Distributed Internet Of Things Devices Research Articles

An Efficient Architecture for Imputing Distributed Data Sets of IoT Networks

In the era of the Internet of Things (IoT), spatially distributed IoT devices collect and store data in a distributed fashion for computational efficiency. However, in IoT networks, due to the fragile device, harsh deployment environment, and unreliable transmission, the possibility of missing data is increasing, which may significantly affect subsequent data processing. Traditional approaches to impute missing data in IoT distributed datasets bring huge communication overheads. In this paper, we develop an efficient architecture for distributed IoT data imputation based on a designed multi-discriminator conditional generative adversarial network. The architecture intelligently learns the characteristics of the distributed datasets to accurately impute missing values. Our experiments are performed using three datasets under two different data missing mechanisms. The experimental results demonstrate that using three datasets, the proposed imputation technique can drastically reduce the imputation error by up to 88.66%, 94.27%, and 95.53% at the premise of low transmission cost, respectively, compared to five state-of-the-art methods.

Federated Learning for IoT Devices With Domain Generalization

Federated Learning (FL) is a distributed machine learning technique that allows numerous Internet of Things (IoT) devices to jointly train a machine learning model using a centralized server for help. Local data never leaves each IoT device in FL, so the local data of IoT devices are protected. In FL, distributed IoT devices usually collect their local data independently, so the dataset of each IoT device may naturally form a distinct source domain. In real-world applications, the model trained over multi-source domains may have poor generalization performance on unseen target domains. To address this issue, we propose FedADG to equip federated learning with domain generalization capability. FedADG employs the federated adversarial learning approach to measure and align the distributions among different source domains via matching each distribution to a reference distribution. The reference distribution is adaptively generated (by accommodating all source domains) to minimize the domain shift distance during alignment. Therefore, the learned feature representation tends to be universal, and thus, it has good generalization performance over the unseen target domains while protecting local data privacy. Intensive experiments on various datasets demonstrate that FedADG has comparable performance with the state-of-the-art.

Protecting IoT Devices from BotNet Threats: A Federated Machine Learning Solution

The proliferation of Internet of Things (IoT) devices has brought unprecedented convenience to our lives, but it has also opened the door to new security challenges. One of the most pressing threats in the IoT landscape is the proliferation of BotNets, which can compromise and control a multitude of devices for malicious purposes. In this paper, we propose a novel approach to address this issue: a Federated Machine Learning Solution for BotNet detection in IoT environments. Our method leverages the collective intelligence of distributed IoT devices while respecting privacy constraints, ensuring that sensitive data never leaves the device. We present a detailed methodology for federated model construction, including data collection, local model training, and secure aggregation. The resulting federated model offers improved accuracy and robustness in BotNet detection, as demonstrated through rigorous evaluation on the N-BaIoT dataset. Our findings underscore the effectiveness of this approach in enhancing IoT device security by detecting and mitigating BotNet threats while safeguarding data privacy. This paper contributes to the advancement of IoT security strategies and provides a framework for protecting IoT devices against evolving threats in a federated and privacy-preserving manner.

Intelligent Reflecting Surface-Assisted Low-Latency Federated Learning Over Wireless Networks

Federated learning (FL) is an emerging technique to support privacy-aware and resource-constrained machine learning, where a base station (BS) will coordinate a set of distributed Internet of Things (IoT) devices to train a shared machine learning model with their local data sets. Nevertheless, due to the frequent interactions between BS and distributed IoT devices for the aggregating/distributing learning model parameters, the performance of FL is fundamentally restricted by the randomness of channel condition. To address this issue, we utilize the intelligent reflecting surface (IRS) to improve the efficiency of learning model aggregation/distribution. In addition, we consider two transmission protocols to enable the model aggregation from IoT devices to BS, i.e., frequency division multiple access (FDMA) and nonorthogonal multiple access (NOMA). For both protocols, we formulate the total training latency minimization problem under the available energy constraints of IoT devices, to jointly optimize the phase shifts of IRS, communication resource scheduling, and transmit power and local computing frequencies of IoT devices. Moreover, we further develop the efficient multidimensional resource management algorithms to solve the formulated training latency minimization problems. Numerical results demonstrate that the proposed IRS-assisted FL systems can achieve significant latency reduction as compared with other benchmark methods, and the NOMA-based model aggregation method exhibits a lower total training latency than the FDMA-based counterpart.

Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection

The proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and innovation. However, this interconnected landscape also presents unique security challenges, necessitating robust intrusion detection mechanisms. In this research, we present a comprehensive study of anomaly detection in IoT networks, leveraging advanced machine learning techniques. Specifically, we employ the Gated Recurrent Unit (GRU) architecture as the backbone network to capture temporal dependencies within IoT traffic. Furthermore, our approach embraces hierarchical federated training to ensure scalability and privacy preservation across distributed IoT devices. Our experimental design encompasses public IoT datasets, facilitating rigorous evaluation of the model's performance and adaptability. Results indicate that our GRU-based model excels in identifying a spectrum of attacks, from Distributed Denial of Service (DDoS) incursions to SQL injection attempts. Visualizations of learning curves, Receiver Operating Characteristic (ROC) curves, and confusion matrices offer insights into the model's learning process, discriminatory power, and classification performance. Our findings contribute to the evolving landscape of IoT security, offering a roadmap for enhancing the resilience of interconnected systems in an era of increasing connectivity.

Towards Semantic Management of On-Device Applications in Industrial IoT

The Internet of Things (IoT) is revolutionizing the industry. Powered by pervasive embedded devices, the Industrial IoT (IIoT) provides a unique solution for retrieving and analyzing data near the source in real-time. Many emerging techniques, such as Tiny Machine Learning (TinyML) and Complex Event Processing (CEP) , are actively being developed to support decision making at the edge, shifting the paradigm from centralized processing to distributed computing. However, distributed computing presents management challenges, as IoT devices are diverse and constrained, and their number is growing exponentially. The situation is even more challenging when various on-device applications (so-called artifacts) are deployed across decentralized IoT networks. Questions to be addressed include how to discover an appropriate function, whether that function can be executed on a certain device, and how to orchestrate a cross-platform service. To tackle these challenges, we propose an approach for the scalable management of on-device applications among distributed IoT devices. By leveraging the W3C Web of Things (WoT) , the capabilities of each IoT device, or more precisely, its interaction patterns, can be semantically expressed in a Thing Description (TD) . In addition, we introduce semantic modeling of on-device applications to supplement an TD with additional information regarding applications on the device. Specifically, we demonstrate two examples of semantic modeling: neural networks (NN) and CEP rules. The ontologies are evaluated by answering a set of competency questions. By hosting the enriched semantic knowledge of the entire IoT system in a Knowledge Graph (KG) , we can discover and interoperate edge devices and artifacts across the decentralized network. This can reduce fragmentation and increase the reusability of IoT components. We demonstrate the feasibility of our concept on an industrial workstation consisting of a conveyor belt and several IoT devices. Finally, the requirements for constructing an IoT semantic management system are discussed.

A Cloud-Based and Dynamic DNS Approach to Enable the Web of Things

Thanks to the evolution in the hardware and software fields, the Internet usage scope is continuously covering smaller and resource-constrained devices. Such devices, commonly called Internet of Things (IoT) devices, with sensing/actuation capabilities, are becoming capable of managing the complexity of communications over the Internet. Nevertheless, the IoT ecosystem is still fragmented due to the different used vertical solutions. This heterogeneity makes IoT devices/systems unable to communicate seamlessly, leading to limited cooperation and tightly coupled deployments. To deal with the interoperability issues in IoT, we propose, in this paper, a Cloud-based approach with a Dynamic Domain Name System (DDNS) mechanism enabling the IoT devices to communicate using the Representational state transfer (REST) model: an approach that follows the Web of things (WoT) paradigm. In particular, the system makes the IoT devices’ hosted resources (e.g., sensors and actuators) able to be steered using globally resolvable (over the Internet) Uniform Resource Locators (URLs) even when deployed behind Network Address Translators (NATs) and firewalls. The system we conceived requires only one public registered domain name to associate, for all the distributed IoT devices, sub-domains of the public one while using a clever routing mechanism. An online implementation of the testbed is provided to show the feasibility of the approach. Further, a performance evaluation of the system is reported to assess the resource usage of the solution.

A Blockchain-based Trust and Reputation Model with Dynamic Evaluation Mechanism for IoT

The rapid development of the Internet of Things (IoT) has dramatically increased the number of distributed IoT devices and users. Trust and Reputation Model (TRM) is a well-known technique for improving the security of IoT, which detects malicious attacks by evaluating user behavior. Since traditional distributed TRMs lack secure and reliable data sharing mechanisms, some works have integrated the TRMs into the trusted blockchains. Nevertheless, they have not realized the security requirements of the comprehensive assessment of user behavior and dynamic evaluation of reputation. Therefore, this paper introduces a Blockchain-based Trust and Reputation Model (BTRM), which evaluates user reputation from many aspects and can resist multiple malicious attacks in the distributed network. Second, we propose a novel Dynamic Evaluation Mechanism (DEM), which reduces the number of reputation evaluations without degrading network security and builds a trusting foundation between long-term inactive users and the network. Eventually, we deploy the proposed model DEM-BTRM in a prototype system of Hyperledger Fabric and compare it with existing reputation evaluation methods. The results show that the DEM-BTRM can comprehensively evaluate user behavior and dynamically detect malicious attacks.

