Smart Internet Of Things System Research Articles

Adaptive TS-ANFIS neuro-fuzzy controller based single phase shunt active power filter to mitigate sensitive power quality issues in IoT devices

The emergence of Internet of Things (IoT) offers numerous functions, such as intelligent sensor integration, remote sensing, and high-speed data transmission, which have found widespread applications in the smart industry and commercial applications. The associative nonlinear effects of a variety of undesirable power quality concerns were resolved by using harmonic mitigation in nonlinear loads and high-performance converters were built on power electronics in conventional systems. Among other performance objectives, total harmonic (THD) distortion analysis and higher order harmonics mitigation is given main concern in smart electronics equipment. This paper proposes an approach to minimize higher-order harmonics due to nonlinear load disturbances in smart IoT devices using the Takagi–Sugeno (TS) Neuro Fuzzy (TS-ANFIS) supervised shunt APF. The proposed work elicits the novel adaptive harmonic mitigation technique in hybrid IoT embedded systems to protect from malfunctioning and to deal with the uncertainty of harmonic signal stimuli in sensitive sensors-based IoT systems. Hysteresis current control is used for the ignition of reference signal under uncertainty of harmonic stimuli. The single phase shunt APF is used to mitigate higher-order harmonics from supply mains, while the implementation of TS-ANFIS supervises the controller action to generate a trigger signal for adequate single phase APF gate excitation. The higher order harmonic current data set is used for error deviation for training neural networks and adaptive control. The estimated adoption of the neuron's empirical weight reduces the total THD to a significant reduction rate of 72.8 % to 0.78 %. The control mechanism is feasible for a range of smart IoT systems to adhere to the standard of 519 (IEEE).

Process Algebraic Approach for Probabilistic Verification of Safety and Security Requirements of Smart IoT (Internet of Things) Systems in Digital Twin

Process algebra can be considered one of the most practical formal methods for modeling Smart IoT Systems in Digital Twin, since each IoT device in the systems can be considered as a process. Further, some of the algebras are applied to predict the behavior of the systems. For example, PALOMA (Process Algebra for Located Markovian Agents) and PACSR (Probabilistic Algebra of Communicating Shared Resources) process algebras are designed to predict the behavior of IoT Systems with probability on choice operations. However, there is a lack of analytical methods in the algebras to predict the nondeterministic behavior of the systems. Further, there is no control mechanism to handle undesirable nondeterministic behavior of the systems. In order to overcome these limitations, this paper proposes a new process algebra, called dTP-Calculus, which can be used (1) to specify the nondeterministic behavior of the systems with static probability, (2) verify the safety and security requirements of the nondeterministic behavior with probability requirements, and (3) control undesirable nondeterministic behavior with dynamic probability. To demonstrate the feasibility and practicality of the approach, the SAVE (Specification, Analysis, Verification, Evaluation) tool has been developed on the ADOxx Meta-Modeling Platform and applied to a SEMS (Smart Emergency Medical Service) example. In addition, a miniature digital twin system for the SEMS example was constructed and applied to the SAVE tool as a proof of concept for Digital Twin. It shows that the approach with dTP-Calculus on the tool can be very efficient and effective for Smart IoT Systems in Digital Twin.

Optimizing Heterogeneity in IoT Infra Using Federated Learning and Blockchain-based Security Strategies

The Internet of Things (IoT) and associated capabilities are becoming indispensable in the planning, operation, and administration of intricate systems of all sizes. High-end learning solutions that go beyond the boundaries of the problem are necessary for addressing the variety of communication concerns (compatibility, secure communication, etc.) in IoT settings. Building machine learning (ML) networks from disparate data sources is a cutting-edge practice known as Federated Learning (FL). In this article, we implement FL between edge-based servers and devices in a sparsely populated cloud to facilitate cohesive learning and the storage of critical information in smart IoT systems. FL enables collaborative training from a common model by aggregating smaller unit models via regulated edge network participants. Further, all the susceptible device’s information and sensitive message transactions are addressed via blockchain technology. Thus, a blockchain-based security mechanism is integrated to secure user privacy and facilitate widespread practical adoption. Finally, a comparison is made between the proposed model and the three best free, open-source Federated Learning models already in use (FedPD, FedProx, and FedAvg). In terms of statistical, and data heterogeneity (>70% SDI, >97% accuracy), the experimental findings suggest that the proposed model performs better than the existing techniques.

