Conventional Internet Of Things Research Articles

AIoT Used for COVID-19 Pandemic Prevention and Control.

The pandemic of COVID-19 is continuing to wreak havoc in 2021, with at least 170 million victims around the world. Healthcare systems are overwhelmed by the large-scale virus infection. Luckily, Internet of Things (IoT) is one of the most effective paradigms in the intelligent world, in which the technology of artificial intelligence (AI), like cloud computing and big data analysis, is playing a vital role in preventing the spread of the pandemic of COVID-19. AI and 5G technologies are advancing by leaps and bounds, further strengthening the intelligence and connectivity of IoT applications, and conventional IoT has been gradually upgraded to be more powerful AI + IoT (AIoT). For example, in terms of remote screening and diagnosis of COVID-19 patients, AI technology based on machine learning and deep learning has recently upgraded medical equipment significantly and has reshaped the workflow with minimal contact with patients, so medical specialists can make clinical decisions more efficiently, providing the best protection not only to patients but also to specialists themselves. This paper reviews the latest progress made in combating COVID-19 with both IoT and AI and also provides comprehensive details on how to combat the pandemic of COVID-19 as well as the technologies that may be applied in the future.

Advancement of IoT System QoS by Integrating Cloud, Fog, Roof, and Dew Computing Assisted by SDN

In the internet of things (IoT) domain, there has currently been a growing interest, leading to the idea of the IoT ecosystem. But the standards, technology, and structures of the conventional IoT framework do not provide the necessary QoS for today's massive data. Thus, for today's IoT ecosystem, a framework called SD-DRFC (software-defined dew, roof, fog, and cloud computing) is suggested in this article. The framework delivers facilities from the closest possible position of end-user gadgets and thus increases the QoS in an IoT system. Clear description about the role and features of each tier is also presented. The path to a multi-tier computational architecture assisted by SDN can be realized from the given detailed literature review. Using the iFogSim simulator, a use case based on the architecture provided is then given and evaluated. This article considers four QoS parameters (latency, network use, cost, and energy consumption). When compared the findings of the simulation, the proposed framework execution performs much better than cloud-only execution.

Multilink Internet-of-Things Sensor Communication Based on Bluetooth Low Energy Considering Scalability

As the growth rate of the internet-of-things (IoT) sensor market is expected to exceed 30%, a technology that can easily collect and processing a large number of various types of sensor data is gradually required. However, conventional multilink IoT sensor communication based on Bluetooth low energy (BLE) enables only the processing of up to 19 peripheral nodes per central device. This study suggested an alternative to increasing the number of IoT sensor nodes while minimizing the addition of a central processor by expanding the number of peripheral nodes that can be processed per central device through a new group-switching algorithm based on Bluetooth low energy (BLE). Furthermore, this involves verifying the relevancy of application to the industry field. This device environment lowered the possibility of data errors and equipment troubles due to communication interference between central processors, which is a critical advantage when applying it to industry. The scalability and various benefits of a group-switching algorithm are expected to help accelerate various services via the application of BLE 5 wireless communication by innovatively improving the constraint of accessing up to 19 nodes per central device in the conventional multilink IoT sensor communication.

IoT Big Data provenance scheme using blockchain on Hadoop ecosystem

The diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).

IoT Service Slicing and Task Offloading for Edge Computing

With the advancement of Internet-of-Things (IoT) technology, various domains, such as smart factories and smart cars have used this new technology to provide value-added services. In addition, technologies, such as multiaccess edge computing (MEC) and network slicing provide another opportunity for the IoT to support more advanced and real-time services that could not have been previously supported. However, the simple integration of such technologies into the IoT does not take the full advantage of MEC and network slicing or the reduction of latency and traffic prioritization, respectively. Therefore, there is a strong need for an efficient integration mechanism for IoT platforms to maximize the benefit of using such technologies. In this article, we introduce a novel architectural framework that enables the virtualization of an IoT platform with minimum functions to support specific IoT services and host the instance in an edge node, close to the end user. As the instance provides its service at the edge node, where the MEC node and network slice are located, the traffic for the end user does not need to traverse back to the cloud. This architecture guarantees not only low latency but also efficient management of IoT services at the edge node. To show the feasibility of the proposed architecture, we conducted an experimental evaluation by comparing the transmission time of both IoT services running on the central cloud and those using sliced IoT functions in the edge gateway. The results show that the proposed architecture provides twice as much transmission speed as that from the conventional cloud-based IoT platform.

