Ubiquitous Connectivity Research Articles

Internet of Everything (IoE) - From Molecules to the Universe

As the Internet of Things (IoT) technologies continue to advance, they are becoming increasingly specialized and compartmentalized into non-interacting application domains, which we call IoXs with X referring to the particular application areas, e.g., Internet of Energy (IoEn), Internet of Vehicles (IoV). This trend has also led to the emergence of unconventional IoXs, such as the Internet of Nano Things (IoNT), further increasing the heterogeneity of the future IoT landscape, in terms of not only the underlying technologies but also the spatiotemporal scale and medium of applications, as well as the material nature of things and the type and semantics of data produced and exchanged. This paper explores the potential synergies and opportunities that may arise from such diversity through the interactions between heterogeneous IoXs, enabling unprecedented applications beyond the current confines of IoT. Inspired by the ubiquitous connectivity and seamless interoperability of the universe, which is a vast network of heterogeneous entities interconnected through various forms of interactions (e.g., chemical, electromagnetic, acoustic, and gravitational), we propose the Internet of Everything (IoE) framework. The IoE framework aims to facilitate cooperation of both existing and future IoXs on a diverse scale ranging from molecules to the universe. We discuss potential IoE applications that can be enabled by such synergies and identify the unique challenges for bringing this holistic IoE picture into reality. To address some of these challenges, we propose a layered network architecture for IoE, which includes an IoE middleware that provides a semantic interface among IoXs based on the introduced concept of 'IoX-as-a-Service'. Lastly, we recommend future research directions for enabling the IoE applications.

High-Capacity Free Space Optics-Based Passive Optical Network for 5G Front-Haul Deployment

With the expansion of Information and Communication Technology, it is important to develop a communication network that can provide high-capacity ubiquitous connectivity. This work proposes an energy-efficient passive optical network (PON) using orthogonal frequency division multiple access (OFDMA) and wavelength division multiplexing (WDM) to facilitate the dense deployment of radio units (RUs) in a beyond 5G (B5G) communication network. High-speed connectivity is ensured by employing a hybrid PON architecture that includes a combination of free space optics (FSO) links and optical fiber (OF) media to carry OFDM and WDM multiplexed traffic. Furthermore, an optical frequency comb generator (OFCG) is utilized at the transmitter module to generate and leverage the spectrum for transmitting information from baseband units (BBUs) to the RUs situated near the end users. The proposed system is analyzed through (i) simulation analysis using Optisystem for transmission capacity computations and (ii) mathematical analysis to determine the total savings in energy. The simulation analysis shows that the given architecture can carry data across 3 km of FSO medium using 512 subcarriers per BBU transmitting at 10 Gbps of data with QPSK-modulated bit sequence. Additionally, energy efficiency shows that the use of an OFCG cuts the total energy usage by 22% at the transmitter module without negatively impacting the system’s high cardinality and transmission capacity.

Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network.

To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G communications, satellite plays a more significant role in it. As users and devices explosively grow, new multiple access technologies are called for. Among the new candidates, rate splitting multiple access (RSMA) shows great potential. Since satellites are power-limited, we investigate the energy-efficient resource allocation in the integrated satellite terrestrial network (ISTN)-adopting RSMA scheme in this paper. However, this non-convex problem is challenging to solve using conventional model-based methods. Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem into a penalized unconstrained one. The reward is maximized while the requirements are satisfied. Moreover, the learning process is time-consuming and unnecessary when little changes in the network. So, an on-off mechanism is introduced to avoid this situation. By calculating the difference between the current state and the last one, the system will decide to learn a new action or take the last one. The simulation results show that the proposed algorithm can outperform other benchmark algorithms in terms of energy efficiency while satisfying the QoS constraint. In addition, the time consumption is lowered because of the on-off design.