Communication-Efficient Semihierarchical Federated Analytics in IoT Networks

The convergence of the Internet of Things (IoT) and data analytics has great potential to accelerate knowledge discovery, while the traditional approach of centralized data collection then processing is becoming infeasible in many applications due to efficiency and privacy concerns. Federated learning (FL) has emerged as a new paradigm that enables model learning across distributed IoT devices without sharing raw data. However, previous works on FL are either relying on a single central server or fully decentralized. In this article, we propose a semihierarchical federated analytics framework combining the advantages of the above architectures. The proposed framework leverages multiple edge servers for aggregating updates from IoT devices and fusing learned model weights without the need of cloud or a central server. Besides, we develop a new local client update rule to further improve the communication efficiency by reducing the communication rounds between IoT devices and edge servers. We analyze the convergence properties of the presenting approach and investigate its characteristics considering the effects of varying parameters, unreliable links, and packet loss. The experimental results demonstrate the effectiveness of our proposed methodology in providing communication-efficient, robust, and fault-tolerant data analytics to IoT networks.

FAHP-Based Reliability Evaluation of Distributed IoT Devices in a Distribution Power Grid

Reliable operation of distributed internet of things (IoT) devices is essential for supporting two-way interaction between energy flow and information flow in a distribution power grid. Traditional reliability evaluation methods suffer from several challenges including complex evaluation indicator selection and inadaptability of fixed weights with actual reliability requirements. In this paper, we first establish a multilevel reliability evaluation indicator system, where the indicators are selected through joint consideration of comprehensiveness and effectiveness. Then, we propose a fuzzy analytic hierarchy process- (FAHP-) based reliability evaluation method, where a three-layer reliability evaluation architecture is established. Specifically, a 0.1-0.9 scaling method is adopted to establish the fuzzy judgment matrix and scale the important relationship among state-layer elements, which can be dynamically adjusted to generate the weights adapting with actual operation state. The scores of distributed IoT devices and corresponding reliability levels can be obtained through the weighted summation of the scores of each layer. Simulation results verify the effectiveness and accuracy of the proposed method through comparing with actual reliability value and two existing evaluation methods.

Energy efficient scheduling and power control of massive MIMO in massive IoT networks

We present various energy efficient (EE) scheduling and power control schemes for Massive MIMO with massive Internet of Things (IoT) connectivity. To support massive IoT devices simultaneously with low latency, the reference signal (RS) is heavily reused. To increase the performance, we reorganize the distributed IoT devices in ascending and in descending orders. We show that the spectral efficiency (SE) is improved based on water-filling effect. Next, we show that a generalized power control scheme is quite effective in increasing the fairness. Nevertheless, it requires a lot of power which is not fitted to low power IoT networks. Based on these facts, we propose sigmoid function based power allocation schemes to compromise between SE/EE performance and the fairness. Numerical results verify that the proposed scheme can achieve arbitrary SE/EE performance tradeoff with satisfactory fairness.

UAV-Assisted Communication Efficient Federated Learning in the Era of the Artificial Intelligence of Things

Artificial Intelligence (AI) based models are increasingly deployed in the Internet of Things (IoT), paving the evolution of the IoT into the AI of things (AIoT). Currently, the predominant approach for AI model training is cloud-centric and involves the sharing of data with external parties. To preserve privacy while enabling collaborative model training across distributed IoT devices, the machine learning paradigm called Federated Learning (FL) has been proposed. The future FL network is envisioned to involve up to millions of distributed IoT devices involved in collaborative learning. However, communication failures and dropouts by nodes can lead to inefficient FL. Inspired by the UAV-assisted communications in 5G heterogeneous networks (HetNet), we propose the UAV-assisted FL in this article. The FL model owner may employ UAVs to provide the intermediate model aggregation in the sky and mobile relay of the updated model parameters from data owners to the model owner. This therefore increases the reach of FL to data owners that face uncertain network conditions and improves the communication efficiency. To incentivize the UAV service providers, we adopt the multi-dimensional contract incentive design as a case study. The incentive compatibility of the contract ensures that the UAVs only choose an incentive package corresponding to its type, for example, traveling cost. The simulation results show that the UAV-assisted FL achieves significant improvement in communication efficiency and validates the incentive compatibility of our contract design.

Spectrum Based Power Management for Congested IoT Networks.

