Next-generation Internet Of Things Research Articles

Optimizing Drone-Based IoT Base Stations in 6G Networks Using the Quasi-opposition-Based Lemurs Optimization Algorithm

The rapid evolution and integration of next-generation Internet-of-things (NG-IoT) applications present new complexities for sixth-generation (6G) mobile communication networks, including the need for extensive connectivity, increased network capacity, and ultralow-latency communications. While ultradense networking, characterized by deploying numerous base stations, offers a potential solution, practical and financial constraints limit its feasibility. Drone-based stations (DBSs) emerge as a flexible alternative, but their optimal positioning remains a critical challenge due to finite energy sources and potential signal degradation. This study introduces a new quasi-opposition-based lemurs optimizer (QOBLO) to address the optimal placement of DBSs in NG-IoT networks. QOBLO combines lemur foraging behaviour with quasi-opposition-based learning to enhance exploration and exploitation in the optimization process. The performance of QOBLO was evaluated across three scenarios and compared with other swarm intelligence algorithms using metrics such as Friedman’s ranking test (FRT) and the Wilcoxon signed-rank test (WSRT). Rigorous simulations emulated real-world conditions and varying network demands, testing QOBLO's adaptability and robustness. Results indicate that QOBLO significantly outperforms other algorithms, achieving a top FRT value of 1.234 and demonstrating superior p values in the WSRT. These findings highlight QOBLO's capability to enhance connectivity, coverage, and energy efficiency in 6G environments. The primary contribution of this research is the development of QOBLO, a scalable and efficient method for optimizing DBS deployment. This new approach improves network performance in complex NG-IoT applications, offering a robust solution to the challenges of 6G networks and ensuring enhanced reliability and sustainability of future communication infrastructures.

Exploring the next generation Internet of Things (IoT) requirements and applications: A comprehensive overview

Exploring the next generation Internet of Things (IoT) requirements and applications: A comprehensive overview

Navigating Industry 4.0 Frontiers: A Scalable and Resilient Next-Generation IoT Framework to Implement Future Advancements in Smart and Adaptive Industrial Systems

The emergence of Industry 4.0 signifies a paradigm shift in industrial systems, characterized by the amalgamation of digital technologies with tangible operations. The goal of this study is to present a state-of-the-art, scalable, and robust Internet of Things (IoT) framework that will enable future innovations in intelligent and adaptable industrial systems to be seamlessly integrated. Our framework gives scalability first priority in response to Industry 4.0's dynamic nature, which is marked by fast technical evolution and rising connection in order to handle the expanding ecosystem of networked devices. The suggested structure places a strong emphasis on resilience and is designed to resist setbacks and guarantee the continuation of vital industrial processes. Our framework improves industrial systems' intelligence by utilizing edge computing, machine learning techniques, and improved communication protocols. This allows the systems to self-adapt to changing situations. Moreover, it adopts a modular architecture that facilitates interoperability and makes it simple to integrate various devices and technologies. Our IoT framework creates a solid, flexible, and future-proof industrial environment with this all-encompassing strategy, enabling businesses to confidently and effectively traverse Industry 4.0's frontiers.

Hybrid Privacy Preserving Federated Learning Against Irregular Users in Next-Generation Internet of Things

While federated learning (FL) is a well-known privacy-preserving (PP) solution, recent studies demonstrate that it still has privacy problems and vulnerabilities, particularly in the context of the Next Generation Internet-of-Things (NG-IoT). Attackers on the server can potentially retrieve sensitive information such as data tabs and memberships. Additionally, current FL studies often overlook critical FL issues such as user dropout and low-quality data, which are prevalent in NG-IoT environments. In this article, we propose a privacy-preserving federated model in hybrid terms based on synchronous + asynchronous manner. This federated model removes the fundamental issues of FL-enabled NG-IoT environments while applying Two-Trapdoor Homomorphic Encryption (TTHE) to ensure the privacy and confidentiality of all components in the proposed model. Furthermore, our server protocol removes the effect of irregular users. In this regard, to control the impact of user dropout, we developed the hybrid LEGATO algorithm, which is asynchronous. To tackle users with low-quality data, we utilize the data-sharing method. Security analysis of the proposed model guarantees correctness, auditing, and PP in a federated setup. Also, in the performance evaluation, we achieved a high level of functionality and accuracy compared to peer works, while system overheads are shown to be lower.

