Internet Of Things Users Research Articles

Federated and Transfer Learning-Empowered Intrusion Detection for IoT Applications

The Internet of Things (IoT) can be described as a considerable number of sensors and physical devices connected to different applications, supported with networking technologies to communicate with other devices and the Internet. With the growing number of IoT users, emerging services, and the need for high availability and data exchange, cyberattacks on those applications have increased in recent years. Therefore, securing IoT applications has allured particular consideration from the industry and research fields. This article illustrates and comprehensively analyzes the effectiveness of using FL/TL trending techniques used with different Machine Learning (ML) and Deep Learning (DL) algorithms to drive the Intrusion Detection Systems (IDS) to secure the IoT applications. The Internet of Medical Things (IoMT) is considered in this article as a use case in which we have demonstrated that using federated and transfer learning can improve model performance, increase learning process speed, reduce the amount of data needed to be trained, and preserve the user's data privacy compared with the traditional learning approaches.

Enhancing secrecy performance of energy harvesting NOMA systems using IoT user scheduling under untrusted users

In this work, multiple energy-limited Internet of Things (IoT) users are intended to communicate with their destination ( D u $D_{u}$ ). The scheduled IoT user harvests the energy from a source signal and uses this energy to forward a mixture of its message and the source message to the D u $D_{u}$ and the source destination based on non-orthogonal multiple access (NOMA). Since the messages are transmitted at the same resource block, the far untrusted user (source destination) has the opportunity to access the message destined for the near user ( D u $D_{u}$ ) by successive interference cancellation. Therefore, an IoT user scheduling strategy is proposed to achieve the reliability and security of transmission in the network. To indicate the superiority of the proposed scheduling scheme and the impact of different parameters on the network performance under untrusted users, the secrecy outage probability of the system is derived by tracking exact and asymptotic analyses and then it is compared with that of benchmarks through simulations. The results reveal the crucial role of the near user threshold secrecy rate as the system's bottleneck and the negligible impact of the power allocation coefficients and the untrusted far user threshold rate on the secrecy performance with increasing the signal-to-noise-ratio.

HeteFL: Network-Aware Federated Learning Optimization in Heterogeneous MEC-Enabled Internet of Things

Federated learning (FL) is an effective paradigm for training a machine-learning model based on data distributed at a large quantity of users in Internet of Things (IoT) without sharing their raw data. However, federated optimization of the global model in heterogeneous IoT—while considering the heterogeneity among users and limited network constraints—remains to be an open challenge. In this article, we propose a novel adaptive federated optimization algorithm, Adp-FedProx, to achieve the optimal learning performance within the limited computation and communication resources at the edge. In particular, we analyze how the training loss is affected by each user’s global update frequency and the time and energy used for learning by obtaining the novel convergence bound of federated training loss in heterogeneous IoT. With our proposed algorithm, all users can dynamically adjust their number of local iterations in each global interval and will not drop out during the training process for resource exhaustion, so as to impair the negative effect of heterogeneity among users and guarantee the convergence of the training model. In addition, we can get the optimal learning performance by minimizing the gap between the final loss function and the optimal one within limited resources. Finally, extensive numerical results demonstrate the algorithmic advantages in adapting system heterogeneity and admirable performance of the proposed methodologies in speeding up FL 5%–10% and reduce the energy consumption in training about 10% compared with FedProx.

Delay-aware distributed program caching for IoT-edge networks.

Edge computing is a novel network architecture that is in proximity to the end devices in an Internet of Things (IoT). As the IoT becoming a major factor in our daily life, provisioning a low response time of the services to IoT users through edge computing is an important problem. Caching necessary program data for the task in an edge node effectively reduces the response time of the computation task. However, due to the increase of IoT users and devices, it is noteworthy that limited-resource edge nodes would receive a number of tasks, having a heavy burden of processing the requests. Therefore, the limited resource and caching space at cloudlet need the careful design of the caching algorithm to utilize the space of multiple edge nodes and relieve the burden of computations. In this paper, we propose a cooperative program caching system that makes different edge nodes cooperatively store program data and cache the replicas of the data requested frequently to handle a number of requests from IoT users. In particular, we develop a cooperative caching algorithm that caches the appropriate number of data replicas depending on the number of requests on each cloudlet and the popularity of the data to minimize the response time. The simulation results show that the proposed cooperative caching algorithm can effectively reduce the response time for IoT users compared to other existing algorithms.