Disk Triboelectric Nanogenerator‐Based Nonvolatile Memory toward Smart Identification System

AbstractWith the rapid advancement of the Internet of things (IoT), security issues of the IoT are emerging because the wireless networks for conventional IoT are easily exposed to hacking. By storing the critical data in a physically separate space, these issues can be suppressed. The nonvolatile memory (NVM) is an attractive solution because the stored data are not erased even after turning off the power. However, the NVM consumes the power for operating and remaining data are exposed to attack. Hence, NVM with high security and low power operation is highly required for IoT platforms. Herein, a disk triboelectric nanogenerator‐based NVM (DTNVM) is developed. The DTNVM can be operated with low power because the reading process of stored data is conducted with triboelectricity. Since the ternary system is adopted, 23 to 119 trits can be stored at the DTNVM by changing the sampling time. The identification information is stored at the DTNVM and 91.3% of consistency of the data with a range of 10% tolerance is recorded as result of the reading. Based on the result, the DTNVM is expected to be utilized in the near future as a next‐generation NVM and for safe identification systems at the IoT.

NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds

Conventional Internet of Things (IoT) applications involve data capture from various sensors in environments, and the captured data then is processed in remote clouds. However, some critical IoT applications (e.g., autonomous vehicles) require a much lower response latency and more secure guarantees than those offered by remote clouds today. Mobile edge clouds (MEC) supported by the network function virtualization (NFV) technique have been envisioned as an ideal platform for supporting such IoT applications. Specifically, MECs enable to handle IoT applications in edge networks to shorten network latency, and NFV enables agile and low-cost network functions to run in low-cost commodity servers as virtual machines (VMs). One fundamental problem for the provisioning of IoT applications in an NFV-enabled MEC is where to place virtualized network functions (VNFs) for IoT applications in the MEC, such that the operational cost of provisioning IoT applications is minimized. In this paper, we first address this fundamental problem, by considering a special case of the IoT application placement problem, where the IoT application and VNFs of each service request are consolidated into a single location (gateway or cloudlet), for which we propose an exact solution and an approximation algorithm with a provable approximation ratio. We then develop a heuristic algorithm that controls the resource violation ratios of edge clouds in the network. For the IoT application placement problem for IoT applications where their VNFs can be placed to multiple locations, we propose an efficient heuristic that jointly places the IoT application and its VNFs. We finally study the performance of the proposed algorithms by simulations and implementations in a real test-bed, Experimental results show that the performance of the proposed algorithms outperform their counterparts by at least 10 percent.

The Impact of Memory-Efficient Bots on IoT-WSN Botnet Propagation

By successfully infecting certain numbers of nodes in an Internet of Things (IoT) platform, botmaster often relies on specific bots (infected nodes) to scan and gather information about the target nodes for onward propagation of the attack. Therefore, the bot’s processing capability to scan and executes the botmaster’s instructions relied on its memory capability. Hence, the bot’s memory efficiency determines the selection or the abandonment of the bot by the botmaster during the propagation of the attack. To defend against IoT botnets attack, there is need to accurately analyze the dynamic characteristics of the botnet propagation in a network. Although conventional IoT botnet propagation models considered certain characteristics of IoT wireless sensor networks (IoT-WSN) in analyzing the propagation time and the size of the botnet infection, the models do not consider the impact of memory-efficient bots on the size of the botnet infection. As such they are not applicable to defend the network against them. To complement this gap, this study proposed an IoT- Susceptible-Infectious-Abandon (IoT-SIA) model to analyze the impact of the memory-efficient bots on the size of the botnet infection. Consequently, based on the numerical simulation results, the memory-efficient bots have shown a direct impact on the size of the botnet infection and the propagation time of the attack.

Series Editorial: Internet of Things and Sensor Networks

Today, the Internet of Things (IoT) continues to evolve as a predominant technical trend. In the face of the global pandemic, many conventional IoT paradigms are, however, expected to shift in response to pressing societal challenges. For instance, we are preparing to observe substantial investments in the telemedicine and healthcare sectors as well as efficient work-from-home solutions. This accentuates the need for edge intelligence in supporting the increasingly massive IoT deployments augmented with machine learning capabilities, among many others.

Tamper Detection and Self-Recovery of Medical Imagery for Smart Health

Social Internet of Things (SIoT) is one of the emerging research areas which integrates the Internet of Things (IoT) with social networking concepts. It envisages better information sharing, by overcoming the scalability issues in IoT. It is supposed to overhaul the smart health industry, as it is a flexible compared to conventional IoT based solutions. It however poses many challenges with data authentication and privacy, being a couple of such issues that need to be addressed. In this paper, we present an efficient watermarking method for tamper detection, localization, and self-recovery of medical images for SIoT-based smart health networks. The cover image is initially divided into four equal parts, with each part progressively divided into 4 × 4 non-overlapping blocks. A watermark is prepared from a group of randomly chosen four, 4 × 4 blocks, determined by chaotic mapping. The information is embedded into 4 sub-blocks mapped utilizing chaotic sequence such that the restoration process can be carried out even if three sub-blocks out of four containing the information, get tampered with. The proposed technique is found to restore almost 1/3rd of the tampered image and produces a high-quality image with the average Peak Signal-to-Noise Ratio value of above 33 dB after the restoration process. The performance of the proposed method for various collage attacks shows its efficacy over other state-of-the-art techniques making it a potential candidate for SIoT driven smart health systems.

