A Review on Performance Analysis of Deep Learning for Task Offloading in Edge Computing

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.

Mobile Internet of Things (IoT) users possess dynamic service requirements in a real IoT system and always try to get services from trusted entities, which we term as facility nodes. However, the trustworthiness of the facility nodes varies from location to location, depending upon the service quality and user feedback. Therefore, a service provider may have different trust scores for its deployed facility nodes in different locations. It is always crucial for an IoT user to ensure that service has been taken from trustworthy nodes. In this article, we consider an IoT service provisioning architecture, where an IoT user requests services from nearby trusted nodes that are deployed by service providers other than the user's own registered service provider. To serve the requests from these unregistered users, the facility nodes incur additional costs. Furthermore, due to heterogeneous service requirements, IoT users prefer composite microservices from a set of facility nodes. In such cases, it is difficult to design a pricing scheme to incentivize the facility nodes. Therefore, we propose a pricing mechanism based on a two-stage Stackelberg game and analyze the interactions between the IoT users and the facility nodes, while considering the trust scores and service charges. In the game model, the facility nodes act as the leaders, and the IoT users act as the followers. Using backward induction, we analyze the existence and uniqueness of the Stackelberg equilibrium. Finally, the numerical results based on the mobility traces of a real map show that the proposed scheme incentivizes both the IoT users and the facility nodes, while ensuring trusted services.

Edge computing is a novel network architecture that is in proximity to the end devices in an Internet of Things (IoT). As the IoT becoming a major factor in our daily life, provisioning a low response time of the services to IoT users through edge computing is an important problem. Caching necessary program data for the task in an edge node effectively reduces the response time of the computation task. However, due to the increase of IoT users and devices, it is noteworthy that limited-resource edge nodes would receive a number of tasks, having a heavy burden of processing the requests. Therefore, the limited resource and caching space at cloudlet need the careful design of the caching algorithm to utilize the space of multiple edge nodes and relieve the burden of computations. In this paper, we propose a cooperative program caching system that makes different edge nodes cooperatively store program data and cache the replicas of the data requested frequently to handle a number of requests from IoT users. In particular, we develop a cooperative caching algorithm that caches the appropriate number of data replicas depending on the number of requests on each cloudlet and the popularity of the data to minimize the response time. The simulation results show that the proposed cooperative caching algorithm can effectively reduce the response time for IoT users compared to other existing algorithms.

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

The proliferation of the Internet of Things (IoT) and wireless sensor networks enhances data communication. The demand for data communication rapidly increases, which calls the emerging edge computing paradigm. Edge computing plays a major role in IoT networks and provides computing resources close to the users. Moving the services from the cloud to users increases the communication, storage, and network features of the users. However, massive IoT networks require a large spectrum of resources for their computations. In order to attain this, resource scheduling algorithms are employed in edge computing. Statistical and machine learning-based resource scheduling algorithms have evolved in the past decade, but the performance can be improved if resource requirements are analyzed further. A deep learning-based resource scheduling in edge computing IoT networks is presented in this research work using deep bidirectional recurrent neural network (BRNN) and convolutional neural network algorithms. Before scheduling, the IoT users are categorized into clusters using a spectral clustering algorithm. The proposed model simulation analysis verifies the performance in terms of delay, response time, execution time, and resource utilization. Existing resource scheduling algorithms like a genetic algorithm (GA), Improved Particle Swarm Optimization (IPSO), and LSTM-based models are compared with the proposed model to validate the superior performances.

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...

Internet of Things Forensics: A Review

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies.

Power line communication (PLC) technology has created a niche use in the Internet of Things (IoT) by offering flexible and reliable connection among power-driven IoT devices/sensors over existing wired networks. In IoT over PLC networks, massive real-time data generated by the ever-growing connected devices will eventually pose an overwhelming burden on the IoT cloud, which in turn severely degrades the network performance. To cope with these issues, edge computing (EC) has emerged as a complement to cloud computing, aiming at offloading a portion of computing in the cloud to the network edges closer to the IoT devices. However, confronting a practical scenario that some electrical devices cannot communicate with wireless and mobile networks directly, existing EC paradigms may not be directly applied to IoT over PLC networks. In this paper, we propose a novel EC-IoT over PLC paradigm to reduce the transmission latency while migrating a portion of computing from the cloud to the edges. First, we develop a distributed EC platform to serve terminal users (TUs) in different IoT systems with various IoT services. Second, we put forth a cache-enabled scheme to store the popular contents from the cloud and edge sensors to reduce redundant data transmissions between TUs and the cloud. Finally, our experimental results demonstrate that the proposed EC-IoT over PLC network can significantly reduce energy consumption and transmission latency.

