Failure-Aware and Delay-Predicted Multipath Virtual Queue Scheduling for Multimedia Transmission in Edge IoT

Multipath Transmission Control Protocol (MPTCP) enables multi-homed devices to establish multiple simultaneous routes for data transmission. Congestion Control (CC) is a fundamental mechanism for implementing and designing MPTCP. The Internet of Things (IoT) networks generate a massive volume of heterogeneous traffic with high dimensional states and diverse QoS characteristics. The existing MPTCP CC algorithms are unable to perform efficiently under highly mobile and dynamic IoT environments. We propose a novel model-free SDN-based adaptive actor-critic deep reinforcement learning framework based on a fuzzy normalized neural network to address the issue of CC for MPTCP in the IoT networks. In the proposed method, an agent can learn efficiently and better approximate the state-action value function of the actor and the action function of the critic to adjust the sub-flows congestion windows size adaptively according to the dynamic condition of a network. Simulation results show that the proposed scheme outperforms the state of the art schemes in terms of the goodput under highly-dynamic IoT environments.

With the growing demand of Internet services, network operators have put significant efforts to improve network error resilience and efficiency. Since there exist different wired/wireless technologies for Internet access such as digital subscriber line (DSL), Ethernet, and worldwide interoperability for microwave access (WiMax), a mobile host can use multiple access networks simultaneously with multipath transmission. Taking the advantage of heterogeneous environment, multipath transmission through the Internet can improve service reliability and network flexibility. Ensuring a reliable end-to-end connection oriented communication with satisfactory quality of service (QoS) and maintaining congestion control and flow control are the main responsibilities of the Transmission Control Protocol (TCP), the dominant transport layer protocol in the Internet. In this paper, we survey the state-of-the-art of multipath transmission techniques for QoS provisioning in wireless Internet access, focusing on the end-to-end transport layer protocols. The main challenges for the design of multipath TCP are reviewed, and the existing transport layer congestion control schemes are categorized. Multipath TCP and stream control transmission protocol (SCTP)-based transport layer protocols are discussed, and their limitations and/or impractical assumptions are addressed. Open research issues on the development of an effective, efficient, and practical multipath TCP protocol are summarized.

With the rapid development of sensing and communication technologies, the Internet of Things (IoT) is becoming a global data generation infrastructure. To utilize the massive data generated by IoT for achieving better system intelligence, machine learning and inference on the IoT data at the edge and core (i.e., cloud) of the IoT are needed. However, the pervasive data collection and processing engender various privacy concerns. While various privacy preservation mechanisms have been proposed in the context of cloud computing, they may be ill-suited for IoT due to the resource constraints at the IoT edge. This thesis primarily studies data obfuscation as a lightweight method to preserve data privacy for cloud-based collaborative machine learning and inference in IoT. Specifically, it presents three approaches: the first is for cascadable, collusion-resilient, and privacy-preserving cloud-based inference and the other two are for privacy-preserving collaborative training of a deep neural network based on distributed IoT data. All approaches protect the privacy contained in the data contributed by distributed IoT devices as the participants against a semi-honest coordinator in the centralized cloud system. They deliver different privacy protection properties. The first approach protects the raw forms and certain privacy attributes of the inference data from the participants by applying participant-specific obfuscation neural networks; the second approach protects the raw forms of the training data contributed by the participants by applying multiplicative random projection; the third approach protects the differential privacy of the contributed training data via additive perturbation. These three approaches are computationally lightweight and can be executed by resource-limited edge devices including smartphones and even mote-class sensor nodes. Extensive performance evaluations performed on multiple datasets and real implementations on IoT hardware platforms show the effectiveness and efficiency of these approaches in protecting data privacy while maintaining the learning and inference performance.

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.

RETRACTED: IoT-Edge Communication Protocol based on Low Latency for effective Data Flow and Distributed Neural Network in a Big Data Environment

The concept of Internet of Things (ioT) has incubated a whole generation of smart applications to resolve problems in society. Despite cloud-based IoT systems inheriting the robustness and scalability of cloud computing, its high latency limits the implementation of time-sensitive applications. To encounter this, Edge-computing-based IoT systems make computing services closer to users which entails lower latencies. New technologies bring new attacks, and Distributed Denial of Service (DDoS) attack has been regarded as one of the major threats to Edge Internet of Things (EIoT) systems. Previous works often focus on the state-of-the-art and the defense mechanism of edge computing. However, there lacks a general standardized method for the security impact analysis, dedicated to EIoT. This paper proposes the DDoS Impact Analysis Index, or DIADex, which is compatible with the IEEE P2668 standard. From the aspects of performance and resources, this method quantifies the impact of DDoS attacks on EIoT systems with a scoring system, which can be used for evaluation in future research and penetration test on EIoT.

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.

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...

Space-ground integrated networks (SGINs) have been recently regarded as a promising way to provide resilient, dependable as well as efficient data transmission in the high-speed railway (HSR) scenario. Applying multipath transmission control protocol (MPTCP) to SGIN can realize data transmission simultaneously via terrestrial and satellite networks. However, since the existing congestion control (CC) mechanisms of MPTCP fail to distinguish between adverse influences (such as packet loss and/or round-trip time increase) caused by congestion and those caused by handovers, it suffers severe performance degradation in the HSR scenario where handover frequently occurs. In this article, we first present the SGIN oriented HSR (SGIN-HSR) with MPTCP. Then leveraging cross-layer information (i.e., reference signal received power), we design a novel cross-layer aided MPTCP CC mechanism targeted at SGIN-HSR based on deep reinforcement learning, which is referred to as HSR-CC, to alleviate performance degradation problems induced by handover. The experimental results show that HSR-CC significantly enhances the goodput and outperforms state-of-the-art MPTCP CC algorithms in SGIN-HSR environments where handover frequently occurs.

The Internet of Things (IoT) is a disruptive technology that underpins Industry 5.0 by integrating various service technologies to enable intelligent connectivity among smart objects. These technologies enhance the convergence of Information Technology (IT), Operational Technology (OT), Core Technology (CT), and Data Technology (DT) networks, improving automation and decision-making capabilities. While cloud computing has become a mainstream technology across multiple domains, it struggles to efficiently manage the massive volume of OT data generated by IoT devices due to high latency, data transfer costs, limited resilience, and insufficient context awareness. Fog computing has emerged as a viable solution, extending cloud capabilities to the edge through a distributed peer-to-peer (P2P) network, enabling decentralized data processing and management. However, IoT networks still face critical challenges, including connectivity, heterogeneity, scalability, interoperability, security, and real-time decision-making constraints. Security is a key challenge in IoT implementations, including secure data communication, IoT edge and fog device identity, end-to-end authentication, and secure storage. This paper presents an efficient blockchain-based framework that creates a secure end-to-end communication cooperative flow IoT network. The framework utilizes a hybrid blockchain network that collaborates to offer a collaborative flow of end-to-end secure communication from end devices to cloud storage. The fog servers will maintain a private blockchain as a next-generation public key infrastructure to identify and authenticate the IoT’s edge devices. The consortium blockchain will be maintained in the cloud and integrated with the permission blockchain system. This system ensures secure cloud storage, authorization, efficient key exchange, and remote protection (encryption) of all sensitive information. To improve the synchronization and block generation, reduce overhead, and ensure scalable IoT network operation, we proposed the threshold signature-based Proof of Stake and Validation (PoSV) consensus. Additionally, lightweight authentication protects resource-constrained IoT nodes using an aggregate signature, ensuring security and performance in real-time scenarios. The proposed system is implemented, and its performance is evaluated using key metrics such as cryptographic processing overhead, consensus efficiency, block acceptance time, and transaction delay. The findings show that threshold signature-based Proof of Stake and Validation (PoSV) consensus, reduces the computational burden of individual signature verification, which results in an optimized transaction latency of 80–150 ms, compared to the previous 100–200 ms without Non-PoSV. Additionally, aggregating multiple signatures from different authentication events reduces signing time by 1.98 ms compared to the individual signature time of 2.72 ms and the overhead of verifying multiple individual transactions is 2.87 ms is significantly reduced to1.46 ms along with authentication delay ranges between 95–180 ms. Hence, the proposed framework improves over existing approaches regarding linear computing complexity, increased cryptographic methods, and a more efficient consensus process.

Multipath TCP (MPTCP) is a backward compatible extension of Transmission Control Protocol (TCP) designed to let an application send data over several TCP connections, usually called subflows. MPTCP presents several opportunities over legacy TCP, especially when these subflows follow disjoint paths: for instance bandwidth aggregation or seamless mobility across networks. While MPTCP is built as an extension of TCP, the scope of the modifications qualifies it almost as a new protocol. This means traditional TCP tools are not enough to analyze MPTCP performance. We thus develop and present an open source application able to analyze MPTCP specific metrics such as the aggregated goodput or inter-subflow retransmissions.

We present TeamBoost, a collaborative solution enabling a client device to leverage the cellular links of its nearby devices to enhance its overall connectivity to the cloud. TeamBoost architecture can serve for numerous internet-of-things (IoT) applications, yet in this work it is proposed as an alternative to conventional and costly H/W upgrades aiming to maintain up-to-date vehicle connectivity experience throughout the vehicles extended lifetime. TeamBoost's approach is to build upon a relatively new and yet well established Multi-Path TCP (MPTCP) protocol, exploiting the natural way that MPTCP splits an end-to-end single TCP stream into multiple MPTCP sub-streams that traverse different communication links. TeamBoost's novel implementation provides a non-trivial extension of MPTCP to TeamBoost's collaborative setting, where sub-streams are relayed through multiple independent devices. To support scalability of the system, the implementation of TeamBoost on the devices is restricted to the user-space, allowing it to be installed as any other application supported by its operating system (OS). Experimental results show the benefits of TeamBoost, in terms of both connectivity performance and robustness.

Optimal load balancing scheduler for MPTCP-based bandwidth aggregation in heterogeneous wireless environments

Object detection, identification and classification techniques have seen many variants and improvements over past two decades. Together with Internet of Things (IoT) devices, improved computational algorithms and cloud support, real-time classification with low-cost devices has already been achieved. This paper discusses the real-time object detection and classification using Microsoft Custom Vision multi-class Machine Learning (ML) model operating at the Edge of IoT network. This paper further examines the use of virtual dockers or containers at the IoT edge devices for better security and isolation by decoupling physical hardware as well that supports multiple applications and services on a single hardware. The experiments are performed using emulated and simulated IoT devices on Microsoft Azure IoT platform for real-time object classification using Custom Vision Machine Learning (ML) models run directly from the edge device. The experimental results are further discussed to validate the model accuracy and its implementation in a future Smart City surveillance environment.

With the continuous development of the Internet of Things (IoT) and the needs of the times, the number of IoT edge devices is rapidly increasing, and to be able to provide users with more reliable information services and decision-making, this paper constructs an IoT system based on cloud-edge collaboration. Based on cloud-edge collaboration, data mining technology is used to categorize data, and regression models and branch neural networks are combined to further improve the IoT system based on cloud-edge collaboration. Finally, the particle swarm algorithm is used to optimize reinforcement learning, and the framework of an IoT system built on cloud-edge partnership is established. Through the data processing experiments on IoT edge computing, the data mining performance of the IoT edge computing-based data mining method is improved by 0.0295, 0.1870, and 0.2059 on the large datasets of Dataset4 and Dataset5, respectively. the average turnaround time is lower than 6.5 for different edge computing power levels. The system rewards are gradually improved from 5.3 to 8.2, and the number of packet loss of the algorithm is less than 5. The feasibility and effectiveness of the IoT data mining method and system based on cloud-edge collaboration are confirmed by this study, and a feasible method for data mining can be applied to IoT systems under cloud-edge collaboration.