UAV-enabled communications using NOMA for 5G and beyond: research challenges and opportunities

Future wireless networks are expected to provide ubiquitous connectivity to a wide range of devices with varying traffic patterns, wherever and whenever it is required. Unmanned aerial vehicles (UAVs) are also considered a potential technique for accommodating massive connections and providing seamless coverage. They can be used as flying base stations (BSs) to take advantage of line-of-sight (LoS) connections and effectively support wireless communication coverage and throughput for 5G and beyond. However, the use of highly mobile and energy-constrained UAVs for wireless communications brings plenty of new challenges. 5G wireless networks require non-orthogonal multiple access (NOMA) to be able to meet heterogeneous requirements for low latency, high dependability, massive connection, better fairness, and high throughput. This paper presents an overview of NOMA-based UAV enabled communications by introducing the background of UAV communication and NOMA schemes. Power allocation schemes are also explored as they are critical controlling mechanisms for performance optimization of NOMA-UAV systems. We also categorize UAV-enabled communication applications for usage in both routine professional settings and emergency scenarios. Finally, we address several open research questions that need to be solved for NOMA, as well as new opportunities and future research trends to be exploited.

Trajectory Design and Resource Allocation for Multi-UAV Networks: Deep Reinforcement Learning Approaches

The future mobile communication system is expected to provide ubiquitous connectivity and unprecedented services over billions of devices. The unmanned aerial vehicle (UAV), which is prominent in its flexibility and low cost, emerges as a significant network entity to realize such ambitious targets. In this work, novel machine learning-based trajectory design and resource allocation schemes are presented for a multi-UAV communications system. In the considered system, the UAVs act as aerial Base Stations (BSs) and provide ubiquitous coverage. In particular, with the objective to maximize the system utility over all served users, a joint user association, power allocation and trajectory design problem is presented. To solve the problem caused by high dimensionality in state space, we first propose a machine learning-based strategic resource allocation algorithm which comprises of reinforcement learning and deep learning to design the optimal policy of all the UAVs. Then, we also present a multi-agent deep reinforcement learning scheme for distributed implementation without knowing a priori knowledge of the dynamic nature of networks. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.

Collaborative Satellite-Terrestrial Edge Computing Network for Everyone-Centric Customized Services

The collaborative Satellite-Terrestrial network plays an important role in the future 6G mobile communication network, which can realize the global ubiquitous connection and support everyonecentric customized services. Taking into account the multi-layer heterogeneity of the Satellite-Terrestrial network, Mobile Edge Computing (MEC) technology can provide users with support for various data business computing services. Most of the existing satellite edge computing regards satellites as relay networks and neglects the deployment of MEC servers on satellites. We first propose a collaborative Satellite-Terrestrial edge computing network that deploys edge servers on LEO satellites. Then, we introduce the collaborative Satellite-Terrestrial network architecture and design a central-edge multiagent collaboration model. Furthermore, it integrates SDN and NFV to achieve optimal control for the collaborative network. Finally, extensive simulations show that the proposed strategy that jointly tasks offloading and resource allocation can achieve higher performance with low complexity.

Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology

Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the offloading and computation of massive latency-sensitive tasks dramatically increase the energy consumption of the network. To address these issues, we first propose a SAGIN architecture with energy-harvesting devices, where the base station (BS) is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power an unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighboring BSs is designed to fully utilize RE to reduce carbon emissions. Secondly, on the basis of task offloading decisions, the UAV trajectory, and the RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emissions.

Attention, ambivalence and algorithms: Publishers in the era of ubiquitous connectivity and expanding platforms

This article scrutinizes how digitalization influences fiction and non-fiction literature publishers in the era of ubiquitous digital connection. The analysis states how a lack of attention is triggering a sense of urgency for the future of literary reading. Further, the digital transition entails an overarching ambivalence. Key stakeholders in literary publishing are experiencing how media on platform-based streaming services is competing with traditional reading. They perceive a battle for time and question the future of reading. From the perspectives of Bourdieu’s theory, the article reveals how penetrating connectivity is leading to a change in the professional habitus. Continuous busyness and increased professional presence are triggering ambivalence between work-related duties and personal well-being. Moreover, the publishing stakeholders reveal an ambivalence in voicing future expectations. While worried about the future of reading, the professional habitus leans on a promising future for the industry.