IEEE Access Special Section Editorial: Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing

The Internet of Things (IoT) technology is not just about the connection among billions of devices but also about enabling a wide range of new capabilities, architectures, or service paradigms. The scale of the IoT is set to have a major economic, social, and environmental impact. However, the implementation of IoT faces various challenges, including sensing ability, energy consumption, power supply, data security, privacy, and data accountability. In order to mitigate these challenges, more and more IoT systems are embracing the new IoT paradigm, such as millimeter-wave (mmWave), sensor-less sensing, backscattering Wi-Fi/RFID, wireless/vision hybrid, blockchain, fat client, and so on. These modes shed light on the emerging technologies that could revolutionize the conventional IoT systems.

Blockchain technology in IoT systems: current trends, methodology, problems, applications, and future directions

The growth of Internet of Things (IoT) took center stage universally due their capability to advance the course of human lives. Consequent upon this, several challenges were thrown up such as security of huge data stored and transmitted through network communication channels. IoT insecurity is partly due to centralization architecture, low computational strength, resource-constrained devices, variation in standards and protocols of communication. From this Systematic Literature Review (SLR), the 85 articles reviewed showed that privacy and security solutions are still being proposed or at conceptual levels, though a number of researchers favored the integration of Blockchain technology, cryptographic and hashing schemes into IoT. The Blockchain technology in IoT systems remains an open area of interest for top researchers especially in evolving frameworks to fit into the centralized architecture, functionality, and scalability demands of conventional IoT systems. In this article, we investigate security and privacy concerns of IoT from the lens of current trends, pertinent challenges, security methodologies, applications, and gaps for future research directions. Most specifically, there is prospect of utilizing cryptographic and hashing schemes offered by Blockchain technology in IoT. Then, high performance and scalable cryptographic schemes (that is, those in the class of lightweight appraoch) are suggested to deal with privacy and security of data in Blockchain-based IoT system. More importantly, this study provides basis for evolving secure and decentralized applications and systems in several domains such as smart farming.

Internet of Video Things: Next-Generation IoT With Visual Sensors

The worldwide flourishing of the Internet of Things (IoT) in the past decade has enabled numerous new applications through the internetworking of a wide variety of devices and sensors. More recently, visual sensors have seen their considerable booming in IoT systems because they are capable of providing richer and more versatile information. Internetworking of large-scale visual sensors has been named Internet of Video Things (IoVT). IoVT has its own unique characteristics in terms of sensing, transmission, storage, and analysis, which are fundamentally different from the conventional IoT. These new characteristics of IoVT are expected to impose significant challenges to existing technical infrastructures. In this article, an overview of recent advances in various fronts of IoVT will be introduced and a broad range of technological and system challenges will be addressed. Several emerging IoVT applications will be discussed briefly to illustrate the potentials of IoVT in a broad range of practical scenarios.

An Efficient Interface for the Integration of IoT Devices with Smart Grids

The evolution of computing devices and ubiquitous computing has led to the development of the Internet of Things (IoT). Smart Grids (SGs) stand out among the many applications of IoT and comprise several embedded intelligent technologies to improve the reliability and the safety of power grids. SGs use communication protocols for information exchange, such as the Open Smart Grid Protocol (OSGP). However, OSGP does not support the integration with devices compliant with the Constrained Application Protocol (CoAP), a communication protocol used in conventional IoT systems. In this sense, this article presents an efficient software interface that provides integration between OSGP and CoAP. The results obtained demonstrate the effectiveness of the proposed solution, which presents low communication overhead and enables the integration between IoT and SG systems.

An Intrusion Detection System on Ping of Death Attacks in IoT Networks

Internet of things (IoT) is almost vulnerable to cyber security attacks due to the limited computational resources of conventional IoT devices. In this paper, ping of death as a cyber security attack is modeled for IoT networks. Attackers increase the length of transmit packets intentionally in order to degrade the network resources. To mitigate ping of death attacks, an intrusion detection system (IDS) is designed to be included in the network gateway to detect attacks and to filter out the over-length packets. The IDS relies on an integer optimization problem minimizing the false alarm probability while maintaining the missed detection probability below a desired level. The problem is solved using a search method. Simulation results demonstrate the impact of the proposed IDS by not allowing the gateway queue lengths to grow unexpectedly and to maintain queues at a normal level. Furthermore, simulation results provide a guideline for the network administrator to configure the IDS parameters such a normal packet length.

