Things Applications Research Articles

TCN-Inception: Temporal Convolutional Network and Inception modules for sensor-based Human Activity Recognition

The field of Human Activity Recognition (HAR) has experienced a significant surge in interest due to its essential role across numerous areas, including human–computer interaction (HCI), healthcare, smart homes, and various Internet of Things (IoT) applications. The power of deep learning methods in performing various classification tasks, including HAR, has been well-demonstrated. In light of this, our paper presents an efficient HAR system developed using a unique deep-learning architecture called TCN-Inception, which is designed for multivariate time series tasks like HAR data, by combining Temporal Convolutional Network (TCN) and Inception modules. The network starts with an Inception module that uses parallel convolution layers with various kernel sizes for feature extraction. It then includes a TCN module with dilated convolutions to grasp extended temporal patterns. Features are merged from different channels, and the use of residual connections and batch normalization improves training and deepens the architecture. We use four public datasets, UCI-HAR, MobiAct, Daphnet, and DSADS to assess the performance of the TCN-Inception model, and it obtains an average accuracy of 96.15%, 98.86%, 92.63%, and 99.50% for each dataset, respectively. Moreover, we compare the TCN-Inception to several deep learning frameworks to verify its performance. Finally, we implement an ablation study using several architectural configurations of the TCN-Inception model.

Design and performance evaluation of SEANet, a software-defined networking platform for the Internet of Underwater Things

This paper presents the design and performance evaluation of the SEANet platform, a software-defined acoustic modem designed for enhancing underwater networking and Internet of Underwater Things (IoUT) applications. Addressing the limitations of traditional acoustic modems, which suffer from low data rates and rigid architectures, SEANet introduces a versatile, adaptive framework capable of reconfiguring all layers of the protocol stack in real-time to accommodate diverse marine applications. The platform integrates high-performance, wideband data converters with modular hardware and software components, enabling real-time adaptation to changing environmental and operational conditions. Experimental evaluations conducted in oceanic settings demonstrate the SEANet capability to significantly exceed the performance of existing commercial underwater modems, supporting data rates up to 150 kbit/s and effectively doubling the performance metrics of conventional systems. Our robust testing also highlights the SEANet proficiency in channel estimation, Orthogonal Frequency-Division Multiplexing (OFDM) link establishment, and interoperability through the JANUS communication standard. Our results underscore the SEANet potential to transform underwater communication technologies, providing a scalable and efficient solution that supports high data rate applications and fosters the expansion of IoUT deployments.

A game theoretic approach for spectral and energy efficient D2D communication in 5G-IoT networks

In the context of fifth generation (5G) technology, Device-to-Device (D2D) communication plays a pivotal role, requiring swift and intelligent decision-making in mode selection and device discovery. This study addresses the challenge of rapid mode selection and device discovery within 5G communication networks, focusing specifically on enhancing spectral and energy efficiency for Internet of Things (IoT) applications. A novel self-centered game theory-based algorithm is introduced to optimize spectral efficiency and support intelligent mode selection. Additionally, the utilization of the support vector machine (SVM) expedites mode selection decisions. For D2D discovery, the Frank-Wolfe method is adopted, significantly improving the differentiation between D2D and Cellular users based on signal strength and interference, thereby enhancing spectral efficiency. The proposed approach maximizes spectral efficiency while adhering to strict power and interference constraints, intelligently partitioning bandwidth into two subparts using game theoretic principles to amplify spectral efficiency. Furthermore, the emphasis on energy efficiency is underscored through iterative calculations to achieve maximum energy-efficient spectral allocation. Numerical analyses validate the efficacy of the proposed technique, revealing substantial improvements in accurately predicted labels. As the number of devices increases, precision and recall rates experience noteworthy enhancements, ultimately leading to superior bandwidth utilization. This research presents a significant contribution to the field of 5G communication, particularly concerning energy efficiency, which is paramount for IoT applications. By accelerating D2D connectivity and optimizing energy and spectrum resources, it advances the goals of energy-efficient D2D communication within 5G-IoT networks.

