, the CPM is used to extract the neighborhood propagation path data, and an autonomous determination algorithm is used to find the number of path clusters at the location of interest. The recorded propagation path information is processed by the Equivalent Base Station (EBS) scheme, and the path clustering is accomplished by using the AGglomerative NESting (AGENS) algorithm. In each path cluster, Inverse Distance Weighted (IDW) algorithm is developed to forecast the path information at the user location. With all path information at user location are forecast, the channel can be reconstructed. The anticipated overhead is substantially lower than that of conventional methods because the computational cost of the procedure only depends on the total number of paths at the nearby locations. Ray-tracing based simulations are carried out to demonstrate the performance of the proposed algorithm. The results show that, when applied to a CPM with a spatial location precision of 5 m, the Normalized Mean Squared Error (NMSE) of the proposed algorithm is nearly identical to that of Linear Minimum Mean Square Error (LMMSE) estimation. The actual achievable date rate can match the needs of upcoming wireless communications and is superior over the conventional LMMSE.

Deep reinforcement learning based trajectory design and resource allocation for task-aware multi-UAV enabled MEC networks

Computing tasks in the air is an important form of mobile edge computing (MEC) to improve the quality of service and enhance network coverage. In this paper, we investigate a multi-UAV cooperative computing model and massive devices access scenario in a service area without infrastructure. There are various types of ground devices with different tasks. Moreover, we consider that the UAV executing tasks of devices need to cache the content that task required. Therefore, we propose a multi-UAV enabled MEC network based on task awareness where each UAV caches some programs to execute tasks offloaded from devices. To minimize completion time, a joint UAV trajectory design, access decision and resource allocation problem is formulated. To address this intractable mixed integer non-linear programming problem, a multi-agent trajectory design and resource allocation (MATR) algorithm is proposed, where the multi-agent deep deterministic policy gradient (MADDPG) is applied. Considering the complexity of high-dimensional continuous action space, we introduce the particle swarm optimization (PSO) algorithm to jointly optimize access decisions, and computation resource allocation to reduce action space. In addition, we discuss the impact of the size of UAV cache space and the location of ground devices on the completion time. Simulation results show that the MATR algorithm can significantly reduce the completion time compared to the baselines.

CFLIT: Coexisting Federated Learning and Information Transfer

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexistence</i> of over-the-air FL and traditional information transfer (IT) in a mobile edge network, where an access point (AP) coordinates a set of devices for over-the-air FL and serves multiple devices for information transfer in the meantime. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexisting federated learning and information transfer (CFLIT)</i> communication framework, where the FL and IT devices share the wireless spectrum in an orthogonal frequency division multiplexing (OFDM) system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. We further derive an analytical expression of the achievable data rate of IT users. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.

A power method based user selection for reconfigurable intelligent surface (RIS) assisted multiuser massive MISO systems

Reconfigurable intelligent surfaces (RISs) have recently emerged as effective solutions for providing massive connectivity. However, an effective RIS user selection method that can support massive devices remains undeveloped. In this study, a power method (PM)-based RIS user selection algorithm for RIS-assisted massive multiple-input multiple-output systems is proposed. It is proven that minimizing the condition number (CN) of the aggregated downlink channel matrix results in the same performance as maximizing the sum rate. Based on this analysis, a PM-based RIS user selection algorithm that utilizes a CN as an RIS user selection metric is proposed here. Although the complexity of conventional user selection algorithm is proportional to the square of the number of base station (BS) antennas ( M 2 ), that of the proposed algorithm is linearly proportional to the number of BS antennas ( M ). Simulation results demonstrate that, under a practical RIS control link, the proposed PMUS algorithm achieves better performance and lower complexity than the conventional user selection algorithm.

Massive Data Connection Architecture Design of Web Platform Based on Blockchain

In order to improve the universality and security of the data management of the Internet of Things and avoid the single point of failure problem in the traditional management architecture, this paper proposes a massive connection management architecture based on blockchain. The blockchain is reconstructed according to the hierarchical structure of blockchain technology, and with reference to the attribute-based access control mechanism, a data exchange model is designed using gossip mode and TLS networking mode to ensure the safe transmission of data. The peer computer network technology, data signature, and consensus mechanism are used to complete the design of the data exchange network layer and realize the design of the blockchain-based massive connection management architecture, so as to effectively avoid the single point of failure problem in the traditional management architecture and greatly reduce the burden of the management center. The access requests of massive devices in 5G application scenarios were simulated, and the results showed that the blockchain-based access control architecture designed in this article can quickly process received requests, with information leakage below 10%, improving the security and processing performance of data connections, achieving efficient processing and management of large-scale data, and providing reliable guarantees for the development of the Internet era.

A Survey of Latest Wi-Fi Assisted Indoor Positioning on Different Principles.