The combination of simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS) and nonorthogonal multiple access (NOMA) brings the necessary full-space degrees of freedom and spatial multiplexing gains for the Internet of Things (IoT) networks. The inherent network heterogeneity and sharing of wireless channels may however increase the exposure of the information interactions to the third party. To address this issue, we propose a covert communication scheme in STAR-RIS assisted NOMA networks over Nakagami-m fading channels, where both downlink and uplink IoT scenarios are considered. Under the NOMA protocol with imperfect successive interference cancelation (SIC), an IoT access point interacts with two IoT users aided by a STAR-RIS without being detected by two wardens. In this scenario, the two IoT users are located on both sides of the STAR-RIS which adopts coherent phase shifting and operates according to the mode switching protocol. To evaluate the wardens’ detection performance, the Kullback-Leibler (KL) divergence is used. Furthermore, the cascaded channel gains of IoT users and wardens are, respectively, characterized as Gamma and complex Gaussian random variables. The closed-form expressions of the expectations of KL divergence and the interruption probabilities for downlink and uplink are derived. To further improve the performance, we formulate the effective covert rate maximization as the joint optimization problems of the transmit power and power allocation coefficient for downlink and uplink, subject to the constraints for covertness, reliability and power budget, which are, respectively, resolved analytically. Extensive simulation results indicate that the proposed scheme improves covertness compared with the benchmarks.

The Internet of Things (IoT) is impacting the world’s connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-based computing paradigm. In the cloud-based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-based computing. To address these challenges, researchers are looking for new computing models for the IoT. Edge computing, a new decentralized computing model, is valued by more and more researchers in academia and industry. The main idea of edge computing is placing data processing in near-edge devices instead of remote cloud servers. It is promising to build more scalable, low-latency IoT systems. Many studies have been proposed on edge computing and IoT, but a comprehensive survey of this crossover area is still lacking. In this survey, we first introduce the impact of edge computing on the development of IoT and point out why edge computing is more suitable for IoT than other computing paradigms. Then, we analyze the necessity of systematical investigation on the edge-computing-driven IoT (ECDriven-IoT) and summarize new challenges occurring in ECDriven-IoT. We categorize recent advances from bottom to top, covering six aspects of ECDriven-IoT. Finally, we conclude lessons learned and propose some challenging

The potential growth of internet of things (IoT) brings people and things together to handle daily tasks in a smart way. The major advancement the IoT offers is quality data sensing and faster data analytics through hurdle-free communication. The increasing number of devices and heterogeneous network natures unwrap more challenges in terms of quality of service. Currently, the deep learning algorithm explores different dimensions of service quality gradually in IoT scenarios. In order to effectively handle a dynamic IoT environment, it is essential that the design of IoT must be supplemented with an intelligent agent for providing effective QoS. The traditional methods are not capable of utilizing historical data to find insights into service quality improvement. In this chapter, a comprehensive analysis of deep learning techniques for improving QoS of the internet of things is carried out. Deep learning solutions for improving QoS and the challenges involved are compared. The deep reinforcement learning (DRL) for improving QoS in IoT and its evaluation technique are also explored.

Federated learning (FL) is emerging as a powerful paradigm for distributed data mining in the context of Internet of Things (IoT) big data. It addresses privacy concerns associated with data outsourcing by enabling local data training and knowledge (i.e., model) sharing. However, simplistic local knowledge sharing can inadvertently expose user privacy to advanced attacks, such as model inversion or gradient leakage. Furthermore, achieving fine-grained and personalized privacy protection for IoT users remains a challenge. In this paper, we propose a novel solution called hierarchical blockchain-empowered cloud-edge orchestrated federated learning (HBCE-FL) to address these challenges. HBCE-FL is designed to provide secure, intelligent, and distributed data analysis for IoT users. To tackle FL’s privacy issues, we develop a multi-level access control encryption and blockchain-based approach for sharing IoT knowledge within the HBCE-FL framework. Our approach classifies IoT users into different levels based on their individual privacy requirements, enabling fine-grained privacy protection. The blockchain is employed for identity authentication, key management, and message sanitization. For scenarios involving IoT users with non-IID data, we integrate federated multi-task learning into HBCE-FL to ensure fairness, robustness, and privacy. Finally, we conduct experiments using classic MNIST and CIFAR10 datasets to validate our approach. The experimental results illustrate that HBCE-FL effectively achieves personalized privacy-preserving FL without losing IoT data availability. Regardless of whether IoT data are homogeneous or heterogeneous, our approach enhances model accuracy and convergence rates by enabling secure IoT knowledge access and sharing for IoT users.

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.

Cloud-assisted Internet of Things (IoT) has become the core infrastructure of smart society since it solves the computational power, storage, and collaboration bottlenecks of traditional IoT through resource decoupling and capability complementarity. The development of a graph database and cloud-assisted IoT promotes the research of privacy preserving graph computation. We propose a secure graph intersection scheme that supports multi-user intersection queries in cloud-assisted IoT in this article. The existing work on graph encryption for intersection queries is designed for a single user, which will bring high computational and communication costs for data owners, or cause the risk of secret key leaking if directly applied to multi-user scenarios. To solve these problems, we employ the proxy re-encryption (PRE) that transforms the encrypted graph data with a re-encryption key to enable the graph intersection results to be decrypted by an authorized IoT user using their own private key, while data owners only encrypt their graph data on IoT devices once. In our scheme, different IoT users can query for the intersection of graphs flexibly, while data owners do not need to perform encryption operations every time an IoT user makes a query. Theoretical analysis and simulation results demonstrate that the graph intersection scheme in this paper is secure and practical.