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

Federated Learning for Internet of Things: A Comprehensive Survey

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

Wireless Communications and Mobile Computing Blockchain-Based Trust Management in Distributed Internet of Things

The development of Internet of Things (IoT) and Mobile Edge Computing (MEC) has led to close cooperation between electronic devices. It requires strong reliability and trustworthiness of the devices involved in the communication. However, current trust mechanisms have the following issues: (1) heavily relying on a trusted third party, which may incur severe security issues if it is corrupted, and (2) malicious evaluations on the involved devices which may bias the trustrank of the devices. By introducing the concepts of risk management and blockchain into the trust mechanism, we here propose a blockchain-based trust mechanism for distributed IoT devices in this paper. In the proposed trust mechanism, trustrank is quantified by normative trust and risk measures, and a new storage structure is designed for the domain administration manager to identify and delete the malicious evaluations of the devices. Evidence shows that the proposed trust mechanism can ensure data sharing and integrity, in addition to its resistance against malicious attacks to the IoT devices.

PFLUA-DIoT: A Pairing Free Lightweight and Unlinkable User Access Control Scheme for Distributed IoT Environments

The Internet of Things (IoT) connects enormous objects through various sensors to facilitate daily life by interconnecting the information space with the decision-makers. Security and privacy are, however, the main concerns in IoT due to the openness of communication channels and the unattended nature of common sensors. To provide security and privacy for sensors and users in IoT-based systems; in 2019, Zhou <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed an unlinkable authentication scheme using bilinear pairings. However, the vulnerability of their scheme against sensor node impersonation attack as proved in this article renders the scheme of their work impractical and insecure. A pairing free lightweight and unlinkable authentication scheme for distributed IoT devices (PFLUA-DIoT) is then proposed in this article. The security of PFLUA-DIoT is proved using the formal method along with a discussion on its provision of security features. The performance and security comparisons show that PFLUA-DIoT provides known security features and provides better performance. Due to the avoidance of bilinear pairing-based expensive operations, PFLUA-DIoT completes authentication in less than half running time as compared with their and related schemes. Therefore, the PFLUA-DIoT can address the security and privacy issues of IoT, practically and efficiently.

DARE: A decentralized association rules extraction scheme for embedded data sets in distributed IoT devices

In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem.

KEEP: Secure and Efficient Communication for Distributed IoT Devices

Security over mobile Internet-of-Things (IoT) devices is critical due to the open nature of distributed wireless communication. To efficiently establish a secure connection between two communication parties, a fast mobile key extraction protocol, KEEP, is proposed. KEEP fastly generates similar bit sequences from two communication parties’ measurements of channel-state information (CSI) of different subcarriers. Then, a distributed “verification-recombination” mechanism is introduced to generate the same encryption key from bit sequences without the public-key authentication, digital signature, or key distribution center of the other party. We implemented real-world experiments using commercial off-the-shelf 802.11n devices to evaluate the performance of KEEP in various scenarios. Theoretical analysis and experimental verification show that KEEP is more secure, effective, and reliable than the state-of-the-art methods.

Dynamic OverCloud: Realizing Microservices-Based IoT-Cloud Service Composition over Multiple Clouds

With the expansion of cloud-leveraged Information and Communications Technology (ICT) convergence trend, cloud-native computing is starting to be the de-facto paradigm together with MSA(Microservices Architecture)-based service composition for agility and efficiency. Moreover, by bridging the Internet of Things (IoT) and cloud together, a variety of cloud applications are explosively emerging. As an example, the so-called IoT-Cloud services, which are cloud-leveraged inter-connected services with distributed IoT devices, dynamically utilize geographically-distributed multiple clouds since mobile IoT devices can selectively connect to the near-by cloud resources for low-latency and high-throughput connectivity. In comparison, most public cloud providers may cause vendor lock-in problems that limit the inter-operable service compositions. Thus, in this paper, we propose a new overlay approach to address the above limitations, denoted as Dynamic OverCloud, which is a specially-arranged razor-thin overlay layer that provides users with an inter-operable and visibility-supported environment for MSA-based IoT-Cloud service composition over the existing multiple clouds. Then, we design a software framework that dynamically builds the proposed concept. We also describe a detailed implementation of the software framework with workflows. Finally, we verify its feasibility by realizing a smart energy IoT-Cloud service with the suggested operation lifecycle.

IoTSim‐Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments

SummaryWith the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data‐driven and real‐time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end‐point networks (eg, Wi‐Fi, 4G, and Bluetooth); (ii) heterogeneity of edge and IoT hardware resources and software stack; (iv) mobility of IoT devices; and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource‐intensive but also time‐intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim‐Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim‐Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim‐Edge is described using three test cases. Results show the varying capability of IoTSim‐Edge in terms of application composition, battery‐oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications.