Future Internet Of Things Networks Research Articles

Enhanced IoT Spectrum Utilization: Integrating Geospatial and Environmental Data for Advanced Mid-Band Spectrum Sharing.

The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks.

Smart Farm Security by Combining IoT Sensor Network and Virtualized Mycelium Network.

In today's world, merging sensor-based security systems with contemporary principles has become crucial. As we witness the ever-growing number of interconnected devices in the Internet of Things (IoT), it is imperative to have robust and trustworthy security measures in place. In this paper, we examine the idea of virtualizing the communication infrastructure for smart farming in the context of IoT. Our approach utilizes a metaverse-based framework that mimics natural processes such as mycelium network growth communication with a security-concept-based srtificial immune system (AIS) and transaction models of a multi-agent system (MAS). The mycelium, a bridge that transfers nutrients from one plant to another, is an underground network (IoT below ground) that can interconnect multiple plants. Our objective is to study and simulate the mycelium's behavior, which serves as an underground IoT, and we anticipate that the simulation results, supported by diverse aspects, can be a reference for future IoT network development. A proof of concept is presented, demonstrating the capabilities of such a virtualized network for dedicated sensor communication and easy reconfiguration for various needs.

Wireless Powered Multiaccess Edge Computing With Slotted ALOHA

In this work, the integration of multi-access edge computing (MEC) with wireless power transfer (WPT) and slotted-ALOHA (SA) is investigated. All aforementioned technologies are considered key enablers for future Internet of Things networks, therefore, their interaction is of great significance. To that end, an energy efficient medium access protocol is designed, where each wireless powered device (WPD) harvests energy in order to either locally process a task or to offload it to the MEC server, by using SA. A non-convex energy minimization problem is formulated, which with the aid of proper transformations and successive convex approximation (SCA) is transformed to an equivalent convex one. Finally, simulation results are presented which aid to extract various insights for the proposed architecture.

Federated Learning Protocols for IoT Edge Computing

In this article, we provide a set of federated learning (FL) protocols for future Internet architectures, which integrate the edge computing with the Internet of Things (IoT) known as “IoT edge computing.” The proposed protocols aim to the efficient implementation of the FL, i.e., distributed intelligence, in future IoT networks, where edge computing will leverage the overall procedure at the edge of the network. We first provide a list of application requirements for such an FL implementation, which result in the architecture of constrained and nonconstrained IoT devices based on a set of Internet engineering task force (IETF) standards. The FL protocols consist of three stages as follows: 1) initial configuration; 2) distributed training; and 3) cloud updates. The specified FL protocols are tested using an experimental IoT platform, which is used to obtain experimental results that provide the performance evaluation of the FL protocols in terms of accuracy, time, and latency. We propose those FL protocols for the next-generation Internet (NGI), where IoT, edge computing, and FL will be blended efficiently for future Internet applications.

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in the fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. In particular, the potential of using emerging deep learning and federated learning techniques for enhancing the efficiency and security of IoT communication are discussed, and their promises and challenges are introduced. Finally, future research directions toward beyond 5G IoT networks are pointed out.

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%, 40%, 32.2%, 27.7%, 15.5%, 11.1%, and 6.6% less energy compared with SA, GA, PSO, BFA, ACO, BA, and GSP, respectively.

6G Internet of Things: A Comprehensive Survey

The sixth-generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) toward a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space–air–ground–underwater communications, Terahertz communications, massive ultrareliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely, healthcare IoTs, Vehicular IoTs and Autonomous Driving, Unmanned Aerial Vehicles, Satellite IoTs, and Industrial IoTs. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.

Throughput and Age of Information in a Cellular-Based IoT Network

This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Johnson-Mehl (JM)</i> tessellation, such that the IoT devices located in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JM cells</i> are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

Fair power allocation in cooperative cognitive systems under NOMA transmission for future IoT networks

To support the massive connectivity in Internet of Things (IoT), several promising techniques like cognitive radio (CR) and non-orthogonal multiple access (NOMA) enables the user to share spectrum resources. This work aims to achieve fairness among secondary users (SUs) in IoT cooperative NOMA-based CR transmission. We design a power allocation algorithm, an independent battery constraint at each node is considered, and power gap among transmissions of two NOMA users is applied for successive interference cancellation. The simulation results show that the proposed framework provides excellent performance and for sufficient available transmission power perfect fairness is achieved.