Smart-IoT Business Process Management: A Case Study on Remote Digital Early Cardiac Arrhythmia Detection and Diagnosis

Cyber-Physical Systems (CPS) and Internet of Things (IoT) systems are mainly in charge of sensing, acting, computing, communicating, managing, and evolving the software integrated in devices. The wide variety of technologies and devices that support IoT systems and the decentralization of services through infrastructures such as fog or edge computing, has led companies to complex business models. These complex business models require decentralized and business-aware architectures driven by the Business Process Management Systems (BPMS) that define the tasks involved in IoT workflows. Well-defined and established IoT BPM are required for companies to remain competitive. However, there are still challenges that must be addressed to smoothly integrate IoT BPM and their architectures into a digitally native company’s daily workflow. In this paper, we present an IoT architecture to support a smart IoT BPM that focuses on addressing the challenges of scheduling, resource allocation, and state management of Smart IoT systems. In particular, the IDOVEN company’s adoption of this Smart IoT BMP in an IoT system for remote digital early cardiac arrhythmia detection and diagnosis is illustrated which has been validated with 2188 patients across the 7 continents.

Differential privacy in edge computing-based smart city Applications:Security issues, solutions and future directions

Fast-growing smart city applications, such as smart delivery, smart community, and smart health, are generating big data that are widely distributed on the internet. IoT (Internet of Things) systems are at the centre of smart city applications, as traditional cloud computing is insufficient for satisfying the critical requirements of smart IoT systems. Due to the nature of smart city applications, massive IoT data may contain sensitive information; hence, various privacy-preserving methods, such as anonymity, federated learning, and homomorphic encryption, have been utilised over the years. Furthermore, limited concern has been given to the resource consumption for data privacy-preserving in edge computing environments, which are resource-constrained when compared with cloud data centres. In particular, differential privacy (DP) has been an effective privacy-preserving method in the edge computing environment. However, there is no dedicated study on DP technology with a focus on smart city applications in the edge computing environment. To fill this gap, this paper provides a comprehensive study on DP in edge computing-based smart city applications, covering various aspects, such as privacy models, research methods, mechanisms, and applications. Our study focuses on five areas of data privacy, including data transmitting privacy, data processing privacy, data model training privacy, data publishing privacy, and location privacy. In addition, we investigate many potential applications of DP in smart city application scenarios. Finally, future directions of DP in edge computing are envisaged. We hope this study can be a useful roadmap for researchers and practitioners in edge computing enable smart city applications.

Research on reliable transmission and application security architecture of power IoT based on energy interconnection

Power Internet of Things (IoT) is an important support for digital innovation service of power energy internet, covering all aspects of power system. Power IoT security defense system may have customer data information leakage during transmission because of the use of traditional means of isolation. This paper proposes a reliable transmission and application security architecture for power smart IoT based on energy interconnection, aiming to solve the reliable transmission and security authentication problems existing in power systems. The paper first analyzes the security risk of the grid wise IoT system, proposes an effective power IoT security transmission scheme, and evaluates the safe and reliable transmission of the grid wise IoT system. Then designed a safe and reliable transmission of the grid smart IoT system, to deal with the traditional power network transmission security and communication security problems. The final application is in the construction of the security transmission platform of the wisdom park of Shanxi Electric Power Company, which provides the corresponding security protection capability in the power IoT through the situational awareness security measures of each layer, and realizes the reliable transmission and security application of the source network load storage and other links in the power IoT environment.

Designing Digital COVID-19 Screening: Insights and Deliberations.

