Next-generation Internet Of Things Research Articles

Review of the theory, principles, and design requirements of human-centric Internet of Things (IoT)

The rapid evolution of Internet of Things (IoT) technologies, with ever more profound implications for humans and societies, has triggered visions and initiatives to re-align the Next-Generation IoT with what works for humans and humanity first. However, despite the increased push towards “human-centric” IoT, it is still poorly understood what “human-centric” actually means in this context, how it is interpreted and embedded into the design, by whom, and for which purposes. To address these questions, we conducted a systematic literature review (N = 84) on the theory, principles, and design requirements of human-centric IoT. A key observation is that, despite the recent increase in research on humane perspectives for IoT, “human-centredness” often still seems to be used primarily as a label and overarching paradigm, not leading to a profound change in the underlying practices. We found no shared understanding of what “human-centric” implies in this context or common agreement on which principles human-centric IoT should be built upon. Furthermore, our analysis confirmed the predominance of technology-oriented fields, with a traditional approach towards user involvement and limited involvement of other disciplines. Overall, our findings point towards an apparent discrepancy between how contributions are positioned and framed (“human-centric”), the practices and assumptions they are based on, and their actual impact and ability to orient existing efforts towards genuine human-centric outcomes and key values. Based on the results, we formulate directions for future research aimed at building a more human-centric and empowering IoT.

AI-Driven Salient Soccer Events Recognition Framework for Next-Generation IoT-Enabled Environments

The salient event recognition of soccer matches in the next-generation Internet of Things (Nx-IoT) environment aims to analyze the performance of players/teams by the sports analytics and managerial staff. The embedded Nx-IoT devices carried by the soccer players during the match capture and transmit data to an artificial intelligence (AI)-assisted computing platform. The interconnectivity of data acquisition devices with an AI-assisted computing platform in the Nx-IoT environment will not only allow the spectators to track the formation of their favorite players during a soccer match but will also enable the managerial staff to evaluate the players’ performance in the soccer match as well as in practice sessions. This Nx-IoT-enabled salient event detection feature can be provided to spectators and sports’ managerial staff as a financial technology (FinTech) service. In this article, we propose an efficient deep-learning-based framework for multiperson salient soccer event recognition in IoT-enabled FinTech. The proposed framework performs event recognition in three steps: 1) frames preprocessing; 2) frame-level discriminative features extraction; and 3) high-level events recognition in soccer videos. Moreover, we introduce a new soccer video events (SVE) data set containing videos of six salient events of soccer games. To provide a strong baseline, we evaluate our newly created SVE data set using different traditional machine learning and deep learning algorithms. We also perform event recognition on untrimmed soccer videos using our proposed framework and compare the results with state-of-the-art methods. The obtained results validate the suitability of our proposed framework for salient event recognition in Nx-IoT environments.

An intelligent model for supporting edge migration for virtual function chains in next generation internet of things

The developments on next generation IoT sensing devices, with the advances on their low power computational capabilities and high speed networking has led to the introduction of the edge computing paradigm. Within an edge cloud environment, services may generate and consume data locally, without involving cloud computing infrastructures. Aiming to tackle the low computational resources of the IoT nodes, Virtual-Function-Chain has been proposed as an intelligent distribution model for exploiting the maximum of the computational power at the edge, thus enabling the support of demanding services. An intelligent migration model with the capacity to support Virtual-Function-Chains is introduced in this work. According to this model, migration at the edge can support individual features of a Virtual-Function-Chain. First, auto-healing can be implemented with cold migrations, if a Virtual Function fails unexpectedly. Second, a Quality of Service monitoring model can trigger live migrations, aiming to avoid edge devices overload. The evaluation studies of the proposed model revealed that it has the capacity to increase the robustness of an edge-based service on low-powered IoT devices. Finally, comparison with similar frameworks, like Kubernetes, showed that the migration model can effectively react on edge network fluctuations.