The Roadmap of Communication and Networking in 6G for the Metaverse

The Metaverse can be regarded as a hypothesized iteration of the Internet, which enables people to work, play, and interact socially in a persist online 3-D virtual environment with an immersive experience, by generating an imaginary environment similar to the real world, including realistic sounds, images, and other sensations. The Metaverse has strict requirements for a fully-immersive experience, large-scale concurrent users, and seamless connectivity, which poses many unprecedented challenges to the sixth generation (6G) wireless system, such as ubiquitous connectivity, ultra-low latency, ultra-high capacity and reliability, and strict security. In addition, to achieve the immersive and hassle-free experience of mass users, the full coverage sensing, seamless computation, reliable caching, and persistent consensus and security should be carefully considered to integrate into the future 6G system. To this end, this paper aims to depict the roadmap to the Metaverse in terms of communication and networking in 6G, including illustrating the framework of the Metaverse, revealing the strict requirements and challenges for 6G to realize the Metaverse, and discussing the fundamental technologies to be integrated in 6G to drive the implementation of the Metaverse, including intelligent sensing, digital twin (DT), space-air-ground-sea integrated network (SAGSIN), multi-access edging computing (MEC), blockchain, and the involved security issues.

Robust Downlink Transmission Design in IRS-Assisted Cognitive Satellite and Terrestrial Networks

Cognitive satellite and terrestrial network (CSTN) is considered as a promising technology to provide ubiquitous connectivity for various users within wide-coverage. This paper proposes a robust downlink transmission scheme for multiple intelligent reflecting surfaces (IRSs) assisted CSTN. Here, the satellite network adopts multigroup multicast transmission scheme to serve many earth stations, while the terrestrial network exploits space division multiple access and multi-IRS-enhanced non-orthogonal multiple access technology to communicate with many terrestrial users. By assuming that these two networks share the same frequency band having only the angular information based imperfect channel state information of each user, we formulate an optimization problem to minimize the total transmit power subject to the constraints of quality-of-service requirement for each user, per-antenna transmit power budgets of satellite and BS, and unit-modulus requirement for each reflecting element. To tackle this mathematically intractable problem, we then employ angular discretization together with the successive convex approximation method to obtain the active beamforming (BF) vectors of satellite and BS, the passive BF vector of IRS, and the power allocation coefficients. Moreover, we propose a generalized zero forcing BF and alternative optimization to obtain the suboptimal solutions of the optimization problem with low computational complexity. Finally, simulation results are given to demonstrate the effectiveness and superiority of the proposed two schemes over the benchmarks.

Load balancing clustering and routing for IoT‐enabled wireless sensor networks

AbstractInternet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy‐efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c‐means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO‐based clustering (GWO‐C) and Cat Swarm Optimization based Energy‐Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.

Business Model Innovation in Manufacturing Firms: Exploring the Driving Forces of Digital Transformation

The adoption of digital technology has become a critical factor in the context of innovation management. The process of digital transformation leads to the realization of concrete business improvements that promote the innovation of entire business models. So far, it has remained unexplored what drives this business model innovation, especially when looking at digitally savvy firms from the manufacturing industry. This study aims to identify the key drivers of digital transformation of business models based on a systematic literature review. A four-step review approach draws on the findings from a core sample of 51 articles from two databases and results in the description of four key drivers for digital transformation of business models, namely ubiquitous connectivity, functional dematerialization, relational decentralization, and flexible scalability. Finally, a conceptual framework of four digital business model archetypes (lighthouse supplier, service enthusiast, data scientist, and platform owner) is proposed that considers a manufacturing firm’s business model exposure to the identified digital transformation drivers. While this study is limited to theoretical findings, it likewise provokes valuable follow-up thoughts for empirical academic work and practitioners from the manufacturing industries.

Special Issue on Green IoT for Future Space–Air–Ground–Ocean-Integrated Networks and Applications

The Internet of Things (IoT) plays a critical role in enabling the seamless integration of disparate devices. Future IoT will rapidly expand its coverage to offer future worldwide omnipresent applications and services by merging communications in diverse spatial domains to build the space–air–ground–ocean-integrated network (SAGOI-Net). SAGOI-Net will include a significant number of battery-powered network nodes, such as satellites, unmanned vehicles, and underwater devices, due to the extremely vast geographic reach and dynamics in free space. Given the battery limitations, high-energy-efficiency communications and networking will be critical to the future system. Green SAGOI-Net seeks to not only bring ubiquitous connectivity to every corner of the globe but also to deliver more data with the same amount of energy.