Dynamic Resource Allocation for Virtual Network Function Placement in Satellite Edge Clouds

Satellite edge computing has become a promising way to provide computing services for Internet of Things (IoT) users in remote areas, which are out of the coverage of terrestrial networks. Nevertheless, it is not suitable for large-scale IoT users due to the resource limitation of satellites. Cloud computing can provide sufficient available resources for IoT users, but it does not meet delay-sensitive services as high network latency. Satellite edge clouds can facilitate flexible service provisioning for numerous IoT users by incorporating the advantages of edge computing and cloud computing. In this paper, we investigate the dynamic resource allocation problem for virtual network function (VNF) placement in satellite edge clouds. The aim is to minimize the network bandwidth cost and the service end-to-end delay jointly. We formulate the VNF placement problem as an integer non-linear programming problem and then propose a distributed VNF placement (D-VNFP) algorithm to address it. The experiments are conducted to evaluate the performance of the proposed D-VNFP algorithm, where Viterbi and Game theory are considered as the baseline algorithms. The results show that the proposed D-VNFP algorithm is effective and efficient for solving the VNF placement problem in satellite edge clouds.

Genetic-based optimization in fog computing: Current trends and research opportunities

Fog computing is a new computational paradigm that emerged from the need to reduce network usage and latency in the Internet of Things (IoT). Fog can be considered as a continuum between the cloud layer and IoT users that allows the execution of applications or storage/processing of data in network infrastructure devices. The heterogeneity and wider distribution of fog devices are the key differences between cloud and fog infrastructure. Genetic-based optimization is commonly used in distributed systems; however, the differentiating features of fog computing require new designs, studies, and experimentation. The growing research in the field of genetic-based fog resource optimization and the lack of previous analysis in this field have encouraged us to present a comprehensive, exhaustive, and systematic review of the most recent research works. Resource optimization techniques in fog were examined and analyzed, with special emphasis on genetic-based solutions and their characteristics and design alternatives. We defined a taxonomy of the optimization scope in fog infrastructures and used this optimization taxonomy to classify the 70 papers in this survey. Subsequently, the papers were assessed in terms of genetic optimization design. Finally, the benefits and limitations of each surveyed work are outlined in this paper. Based on these previous analyses of the relevant literature, future research directions were identified. We concluded that more research efforts are needed to address the current challenges in data management, workflow scheduling, and service placement. Additionally, there is still room for improved designs and deployments of parallel and hybrid genetic algorithms that leverage, and adapt to, the heterogeneity and distributed features of fog domains.

Dynamic Spectrum Access for Internet-of-Things Based on Federated Deep Reinforcement Learning

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework.

Investigating Wireless and Internet of Things Technologies Security Threats and Attacks

Wireless and mobile devices are part of our lives. Wireless technology encompasses Internet of Things (IoT). Some key features of these devices are sensors, connectivity, and artificial intelligence and they can be found in health clinics, homes, buildings, vehicles, cities, wearables, etc. However, wireless and IoT technologies are sources of a variety of security threats to privacy and data and are becoming targets for attackers or hackers. In this paper, the authors strive to answer to the following research questions: 1) What types of threats can wireless and IoT technologies pose? 2) What type of threats can be exploited for attack and how? 3) What techniques are used to mitigate the threats and attacks? 4) What can wireless and IoT users do to protect their privacy and data? As a result, we investigate different types of security threats and attacks, and common security countermeasures used in wireless and IoT.

