A Novel Deep-Learning-Based Robust Data Transmission Period Control Framework in IoT Edge Computing System

With the explosive growth of the Internet of Things (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated as IoT services, and user requests can be achieved through the composition of data and/or computation-intensive IoT services. Considering the prediction-uncertainty of forthcoming requests, certain IoT services may (i) not be hosted currently by appropriate IoT devices, or (ii) such an IoT service exists, but its non-functional properties may hardly be satisfied with respect to certain constraints prescribed by requests. To address this challenge, this paper proposes an efficiency-aware service Migration Scheduling (denoted eMS) mechanism in edge networks, in order to migrate IoT services on-demand, and thus, to optimally settle non-satisfiable constraints. Specifically, IoT services are re-scheduled, such that certain IoT services are migrated from their hosting IoT devices to neighboring ones, while minimizing the energy consumption and average delay caused by this service re-scheduling operation. We formulate this service re-scheduling as a multi-objective and multi-constraint optimization problem, which is solved through integrating the greedy algorithm into the fast non-dominated sorting and crowded-comparison operators as the hybrid genetic algorithm (G-NSGA-II). Based on real-life datasets provided by an oil pipeline monitoring project, extensive experiments are conducted, and evaluation results show that our eMS is promising in reducing the energy consumption and average delay of service re-scheduling in comparison with the state-of-art’s techniques.

The Internet of Things (IoT) enabled technologies to have proliferated due to their abilities to capture and exchange quality information. This empirical study aims to investigate the factors influencing the intention to use IoT services in healthcare by young physicians. An integrated model based on the theory of planned behaviour (TPB) and diffusion of innovations (DOI) theories are drawn upon and analyzed through a Structural Equation Modelling (SEM) with importance-performance map analysis (IPMA). Data were collected through a structured survey questionnaire. The results indicated that behavioural intention to use IoT in healthcare services vastly affected by the attitude and value co-creation. Attitude was found to be a principal determinant followed by the relative advantage towards behavioural intention to use IoT services. The novelty of this empirical study is that the proposed integrated model provides a meaningful application of the young physicians’ intention to use IoT in healthcare in developing countries.

<title>Abstract</title> The Internet of Things (IoT) services have seen rapid growth in recent years. However, existing research has yet to propose a robust method for quantitatively evaluating IoT services, nor has it adequately addressed the impact of cooperation and competition among IoT service providers on pricing strategies. This paper develops a duopolistic Cournot competition model involving data providers, service providers, and consumers. IoT service providers leverage machine learning to train models that generate user-centric IoT services, which they can then market and monetize for profit. We introduce a Logistic-curved service-quality function, with the amount of data used for machine learning as the primary metric for IoT service quality. The competitive behavior between IoT service providers in a Cournot duopoly market is examined, and we derive the optimal IoT service pricing and data procurement strategies that maximize profits for both parties. Next, we explore the possibility of bundling IoT services at discounted prices, categorizing four distinct scenarios based on the relationship between service quality and pricing. We calculate the pricing of IoT service bundles and determine the optimal data purchase quantities for both providers to maximize their profits. Finally, we incorporate a price-fluctuation factor to account for market uncertainty, refining the profit-function model to better reflect real-world market dynamics. The research can provide decision-making references for IoT service pricing, and the pricing mechanism based on machine learning can effectively mitigate the biases caused by price fluctuations.

In order to reduce unnecessary data transmissions from Internet of Things (IoT) sensors, this article proposes a multivariate-time-series-prediction-based adaptive data transmission period control (PBATPC) algorithm for IoT networks. Based on the spatio-temporal correlation between multivariate time-series data, we developed a novel multivariate time-series data encoding scheme utilizing the proposed time-series distance measure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {ADMWD}$ </tex-math></inline-formula> . Composed of two significant factors for a multivariate time-series prediction, i.e., the absolute deviation from the mean (ADM) and the weighted differential (WD) distance, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {ADMWD}$ </tex-math></inline-formula> considers both the time distance from a prediction point and a negative correlation between the time-series data concurrently. Utilizing the convolutional neural network (CNN) model, a subset of IoT sensor readings can be predicted from encoded multivariate time-series measurements, and we compared the predicted sensor values with actual readings to obtain the adaptive data transmission period. Extensive performance evaluations show a substantial performance gain of the proposed algorithm in terms of the average power reduction ratio (approximately 12%) and average data reconstruction error (approximately 8.32% MAPE). Finally, this article also provides a practical implementation of the proposed PBATPC algorithm via the HTTP protocol under the IEEE 802.11-based WLAN network.