Analyzing the suitability of IEEE 802.11ah for next generation Internet of Things: A comparative study

Hyperconnectivity, real-time processing, and interoperable communication facilities are key to the Next Generation (NG) Internet of Things (IoT). However, existing IoT architectures employ a diverse range of communication technologies to support desired data rates and coverage range, making them more complex to manage. The emerging IEEE 802.11ah standard, marketed as WiFi HaLow™, enables seamless communication among large-scale and energy-efficient sensor/actuator nodes to the Internet. IEEE 802.11ah introduces key features like Restricted Access Window (RAW) for reducing contention, Hierarchical Traffic Indication Map (TIM) and Association Identifier (AID) for efficient scheduling and device identification, and multiple Modulation and Coding Schemes (MCSs) for adaptive data transmission, enhancing IoT connectivity in Next-Generation IoT (NGI) communication. In this paper, we discuss IEEE 802.11ah for NGI concerning different requirements in terms of communication and computation. We survey the suitability of 802.11ah for NGI as compared to the existing state-of-the-art connectivity solutions. Thereafter, an IEEE 802.11ah-based NGI architecture and protocol stack are proposed and discussed, according to the different distinct features of this novel technology. Finally, we conclude by enumerating research and future directions for the proposed network architecture.

Massive Connectivity and Low-Latency for Next-Generation Internet of Things: A Filtered OFDM-based Deep Learning Approach

Abstract The Internet of Things (IoT) is one of the numerous services offered by sixth-generation (6G) mobile communications. It is necessary to meet heterogeneous criteria for large connectivity and low latency to serve the various kinds of IoT applications. In order to concurrently provide enormous connectivity and meet the low-latency conditions in the uplink IoT network, we proposed filtered orthogonal frequency division multiplexing (FOFDM) based on a service group adopting the deep learning (DL) technique. The proposed FOFDM-DL platform focuses on two key areas: first, it works for the concurrence of different time-frequency granularity appropriate for distinct service-based grouping, and secondly, it facilitates low latency and massive connectivity to deliver dependable communications. The suggested FOFDM-DL architecture may accommodate the needs of massive machine-type communication referred as mMTC and ultra-reliable and low-latency communications known as uRLLC concurrently for the next-generation communication systems. However, the uRLLC and mMTC requirements can only be supported separately by the new radio (NR)-5G, beyond 5G (or 6G). In comparison to the traditional scheme, simulation results demonstrate that the suggested FOFDM-DL platform works surprisingly well in the next-generation IoT network.

Design and implementation of hybrid low power wide area network architecture for IoT applications

The rapid proliferation of Internet of Things (IoT) devices and applications has resulted in an increasing demand for Low Power and Wide Area Network (LPWAN) solutions. The adoption of IoT networks still faces several challenges, despite the rapid advancement of low-power communication technology. Homogenizing this sector requires allowing interoperability between many technologies, which is now one of the largest obstacles. In this article, we present the design and implementation of the hybrid LPWAN architecture that can accomplish wide-area communication coverage and low-power consumption for IoT applications by leveraging two LPWAN technologies, Wireless Smart Ubiquitous Network (Wi-SUN) and Long Range (LoRa). In particular, LoRa is used for long-range communication, and Wi-SUN for a low-latency mesh network. Additionally, we implemented smart street light controlling system as a real-world deployment at the university campus to showcase the efficiency of the hybrid network. Our results demonstrate that the hybrid LPWAN architecture provides a better coverage and capacity while consuming less power than that of the LoRa or Wi-SUN network. The results of this study demonstrate the effectiveness of the proposed hybrid LPWAN architecture as a viable solution for next-generation IoT applications.

Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment

The Internet of Things (IoT) is the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT platforms. The effects of these intrusions lead to economic and physical damage. The conventional IoT security approaches are unable to handle the current security problems since the threats and attacks are continuously evolving. In this background, employing Artificial Intelligence (AI) knowledge, particularly Machine Learning (ML) and Deep Learning (DL) solutions, remains the key to delivering a dynamically improved and modern security system for next-generation IoT systems. Therefore, the current manuscript designs the Honey Badger Algorithm with an Optimal Hybrid Deep Belief Network (HBA-OHDBN) technique for cyberattack detection in a blockchain (BC)-assisted IoT environment. The purpose of the proposed HBA-OHDBN algorithm lies in its accurate recognition and classification of cyberattacks in the BC-assisted IoT platform. In the proposed HBA-OHDBN technique, feature selection using the HBA is implemented to choose an optimal set of features. For intrusion detection, the HBA-OHDBN technique applies the HDBN model. In order to adjust the hyperparameter values of the HDBN model, the Dung Beetle Optimization (DBO) algorithm is utilized. Moreover, BC technology is also applied to improve network security. The performance of the HBA-OHDBN algorithm was validated using the benchmark NSLKDD dataset. The extensive results indicate that the HBA-OHDBN model outperforms recent models, with a maximum accuracy of 99.21%.

Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System

With the emergence of the 5G network, the count of analysis papers associated with the 6G Internet of Things (IoT) has rapidly increased due to the rising attention of researchers in next-generation technology, 6G networks and IoT techniques. Owing to this, grasping the overall research topics and directions is a complex task. To mutually address the significant issues of 6G cellular IoT, i.e., information transmission, data aggregation and power supply, we proposed a variance-based integrating model for the 6G-IoT approach that considers energy, communication and computation (ECC). Initially, the base station (BS) charges huge IoT devices concurrently utilizing WPT in the downlink. After that, IoT devices gather the energy to perform the communication task and the computation task in the uplink in a similar spectrum. Also, the model integrates the optimization of transmit beams via the Improved Ant Colony Optimization (IACO) model to balance the system performance, power consumption and computational complexity. Further, this study exploited activated Remote Radio Units (RRUs) to improve the network performance and energy efficiency in the downlink model. The simulation outcomes evaluate the performance of the proposed work over the conventional models concerning error analysis. From the results, the MSE value in the IACO work is much lower, around 0.011, while the compared schemes achieved comparatively higher MSE values.

Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey

Artificial intelligence (AI) and Machine Learning (ML) are widely employed to make the solutions more accurate and autonomous in many smart and intelligent applications in the Internet of Things (IoT). In these IoT applications, the performance and accuracy of AI/ML models are the main concerns; however, the transparency, interpretability, and responsibility of the models’ decisions are often neglected. Moreover, in AI/ML-supported next-generation IoT applications, there is a need for more reliable, transparent, and explainable systems. In particular, regardless of whether the decisions are simple or complex, how the decision is made, which features affect the decision, and their adoption and interpretation by people or experts are crucial issues. Also, people typically perceive unpredictable or opaque AI outcomes with skepticism, which reduces the adoption and proliferation of IoT applications. To that end, Explainable Artificial Intelligence (XAI) has emerged as a promising research topic that allows ante-hoc and post-hoc functioning and stages of black-box models to be transparent, understandable, and interpretable. In this paper, we provide an in-depth and systematic review of recent studies that use XAI models in the scope of the IoT domain. We classify the studies according to their methodology and application areas. Additionally, we highlight the challenges and open issues and provide promising future directions to lead the researchers in future investigations.

Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

Spectrum-Efficient Analog Pulse Index Modulation for High-Volume Wireless Data Telemetry