Water droplets play a role in Internet of Things applications

Water droplets play a role in Internet of Things applications

Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

Robust Encryption Framework for IoT Devices Based on Bit-plane Extraction, Chaotic Sine Models, and Quantum Operations

In the domain of Internet of Things (IoT) applications, quantum computers offer the promise of addressing complex computational issues that traditional cryptographic methods find challenging. The unparalleled computational power of quantum computers makes existing cryptographic schemes, tailored to mitigate computational complexity, ineffective. This research introduces a robust encryption scheme designed specifically for digital data exchanged between IoT devices such as smartphones, smart sensors, and smart health monitoring devices. The proposed encryption framework is based on bit-plane extraction-based encryption, the chaotic sine model, the hyperchaotic map, the quantum baker map, and quantum operations such as quantum selective scrambling. The proposed methodology involves three key phases: the first and last phase related to permutation, which is performed at the bit level based on discrete bit-plane scrambling, bit-plane extraction, and hyperchaotic mapping. However, the second phase is based on diffusion, which depends on the chaotic sine model. During the initial stage, creating correlations among image pixels, along with a couple of chaotic sequences using a quantum baker map, is accomplished through a superposition of quantum-states. Before applying diffusion, the pixels of the grayscale image are encoded using a single qubit, and quantum operations are based on a quantum circuit featuring swap gates. To introduce pixel-level permutation, a hyperchaotic map generates random sequences within the range of 0 to 255. A circular shift further shuffles the pixel rows and pixel columns of the permuted image pixels. To enhance the digital image security further, bit-planes are extracted from the permuted bit-planes. Then, the bits within each bit plane are scrambled using a random sequence generated by the hyperchaotic map. Finally, the encoded bit planes are merged to produce a final encrypted image. Experimental results demonstrate the effectiveness of the proposed methodology, with security parameters such as correlation, entropy, and key space measuring at 0.0001, 7.999, and >2100, respectively. Furthermore, to assess performance in relation to computational complexity, a detailed analysis of computational time is conducted. The results reveal that the proposed encryption scheme can execute all encryption steps presented in the proposed method in less than one second. These findings demonstrate the secure transmission of digital data between IoT devices and show the suitability of the proposed encryption scheme for real-time applications.

Multi-valued verification of commitment systems with uncertainty and inconsistency in multi-source data settings

In the dynamic landscape of Internet of Things (IoT) applications within multi-source data environments, ensuring the reliability and correctness of system communications has become a paramount concern. This is particularly evident in the presence of commitment protocols with inconsistency and uncertainty. This paper tackles these challenges by introducing a new logic, termed Six-Values Computation Tree Logic for Commitment (6V-CTLC), specifically crafted to adeptly model IoT systems with both inconsistency and uncertainty. Employing this logic, we devise an innovative reduction-based multi-valued model checking approach to verify the systems under scrutiny. Our method is implemented through a Java transformation tool we developed to translate the 6V-CTLC logic to the classical logic of commitment (CTLC) and seamlessly interfaces with the efficient model checker MCMAS+. Applying this approach, we verify an abstracted 6V-CTLC model featuring uncertainty and inconsistency, as well as the original model of our system before abstraction. Furthermore, we assess the scalability of our approach through ten experiments, comparing the results obtained from verifying the two models. The findings demonstrate the effectiveness of system abstraction in mitigating the state explosion problem, while the developed multi-valued model checking technique yields precise results.