Due to the global COVID-19 pandemic, public health control and screening measures have been introduced at healthcare facilities, including those housing our most vulnerable populations. These warning measures situated at hospital entrances are presently labour-intensive, requiring additional staff to conduct manual temperature checks and risk-assessment questionnaires of every individual entering the premises. To make this process more efficient, we present eGate, a digital COVID-19 health-screening smart Internet of Things system deployed at multiple entry points around a children's hospital. This paper reports on design insights based on the experiences of concierge screening staff stationed alongside the eGate system. Our work contributes towards social-technical deliberations on how to improve design and deploy of digital health-screening systems in hospitals. It specifically outlines a series of design recommendations for future health screening interventions, key considerations relevant to digital screening control systems and their implementation, and the plausible effects on the staff who work alongside them.

A Decentralized Trust Establishment Protocol for Smart IoT Systems

The Internet of Things (IoT) is a widely deployed technology to provide innovative services in many several fields such as industry, logistics, healthcare, and energy management. The integration of artificial intelligence, machine learning, edge/cloud computing, 5G networks, big data analytics, and other emerging technologies in IoT systems is leading to the emergence of Smart Systems that support devices to make autonomous decisions in order to improve and facilitate people’s lives. These smart IoT devices produce a huge amount of sensitive data. Thus, establishing Trust between these devices is vital to ensure the security of the sensed data and for the normal functionality of smart systems. To address these challenges, several Trust establishment protocols have been proposed, but they have some limitations such as centralization, the need for human intervention, and the lack of adaptability to smart devices with computation and storage constraints. In this paper, a decentralized trust establishment protocol is proposed for smart IoT devices and is applicable for different scenarios. The protocol provides decentralized identity management, mutual authentication, access control, and secure communication by combining Blockchain technology with PV-SAS-MCA message cross-authentication protocol based on Short Authenticated Strings (SAS). To the best of our knowledge, this research is the first to propose a new approach that uses Blockchain as an extra distributed and authenticated channel to exchange Short Authenticated Strings without physical interaction. This helps to allow entities in the Smart IoT environment not only to identify each other but also to exchange their public keys in a secure manner. Moreover, the proposed protocol uses Elliptic Curve Cryptography (ECC) to secure communications with significantly smaller key sizes and lower computation overhead. Experimental results reveal that the time required to complete the authentication process is less than 90 ms which is a superior performance when compared to state-of-the-art decentralized authentication mechanisms.

A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph

Internet of Things (IoT) infrastructure connects consumer electronics, household appliances, and other smart gadgets. For designing IoT systems and applications, different architectures and protocols are being used. The design quality of an IoT system and its services is assessed with quality of service (QoS) factors such as complexity, functional appropriateness, performance, efficiency, compatibility, maintainability, portability, and usability. The existing methods in the literature focus on measuring the quality of an IoT system during execution time; however, there is a need to define some standards that may be used to determine the quality of an IoT system from its design. This work models the IoT EcoSystem as a schema graph and presents a suite of metrics to determine the design quality of an IoT system and its services under the defined QoS factors. The presented metrics are mapped to the ISO-9126 QoS factors to ensure the standards and quality of IoT services during the design phase. The proposed metrics are validated with benchmarking on a case study smart healthcare IoT system. The results proved that the presented metrics are practical to quantify the quality of IoT design models under QoS factors, and the suite of metrics is able to compare differently designed IoT services.

Editorial “Green Computing, Monitoring and Assessing of Smart IoT Systems and Components”

Information and communication technologies and IT-based systems are, on the one side, a mean for energy systems and grids monitoring, control and instrumentation, power low management and safety ensuring. On the other side, IT-based systems, networks and infrastructures are weighty consumers of energy and other resources. The most challengeable IT domains, which can be called energy or power consumption critical, are mobile and Internet of Things (IoT) systems, cloud services, sensor networks.

Energy-Efficient Offloading for DNN-Based Smart IoT Systems in Cloud-Edge Environments

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.