Spectral efficient designs of MIMO‐based CR‐NOMA for Internet of Things Networks

SummaryDue to rapid increase in the wireless‐connected Internet‐of‐things (IoT) devices, the communication among devices is a critical task as it relies on the radio‐frequency spectrum, which is a limited resource. Therefore, additional spectral efficient communication designs are required for future IoT networks. By keeping this in mind, we have exploited individually spectral efficient technologies such as the cognitive radio (CR), non‐orthogonal multiple access (NOMA), and multiple‐input multiple‐output (MIMO) simultaneously that is MIMO‐based CR‐NOMA (MIMO‐CR‐NOMA) and have proposed as well as analyzed a potential framework for its downlink scenario. The closed‐form expressions for the throughput at each user due to the number of transmitting and receiving antennas are derived numerically. Further, we have derived mathematical expressions for the sum throughput of different frameworks such as CR‐NOMA, CR‐MIMO, and MIMO‐CR‐NOMA systems. Moreover, the proposed frameworks are simulated, and the relationship between two NOMA users each with multiple antennas is illustrated with simulation results.

The Complexity–Performance Tradeoff in Resource Allocation for URLLC Exploiting Dynamic CSI

The challenging applications envisioned for the future Internet of Things networks are making it urgent to develop fast and scalable resource allocation algorithms able to meet the stringent reliability and latency constraints typical of the ultra-reliable, low-latency communications (URLLCs). However, there is an inherent tradeoff between complexity and performance to be addressed: sophisticated resource allocation methods providing optimized spectrum utilization are challenged by the scale of applications and the concomitant stringent latency constraints. Whether nontrivial resource allocation approaches can be successfully applied in large-scale network instances is still an open question that this article aims to address. More specifically, we consider a scenario in which channel-state information (CSI) is used to improve spectrum allocation in a radio environment that experiences channel time correlation. Channel correlation allows the usage of CSI for longer time before an update, thus lowering the overhead burden. Following this intuition, we propose a dynamic pilot transmission allocation scheme in order to adaptively tune the CSI age. We systematically analyze the improvement of this approach applied to a sophisticated, recently introduced graph-based resource allocation method that we extend here to account for CSI. The results show that even in very dense networks and accounting for the higher computational time of the graph-based approach, this algorithm is able to improve spectrum efficiency by over 12% as compared to a greedy heuristic, and that dynamic pilot transmissions allocation can further boost its performance in terms of fairness, while concomitantly further increase spectrum efficiency of 3%-5%.

A Novel Interference Cancellation Scheme for Bistatic Backscatter Communication Systems

The bistatic backscatter is a promising paradigm for the future internet of things network. To tackle the key challenge of strong direct emitter-reader interference, this letter proposes a novel interference cancellation scheme with minimal hardware complexity by exploiting the periodicity of sinusoidal radio frequency (RF) carrier. Based on the interference-free signal, we pursue a detailed investigation on the optimal detector design and characterize its closed-form bit error rate (BER) expression. Also, to demonstrate the superiority of the proposed optimal detector, the popular energy detector is presented to serve as a benchmark. In addition, a simple estimation method is designed to obtain the information of certain parameters required for constructing the thresholds and test statistics. Simulations results demonstrate the superiority of the proposed optimal detector.

Collision Observation-based Optimization of Low-power and Lossy IoT Network using Reinforcement Learning

The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic and lossy environment and impact performance. The ranking mechanism also requires large control overheads to update rank information. The resource-constrained IoT devices operating in a low-power and lossy network (LLN) environment need an efficient solution to handle these problems. Reinforcement learning (RL) algorithms like Q-learning are recently utilized to solve learning problems in LLNs devices like sensors. Thus, in this paper, an RL-based optimization of dense LLN IoT devices with heavy heterogeneous traffic is devised. The proposed protocol learns the collision information from the MAC layer and makes an intelligent decision at the network layer. The proposed protocol also enhances the operation of the trickle timer algorithm. A Q-learning model is employed to adaptively learn the channel collision probability and network layer ranking states with accumulated reward function. Based on a simulation using Contiki 3.0 Cooja, the proposed intelligent scheme achieves a lower packet loss ratio, improves throughput, produces lower control overheads, and consumes less energy than other state-of-the-art mechanisms.

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns.

The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.

Resource Allocation and 3-D Placement for UAV-Enabled Energy-Efficient IoT Communications

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

Complete Target Coverage in Radio Frequency and Solar-Powered Sensor Networks

Future Internet of Things networks are likely to have radio frequency (RF) energy harvesting sensor devices and also solar-powered hybrid access points (HAPs) that help monitor one or more targets such as assets in a warehouse. In this setting, we outline three contributions that ensure devices' monitor targets at all times and upload the maximum number of samples to an HAP in order to ensure high monitoring quality. First, we outline a mixed integer linear program (MILP) and use it to optimize the transmission power of HAPs and time used by devices to harvest RF energy, monitor target(s), and data transmissions. Second, we propose two heuristic algorithms to solve large problem instances. The results show that our heuristics achieve almost 97% of the optimal result computed by the MILP.

GoodPut, Collision Probability and Network Stability of Energy-Harvesting Cognitive-Radio IoT Networks

Due to the ever-expanding applications of the Internet-of-Things (IoT), designing energy- and spectrally-efficient transmission schemes to support massive connections and devices is inevitable and still challenging. Thus, energy-harvesting (EH) and cognitive-radio (CR) systems are becoming more inseparable for future IoT networks. This paper analyzes the performance of EH-CR-IoT networks, where closed-form expressions for network metrics, such as GoodPut, collision probability and average packet delay are derived. In addition to the interference caused by spectrum sensing errors, our analysis also incorporates the primary user (PU) return interference into the different network metrics. Furthermore, the effect of primary network traffic behavior and IoT network parameters are investigated. To account for delay-sensitive packets, the average end-to-end delay of packets as well as delay violation probability in the IoT network are mathematically formulated and analyzed as quality-of-service (QoS) measures for network stability. Moreover, the derived metrics can be utilized to optimize the Goodput, subject to various practical constraints. Simulations are also performed to verify the theoretical results. Above all, the effect of energy-harvesting rate on GoodPut and IoT network stability is explored, which provides insights into determining the physical structure of the energy-harvesting system.

Random Space–Time Line Code With Proportional Fairness Scheduling

As the demand for and services of the Internet of Things (IoT) increase, the dense deployment of a massive number of devices is expected in future IoT networks. Accordingly, an appropriate scheduling method will be required to fairly support the overwhelmingly large number of devices or users operating in practical environments. In this paper, we consider a single-cell downlink network in which one transmitter, such as an access point and base station, supports the massive number of users by using a space–time line code (STLC) scheme. Here, to resolve a prohibitively large amount of channel state information (CSI) feedback from the users to the transmitter, we propose a new STLC scheme called random STLC that uses a random vector for the STLC encoding. Furthermore, a proportional fairness (PF) scheduler is modified with a priority factor that is proportional to the devised alternative signal-to-interference-plus-noise ratio. By a comparison with an optimal STLC system that uses full CSI with a PF scheduler, we justify the benefit of the proposed random STLC with a PF scheduler using partial CSI for massive-user networks. Numerical results obtained from a rigorous simulation confirm that the proposed scheme can provide a comparable to achievable rate and fairness with massive number of users together with affordable feedback overhead in massive networks.

SH-BlockCC: A secure and efficient Internet of things smart home architecture based on cloud computing and blockchain technology

The growing demand for human-independent comfortable lifestyle has emboldened the development of smart home. A typical keenly intellective home includes many Internet of things contrivances that engender processes and immensely colossal data to efficiently handle its users’ demands. This incrementing demand raises a plethora of concern cognate to a smart home system in terms of scalability, efficiency, and security. All these issues are tedious to manage, and the existing studies lack the granularity for surmounting them. Considering such a requisite of security and efficiency as a quandary at hand, this article presents a secure and efficient smart home architecture, which incorporates the blockchain and the cloud computing technologies for a cumulated solution. Because of the decentralized nature of blockchain technology, it can serve the processing services and make the transaction copy of the collected sensible user data from smart home. To ensure the security of smart home network, our proposed model utilizes the multivariate correlation analysis technique to analyze the network traffic and identify the correlation between traffic features. We have evaluated the performance of our proposed architecture using different parameters like throughput and discovered that blockchain is an efficient security solution for the future Internet of things network.