As the location-based service (LBS) plays an increasingly important role in real life, the topic of positioning attracts more and more attention. Under different environments and principles, researchers have proposed a series of positioning schemes and implemented many positioning systems. With widely deployed networks and massive devices, wireless fidelity (Wi-Fi) technology is promising in the field of indoor positioning. In this paper, we survey the authoritative or latest positioning schemes for Wi-Fi-assisted indoor positioning. To this end, we describe the problem and corresponding applications, as well as an overview of the alternative methods. Then, we classify and analyze Wi-Fi-assisted indoor positioning schemes in detail, as well as review related work. Furthermore, we point out open challenges and forecast promising directions for future work.

Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks

AbstractThe internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum access (OSA) has been addressed widely in the past decade. However, the tradeoff between two key indexes, the throughput of cognitive radio network (CRN) and the delay of secondary user (SU), is rarely mentioned so far, which ignites the authors’ work in this paper. Taking into account a channel handoff (CH) based multi‐channel OSA scenario, the authors first analyze the opportunistic transmission performance of SU, and model the throughput of CRN as well as the handoff delay of SU. Then, the authors build up a delay‐constraint throughput optimization problem, and thus formulate the throughput‐delay tradeoff for OSA. Finally, the optimal traffic rates of SU for a good throughput‐delay tradeoff are derived according to maximizing the throughput of CRN. Theoretical and simulated results show that to enhance the throughput and to reduce the delay do not conflict always. By well adjusting the traffic rates of SU according to the traffic rates of primary user, the throughput of CRN can be improved while the handoff delay can be kept under a given level.

Energy-Efficient Scheduling for Search-Space Periods in NB-IoT Networks

One of the significant properties of narrowband Internet of Things (NB-IoT) devices is supporting a long battery life. In the NB-IoT, devices have different search-space periods based on channel qualities. However, because a subframe of 180 kHz is a basic unit in the downlink, a base station can generally only simultaneously schedule devices with the same search-space period and allocate radio resources at the start of the period. If scheduling does not consider search-space periods, a base station will miss the start of the search-space period of some devices or sacrifice some devices. Massive devices will attempt multiple periods for blind decoding (BD), resulting in huge energy consumption. This article investigates a new direction in reducing the energy consumption of devices by reducing the BD and idle time for the NB-IoT. The challenge of the target energy-efficient scheduling problem is how to schedule search-space periods to reduce each device's BD and idle time subject to the data requirements. We propose an algorithm to determine each device's search-space period and an energy-efficient scheduling algorithm to schedule the search-space periods. The simulation results show that, compared with two baselines, the proposed algorithms reduce the energy consumption by 77%.

Time-Frequency Domain Non-Orthogonal Multiple Access for Power Efficient Communications

We propose a power efficient non-orthogonal multiple access (NOMA) scheme suitable for Internet of Things (IoT) and device-to-device (D2D) communications. OFDM-based power-domain NOMA (PD-NOMA) is one of the promising approaches to achieve simultaneous connections of massive devices. Nevertheless, the problem of high peak-to-average power ratio (PAPR) is a major challenge, since power amplifier (PA) efficiency is a critical factor for IoT and D2D devices consisting of low-cost hardware components. This paper overcomes this problem by introducing time-frequency domain NOMA (TF-NOMA), where OFDM and DFT-spread OFDM (DFT-s-OFDM) are superposed over the time-frequency domain. By utilizing the low PAPR property achieved by DFT-precoding, TF-NOMA can efficiently reduce the PAPR of the downlink signal transmitted by the base station (BS) as well as the uplink signals of the devices located far from BS. As a result, the proposed TF-NOMA can operate with higher PA efficiency than the conventional OFDM-based PD-NOMA. Furthermore, the insertion of DFT-precoding with the aim of PAPR reduction also enhances the error propagation resistance due to the resulting Gaussian mixture signal property. Throughout the mathematical analysis and computer simulations, we demonstrate that TF-NOMA achieves better error rate performance than the conventional PD-NOMA with significant improvement of PA efficiency.

A 28/32/38 GHz Circularly Polarized DRA Micro-strip Antenna for 5G mm-Wave Applications