Spectrum-efficient, high-density, and high-speed data transmission are essential to store and exchange sensitive information through Internet-of-Things (IoT) networks. However, the resource-constrained IoT devices and the limited existing frequency spectrum pose a significant challenge to employing the more efficient technique for the next-generation technology. This paper presents an innovative pulse index modulation (PIM) architecture with a randomization technique for analog pulse-based packet-less high-volume data transmission. The pulse-based compression simplifies the encoding and decoding schemes for short-range wireless telemetry. The pulse randomization technique creates the pulse blinding by reordering the pulse positions and adds an extra level of physical layer security. This paper describes the design, simulation, and prototype development of PIM to validate the feasibility and compatibility of pulse-based data telemetry using the standard architecture. Simulation results show that the proposed PIM increases the data compression ratio by 2.61 times and improves the processing time by 5.56 time compared to the Huffman coding for a 3-bit system. Test results show that the BER is 7.5×10-4 at 10.0 dB SNR, which satisfies the lower bound for data communication and increases the transmission speed k-times by supporting 2k*k bits data using 2k number of pulses, where k is a non-negative integer number. The proposed PIM has the potential to improve data rate with added security to support the next-generation IoT networks.

RLCS: Towards a robust and efficient mobile edge computing resource scheduling and task offloading system based on graph neural network

As the Internet of Things (IoT) keeps evolving, next-generation IoT (NG-IoT) scenarios empower various applications which require low-latency connections or high bandwidth support. Mobile edge computing (MEC) is then proposed to provide users with low-latency computing services. However, the massive and heterogeneous nature of user devices and MEC servers also brings some new challenges for resource management and task offloading. Existing works have some shortcomings because of adopting a coarse-grained task model or neglecting task graph information. The booming of artificial intelligence (AI) provides us with a more robust approach to addressing these issues. In this paper, we propose an NG-IoT user task offloading and resource scheduling architecture in the MEC scenario. We formulate our problem objective as minimizing average user task completion time (TCT). To solve the problem, we propose a Reinforcement Learning based algorithm for Container Scheduling (RLCS) and cooperating with the graph convolutional network (GCN) technique. We perform RLCS training and evaluate RLCS performance in the simulated environment. Evaluation results indicate that RLCS outperforms other baselines (e.g., reinforcement learning based algorithm, heuristic algorithm) in multiple experimental settings.

Geometry-guided multilevel RGBD fusion for surface normal estimation

Developments in 3D computer vision have advanced scene understanding and 3D modeling. Surface normal estimation is a basic task in these fields. In this paper, we propose a geometry-guided multilevel fusion scheme for high-quality surface normal estimation by exploiting texture and geometry information from color and depth images. The surface normal is progressively predicted with a coarse-to-fine strategy. First, an initial surface normal (IniNormal) Nini is predicted by a hierarchical confidence reweighting convolution neural network to merge texture and geometry information in a CNN feature level. Although a general accuracy is achieved, the long tail problem makes the IniNormal always fails in special areas where the depth map is high-quality while the intensity interference is challenging, such as repeating textures and abnormal exposures. Further, a traditional geometry-consistent based surface normal(GeoNormal) Ngeo is calculated based on traditional constraints, and a surface normal level fusion module is designed to remap the depth to different representations and reconsider scene information. Then, the final clear surface normal N is estimated by adaptively reintegrating the IniNormal and GeoNormal in a decision level. To overcome disturbances in the dataset and ensure the trainability of the network, a carefully designed hybrid objective function and an annealed term are applied. An explainable analysis is attached. The experimental results on two benchmark datasets demonstrate that the proposed GMLF(geometry-guided multilevel RGBD fusion for surface normal estimation) can achieve better quantitative and qualitative performance. The proposed method may be useful for robots and auto-driving which can be applied in the next-generation Internet-of-Things (NG-IoT).

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website's content as a technical resource and reference.

Multi-agent reinforcement learning enabled link scheduling for next generation Internet of Things

In next generation Internet of Things (NG-IoT) networks, numerous pieces of information are aggregated from the user devices and sensor nodes to the local computing units for further computing to support high-level applications. Those multitudinous transmission demands have raised new challenges for current link scheduling protocols. The centralized link scheduling protocols are inappropriate in some large-scale NG-IoT scenarios. The previously distributed link scheduling uses the randomized transmission scheme to avoid interference, making it hard to utilize the bandwidth resources fully. The multi-agent machine learning (MAML) technique is a potential approach to finding the most optimal link scheduling strategy. At the same time, the over-large state space will take a long time for them to approach the optimal solution, which reduces the practicality of the MAML. To fully utilize the bandwidth resource and improve the efficiency of link scheduling, this paper studies a multi-agent reinforcement learning enabled link scheduling problem. Different from the conventional MAML techniques that randomly select a state to do their exploration, in our multi-agent reinforcement learning algorithm, a good state is firstly obtained within polynomial time steps by executing a distributed and randomized sub-algorithm. We say a state is good if it is not far from the optimal state. Then, our multi-agent reinforcement learning scheme starts from the good state and does its exploration with an ɛ greedy scheme, which significantly reduces the time steps to get close to the optimal link scheduling strategy. Extensive simulations are conducted to investigate the performance of our work.