Proposed Model of Internet of Things Adoption for Higher Education Institution

In the light of the information technology revolution, the emergence of the Internet of Things (IoT) is an important development that can enhance the efficiency of daily activities, such as increasing communication efficiency for users around the world. Within this context, few users realize the benefits of using IoT services. In general, most of the previous studies focused on technical aspects of the Internet of Things, such as architectural dimensions and wireless sensor networks, and the studies did not focus on examining the perceptions of IoT users. The aim of this study is to determine the factors that affect users' adoption of IoT services in an educational institution in Iraq. Random samples were used to collect data from 302 (faculty) at Northern Technical University. The UTAUT model was used with the addition of two factors (privacy, trust), as current research confirmed the importance of these two factors in the users' decision to adopt new technologies. The current study found that social influence is the most important factor regarding behavioral intention (BI) to use IoT services followed by expected effort, privacy and performance expectations. Facilitating conditions have an impact on the behavior of using IoT services. Finally, the study recommends decision makers in higher education institutions to use Internet of Things services and work to increase users' awareness of its benefits.

Fairness-Based Resource Allocation for Multiple Weights Opportunistic Beamforming in Internet of Things Networks

Multiple-input–multiple-output (MIMO) is a promising technique in Internet of Things (IoT) Networks, which can effectively multiplex more IoT users and improve the spectrum efficiencies (SEs) of the users. However, when the number of IoT users is large, the complexity becomes enormous. Moreover, perfect channel state information (CSI) is a basic assumption in MIMO systems, which is impractical with a large number of IoT users. In order to reduce the complexity and break the CSI limitation, an opportunistic-beamforming (OBF)-based IoT network is proposed, which can achieve high SE with low complexity and feedback under a large number of IoT users’ conditions. Then, to solve the unfairness problem and further improve SE, a downlink multiple-weight and multiple receive antenna OBF (MW-OBF-MRA) system with the proportional fairness (PF) strategy is proposed, where the transmitter and receiver are jointly designed. Moreover, a joint resource allocation scheme is provided to achieve the maximum SE with PF constraints. Numerical results show that our proposed system can achieve fairness with the minimum SE loss and provide better SE than the other beamforming schemes with the limited feedback information.

A value-added IoT service for cellular networks using federated learning

The number of Internet-of-Things (IoT) devices is expected to reach 64 billion by 2025. These IoT devices will mostly use cellular networks for transferring a huge amount of IoT data to the cloud for machine learning (ML) based forecasting. Keeping in view a large number of application scenarios for highly resource constraint IoT devices connected with the cellular networks, we propose a value-added IoT service (VAIS) for the cellular network operators based on the federated learning (FL) paradigm. Through simulation experiments, we show, for real air quality data and specific ML models, the proposed VAIS reduces the backhaul data by 70× and requires less energy than its equivalent cloud-based conventional approach, however, with a slight increase in communication time. From the insights we gained, we believe that a properly designed VAIS would efficiently utilize network resources, not only reduce management for IoT users but also the operating costs for cellular operators, and encourage IoT applications on limited backhaul cellular networks.

Towards Privacy by Design on the Internet of Things (IoT) Use: A Qualitative Descriptive Study

From remote control and surveillance to energy and health monitoring, IoT devices provide cutting-edge services to improve our lives. Nevertheless, IoT technologies raise complicated, innovative privacy-related issues that might limit their widespread adoption. Due to the passive nature of many IoT devices, it may be challenging for users to understand that their personal information is being gathered. Therefore, this paper aims to examine the Privacy by Design IoT application idea. We adapted the seven principles of Ann Cavoukian's Privacy by Design, which were first developed. In this study's qualitative descriptive methodology with interview sections are used. The results indicate that the majority of IoT users agree with and have high expectations for the Privacy by Design concept to be one of the remedies to the imbalance between concerns about the risks and benefits of using IoT.