RRA-FFSCIL: Inter-intra classes representation and relationship augmentation federated few-shot incremental learning

Federated learning (FL), as a distributed machine learning paradigm, enables on-device model training and inference without data updates or privacy breaches, promoting edge intelligence in Industrial Internet of Things (IIoTs) applications. However, FL applying in IIoTs faces some challenges in practice: (1) the catastrophic forgetting of old data resulting from a streaming reception for arriving data; (2) the forgetting exacerbated by the non-i.i.d. data distribution across devices; (3) the overfitting caused by insufficient sample size of new data (i.e., few samples). To address the above problems, we propose an inter-intra classes representation and relationship augmentation federated few-shot incremental learning framework called RRA-FFSCIL, effectively mitigating catastrophic forgetting from local and global perspectives. Specifically, forgetting from streaming data is alleviated by designed modules in the client side of RRA-FFSCIL: a random few-shot (FS) episodic adaptation strategy, a class-projection relation strengthening loss function, and a class-aware distribution balancing loss function. Meanwhile, the mitigation of global forgetting from non-i.i.d. of distributions across devices thanks to selecting the best old models on the server to assist local class relation enhancement. We conduct extensive experiments on three datasets (cifar100, tinyImageNet, miniImageNet) to validate the effectiveness and superiority of RRA-FFSCIL in addressing the forgetting problem associated with streaming data.

User authentication using gait and enhanced attribute-based encryption: a case of smart home

With the increasing popularity of the internet of things (IoT) application such as smart home, more data is being collected, and subsequently, concerns about preserving the privacy and confidentiality of these data are growing. When intruders attack and get control of smart home devices, privacy is compromised. Attribute-based encryption (ABE) is a new technique proposed to solve the data privacy issue in smart homes. However, ABE involves high computational cost, and the length of its ciphertext/private key increases linearly with the number of attributes, thus limiting the usage of ABE. This study proposes an enhanced ABE that utilises gait profile. By combining lesser number of attributes and generating a profiling attribute that utilises gait, the proposed technique solves two issues: computational cost and one-to-one encryption. Based on experiment conducted, computational time has been reduced by 55.27% with nine static attributes and one profile attribute. Thus, enhanced ABE is better in terms of computational time.

Cometes: Cross-Device Mapping for Energy and Time-Aware Deployment on Edge Infrastructures

As the Internet of Things (IoT) application development falls more on libraries and tools, software developers design applications targeting power-constrained Edge-computing devices. However, not only the Quality-of-Service (QoS) but also energy savings remain major challenges. This article presents Cometes, a complete tool-flow that extends modern SDK tools, assisting developers in selecting resources towards the desired trade-off between execution time and energy consumption, without exceeding the power limitations of the Edge clusters. Cometes runs at application design time, on top of Kubernetes, and exploits cross-device estimation methods as well as traditional multi-objective optimizers to enable efficient resource allocation. The presented solution achieves up to 66% energy savings, up to 50% speed-up and an average improvement of 7%, compared to the placement policy of the lightweight Kubernetes (k3s).

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption.

Performance Limit: Fuzzy Logic Based Anti-Collision Algorithm for Industrial Internet of Things Applications

Passive RFID has the advantages of rapid identification of multi-target objects and low implementation cost. It is the most critical technology in the Industrial Internet of Things information-gathering layer and is extensively applied in various industries, such as smart production, asset management, and monitoring. The signal collision caused by the communication between the reader/writer and tags sharing the same wireless channel has caused a series of problems, such as the reduction of the identification efficiency of the reader/writer and the increment of the missed reading rate, thus restricting the further development of RFID. At present, many hybrid anti-collision algorithms integrate the advantages of Aloha and TS algorithms to optimize RFID system performance, but these solutions also suffer from performance bottlenecks. In order to break through such performance bottleneck, based on the ISE-BS algorithm, we combined the sub-frame observation mechanism and the Q value adjustment strategy and proposed two hybrid anti-collision algorithms. The experimental results show that the two algorithms proposed in this paper have obvious advantages in system throughput, time efficiency and other metrics, surpassing existing UHF RFID anti-collision algorithms.