Swarm Intelligence-Based Task Scheduling for Enhancing Security for IoT Devices

Due to the great advancement in computation, communication and control technologies, the Internet of Things (IoT) can provide ubiquitous connectivity for anyone and anything at any time and any place, leading to a revolution in information society. Protecting devices against various security threats is one of the most important challenges in IoT since IoT applications are generally security-critical systems while IoT devices are often poorly secured. For IoT devices, employing security services provided by smart gateways or edge/cloud servers to defend against various threats is an effective way to enhance their security. However, the finite battery energy of devices and the limited fund of device users hinder the wide application of security services in IoT. This necessitates the demand for designing new methodologies to tackle the trade-off among security, energy and fund of IoT devices. Therefore, this paper attempts to optimize system security of IoT devices under energy and fund constraints. Specifically, to formulate the energy and fund constrained security optimization problem, we firstly propose a pricing model for the security services provided by the smart gateway. We then formulate the problem as a mixed-integer linear programming (MILP) problem. Since using a solver to address the MILP problem may be time-consuming, we leverage the swarm intelligence technique to design a new task scheduling scheme that can efficiently solve the optimization problem. Extensive experiments are conducted to validate our proposed MILP and swarm intelligence based task scheduling algorithms. Simulation results show that our scheme outperforms two state-of-the-art methods in improving system quality of security and guaranteeing schedule feasibility.

Downlink Training Sequence Design Based on Waterfilling Solution for Low-Latency FDD Massive MIMO Communications Systems

Future generations of wireless communications systems are expected to evolve toward allowing massive ubiquitous connectivity and achieving ultra-reliable and low-latency communications (URLLC) with extremely high data rates. Massive multiple-input multiple-output (m-MIMO) is a crucial transmission technique to fulfill the demands of high data rates in the upcoming wireless systems. However, obtaining a downlink (DL) training sequence (TS) that is feasible for fast channel estimation, i.e., meeting the low-latency communications required by future generations of wireless systems, in m-MIMO with frequency-division-duplex (FDD) when users have different channel correlations is very challenging. Therefore, a low-complexity solution for designing the DL training sequences to maximize the achievable sum rate of FDD systems with limited channel coherence time (CCT) is proposed using a waterfilling power allocation method. This achievable sum rate maximization is achieved using sequences produced from a summation of the user’s covariance matrices and then applying a waterfilling power allocation method to the obtained low-complexity training sequence. The results show that the proposed TS outperforms the existing methods in the medium and high SNR regimes while reducing computational complexity. The obtained results signify the proposed TS’s feasibility for practical consideration compared with the existing DL training sequence designs.

Studying the dimensions of m-interactivity and customers' engagement in m-commerce applications.

These days, technology has a significant role in the success of selling and buying goods through m-commerce applications. Therefore, this study aims to study the impact of dimensions of M-interactivity on customers' engagement in m-commerce and the effect of customers' attention in m-commerce on customer loyalty towards m-commerce applications. The questionnaire was distributed among Saudi citizens. The five hundred questionnaire links were distributed randomly among Saudi citizens. The partial least squares (PLS) were implemented to analyse the data to test the proposed hypotheses and get the rest of the results. The results revealed that the questionnaire used in this study was valid and reliable. Also, it revealed that active control, connectedness, responsiveness, and synchronicity positively impact customers' engagement in m-commerce applications. Furthermore, it showed that ubiquitous connectivity and personalization do not positively impact customers' attention in m-commerce. In addition, the results revealed that customer engagement positively impacts customer loyalty (LO) in m-commerce applications.

Time-aware neural ordinary differential equations for incomplete time series modeling.

Internet of Things realizes the ubiquitous connection of all things, generating countless time-tagged data called time series. However, real-world time series are often plagued with missing values on account of noise or malfunctioning sensors. Existing methods for modeling such incomplete time series typically involve preprocessing steps, such as deletion or missing data imputation using statistical learning or machine learning methods. Unfortunately, these methods unavoidable destroy time information and bring error accumulation to the subsequent model. To this end, this paper introduces a novel continuous neural network architecture, named Time-aware Neural-Ordinary Differential Equations (TN-ODE), for incomplete time data modeling. The proposed method not only supports imputation missing values at arbitrary time points, but also enables multi-step prediction at desired time points. Specifically, TN-ODE employs a time-aware Long Short-Term Memory as an encoder, which effectively learns the posterior distribution from partial observed data. Additionally, the derivative of latent states is parameterized with a fully connected network, thereby enabling continuous-time latent dynamics generation. The proposed TN-ODE model is evaluated on both real-world and synthetic incomplete time-series datasets by conducting data interpolation and extrapolation tasks as well as classification task. Extensive experiments show the TN-ODE model outperforms baseline methods in terms of Mean Square Error for imputation and prediction tasks, as well as accuracy in downstream classification task.