In modern communication system, mobile communication spectrum, below 3 GHz bands, faces more shortage and is not able to serve people with more efficient device connectivity and required data speed. Upon this background, 5G wireless communication – the 5th generation mobile technology standards are able to deliver high data rates, low latency communications, and massive device connectivity. For the efficient deployment of the 5G systems, and in order to support higher bandwidth, there is a dire need to design a compact and efficient antenna at mm wave frequencies (26/28 and 38). The resulting antenna, as the outcome, has been established to possess a compact size of 10 × 12 × 0.254 mm3 that covers three bands from the frequency spectra. The lower band resonant frequency is located at 26.74 GHz, the range between 25.44 GHz and 28.37 GHz, the middle band resonant frequency is located at 32 GHz, the range between of 31.08 GHz and 32.48 GHz, and the higher resonant frequency is located at 38.76 GHz, the range between 36.04 GHz and 40.34 GHz. In this article a modified novel structure of partial ground plane and DRA are used to get the better radiation enhancement and circular polarization. In this, the circularly polarized bands consist of the axial ratios &lt;3 dB which are 27.95 GHz to 28.37 GHz, 31.08 GHz to 31.67 GHz, and 36.04 GHz to 38.73 GHz. The gain achieved is greater than 6.5 dBi and the efficiency is greater than 96% over the three bands. Ultimately, it is proved to the right option for 5G mm wave applications.

Role of a 24-hour Ambulatory Internet of Things System in Preeclampsia Monitoring: Technologies, Challenges, and Future Path Survey

The Internet of Things (IoT) is a technology that integrates different sensor actuators, working together for data management towards efficient communication within the digital world. IoT has been applied in many sectors to achieve sustainable development goals. Massive devices and a huge amount of data have been the major components of the technology, which has presented new challenges. IoT has been applied in healthcare to improve several ways of managing health, including antenatal care. Worldwide, the cost of having preeclampsia monitoring has been a major concern. A 24-hour ambulatory IoT system, an integration of a smartwatch, a mobile device, and a cloud-based application, is one of the technologies used to help in preeclampsia monitoring. IoT and its functionalities have been evaluated in previous studies and assessments. However, they concentrated on its application in other areas, such as animal husbandry, and little on ambulatory care. The impact of a real-time ambulatory IoT system on preeclampsia monitoring are comprehensively and methodically examined in this paper, focusing on three categories: the challenges and its benefits in ambulatory care. The application’s effects, performance, and safety have been thoroughly described. Generally, this paper explores potential initiatives of the IoT system to address existing ambulatory care issues.

Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things

Industrial Internet-of-Things (IIoT) applications generate data in varying amounts with diverse quality of service requirements. The adaptive network access approach and distributed resource management in IIoT networks can reduce the communication overheads caused by centralized resource management approaches. In this regard, statistical learning is a promising tool for addressing decision-making problems in a dynamic environment. This paper considers uplink dominant IIoT networks in which massive devices generate delay-sensitive and delay-tolerant data and communicate over shared radio resources. We propose a novel grant-free access scheme using a statistical learning approach that enables IIoT entities to perform delay-sensitive and delay-tolerant transmissions over dynamically partitioned resources in a prioritized manner. In order to improve utilization of available radio resources, we design an adaptive network access mechanism operating in a semi-distributed manner. This mechanism enables end devices to use their transmission history to choose between static and dynamic resource allocation-based grant-free schemes in a dynamic environment. Simulation results show that average latency and resource utilization vary in grant-free access schemes employing static and dynamic resource allocations. Thus, compared to a single transmission scheme, the proposed adaptive network access offers better channel utilization while meeting the application-specific latency bound in IIoT networks.

Next-Generation URLLC With Massive Devices: A Unified Semi-Blind Detection Framework for Sourced and Unsourced Random Access

This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA), which enables next-generation ultra-reliable low-latency communications (URLLC) with a massive number of devices. Specifically, the active devices transmit their uplink access signals in a grant-free manner to realize ultra-low access latency. Meanwhile, the base station aims to achieve ultra-reliable data detection under severe inter-device interference without exploiting explicit channel state information (CSI). We first propose an efficient transmitter design, where a small amount of reference information (RI) is embedded in the access signal to resolve the inherent ambiguities incurred by the unknown CSI. At the receiver, we further develop a successive interference cancellation-based semi-blind detection scheme, where a bilinear generalized approximate message passing algorithm is utilized for joint channel and signal estimation (JCSE), while the embedded RI is exploited for ambiguity elimination. Particularly, a rank selection approach and a RI-aided initialization strategy are incorporated to reduce the algorithmic computational complexity and to enhance the JCSE reliability, respectively. Besides, four enabling techniques are integrated to satisfy the stringent latency and reliability requirements of massive URLLC. Numerical results demonstrate that the proposed semi-blind detection framework offers a better scalability-latency-reliability tradeoff than the state-of-the-art detection schemes dedicated to sourced or unsourced RA.

SED-SGC: A scalable, efficient, and distributed secure group communication scheme based on superlattice PUF

A critical challenge in group communication is to guarantee the authenticity, integrity, and confidentiality of sensitive data, especially in scenarios with potential and prospective massive device access, such as the Internet of Everything (IoE). This paper explores a scalable, efficient, distributed secure group communication (SED-SGC) scheme that employs superlattice Physical Unclonable Function (PUF), tree structure, and the Decision Diffie–Hellman Problem. In SED-SGC, superlattice PUF facilitates lightweight, high-security mutual authentication and key generation. The Decision Diffie–Hellman Problem and tree structure construct group key agreement and dynamic member management protocol with logarithmic complexity, achieving forward and backward secrecy. Also, we demonstrate that SED-SGC can effectively guarantee security against multiple attack methods such as collusion attacks, man-in-the-middle attacks (MITM), and impersonation attacks.