Congestion and throughput optimization protocol for providing better quality of service and experience

<p>Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.</p>

A Confounding-Domain Randomized Approach for Providing Source Location Privacy Preservation for Wireless Sensor Networks

Wireless sensor networks (WSNs) have advanced technologically, allowing for a wide range of Internet of Things (IoT) applications. The Internet of Things (IoT) provides a platform for connecting items to achieve a specific goal with minimal human intervention. Various practical sectors, such as environmental monitoring and defense, demand IoT support with maximum object connectivity, and low human participation, yet face security concerns from strong unauthorized entities. As location privacy is one of these major vulnerabilities, continual monitoring of contextual information may lead the attacker to the asset's position. The location privacy of the source node that detects and reports the presence of an event is critical. In this study, we provide a source location privacy (SLP) preservation technique based on confounding domain angular routing (SLP-CDAR) that attempts to improve security while increasing network lifespan. To avoid traffic analysis attacks, packets first undergo a random walk, followed by a perplexing time domain routing that creates additional diversionary pathways. The packets are then routed via angle routing, followed by a random path, and finally delivered to the base station using the forward random walk technique. The experimental results prove that the proposed scheme enhances the location privacy strength while providing an improved network lifetime, and a swift increase in the entropy.

Energy-Aware Microservice-Based Application Deployment in UAV-Based Networks for Rural Scenarios

Yearly, the rates of Internet penetration are on the rise, surpassing 80% in developed nations. Despite this progress, over two billion individuals in rural and low-income regions face a complete absence of Internet access. This lack of connectivity hinders the implementation of vital services like remote healthcare, emergency assistance, distance learning, and personal communications. To bridge this gap and bring essential services to rural populations, this paper leverages Unmanned Aerial Vehicles (UAVs). The proposal introduces a UAV-based network architecture and an energy-efficient algorithm to deploy Internet of Things (IoT) applications. These applications are broken down into microservices, strategically distributed among a subset of UAVs. This approach addresses the limitations associated with running an entire IoT application on a single UAV, which could lead to suboptimal outcomes due to battery and computational constraints. Simulation results conducted in a realistic scenario underscore the effectiveness of the proposed solution. The evaluation includes assessing the percentage of IoT requests successfully served to users in the designated area and reducing the energy consumption required by UAVs during the handling of such requests.

Adaptive Transmissions for Batteryless Periodic Sensing

Batteryless, self-sustaining embedded sensing devices are key enablers for scalable and long-term operations of Internet of Things (IoT) applications. While advancements in both energy harvesting and intermittent computing have helped pave the way for building such batteryless IoT devices, a present challenge is a system design that can utilize intermittent energy to meet data requirements from IoT applications. In this paper, we take the requirement of periodic data sensing and describe the hardware and software of a batteryless IoT device with its model, design, implementation, and evaluation. A key finding is that, by estimating the non-linear hardware charging and discharging time, the device software can make scheduling decisions that both maintain the selected sensing period and improve transmission goodput. A hardware–software prototype was implemented using an MSP430 development board and LoRa radio communication technology. The proposed design was empirically compared with one that does not consider the non-linear hardware characteristics. The result of the experiments illustrated the nuances of the batteryless device design and implementation, and it demonstrated that the proposed design can cover a wider range of feasible sensing rates, which reduces the restriction on this parameter choice. It was further demonstrated that, under an intermittent supply of power, the proposed design could still keep the device functioning as required.

Dynamic Key Generation Using GWO for IoT System

One well-known technological advancement that significantly impacts many things is the Internet of Things (IoT). These include connectivity, work, healthcare, and the economy. IoT can improve life in many situations, including classrooms and smart cities, through work automation, increased output, and decreased worry. However, cyberattacks and other risks significantly impact intelligent Internet of Things applications. Key generation is essential in information security and the various applications that use a distributed system, networks, or Internet of Things (IoT) systems. Several algorithms have been developed to protect IoT applications from malicious attacks; since IoT devices usually have small memory resources and limited computing and power resources, traditional key generation methods are inappropriate because they require high computational power and memory usage. This paper proposes a method of Dynamic Key Generation Method (DKGM) to overcome the difficulty using a specific chaotic map called the Zaslavskii Map and a swarm intelligent algorithm for optimization called Grey Wolf Optimizer (GWO). DKGM's ability to generate several groups-seed numbers using the Zaslavskii map depends on various initial parameters. GWO selects strong generated numbers depending on the randomness test as a fitness function. Three wolfs GWα, GWβ, and GWΩ, are used to simulate the behavior of a pack of grey wolves when attacking prey. The speed and position of each wolf are updated depending on the best three wolves. Finally, use the sets GWα in the round, GWβ in the subkey, and GWΩ in shifting operations of the Chacha20 hash function. The dynamic procedure was used to improve the high-security analysis of the DKGM approach over earlier methods. Simulations show that the suggested method is preferable for IoT applications.