Billions of devices in the Internet of Things (IoT) collect sensitive data about people, creating data privacy risks and breach vulnerabilities. Accordingly, data privacy preservation is vital for sustaining the proliferation of IoT services. In particular, privacy-preserving IoT connects devices embedded with sensors and maintains the data privacy of people. However, common misconceptions exist among IoT researchers, service providers, and users about privacy-preserving IoT. This article refutes five common misconceptions about privacy-preserving IoT concerning data sensing and innovation, regulations, and privacy safeguards. For example, IoT users have a common misconception that no data collection is permitted in data privacy regulations. On the other hand, IoT service providers often think data privacy impedes IoT sensing and innovation. Addressing these misconceptions is essential for making progress in privacy-preserving IoT. This article refutes such common misconceptions using real-world experiments and online survey research. First, the experiments indicate that data privacy should not be perceived as an impediment in IoT but as an opportunity to increase customer retention and trust. Second, privacy-preserving IoT is not exclusively a regulatory problem but also a functional necessity that must be incorporated in the early stages of any IoT design. Third, people do not trust services that lack sufficient privacy measures. Fourth, conventional data security principles do not guarantee data privacy protection, and data privacy can be exposed even if data is securely stored. Fifth, IoT decentralization does not attain absolute privacy preservation.

Internet of Things (IoT) services have grown substantially in recent years. Consequently, IoT service providers (SPs) are emerging in the market and competing to offer their services. Many IoT applications utilize these services in an integrated manner with different Quality-of-Service (QoS) requirements. Thus, the provisioning of end-to-end QoS is getting more indispensable for IoT platforms. However, provisioning the system by using only QoS metrics without considering user experiences is not sufficient. Recently, Quality of Experience (QoE) model has become a promising approach to quantify actual user experiences of services. A holistic design approach that considers constraints of various QoS/QoE metrics together is needed to satisfy requirements of these applications and services. Besides, IoT services may operate in environments with limited resources. Therefore, effective management of services and system resources is essential for QoS/QoE support. This paper provides a comprehensive survey for the state-of-the-art studies on IoT services with QoS/QoE perspective. Our contributions are threefold: (1) QoE-driven architecture is demonstrated by classifying vital components according to QoE-related functions in prior studies, (2) QoE metrics and QoE optimization objectives are classified by corresponding system and resource control problems in the architecture, and (3) QoE-aware resource management e.g., QoE-aware offloading, placement and data caching policies with recent Machine Learning approaches are extensively reviewed.

Pricing Models for the Internet of Things (IoT): Game Perspectives

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Providing Internet of Things (IoT) environments with service level guarantee is a challenging task for improving IoT application usage experience. We specify in this paper an IoT architecture enabling an IoT Service Level Agreement (iSLA) achievement between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). In order to guarantee the IoT applications’ requirements, Quality of Service (QoS) mechanisms should be implemented within all the layers of the IoT architecture. Thus, we propose a specific mechanism for the lowest layer of our service level based IoT architecture (i.e., sensing layer). It is an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism enabling to take into consideration the requirements of real-time IoT services. Our access method called QBAIoT (QoS based Access for IoT) extends IEEE 802.15.4 systems by creating a new contention access period for each specified traffic class in the iSLA. Furthermore, due to the huge number of IoT connected devices, self-configuring capability provisioning is necessary for limiting human intervention and total cost of ownership (TCO). Thus, we integrate a self-configuring capability to the QBAIoT access method by implementing the MAPE-K closed control loop within the IoT High Level Gateway (HL-Gw) of our proposed QoS based IoT architecture.