The Best of Both Worlds: A General Architecture for Data Management in Blockchain-enabled Internet-of-Things

The rapid proliferation of Internet-of-Things (IoT) devices has brought great challenges of data management, i.e., storing, retrieving and manipulating a large volume of IoT data. Conventional IoT systems rely on centralized architectures to manage IoT data, hence suffering from limited scalability, lack of transparency, and single point of failure issues. As such, we employ blockchain as a distributed ledger to support the decentralized approach of data management in IoT systems, where IoT data are stored in the deployed blockchain for further utilization, e.g., retrieve and audit. A general architecture combining blockchain and IoT systems is presented. Nevertheless, as the resource constraints of IoT devices may still exist during the process of data transmissions from IoT devices to the blockchain network, we propose a case study of a learning-assisted resource allocation method to support intelligent data management. The numerical results show that the proposed scheme achieves superior performance compared with baseline solutions.

Energy Adaptive Intrusion Detection System for Energy Harvesting IoT

Internet of Things (IoT) is an emerging technology that makes network of physical objects which can identify, communicate and share information through Internet. The edge of IoT network are mostly simple sensors. The success of the IoT application depends on the quality of sensor data at the right time, this leads to the requirement of IoT devices be long term, self-sustaining and have the ability to harvest their required energy from deployed environment. Such devices incur additional security challenges because of prolonged life time and change in the life cycle of devices. A novel intrusion detection system is designed for energy harvesting 6LoWPAN based IoT network considering the energy scavenging characteristics of devices in addition to conventional IoT. The simulation results confirm that the proposed intrusion detection system is efficient and accurate in detecting the attacks.

Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this article, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices’ transmission schedule over time, while ensuring the reliability of data collection and required 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.

A multi-device multi-tasks management and orchestration architecture for the design of enterprise IoT applications

Enterprise Internet of Things (EIoT) is the next advancement in technology. EIoT allows the involvement of embedded devices to participate in business processes to automate enterprise operations. EIoT demands the modelling of services based on tasks to automate business processes and to allow easy adaptation to new business models. It is anticipated to enhance efficiency, align physical operations on a real-time basis and provide analytics for fostering more customer-centric business. The architecture is the cornerstone for any application, and currently, there exist a variety of architectures for enterprise systems and IoT addressing the requirements of the respective domains. The reference architectures of conventional IoT platforms perform orchestration at the service level, which, despite its success, does not align well with the demands of enterprises processes. This paper proposes a reference architecture to consider both the requirements of EIoT as well as the conventional Internet of Things (IoT). This paper is a novel attempt to propose orchestration at the task level, considering the business process modelling of enterprise systems. This level of orchestration is scalable and flexible due to the automation at a more granular level. In contrast, the service-level orchestration works well for general IoT application but is not suited for enterprises whose sole aim is the modelling of enterprise operation at the very task level. Therefore, a reference architecture named a multi-device multi-tasks management and orchestration (MDMT-MOA) based on a top-down methodology is proposed addressing the needs of IoT and enterprise applications. A case study is implemented on top of the architecture, and the performance is evaluated using various metrics related to EIoT. Results indicate that the proposed architecture is efficient due to lightweight payload, scalable, fault-tolerant and flexible to adapt with new business models.

NOMA-Enabled Mobile Edge Computing for Internet of Things via Joint Communication and Computation Resource Allocations

The past decades have witnessed an explosive growth of the Internet of Things (IoT) services requiring intensive computation resources. The conventional IoT devices, however, are usually equipped with very limited computation resources, which results in degraded quality of experience when executing the resource-hungry applications. Mobile edge computing (MEC), which enables smart terminals (STs) to offload parts of their computation workloads to the edge servers located at cellular base stations (BSs), has provided a promising approach to address this issue. In this article, we investigate the nonorthogonal multiple access (NOMA)-enabled multiaccess MEC. Specifically, by exploiting the advanced NOMA, an ST can simultaneously offload its computation workloads to different edge servers (ESs), which thus reduces the overall delay in completing the ST's computation workloads. To study this problem, we formulate a joint optimization of the computation resource allocations at the ESs, the ST's offloaded workloads and its radio resource allocations for NOMA transmission, with the objective of minimizing a system wise cost that accounts for the overall delay in finishing the ST's total computation workload and the total computation resource usage cost at the ESs. Despite the nonconvexity of the joint optimization problem, we exploit its layered structure and propose an efficient layered algorithm to find the optimal solution. By exploiting the optimal offloading solution of a single ST, we further investigate the scenario of multiple STs and propose two algorithms to determine the optimal grouping among different ESs for serving the STs, with one algorithm aiming at minimizing the total cost of all STs and the other algorithm aiming at determining the Nash stable grouping for the ESs. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed NOMA-enabled multiaccess computation offloading.