RecIoT: A Deep Insight into IoT-Based Smart Recommender Systems

Typically, Internet of Things (IoT) is perceived as the technology for connecting people, devices, vehicles, home appliances, etc., and 212 billion heterogenous devices are presumed to be connected by 2020 under its umbrella. This domain is gaining popularity worldwide because of the wave it has brought in both research and industrial sector. Smart applications of this scenario provide assistance and services to the users in smart homes, smart cities, smart retail, agriculture, healthcare, etc. However, researchers are now exploring the potential of IoT systems in other domains, one of them being recommender systems. In recent years, the focus of researchers has drifted towards utilizing recommender systems to improve offered selections to IoT users. Therefore, the aim of this paper is to study the present practices, approaches, and challenges in the aforementioned domain along with possible solutions. Briefly, the contributions of this paper are as follows: (1) providing an overview of the prevailing scenario in a range of application domains associated with IoT based recommender systems, (2) providing a framework of some real-world applications, (3) outlining key areas for the improvement towards context-aware personalized services in the future research, and (4) providing a bibliometric trend analysis prevalent in the concerned domain.

A Survey of Trust Management Schemes for Social Internet of Things

Social Internet of Things (SIoT) involves integrating social networking concepts in the Internet of Things (IoT) to enhance social interactions among IoT objects and users. SIoT is envisaged to provide adequate service selection and discovery. Trust is an essential factor whenever social concepts are discussed in communication networks. Trust usually leads to a mutual relationship between two parties (i.e., the trustor and trustee) where they both enjoy mutual benefits. For secure social relationships, Trust management (TM) is a crucial feature of SIoT. The primary aim of this work is to provide a comprehensive review of trust management proposals/schemes available for SIoT. Four main trust calculation algorithms for trust management were selected for this review, and they were examined in detail. The IEEE Xplore, Scopus, ResearchGate, and Google Scholar databases were searched for articles containing the terms "Trust aggregation approaches in IoT", and "Trust computation in SIoT" with a particular emphasis on works published between 2018 and 2021. The paper also discussed the pros and cons of each TM technique, trust metrics/features, contributions, and limitations of the state-of-the-art SIoT TM proposals in the literature. The paper further provides open issues and possible research directions for entry-level researchers in the domain of SIoT.

Virtual Network Function Placement in Satellite Edge Computing With a Potential Game Approach

Satellite networks, as a supplement to terrestrial networks, can provide effective computing services for Internet of Things (IoT) users in remote areas. Due to the resource limitation of satellites, such as in computing, storage, and energy, a computation task from an IoT user can be divided into several parts and cooperatively accomplished by multiple satellites to improve the overall operational efficiency of satellite networks. Network function virtualization (NFV) is viewed as a new paradigm in allocating network resources on-demand. Satellite edge computing combined with the NFV technology is becoming an emerging topic. In this paper, we propose a potential game approach for virtual network function (VNF) placement in satellite edge computing. The VNF placement problem aims to minimize the deployment cost for each user request, furthermore, we consider that a satellite network should provide computing services for as many user requests as possible. We formulate the VNF placement problem as a potential game to maximize the overall network payoff and analyze the problem by a game-theoretical approach. We implement a decentralized resource allocation algorithm based on a potential game (PGRA) to tackle the VNF placement problem by finding a Nash equilibrium. Finally, we conduct the experiments to evaluate the performance of the proposed PGRA algorithm. The simulation results show that the proposed PGRA algorithm can effectively address the VNF placement problem in satellite edge computing.

Do desire, anxiety and personal innovativeness impact the adoption of IoT devices?

PurposeThis study aims to investigate the threat phenomenon as perceived by Internet of Things (IoT) users and examines the role of anxiety, desire and personal innovativeness in the behavioral intention toward the usage of IoT devices.Design/methodology/approachA unified research model is developed based on the protection motivation theory, theory of reasoned action, theory of self-regulation and the review of relevant theoretical, empirical and practitioner literature. Data were collected from 315 assistive IoT device users and analyzed using partial least squares structural modeling.FindingsThe results indicate strong support for the proposed research model. All relationships, except one, were significant at the 0.05 level. Desire was found to play a direct as well as a moderating role between fear and behavioral intention to continue using assistive IoT devices, which was also directly influenced by anxiety and personal innovativeness.Research limitations/implicationsUnderstanding the security behaviors of IoT users will help researchers and practitioners develop preventive measures and robust security solutions for the IoT devices to avert any threats from cyber-attacks and to boost users’ confidence levels. Future research will benefit from replicating the study using longitudinal data.Originality/valueThis study is one of the first studies that integrate multiple perspectives to present a holistic research model. To the authors’ knowledge, anxiety, desire and personal innovativeness, key factors influencing fear and behavioral intention, have not been studied in the domain of adoption of IoT assistive devices. Additionally, the study offers a new dimension to IoT users’ security behaviors.

