Cellular Internet Of Things Networks Research Articles

Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks

Federated learning (FL) is a promising technique to provide intelligent services for the internet of things (IoT). By transmitting the model parameters instead of user data between the client and central server, FL greatly improves the user privacy and reduces transmission latency. However, due to the fading effects of the wireless channel, the outage of wireless transmission degenerates the learning efficiency when FL is applied in wireless IoT networks. In order to address this issue, we investigate the joint optimization of client selection and wireless resource allocation in FL-aided cellular IoT networks. By taking both the amount of training data and wireless resource consumption into consideration, we formulate the problem as a mixed integer non-linear programming to maximize the utility of the network. To solve the problem effectively, an alternative direction-based algorithm is proposed by decomposing the original problem into two sub problems. The simulation results indicate that the proposed algorithm substantially improves the FL learning performance and reduces the consumption of wireless resources compared with existing methods.

Performance Analysis of Cellular Ultradense IoT Networks With Wireless Backhauls

The rising era of smart living requires unprecedented advancements in cellular networks to support the communications of the Internet of Things (IoT), referred to as Machine-Type Communication (MTC). Hence, we consider an Ultra-Dense Network (UDN) environment supported by wireless backhaul links and investigate the achievable performance gains for MTC. By doing so, we avoid the complexity, cost, and/or infeasibility of providing fiber backhaul links for the massive number of Small Cells (SCs) found in UDNs. We utilize the Millimeter Wave (mmWave) band to support the communications between the IoT Devices (IoTDs) and their serving SCs. By doing so, the excess available bandwidth can be used to support a massive number of IoTDs while the propagation characteristics of the mmWave signals can be exploited to mitigate the severe Inter-Cell Interference (ICI) found in UDNs. In this regard, we formulate a mathematical framework using tools from stochastic geometry to derive analytical expressions for the density of supported IoTDs and the average capacities per SC on both the Access Link (AL) and the Backhaul Link (BH). In addition, we obtain a tight lower bound of the average capacity per SC under the considered wireless limited-capacity backhaul links. The obtained results show the existence of an optimal active SC density that maximizes SC utilization.

A Game-Theoretic Incentive Mechanism for Battery Saving in Full Duplex Mobile Edge Computing Systems With Wireless Power Transfer

Mobile edge computing (MEC) is a promising paradigm to handle the mismatch between computation-intensive applications and resource-limited devices. Nevertheless, as most internet of things (IoT) terminals are battery-limited, the computation gain of MEC may be compromised due to insufficient battery energy for task offloading. Wireless power transfer (WPT) and full duplex (FD) communications are economical charging and transmission methods for battery-limited IoT terminals. However, when integrating wireless power transfer and FD into MEC, the incentive problem should be jointly addressed with task offloading, because the WPT facilities and their powered IoT nodes belong to different service operators. In this paper, we investigate the efficiency of WPT from the perspective of battery saving, and propose an efficient wireless powered task offloading and incentive mechanism in FD MEC-enabled cellular IoT networks. The battery saving efficiency, which addresses both the total cost of WPT and saved energy of battery, is proposed as the performance metric. By adopting this metric as the utility function of the network operator (NO), the task offloading and incentive problem are jointly formulated as a Stackelberg game. We then propose an efficient alternating direction iteration-based algorithm to solve its equilibrium efficiently. Simulation results demonstrate the benefits of our algorithm in battery saving by comparisons with utility oriented benchmarks. Moreover, it reveals the tradeoff between the utility of NO and battery saving, which verifies the positive effects of FD communications and WPT in improving the efficiency of battery saving.