Next-Generation IoT Devices: Sustainable Eco-Friendly Manufacturing, Energy Harvesting, and Wireless Connectivity

This invited paper presents potential solutions for tackling some of the main underlying challenges toward developing sustainable Internet-of-things (IoT) devices with a focus on eco-friendly manufacturing, sustainable powering, and wireless connectivity for next-generation IoT devices. The diverse applications of IoT systems, such as smart cities, wearable devices, self-driving cars, and industrial automation, are driving up the number of IoT systems at an unprecedented rate. In recent years, the rapidly-increasing number of IoT devices and the diverse application-specific system requirements have resulted in a paradigm shift in manufacturing processes, powering methods, and wireless connectivity solutions. The traditional cloud-centering IoT systems are moving toward distributed intelligence schemes that impose strict requirements on IoT devices, e.g., operating range, latency, and reliability. In this article, we provide an overview of hardware-related research trends and application use cases of emerging IoT systems and highlight the enabling technologies of next-generation IoT. We review eco-friendly manufacturing for next-generation IoT devices, present alternative biodegradable and eco-friendly options to replace existing materials, and discuss sustainable powering IoT devices by exploiting energy harvesting and wireless power transfer. Finally, we present (ultra-)low-power wireless connectivity solutions that meet the stringent energy efficiency and data rate requirements of future IoT systems that are compatible with a batteryless operation.

FogAI: An AI-supported fog controller for Next Generation IoT

In this paper, we present a novel artificial intelligence-based fog controller, called FogAI that provides a versatile control mechanism to the fog layer. FogAI not only abstracts the control mechanism from the fog environment but also offers potential solutions for the problems of fog-based Next Generation Internet of Things (NGIoT) systems. To this end, we first present a comprehensive examination of challenging issues in Fog Computing (FC). Then, we outline possible FogAI based solutions to these challenges from different perspectives. To illustrate the feasibility of our FogAI concept, we design a use case scenario for task offloading problem in FC. Then, we propose a Deep Q-Learning (DQL) algorithm that autonomously performs task offloading in delay-sensitive and computationally-intensive IoT applications and test it on FogAI. The results show that the proposed FogAI-assisted DQL algorithm is superior to existing offloading policies.

LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networks

LoRa wireless networks are considered as a key enabling technology for next generation internet of things (IoT) systems. New IoT deployments (e.g., smart city scenarios) can have thousands of devices per square kilometer leading to huge amount of power consumption to provide connectivity. In this paper, we investigate green LoRa wireless networks powered by a hybrid of the grid and renewable energy sources, which can benefit from harvested energy while dealing with the intermittent supply. This paper proposes resource management schemes of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway energy efficiency. First, the problem of grid power consumption minimization while satisfying the system's quality of service demands is formulated. Specifically, both scenarios the uncorrelated and time-correlated channels are investigated. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Since the optimal solution is obtained with high complexity, online resource management heuristic algorithms that minimize the grid energy consumption are proposed. Finally, taking into account the channel and energy correlation, adaptable resource management schemes based on Reinforcement Learning (RL), are developed. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

On the Performance of Uplink Rate-Splitting Multiple Access

The use of new multiple access schemes emerges as a necessity in order to efficiently cope with the massive connectivity, reliability and high throughput requirements of the next generation Internet of Things. In this letter, we study the performance of an uplink rate-splitting multiple access (RSMA) network with two sources, in terms of outage probability and throughput. Specifically, we derive closed-form expressions for the outage probability of the transmitted messages and then, we utilize them to derive the throughput of the sources in a random access network with a medium access control protocol based on slotted ALOHA and RSMA.

Virtualized Network Function Forwarding Graph Placing in SDN and NFV-Enabled IoT Networks: A Graph Neural Network Assisted Deep Reinforcement Learning Method

With an ambitious increase in the number of Internet of Things (IoT) terminals, IoT networks face a huge challenge which is providing diverse and complex network services with different requirements on a common infrastructure. To solve this challenge, Software Defined Network (SDN) and Network Function Virtualization (NFV) are adopted to build next-generation IoT networks which are softwarized and virtualized. This way, network functions are virtualized as Virtualized Network Functions (VNFs) and a network service consists of a set of VNFs. One of the main challenges for realizing this paradigm is the optimal resource allocation for VNFs. Most existing works assumed that services are represented as Service Function Chains (SFCs) which are chains. However, network services in IoT networks are more complex and diverse, therefore, more appropriate representations are Virtualized Network Function Forwarding Graphs (VNF-FGs) which are Directed Acyclic Graphs (DAGs). Previous works failed to exploit this special graph structure, which makes them sub-optimal or non-applicable for IoT networks. In this paper, we investigate the VNF-FG placing problem in dynamic IoT networks where DAG-represented services arrive and depart. To fully exploit the graph structures of services and handle the complexity of dynamic IoT networks, we combine a novel neural network structure Graph Neural Network (GNN) with Deep Reinforcement Learning (DRL) and propose an efficient algorithm for VNF-FG placing, which is called Kolin. Extensive simulation results suggest that Kolin outperforms the state-of-the-art solutions in terms of system cost, acceptance ratio, and computation complexity.