Towards a new semantic middleware for service description, discovery, selection, and composition in the Internet of Things

AbstractThe emergence of the Internet of Things (IoT) allows the integration of everyday devices such as tags, sensors, and actuators into the Internet. IoT also provides handy and automatic techniques to interoperate ad‐hoc, heterogeneous, and mobile networks and simplify data sharing among devices by abstracting their capabilities as services. Nevertheless, the large number and heterogeneity of encompassed networks/devices and the diversity of the offered resources/services make the IoT service description, discovery, selection, and composition a challenging task. To deal with these issues, we introduce, in this article, a semantic middleware for IoT applications based on service composition. Such a solution supports the semantic description of IoT resources, including services and user requests, and provides a modular, end‐to‐end, and loosely coupled request resolution process that comprises a context‐aware service discovery, a semantic service selection, and an automatic lightweight service composition. The proposal is illustrated and extensively evaluated on a restricted smart‐city scenario. Obtained results show that the proposed service discovery, selection, and composition sub‐processes improve the scalability of equivalent state‐of‐the‐art solutions by around 15, 20, and 40, respectively.

Finite Block Length Analysis of RIS-Assisted UAV-Based Multiuser IoT Communication System With Non-Linear EH

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations.

Short-Packet Communications in NOMA-CDRT IoT Networks With Cochannel Interference and Imperfect SIC

In this paper, we propose short-packet communications (SPCs) with non-orthogonal multiple access (NOMA) for coordinated and direct relay transmission (CDRT) Internet-of-Things (IoT) networks to enhance spectrum utilization and system performance. In this system, a source transmits its packets to a near IoT user via direct transmission and a far IoT user via selective relaying transmission under practical conditions of imperfect successive interference cancellation and cochannel interference. We derive the closed-form and asymptotic expressions for the average block-error-rate (BLER), throughput, and the goodput of the considered system over Rayleigh fading channels. To further improve the considered system, we formulate the system throughput maximization problem. Numerical results show that our proposed system outperforms the orthogonal multiple access and conventional NOMA-CDRT ones in terms of both BLER, system throughput, and goodput. In addition, by jointly optimizing power allocation coefficients and channel coding ratios, the system throughput performance is maximized. Furthermore, for a given block-length, the proposed system with short messages achieves superior latency and reliability compared to the long-message one. Monte Carlo simulations are presented to confirm the theoretical analysis.

Adaptive Access Selection Algorithm for Multi-Service in 5G Heterogeneous Internet of Things

5G heterogeneous network system incorporates multiple radio access technologies (RATs), which enables the massive connection of Internet of things (IoT) devices and popularity of diverse IoT services. However, with the tremendous growth of IoT connections, personalization of service requirements and deepening of network heterogeneity, how to adaptively optimize the quality of experience (QoE) of IoT users in any motion state while balancing network load is still a major challenge in 5G system. Therefore, this paper proposes an innovative access selection mechanism named REMNS that allows users requesting IoT services to obtain optimal QoE in 5G heterogeneous networks. In particular, a network pre-assessment mechanism based on fuzzy logic is exploited to filter available networks by user devices. Furthermore, REMNS devises a comprehensive preference evaluation framework based on the subjective-oriented Analytic Hierarchy Process (AHP) and objective-oriented Entropy Weight Method (EWM) to measure the preference degrees of each IoT service for network attributes. Subsequently, we put forward a relative entropy based multi-service access selection algorithm to make servers rapidly sort optimal network from the filtered available networks, so as to enhance QoE for users under the constraint of limited network capacities. The evaluation results demonstrate that the REMNS can effectively maintain stable service connections and significantly improve user QoE.