Medium Access Control Techniques for Massive Machine-Type Communications in Cellular IoT Networks

A key component of the Internet of things (IoT) ecosystem is wide-area network connectivity, for which cellular network technologies are a promising option through their support of massive machine-type communications (mMTC). However, numerous devices transmitting sporadically small data packets in a highly synchronized way can generate overload on the radio access network. This situation leads to a shortage of resources, especially those associated with the random access procedure for contention, control, and data transmission, causing preamble collision, control message blocking, and data collision. As a result, mMTC traffic can jeopardize the provisioning of quality of service (QoS) to end-devices, decrease the network efficiency, and increase access latency and device energy consumption. This paper proposes medium access control (MAC) techniques for addressing problems related to the support of mMTC in cellularnetworks. First, a solution for allocating control resources with QoS provisioning and access differentiation is provided, including a resource management model, a scheduling algorithm, and a message prioritization policy. Second, a mechanism for reducing data collisions is also proposed. This solution comprises a protocol in which every device with scheduled retransmission uses a probabilistic policy to decide whether to retransmit, a novel method at the device to estimate the number of nodes trying random access, and two different retransmission policies employing this estimation. Results show that the proposals support QoS, decrease access latency, decrease the device energy consumption, and increase resource utilization under massive random access.

Resource-Efficient Parallelized Random Access for Reliable Connection Establishment in Cellular IoT Networks.

The role of various internet-of-things (IoT) devices responsible for data collection and reporting becomes more important in the era of Industry 4.0. Due to the various advantages (e.g., wide coverage, robust security, etc.), the cellular networks have been continuously evolved to accommodate IoT scenario. In IoT scenario, connection establishment is essential and primary for enabling IoT devices to communicate with centralized unit (e.g., base station (BS)). This connection establishment procedure in cellular networks, random access procedure, is generally operated in a contention-based manner. So, it is vulnerable to simultaneous connection requests from multiple IoT devices to the BS, which becomes worse as the contention participants increase. In this article, we newly propose a resource-efficient parallelized random access (RePRA) procedure for resource-efficiently ensuring reliable connection establishment in cellular-based massive IoT networks. Key features of our proposed technique are twofold: (1) Each IoT device simultaneously performs multiple RA procedures in parallel to improve connection establishment success probability, and (2) the BS handles excessive use of radio resources based on newly proposed two types of redundancy elimination mechanisms. Through extensive simulations, we evaluate the performance of our proposed technique in terms of connection establishment success probability and resource efficiency under various combinations of control parameters. Consequently, we verify the feasibility of our proposed technique for reliably and radio-efficiently supporting a large number of IoT devices.

Self-Organized Low-Power Multihop Failover Protocol for a Cellular-Based Public Safety Device Network

This study proposes a network failover protocol for public safety devices, such as fire alarms and gas leak detectors using a cellular (i.e., 5G and LTE) Internet of Things (IoT) network, and evaluates its performance through experiments. The proposed protocol recovers communication functions by establishing a detour route through the secondary network (i.e., 447-MHz narrowband wireless network) when a problem occurs in the primary network (i.e., cellular network) in a disaster situation. All operations of the proposed protocol are performed autonomously by self-organization and collaboration of distributed nodes without centralized control. Thus, the communication function is maintained until all physically configurable routes are cut without any network reconfiguration process, even under adverse conditions, where nodes and communication paths status frequently change in real time due to a disaster. In addition, this failover protocol reduces power consumption by activating the secondary network only at the moment when network failover is necessary by using the novel self-organized low-power multihop relay wake-up scheme proposed in this study. Therefore, the proposed protocol can be easily applied to IoT devices that are often operated only by batteries. The proposed protocol also has the advantage of immediate use without any modification of the current cellular IoT network.

Resource Efficiency Analysis of Random Access Parallelization Technique for Cellular IoT Networks

Recently, a random access parallelization (RAP) technique for cellular internet-of-things (IoT) networks was proposed in [1], and RA failure probability was the performance metric of interest. Even though the performance gain in terms of the RA failure probability comes from the sacrifice of the resource efficiency, this point was not well discussed in [1]. We believe that providing supplementary descriptions on the RAP technique from the viewpoint of the resource efficiency is highly required to comprehensively understand for the RAP technique. To this end, in this letter, we newly provide the mathematical analysis on the resource efficiency of the RAP technique and verify its validity through simulations. Finally, we formulate an optimization problem taking the resource efficiency into account and numerically find its solution.