Development Strategy of Smart Internet of Things System

The Internet of Things (IoT) is an important component of the new digital infrastructure and is deeply integrated with the fifth-generation mobile communication (5G), big data, cloud computing, artificial intelligence (AI), blockchain, and digital twin. It is profoundly changing the technology system and promoting the digital economy, ushering in a new stage of smart IoT system in which everything is connected. This paper reviews the development status of IoT in China, proposes the concept of smart IoT system (IoT 2.0), and expounds on the implications, architecture, technical pedigree, and key enabling technologies. Practical cases of smart IoT system are explored considering the application scenarios of intelligent manufacturing, smart agriculture, smart grid, smart healthcare, intelligent transportation, and intelligent environmental protection, demonstrating the application values of the smart IoT system. Furthermore, we suggest that a technology integration innovation project that integrates IoT, AI, 5G, and new application field technologies should be implemented; focus should be placed on the research, development, and industrialization of intelligent products such as smart IoT systems / cloud native platforms / low-code (no-code) application development environments and toolsets, high-end sensors for smart IoT systems, and IoT chips / special components; and application demonstration of cloud-edge-end collaborative, autonomous controllable, safe, and credible smart IoT systems should be conducted.

Efficient Compression Method Based on Object Model for Massive Real-time Data in Power IOT

With rapid development of the Internet of things (IOT) in power field, equipment of various majors began to access the smart IOT system on a large scale. In this paper, a new data compression algorithm based on object model (CABM) is proposed to solve the problems of large-scale applications of power Internet of Things, such as high-frequency transmission of large quantities, real-time short data can not be effectively compressed, resulting in low efficiency of cloud edge information transmission and serious waste of bandwidth resources. The CABM adds a compression and decompression module between the IOT management platform and the edge agent; the IOT management platform automatically generates a compression dictionary based on the object model file and saves it synchronously in the edge agent. During data transmission, both the sender and receiver complete automatic compression and decompression according to the compression dictionary. The experimental results show that for real-time message data below 1KB, the compression rate using CABM algorithm is 3-5 times higher than that using GZIP compression algorithm; and the compression time is 10-20 times lower than that using GZIP; the efficiency using CABM algorithm is 4.5 times higher than that using GZIP algorithm; that is, CABM can reduce redundant data transmission between cloud edges and improve the efficiency of cloud edge transmission.

A Full Lifecycle Authentication Scheme for Large-scale Smart IoT Applications

The rapid development of IoT (Internet of Things) brings great convenience to people through the utilization of IoT applications, but also brings huge security challenges. Existing IoT security breaches show that many IoT devices have authentication flaws. Although many IoT authentication schemes were proposed, they are not applicable to recent smart IoT applications covering IoT device, back-end sever, and user-end mobile applications. To build the first line of defense for trending IoT systems, this paper proposes a new authentication scheme. The proposed scheme first models the entire life cycle of the IoT device for real-world scenarios of smart IoT systems, which contains factory manufacturing, daily usage, and system resetting. For each stage in the life cycle, the proposed scheme employs efficient symmetric key mechanisms to achieve the authentication between IoT device, back-end server, and mobile application. The proposed scheme supports both server-free local area network communication and sever-involved remote public area communication. Formal security verification shows that the proposed scheme resists existing attacks. The open-source experimental evaluations also show that the proposed scheme is efficient and promising for practical usage.

Guest Editorial Special Issue on Smart IoT System: Opportunities by Linking Cloud, Edge, and AI

Recently, the Internet of Things (IoT) technologies have made their entrances into many fields, such as smart city, healthcare, intelligent transportation, forest protection, and environmental monitoring.

Efficient Anomaly Detection for Smart Hospital IoT Systems.

In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

Addressing Security and Privacy Issues of IoT Using Blockchain Technology

Internet of Things (IoT) has been the most emerging technology in the last decade because the number of smart devices and its associated technologies has rapidly grown in both industrial and research prospectives. The applications are developed using IoT techniques for real-time monitoring. Due to low processing power and storage capacity, smart things are vulnerable to the attacks as existing security or cryptography techniques are not suitable. In this study, we initially review and identify the security and privacy issues that exist in the IoT system. Second, as per blockchain technology, we provide some security solutions. The detailed analysis, including enabling technology and integration of IoT technologies, is explained. Finally, a case study is implemented using the Ethererum-based blockchain system in a smart IoT system and the results are discussed.