LoRaWAN: The dilemma of optimal transmission parameter

Low-Power Wide-Area Network (LPWAN) technologies, particularly LoRaWAN, have gained significant attention for their ability to facilitate long-range communication with low power consumption, making them ideal for Internet of Things (IoT) applications. However, achieving optimal performance in LoRaWAN systems requires careful selection of transmission parameters such as spreading factor, bandwidth, and coding rate. This paper addresses the dilemma faced by network designers and operators in determining the optimal transmission parameters for LoRaWAN deployments. We provide an overview of the key parameters and their impact on network performance, considering factors such as latency, packet delivery rate, and energy efficiency. Furthermore, we discuss the trade-offs involved in parameter selection. Through simulations, we evaluate the performance of different parameter configurations under various network conditions and provide insights into achieving the best balance between coverage, capacity, and energy consumption in LoRaWAN deployments. Findings from the study established that using high coding rate and bandwidth only provides minimal improvement in packet delivery rate particularly at large distances but at the cost energy consumption while spreading factor remains the most important transmission parameter in LoRa networks.

Analytic Hierarchy Process Assessment Framework for Blockchain in Renewable Energy

The urgency to reach net-zero emissions until 2050 has created a vast interest in innovative technologies for renewable energy sector. At the same time the need for trusty and unfailing solutions will rise considering the increasing amount of decentralized and decarbonized energy systems. It has previously been described in the literature that the aforementioned characteristics could be combined by blockchain technology. Therefore, this study focuses on the practical side – the opinion of industry experts about the use of blockchain technology in renewable energy. By using analytic hierarchy process analysis, the goal of this study is to develop a methodology for the selection of the most appropriate blockchain applications in renewable energy and to identify and evaluate the possible types of use taking into account socio-economic, political and legal, technological and environmental factors. The results of this study highlights the important influence of political, legal and technological factors like involvement of government institutions and possible attacks to the system, but considers the economic factors as the least significant for the introduction and use of blockchain technology in the renewable energy sector. The most promising use cases for blockchain would be associated with reliable and immutable certificates of origin for renewable energy and use in smart grid (including smart metering), smart homes and relevant Internet of Things applications. The use of crypto-assets and initial coin offerings for renewable energy development should be viewed with precaution and willingness to inform and educate considering social factors like public opinion and societies’ knowledge on crypto-assets.

Critical Concerns of Deploying Blockchain in Internet of Things Applications

The Internet of Things (IoT) and blockchain are both recognized as cutting-edge, popular technologies. In contrast to IoT, which pertains to the spread of linked equipment via supplying information over the Internet, blockchain offers innovative data storage and management avenues. Even as blockchain demands real-time data application and IoT specifies mechanisms to securely store and handle data overflows, a combination of the two seems promising. Blockchain, a technology created with the cryptocurrency Bitcoin, could meet the needs of the IoT. However, combining blockchain with the IoT might present a multitude of issues due to the features of blockchain and IoT technology. Although many articles have been released on the blockchain and the IoT, the concerns with this combination are still vague and dispersed. In light of this, this study seeks to give an overview of the problems that have the biggest impact on blockchain-based IoT by reviewing the pertinent peer-reviewed articles. This article also addresses some suggestions for lessening the impact of these concerns. The study considered peer-reviewed articles published within the last five years, focusing on topics related to blockchain and IoT integration. We identified 44 articles for this review. The study's significance is that it not only expands the scholarly understanding of this complex intersection between blockchain and IoT but also provides actionable insights that can drive innovation and enhance the reliability and security of IoT ecosystems in practical settings.