Analytical Guarantees for Hyperparameter Free RFF Based Deep Learning in the Low-Data Regime

Deep-learning (DL) has emerged as promising for diverse signal-processing tasks concerning the next-generation of communication-systems, Internet of Things (IoT), and in circuit-design. However, existing DL based signal-processing methods are well-known to require large datasets to achieve good generalization. Moreover, the existing DL-based solutions are known for their sensitivity to the hyperparameter-choices and the DL-architecture. In this regard, existing DL-paradigms, such as meta-learning are useful to find optimal hyperparameter-values and architectures. In this work, generalized analytical results are provided for random Fourier feature-based DL (RFF-DL) that guarantee a degree of hyperparameter-independence and significantly improved generalization under data-limited applications. The benefits of RFF-DL promised by the presented analytical contributions are validated by a case study that improves low-pilot training for the next-generation IoT using the hyperparameter-free RFF based DL.

Towards Spoofing Resistant Next Generation IoT Networks

The potential vulnerability to wireless spoofing attacks is still a critical concern for Next Generation Internet of Things (NGIoT) networks which may result in catastrophic consequences in mission–critical applications. Conventional solutions may impose additional signal processing, protocol, and latency overheads which are inappropriate for NGIoT networks designed to provide high–speed and low–latency connections for a large number of resource–constrained IoT devices. In this paper, we utilize the uniqueness of beam pattern features in mmWave–enabled devices and propose a scalable security mechanism for the detection of wireless spoofing attacks in NGIoT networks. This uniqueness is proven to exist due to the non–ideal manufacturing of antenna arrays used in mmWave–enabled devices. In our approach, when legitimate mmWave–enabled IoT devices enrol into the network, their unique beam features are learned by a learning model developed at the network server. Then, during data transmission, network base stations (gNBs)/Access Points (APs) measure the beam features from the received RF signals and send them to the network server for the detection of anomalies. We develop our learning model based on Deep Autoencoders (DAEs) that are an effective tool for anomaly detection. Fortunately, the beam feature extraction can be performed using the beam searching mechanism that is already provided in mmWave standards (5G–NR and IEEE 802.11ad). Thus, feature extraction does not introduce any signal processing overheads to the system. Moreover, the proposed mechanism imposes zero computation/communication overhead to the resource—constrained IoT nodes. In our experiments, we reached 98.6% accuracy in the detection of illegitimate devices which confirms the effectiveness of the proposed approach.

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

The emergence of Beyond 5G (B5G) and 6G networks translated personal and industrial operations highly effective, reliable, and gainful by speeding up the growth of next generation Internet of Things (IoT). Industrial equipment in 6G encompasses a huge number of wireless sensors, responsible for collecting massive quantities of data. At the same time, 6G network can take real-world intelligent decisions and implement automated equipment operations. But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken. This has become mandatory for optimal resource allocation in 6G-enabled industrial applications. In this scenario, the current research paper introduces a new metaheuristic resource allocation strategy for cluster-based 6G industrial applications, named MRAS-CBIA technique. MRAS-CBIA technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial applications. The proposed MRAS-CBIR technique involves three major processes. Firstly, Weighted Clustering Technique (WCT) is employed to elect the optimal Cluster Heads (CHs) or coordinating agents with the help of three parameters namely, residual energy, distance, and node degree. Secondly, Decision Tree-based Location Prediction (DTLP) mechanism is applied to determine the exact location of Management Agent (MA). Finally, Fuzzy C-means with Tunicate Swarm Algorithm (FCM-TSA) is used for optimal resource allocation in 6G industrial applications. The performance of the proposed MRAS-CBIA technique was validated and the results were examined under different dimensions. The resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.

Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches

The emergence of next-generation Internet-of-Things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.