Adaptive Rate NOMA for Cellular IoT Networks

Internet-of-Things (IoT) technology is envisioned to enable a variety of real-time applications by interconnecting billions of sensors/devices. These IoT devices rely on low-power wide-area wireless connectivity for transmitting, mostly fixed- but small-size, status updates of the random processes observed by them. Owing to their ubiquity, cellular networks are seen as a natural candidate for providing reliable wireless connectivity to IoT devices. Given the massive number of IoT devices, enabling non-orthogonal multiple access (NOMA) for the mobile users and IoT devices is appealing in terms of the efficient utilization of spectrum compared to the orthogonal multiple access (OMA). For instance, the uplink NOMA can also be configured such that the mobile users adapt their transmission rates depending upon the channel conditions while the IoT devices transmit at a fixed rate. For this setting, we analyze the ergodic capacity of the mobile users and the mean local delay of IoT devices using stochastic geometry. Our analysis demonstrates that the aforementioned NOMA configuration provides better ergodic capacity for mobile users compared to OMA when delay constraint of IoT devices is strict. We also show that NOMA supports a larger packet size at IoT devices than OMA under the same delay constraint.

Semi-supervised Transformer Network for Anomaly Detection in Cellular Internet of Things

Because of the lightning-fast expansion of the Internet of Things (IoT) technologies, an enormous amount of data has been produced. This traffic can be mined for information that can be used to identify and avoid intrusions into IoT networks. Despite the significant efforts that have been put into labeling Internet of Things traffic records, the total number of labeled records is still quite low, which makes it more difficult to detect intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (S2T-Net), in which we propose a temporal transformer module to empower the model to learn valuable interactions in cellular data. An improved spatial transformer is presented to capture local representation in the cellular traffic flow. At the same time, a multilevel semi-supervised training technique is used to account for the consecutive structure of the IoT traffic information. In order to provide effective real-time threat intelligence, the suggested S2T-Net can be tightly coupled into a cellular IoT network. Last but not least, empirical assessments on two current databases (CIC-IDS2017 and CIC-IDS2018) show that S2T-Net boosts intrusion detection accuracy and resilience while retaining resource-efficient computing.

Energy-Saving Solutions for Cellular Internet of Things–A Survey

The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

Multiagent Reinforcement Learning Meets Random Access in Massive Cellular Internet of Things

Internet of Things (IoT) has attracted considerable attention in recent years due to its potential of interconnecting a large number of heterogeneous wireless devices. However, it is usually challenging to provide reliable and efficient random access control when massive IoT devices are trying to access the network simultaneously. In this article, we investigate methods to introduce intelligent random access management for a massive cellular IoT network to reduce access latency and access failures. Toward this end, we introduce two novel frameworks, namely, local device selection (LDS) and intelligent preamble selection (IPS). LDS enables local communication between neighboring devices to provide cluster-wide cooperative congestion control, which leads to a better distribution of the access intensity under bursty traffics. Taking advantage of the capability of reinforcement learning in developing cooperative multiagent policies, IPS is introduced to enable the optimization of the preamble selection policy in each IoT clusters. To handle the exponentially growing action space in IPS, we design a novel reinforcement learning structure, named branching actor–critic, to ensure that the output size of the underlying neural networks only grows linearly with the number of action dimensions. Simulation results indicate that the introduced mechanism achieves much lower access delays with fewer access failures in various realistic scenarios of interests.

On Edge Caching in Satellite — IoT Networks

The implementation of the Internet of Things (IoT) is mostly done through cellular networks, which do not cover the whole world. In addition, the explosive growth of global Internet access demand introduces the need for integrating satellites with cellular IoT networks for coverage extension and backhaul offloading. Operating hybrid satellite-IoT (SIoT) networks, however, might incur excessive service latency and high satellite bandwidth consumption. To tackle these issues, edge caching technology has been considered in SIoT. This article reviews existing research on edge-caching-based SIoT networks with illustrative performance evaluation. Various caching design criteria with a focus on two-tier cache-enabled SIoT are discussed. In addition, open research problems on edge caching in SIoT are identified as future research directions and opportunities.