5G network is considered as a key enabler in meeting continuously increasing demands for the future Internet of Things (IoT) services, including high data rate, numerous devices connection, and low service latency. To satisfy these demands, network slicing and fog computing have been envisioned as the promising solutions in service-oriented 5G architecture. However, security paradigms enabling authentication and confidentiality of 5G communications for IoT services remain elusive, but indispensable. In this paper, we propose an efficient and secure service-oriented authentication framework supporting network slicing and fog computing for 5G-enabled IoT services. Specifically, users can efficiently establish connections with 5G core network and anonymously access IoT services under their delegation through proper network slices of 5G infrastructure selected by fog nodes based on the slice/service types of accessing services. The privacy-preserving slice selection mechanism is introduced to preserve both configured slice types and accessing service types of users. In addition, session keys are negotiated among users, local fogs and IoT servers to guarantee secure access of service data in fog cache and remote servers with low latency. We evaluate the performance of the proposed framework through simulations to demonstrate its efficiency and feasibility under 5G infrastructure.

Cyber-physical system (CPS) security has become more important in the age of Industry 4.0 because of the quick integration of automation and the Internet of Things. The goal of this project is to create a strong intrusion detection and control system that can recognize and lessen security risks in CPS settings. The suggested approach makes use of deep learning (DL) and reinforcement learning (RL) techniques. To guarantee data consistency, pre-processing procedures such as mean-based imputation and min–max scaling come after data collection. ADASYN data augmentation is used to address class imbalance, while entropy analysis and statistical techniques are used to extract key features. The intrusion detection phase uses a combination of deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BI-LSTM) networks to capture both spatial and temporal relationships in the data, while a hybrid feature selection technique improves the model’s performance. A deep Q-network (DQN) handles attack mitigation and uses reinforcement learning to adjust to new threats. Detecting attack patterns with high sensitivity (0.984), specificity (0.983), and accuracy (0.991626) for dataset 1, the accuracy of dataset 2 is 0.985 for 70% of training and 0.988 for 80% of training, and the Proposed-DBID-Net architecture enhances CPS security in Industry 4.0. The evaluation phase emphasizes how crucial feature selection is to maximize the model’s accuracy. In conclusion, this study offers a thorough and flexible method for protecting CPS in Industry 4.0, guaranteeing accuracy and scalability across changing cyber threats.

In this work we discuss and propose a marketplace concept for the Internet of Things (IoT). We start from the vision that unlike the current IoT landscape where tech and consulting companies provide complete “solutions” for some vertical markets, in future we might witness an “uberization” of IoT, where every person or company will be able to offer or “consume” sensor data, actuator access and data analytics services to/from anybody else, leading to several marketplaces where these IoT services will be traded and exchanged. In this line, we assume that data and information about the places, things and persons will be the major asset in our future society, and that physical objects and cloud-based computing environments will be simply the resources that will allow greater interaction between the physical and digital world. In this paper, we present our initial analysis about the main principles and technologies enabling future trade of sensor data and actuation access, and how it might affect the economic organization of our society today.

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

Nowadays, providing Internet of Things (IoT) environments with service level guarantee is a challenging task. We describe in this paper a service level based IoT architecture that enables an IoT Service Level Agreement (iSLA) achievement between an IoT Service Provider and an IoT Client. This IoT SLA specifies the requirements of an IoT service in a specific application domain (e-health, smart cities, etc.). In order to guarantee these requirements, QoS mechanisms should be implemented within the IoT architecture. Thus, we propose an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism to ensure the requirements of an IoT e-health service. Our approach called QBAIoT (QoS based Access for IoT) consists in creating different contention access periods corresponding to different specified traffic classes. Each of these periods within the QoS based adapted IEEE 802.15.4 superframe is specific for a traffic type. A QoS based contention access period called QoS CAP is configured with a number of slots during which only IoT objects belonging to the same QoS class can send data.

SummaryRecently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required.In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.