Intelligent Trust-Based Public-Key Management for IoT by Linking Edge Devices in a Fog Architecture

Due to memory and processing limitations, Internet-of-Things (IoT) devices require external fog servers to perform some of their tasks. However, this offloading of tasks comes at the cost of more interactions whose security cannot be guaranteed without the authentication and key management scheme. Traditional prescriptions, such as those used for securing the Web, require referring to central agents, such as certificate authorities (CA) or online certificate status protocol (OCSP) responders, that sit in the cloud. This poses many challenges, including additional communication costs and repetitive delays which work against the low latency and energy efficiency goals of edge networking. In this article, we propose a novel semidecentralized public-key management scheme for smart IoT systems in which devices intelligently decide whether to look for the keying material locally at the edge or refer to the cloud for this purpose. The result is a security architecture that links IoT devices, fog servers, and cloud, but with minimal dependency on the latter. In the proposed solution, devices work collaboratively to deliver revocation lists and digital certificates of fog servers to each other. The decision to go for edge nodes or cloud CA/OCSP responders is made intelligently by each node upon learning its neighborhood and network statistics. The core idea is based on the Web of trust, but unlike that, whenever a material is not found locally, cloud servers are queried. Experiments show that through this intelligent approach, the cost of key management operations, e.g., delay, can be reduced by up to 50%.

An Agent-Based Approach to Realize Emergent Configurations in the Internet of Things

The Internet of Things (IoT) has enabled physical objects and devices, often referred to as things, to connect and communicate. This has opened up for the development of novel types of services that improve the quality of our daily lives. The dynamicity and uncertainty of IoT environments, including the mobility of users and devices, make it hard to foresee at design time available things and services. Further, users should be able to achieve their goals seamlessly in arbitrary environments. To address these challenges, we exploit Artificial Intelligence (AI) to engineer smart IoT systems that can achieve user goals and cope with the dynamicity and uncertainty of their environments. More specifically, the main contribution of this paper is an approach that leverages the notion of Belief-Desire-Intention agents and Machine Learning (ML) techniques to realize Emergent Configurations (ECs) in the IoT. An EC is an IoT system composed of a dynamic set of things that connect and cooperate temporarily to achieve a user goal. The approach enables the distributed formation, enactment, adaptation of ECs, and conflict resolution among them. We present a conceptual model of the entities of the approach, its underlying processes, and the guidelines for using it. Moreover, we report about the simulations conducted to validate the feasibility of the approach and evaluate its scalability.

Robustness Evaluations of Sustainable Machine Learning Models against Data Poisoning Attacks in the Internet of Things

With the increasing popularity of the Internet of Things (IoT) platforms, the cyber security of these platforms is a highly active area of research. One key technology underpinning smart IoT systems is machine learning, which classifies and predicts events from large-scale data in IoT networks. Machine learning is susceptible to cyber attacks, particularly data poisoning attacks that inject false data when training machine learning models. Data poisoning attacks degrade the performances of machine learning models. It is an ongoing research challenge to develop trustworthy machine learning models resilient and sustainable against data poisoning attacks in IoT networks. We studied the effects of data poisoning attacks on machine learning models, including the gradient boosting machine, random forest, naive Bayes, and feed-forward deep learning, to determine the levels to which the models should be trusted and said to be reliable in real-world IoT settings. In the training phase, a label modification function is developed to manipulate legitimate input classes. The function is employed at data poisoning rates of 5%, 10%, 20%, and 30% that allow the comparison of the poisoned models and display their performance degradations. The machine learning models have been evaluated using the ToN_IoT and UNSW NB-15 datasets, as they include a wide variety of recent legitimate and attack vectors. The experimental results revealed that the models’ performances will be degraded, in terms of accuracy and detection rates, if the number of the trained normal observations is not significantly larger than the poisoned data. At the rate of data poisoning of 30% or greater on input data, machine learning performances are significantly degraded.