Meta-Material Sensor-Based Internet of Things for Environmental Monitoring by Deep Learning: Design, Deployment, and Implementation

Using widely deployed Internet of Things (IoT) sensors to perceive the environmental distribution is crucial in many IoT applications, such as intelligent healthcare and smart home. As using traditional sensors will lead to high costs and maintenance, it is important to design the next-generation IoT sensor to reduce the cost of ubiquitous deployment. For this purpose, we propose a novel IoT system based on low-cost and fully passive meta-material sensors. Specifically, the meta-material sensors can sense multiple environmental conditions such as temperature and humidity levels, and transmit back the information by signal reflection, simultaneously. With the information contained in the received signals, a wireless receiver can obtain detailed environmental distributions. However, it is not trivial to achieve high sensing accuracy in the meta-material sensor based IoT system because the structure of the meta-materials, the deployment positions of sensors, and the reconstruction function for environmental distributions need to be jointly optimized. To handle this challenge, we propose an algorithm to design the meta-material based IoT system with the help of a deep learning approach. Simulation results verify that the proposed algorithm effectively maximizes the sensing accuracy. Experimental evaluations also show that the proposed scheme can obtain humidity distribution with an accuracy of over 93%.

Interpretable intrusion detection for next generation of Internet of Things

This paper presents a new framework for intrusion detection in the next-generation Internet of Things. MinMax normalization strategy is used to collect and preprocess data. The Marine Predator algorithm is then used to select relevant features to be used in the learning process. The selected features are then trained with an advanced and state-of-the-art recurrent neural network that includes an attention mechanism. Finally, Shapely values are calculated to determine how much each feature contributes to the final output. The dataset NSL-KDD was used for intensive simulations. The results show the advantages of the proposed system as well as its superiority over state-of-the-art methods. In fact, the proposed solution achieved a rate of more than 94% for both true negative and true position, while the rates of the existing solutions are below 90% for the challenging NSL-KDD datasets.

Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins

The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud​ matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implementation of the edge–cloud collaboration framework.

ASSIST-IoT: A Modular Implementation of a Reference Architecture for the Next Generation Internet of Things

Next Generation Internet of Things (NGIoT) addresses the deployment of complex, novel IoT ecosystems. These ecosystems are related to different technologies and initiatives, such as 5G/6G, AI, cybersecurity, and data science. The interaction with these disciplines requires addressing complex challenges related with the implementation of flexible solutions that mix heterogeneous software and hardware, while providing high levels of customisability and manageability, creating the need for a blueprint reference architecture (RA) independent of particular existing vertical markets (e.g., energy, automotive, or smart cities). Different initiatives have partially dealt with the requirements of the architecture. However, the first complete, consolidated NGIoT RA, covering the hardware and software building blocks, and needed for the advent of NGIoT, has been designed in the ASSIST-IoT project. The ASSIST-IoT RA delivers a layered and modular design that divides the edge-cloud continuum into independent functions and cross-cutting capabilities. This contribution discusses practical aspects of implementation of the proposed architecture within the context of real-world applications. In particular, it is shown how use of cloud-native concepts (microservices and applications, containerisation, and orchestration) applied to the edge-cloud continuum IoT systems results in bringing the ASSIST-IoT concepts to reality. The description of how the design elements can be implemented in practice is presented in the context of an ecosystem, where independent software packages are deployed and run at the selected points in the hardware environment. Both implementation aspects and functionality of selected groups of virtual artefacts (micro-applications called enablers) are described, along with the hardware and software contexts in which they run.