Resource-Optimized Recursive Access Class Barring for Bursty Traffic in Cellular IoT Networks

A massive number of Internet-of-Things (IoT) and machine-to-machine (M2M) communication devices generate various types of data traffic in cellular IoT networks: periodic or nonperiodic, bursty or sporadic, etc. In particular, bursty and nonperiodic traffic may cause an unexpected network congestion and temporary lack of radio resources. In order to effectively accommodate such bursty and nonperiodic traffic, we propose a novel recursive access class barring (R-ACB) technique to optimally utilize the available resources associated with the random access procedure (RAP) that consists of multiple steps in cellular IoT networks, while existing ACB schemes only considered the resource of the first step of RAP, i.e., the number of available preambles. The proposed R-ACB technique consists of two main parts: 1) online estimation of the number of active IoT/M2M devices who have data to transmit to an eNodeB and 2) adjustment of the ACB factor that indicates the probability that an active device sends a preamble to eNodeB. It is notable that the estimation and the adjustment recursively affect each other when R-ACB operates. In addition, we also propose mathematical models to analyze the performance of R-ACB in terms of total service time, average access delay, resource efficiency, and energy efficiency (EE). Through extensive computer simulations, we show that the proposed R-ACB technique outperforms the conventional ACB schemes.

Low-Complexity Data Collection Scheme for UAV Sink Nodes in Cellular IoT Networks

Owing to the nature of battery-operated sensors, the residual energy of their battery should be considered when a sink node collects sensory data from them. If a sink attempts to collect data from a sensor that does not have sufficient residual energy to transmit data, the utilization of wireless communication resources may be degraded. However, requiring a periodic residual energy report of the sensors considerably reduces the energy efficiency of them. Consequently, the sink node must be able to estimate the residual energy of the sensors without additional information exchange before attempting to collect data. To tackle this problem, we propose a data collection scheme for mobile sinks in cellular Internet-of-things (IoT) networks. The scheme consists of two phases. In the first phase, the mobile sink estimates the residual energy of the surrounding sensors. To reduce the complexity, we design a state diagram composed of three states based on the Markov chain of the sensor. The sink node calculates the communicable likelihood of each sensor based on the state diagram and collects data from the sensor with the highest likelihood. In the second phase, a cellular base station (BS) selects the appropriate mobile sink to improve the signal-to-interference-plus-noise ratio (SINR) of the signal from a cellular user. Each sink node delivers the bidding price to the BS based on the likelihoods of the sensors. The BS determines an appropriate sink node to allocate resources based on the received bidding prices. Moreover, we show the performance of the proposed method in terms of SINR and IoT network utilization through simulation.

Resource-Hopping-Based Grant-Free Multiple Access for 6G-Enabled Massive IoT Networks

Grant-free multiple access (GFMA) is an emerging technology to accommodate a massive number of devices for 6G-enabled Internet of Things (IoT) networks. The main advantages of GFMA are to efficiently reduce control signaling overhead for resource scheduling while improving resource efficiency. In this article, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">novel</i> resource-hopping-based GFMA (RH-GFMA) framework with resource hopping schemes for providing massive connectivity in 6G cellular IoT networks, where each IoT device is allowed to access physical radio resources by using a preassigned resource hopping pattern without not only resource request but also grant procedure, which is the so-called “one-shot” noninteractive multiple access. We exploit three types of resource hopping schemes in the proposed RH-GFMA framework: 1) random hopping; 2) resource group hopping; and 3) Latin-square group hopping. We mathematically analyze the RH-GFMA system performance in terms of the hopping pattern collision probability, maximum allowable packet delay, and interference-over-thermal. Finally, we derive an accommodation capacity of the proposed RH-GFMA framework, which is defined as the expected number of IoT devices accommodated in a cell under a maximum allowable packet-delay requirement and an interference-over-thermal constraint. With the proposed GFMA, massive IoT devices are expected to be efficiently accommodated in 6G wireless networks, while satisfying strict latency and reliability requirements.