Over-the-Air Antenna Array Calibration for mmWave Hybrid Beamforming Systems Based on Monte Carlo Markov Chain Method

The antenna phase calibration is critical for the millimeter wave (mmWave) hybrid beamforming communication system. Especially, with the increasing number of antennas being deployed in future massive MIMO systems, it is unrealistic to calibrate each antenna path in a microwave anechoic chamber. This work proposes a phase calibration based on over-the-air (OTA) scheme for mmWave hybrid beamforming systems, where the calibration process is performed automatically by the online signal transmission. The challenge for the OTA-based phase calibration is the imperfect channel estimation due to the phase error. To solve the challenge, we simplify the channel estimation process by integrating the beam alignment and phase calibration process. Consequently, the phase calibration is formatted to an MMSE-criterion based optimization problem solved by the Monte Carlo Markov Chain (MCMC) with probabilistic conversion improved Gibbs sampling method. Furthermore, an alternating iteration algorithm is developed to calibrate phase deviation for both the transmitter and receiver. We also extend our method to solve the joint amplitude and phase calibration problem. According to the simulation result, the calibration results outperform existing methods when the signal-to-noise ratio is less than 15dB. Besides, the proposed method exhibits a high beamforming gain during the calibration process, which paves the way for future massive device deployment in a practical system.

Overview of Internet of Medical Things Security Based on Blockchain Access Control

The Internet of Things provides convenience to health systems, especially for remote monitoring of patient physical indicators. While providing convenience, there may be more security vulnerabilities in protecting patient and doctor information and storing health data effectively. As an important research branch in the field of the Internet of Things, the Internet of Medical Things is important for the overall improvement of public health in terms of how to safely conduct technology development and application research and to effectively implement healthcare needs. Blockchain technology is decentralized and untrusted as well as prevents tampering with data and reduces the cost of trust. Its good performance has a strong developmental nature in the healthcare field. This paper analyses how to solve security problems through access control under the Internet of Medical Things, and optimizes three access control methods. The Internet of Medical Things accesses control approach that introduces blockchain technology enhances computational and storage capabilities and is a good solution to the problem of third-party trustworthiness. Even in the face of the rapid growth of end devices, blockchain technology can solve some of the problems arising from access control of massive devices through three directions: hierarchical management, compressed storage and performance optimization. Finally, it provides directions for future research on the security aspects of blockchain technology under the Internet of Medical Things.

Security Authentication Protocol for Massive Machine Type Communication in 5G Networks

As one of the three major applications of 5G, massive machine type communication (mMTC) is mainly oriented to network access scenarios for massive devices. mMTC focuses on solving the problem that traditional mobile communication cannot well support the Internet of Things and vertical industry applications. According to the current 3GPP standard, these massive devices still use the traditional authentication process to realize mutual authentication with 5G core network, which brings a lot of communication and computing overhead. In addition, privacy protection will also be threatened in the authentication process. In order to alleviate the signaling congestion during authentication and solve the insecurity in authentication, this paper proposes a group authentication scheme for mMTC. Due to the characteristics of low power consumption and massive connection, the scheme mainly adopts lightweight encryption operation to avoid the computational burden of equipment and server. We verify the security of our scheme by using BAN logic to formally analyze the scheme. Then, through informal analysis, our proposed scheme can not only avoid signaling blocking and provide mutual authentication but also resist various possible attacks. Through performance evaluation, it is proved that our scheme has better efficiency.

Backscatter‐assisted Non‐orthogonal multiple access network for next generation communication

AbstractNon‐orthogonal multiple access (NOMA) technique introduces spectrum cooperation among different users and devices, which improves spectrum efficiency significantly. Energy‐limited devices benefit from the backscatter (BAC) technique to transmit signals without extra energy consumption. The combination of NOMA and BAC provides a promising solution for Internet of Things (IoT) networks, where massive devices simultaneously transmit and receive signals. This study investigates a system model with two NOMA downlink users and an uplink device. The aim is to maximise the data rate of the uplink device by optimising the power allocation coefficient and the backscattering coefficient. Meanwhile the quality of service requirements of two NOMA users are guaranteed. The closed‐form solution of two optimisation variables is derived, and an alternating algorithm is also proposed to solve the formulated optimisation problem efficiently. The proposed system verifies the feasibility of IoT devices being added into existing networks and provides a promising solution for wireless communication networks in the future.