Smart and Solar Greenhouse Covers: Recent Developments and Future Perspectives

The examination of recent developments and future perspectives on smart and solar greenhouse covers is significant for commercial agriculture given that traditional greenhouse relied on external energy sources and fossil fuels to facilitate lighting, heating and forced cooling. The aim of this review article was to examine smart and solar materials covering greenhouse. However, the scope was limited to intelligent PhotoVoltaic (PV) systems, optimization of some material properties including smart covers, heat loading and the use of Internet of Things (IoT) to reduce the cost of operating greenhouse. As such, the following thematic areas were expounded in the research; intelligent PV systems, optimization of the Power Conversion Efficiency (PCE), Panel Generator Factor (PGF) and other material properties, heat loading future outlook and perspectives. The intelligent PV section focused on next-generation IoT and Artificial Neural Networks (ANN) systems for greenhouse automation while the optimization of material parameters emphasized quantum dots, semi-transparent organic solar cells, Pb-based and Pb-based PVs and three dimensional (3D) printing. The evaluation translated to better understanding of the future outlook of the energy-independent greenhouse. Greenhouse fitted with transparent PV roofs are a sustainable alternative given that the energy generated was 100% renewable and economical. Conservative estimates further indicated that the replacement of conventional sources of energy with solar would translate to 40–60% energy cost savings. The economic savings were demonstrated by the Levelized cost of energy. A key constraint regarded the limited commercialization of emerging innovations, including transparent and semitransparent PV modules made of Pb-quantum dots, and amorphous tungsten oxide (WO3) films, with desirable electrochromic properties such as reversible color changes. In addition to intelligent energy harvesting, smart IoT-based materials embedded with thermal, humidity, and water sensors improved thermal regulation, frost mitigation and prevention, and the management of pests and disease. In turn, this translated to lower post-harvest losses and better yields and revenues.

Hybrid NOMA for Future Radio Access: Design, Potentials and Limitations

Next-generation internet of things (IoT) applications need numerous low-powered wireless mobile devices to connect with each other, having ultra-reliability and low latency. Non-orthogonal multiple access (NOMA) is a promising technology to address massive connectivity for 5G and beyond by accom- modating several users within the same orthogonal resource block. Therefore, this article explores hybrid NOMA (HNOMA) for massive multiple access in the uplink scenarios due to its higher spectral efficiency. The HNOMA includes both power domain and code domain NOMA method due to diverse channel conditions in practice. We highlight that polar coded based data transmission can achieve higher reliability and lower latency in HNOMA-based wireless networks. Further, at the base station (BS), channel state information (CSI) of each link is not perfectly available or very complex to estimate due to non-orthogonal connections. Therefore, we analyze and review the performance of uplink based system involving HNOMA transmission in imperfect CSI. Furthermore, we summarize some key technical challenges and their potential solutions in futuristic IoT applications using HNOMA transmission. Finally, we offer some design guidelines for HNOMA-based systems using a deep learning approach to implement adaptive and efficient wireless networks.

Improved Secure and Lightweight Authentication Scheme for Next-Generation IoT Infrastructure

The Internet of Things (IoT) is a huge network formed by connecting various information sensing devices through the Internet. Although IoT has been popularized in many fields, connected devices can be used only when network security is guaranteed. Recently, Rana et al. proposed a secure and lightweight authentication protocol for the next-generation IoT infrastructure. They claim that their protocol can resist major security attacks. However, in this study, we prove that their protocol is still vulnerable to offline password guessing attacks and privilege internal attacks. In order to solve these shortcomings, we propose an improved protocol, which is proved to be secure by formal and informal analysis. In addition, after comparing the time and memory consumption with other protocols, we find that our protocol has more advantages.

Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach

The ubiquitous nature of Internet of Things (IoT) devices has posited many challenges that need innovative solutions in the 5G era. Software defined networks (SDNs) are becoming indispensable in managing several aspects of next-generation IoT networking that arise from the need to control highly heterogeneous, geographically dispersed, mobile IoT devices. One such aspect is cache management at the edge. Recently, multiple forms of edge resources, including mobile device clouds and micro-edge data centers have emerged to provide scalable cache placement locations that reduce the costs for the mobile network operator (MNO). As all of these service locations are registered with the MNO (or links established after registration with the 5G base station, BS), content should be placed according to the user's demand and the cost the user is willing to pay to receive the desired level of QoS. To this end, it is important to understand the future popularity of the content for its optimal placement considering the highly dynamic user mobility. In this article, we address two key aspects of a mobile IoT network: security and seamless connectivity for data delivery. We rely on the federated learning (FL) architecture, which enables harnessing data and computational capabilities at end-user devices to train machine learning models. We study FL concepts in the domain of edge computing for IoT use cases, such as caching. We draw conclusions from various state-of-the-art models and posit several challenges that can be overcome via a novel proposed control algorithm.

Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach

Convergence of Augmented Reality (AR) and Next Generation Internet-of-Things (NG-IoT) can create new opportunities in many emerging areas, where the real-time data can be visualized on the devices. Integrated NG-IoT network, AR can improve efficiency in many fields such as mobile computing, smart city, intelligent transportation and telemedicine. However, limited by capability of mobile device, the reliability and latency requirements of AR applications is difficult to meet by local processing. To solve this problem, we study a binary offloading scheme for AR edge computing. Based on the proposed model, the parts of AR computing can offload to edge network servers, which is extend the computing capability of mobile AR devices. Moreover, a deep reinforcement learning offloading model is considered to acquire B5G network resource allocation and optimally AR offloading decisions. First, this offloading model does not need to solve combinatorial optimization, which is greatly reduced the computational complexity. Then the wireless channel gains and binary offloading states is modeled as a Markov decision process, and solved by deep reinforcement learning. Numerical results show that our scheme can achieve better performance compared with existing optimization methods.

Service Deployment Strategy for Predictive Analysis of FinTech IoT Applications in Edge Networks

The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for financial technology (FinTech). The emergence of the next-generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyze the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this article, we develop a ranked-based service deployment strategy and an artificial intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users’ satisfaction ratio. Besides that, an efficient service deployment strategy is developed using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Hall's$ </tex-math></inline-formula> theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVMs) algorithm is used at edge networks for analyzing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively.

An IoT-enabled real-time overhead view person detection system based on Cascade-RCNN and transfer learning

Internet of things (IoT) is transforming technological evolution in several practical applications. These applications range from smart cities, smart healthcare to intelligent video surveillance, where the primary interest is person monitoring and detection. The amalgamation of Artificial Intelligence (AI) and IoT-based techniques maintain a balance between computational cost and efficiency that is essential for next-generation IoT networks. In this context, a real-time IoT-enabled people detection system is introduced. The developed system performs image processing task over the cloud using an internet connection, thus reduces the computational cost by processing high-resolution images over the cloud. For person detection, a pre-trained Cascade RCNN, a deep learning approach is used. It is an object detection architecture, seeks to address discrediting performance with increased Intersection over Union (IoU) thresholds. As the architecture is pre-trained with COCO data set and the person body’s appearance in overhead perspective is significantly different; thus, additional training is performed to enhance the detection results. Taking advantage of transfer learning architecture is trained for overhead person images, and the newly trained feature layer is added to the existing architecture. Experimental outcomes reveal that additional training increases the detection architecture’s performance with an accuracy rate of 0.96.

Optimization of Ultra-Dense Wireless Powered Networks.

The internet-of-things (IoT) is expected to have a transformative impact in several different domains, including energy management in smart grids, manufacturing, transportation, smart cities and communities, smart food and farming, and healthcare. To this direction, the maintenance cost of IoT deployments has been identified as one of the main challenges, which is directly related to energy efficiency and autonomy of IoT solutions. In order to increase the energy sustainability of next-generation IoT, wireless power transfer (WPT) emerged as a promising technology; however, its effectiveness is hindered as the distance between the base station and the wireless powered IoT devices increases. To counter this effect, decentralized approaches based on the use of distributed densely deployed remote radio heads (RRHs) can be utilized to diminish the distance between the transmitting and the receiving nodes. A trade-off ensues from the use of RRHs as power beacons (PBs) or access points (APs) that enable either energy transfer during downlink or information reception during uplink, respectively. To balance this trade-off, in this work, the maximization of the ergodic rate in ultra-dense wireless powered networks is investigated. In more detail, three different protocols are introduced, optimized, and compared to each other: density splitting, time splitting, and hybrid time and density splitting, which are based on the optimization of the portion of the number of RRHs that are employed as PBs or APs at a specific time instance. Additionally, two different policies are taken into account regarding the PBs’ power constraint. The formulated problems that correspond to the combination of the proposed protocols with each of the two considered power constraint policies are optimally solved by using convex optimization tools and closed-form solutions are derived that result to useful insights. Finally, numerical results are provided, which illustrate the ergodic rate achieved by each of the proposed protocols and offer interesting conclusions regarding their comparison, which are directly linked to design guidelines and the required capital and operational expenses.