Backhaul-Capacity-Aware Interference Mitigation Framework in 6G Cellular Internet of Things

To meet the increasing demands for wide-band communications and network densification, a new paradigm of millimeter-wave (mmWave)-enabled integrated access and backhaul (IAB) is urgently needed in the sixth-generation (6G) cellular Internet-of-Things (IoT) network. However, the mmWave-enabled IAB technology brings new challenges on network capacity in terms of the differentiated backhaul capacities of small-cell base stations and the various interferences among access links in the 6G cellular IoT network. Therefore, this article proposes a joint traffic load-balancing and interference mitigation framework to maximize the network capacity for 6G cellular IoT services. A novel two-step resource allocation scheme is designed by optimizing the user equipment association and the transmit power allocation (PA), iteratively. Moreover, to minimize both the backhaul burden and the interference, a novel backhaul capacity and interference-aware matching utility function using the many-to-many matching model is designed to measure the interference penalty and the backhaul capacity. By transforming the nonconvex PA subproblem into a convex problem using the successive convex approximation method, both upper and lower bounds of the optimal transmit power are theoretically achieved. Simulation results prove that the proposed algorithms can significantly improve the network sum rate by 71.9% compared to the conventional algorithms, and the successful transmission probability can be well guaranteed.

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.

Enhancing the Reliability of Cellular Internet of Things through Agreement

Because the Internet of Things (IoT) can provide a global service network through various smart devices, the IoT has been widely used in smart transportation, smart cities, smart healthcare, and factory automation through the Internet connection. With the large-scale establishment and 5G (fifth generation) wireless networks, the cellular Internet of Things (CIoT) will continue to be developed and applied to a wide range of applications. In order to provide a reliable application of CIoT, a safe and reliable network topology MECIoT is proposed in this study. To improve the reliability and fault-tolerant capability of the network proposed, the problem of reaching agreement should be revisited. Therefore, the applications in the system can still be performed correctly even if some processing units (PUs) in the system have failed. In this study, a new protocol is proposed to allow all normal PUs in MECIoT to reach an agreement with the minimum amount of data exchanges required and the maximum number of failed PUs allowed in MECIoT. In the end, the optimality of the protocol has been proven by mathematical method.

Interference aided cooperative SWIPT for cellular IoT networks towards 5G and beyond

To meet the anticipated challenges of the fifth-generation and beyond wireless networks, multiple candidate technologies are needed to be integrated. Motivating by this, two cooperative simultaneous wireless information and power transfer (SWIPT) protocols are proposed in this study for cellular internet of things (IoT) networks by exploiting non-orthogonal multiple access considering two different scenarios. In the proposed SWIPT protocols, both the signal of source and co-channel interference (CCI) signals are exploited as a potential source of energy. The source considered as a cellular base station communicates with its user via the assistance of energy-constrained IoT nodes acting as relays. In the first scenario, one of the assisting IoT nodes acts as a best relay to forward source information and its own information simultaneously in the same channel by exploiting non-orthogonal multiple access. The best relay is selected based on the maximum harvested energy from the signal of source and CCI signals. Second scenario is the extended version of the first scenario, where two extra energy constraint IoT transmitter–receiver pairs are assumed to be present. To evaluate the performance of the proposed protocols, delay-tolerant throughput, outage probability, delay-limited throughput, and energy efficiency are investigated over Nakagami-m fading channels. Finally, comparative results with the existing strategies are demonstrated to show the effectiveness of the proposed protocols.

Group-based delivery of critical traffic in cellular IoT networks

Fifth generation (5G) networks are expected to connect a huge number of Internet of Things (IoT) devices in many usage scenarios. The challenges of typical massive IoT applications with sporadic and short packet uplink transmissions are well studied, while not enough attention is given to the delivery of content of common interest, such as software/firmware updates and remote control, towards IoT devices in emerging point-to-multipoint scenarios. Moreover, the delivery of delay-sensitive IoT traffic is not sufficiently addressed in the literature. In this work we (i) identify the drawbacks of the current Single-Cell Point-to-Multipoint (SC-PTM) solution for unplanned critical traffic delivery in cellular IoT (cIoT) networks, and (ii) propose paging and multicast schemes for a fast distribution of critical updates after, e.g., bug fixes or system failures. We benchmark the performance of the proposed paging scheme against similar solutions available in the literature. Our extended SC-PTM framework is energy efficient and guarantees low service latency, as demonstrated both analytically and by simulations.