A Novel Multi-Attack IDS Framework for Intelligent Connected Terminals Based on Over-the-Air Signature Updates

Modern terminals are developing toward intelligence and ubiquitous connection. Such ICTs (intelligent connected terminals) interact more frequently with the outside world and expose new attack surfaces. IDSs (intrusion detection systems) play a vital role in protecting ICT security. Multi-attack IDSs that can cover both intra-terminal and inter-terminal networks are a promising research direction for improving detection accuracy and the strength of security protection. However, a major challenge is the frequent dynamic signature updates across the network boundary, which cause significant computational overheads and result in losses in detection performance. In light of this, we propose a novel IDS framework based on OTA (over-the-air) signature updates to implement multi-attack detection. It updates the attack signatures of the target ICTs and adds the new attack signatures to the signature database in order to minimize the local memory storage and computing resources. It employs a CNN (convolutional neural network) based on an auto-encoder to achieve multi-attack detection, which can ensure the detection accuracy of multi-attacks with the multiple classification function. We evaluated our framework on four types of real-world ICT attack data, drawing comparisons with four widely used IDS schemes, and demonstrated the non-negligible superiority of our scheme over all benchmarks in terms of accuracy, recall, precision, and F1-score. Our work represents an important step toward an IDS that can detect multi-attacks in both intra-terminal and inter-terminal networks.

Adaptive Parameters for LoRa-Based Networks Physical-Layer.

Sub-GHz communication provides long-range coverage with low power consumption and reduced deployment cost. LoRa (Long-Range) has emerged, among existing LPWAN (Low Power Wide Area Networks) technologies, as a promising physical layer alternative to provide ubiquitous connectivity to outdoor IoT devices. LoRa modulation technology supports adapting transmissions based on parameters such as carrier frequency, channel bandwidth, spreading factor, and code rate. In this paper, we propose SlidingChange, a novel cognitive mechanism to support the dynamic analysis and adjustment of LoRa network performance parameters. The proposed mechanism uses a sliding window to smooth out short-term variations and reduce unnecessary network re-configurations. To validate our proposal, we conducted an experimental study to evaluate the performance concerning the Signal-to-Noise Ratio (SNR) parameter of our SlidingChange against InstantChange, an intuitive mechanism that considers immediate performance measurements (parameters) for re-configuring the network. The SlidingChange is compared with LR-ADR too, a state-of-the-art-related technique based on simple linear regression. The experimental results obtained from a testbed scenario demonstrated that the InstanChange mechanism improved the SNR by 4.6%. When using the SlidingChange mechanism, the SNR was around 37%, while the network reconfiguration rate was reduced by approximately 16%.

Secure Transmission for THz-Empowered RIS-Assisted Non-Terrestrial Networks

The non-terrestrial networks (NTNs) are recognized as a key component to provide cost-effective and high-capacity ubiquitous connectivity in the future wireless communications. In this paper, we investigate the secure transmission in a terahertz (THz)-empowered reconfigurable intelligent surface (RIS)-assisted NTN (T-RANTN), which is composed of a low-Earth orbit satellite transmitter, an RIS-installed high-altitude platform (HAP) and two unmanned aerial vehicle (UAV) receivers, only one of which is trustworthy. An approximate ergodic secrecy rate (ESR) expression is derived when the atmosphere turbulence and pointing error due to the characteristics of THz as well as the phase errors resulting from finite precision of RIS and imperfect channel estimation are taken into account simultaneously. Furthermore, according to the statistical and perfect channel state information of the untrustworthy receiver, we optimize the phase shifts of RIS to maximize the lower bound of secrecy rate (SR) and instantaneous SR, respectively, by using semidefinite relaxation method. Simulation results show that both the approximate expression for the ESR and the optimization algorithms are serviceable, and even when the jitter standard variance of the trustworthy receiver is greater than that of the untrustworthy one, a positive SR can still be guaranteed.