Privacy In Internet Of Things Research Articles

Prism Blockchain Enabled Internet of Things with Deep Reinforcement Learning

This paper presents a Deep Reinforcement Learning based IoT enabled Prism BLockchain. The recent advancements in the field of Internet of Things (IoT) motivate the development of a secure infrastructure for storing and sharing vast amounts of data. Blockchain, a distributed and immutable ledger, is best known as a potential solution to data security and privacy for IoT. The scalability of blockchain, which should optimize the throughput and handle the dynamics of the IoT environment, becomes a challenge due to the enormous amount of IoT data. The critical challenge in scaling blockchain is to guarantee decentralization, latency, and security of the system while optimizing the transaction throughput. This paper presents a deep reinforcement learning (DRL)-based performance optimization for blockchain-enabled IoT. We consider one of the recent promising blockchains, Prism as the underlying blockchain system because of its good performance guarantees. We integrate the IoT data to Prism Blockchain and optimize the performance of the system by leveraging Proximal Policy Optimization (PPO) method. The DRL method helps to optimize the blockchain parameters like mining rate and mined blocks to adapt to the environment dynamics of the IoT system. Our results show that the proposed method can improve the throughput of Prism blockchain based IoT systems while preserving Prism performance guarantees. Our scheme can achieve 1.5 times more system rewards than IoT integrated Prism. In our experimental setup, the proposed scheme could improve the average throughput of the system by about 6,000 transactions per sec compared to Prism.

Understanding perspectives for product design on personal data privacy in internet of things (IoT): A systematic literature review (SLR)

As the number of Internet users grows, the increase in smart devices interconnected through the Internet of Things (IoT) have contributed to improvements in the functionality of everyday products and enhancement of user experience. Yet, they affect user privacy and render personal data more vulnerable. To foster a digital future fully aware of user privacy requirements, a line of design research emerges that focuses on balancing product innovation with user data protection. This matter relates to sociocultural, economic, and technological aspects, and its core is a human-centered design strategy. Still, there is a gap in academic research oriented towards guiding product developers on how to consider personal data privacy concerns when designing honest IoT devices. To define this gap and delve deeper into this relevant topic, this paper presents a systematic literature review of recent academic research using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. This review focuses on prevalent research topics such as data privacy, personal data, data surveillance, and user behaviour in IoT. The result is a state-of-the-art compilation of 45 scientific studies mapping the most relevant concepts and approaches for product development in the last ten years of research, aligned with some central research questions. The Discussion and Conclusion sections provide a deep understanding of the complexity of the fast-changing landscape of privacy and personal data management using IoT products. Finally, this study proposes future academic research directions devoted to providing product designer specific, specialised help from different (yet interconnected) scientific approaches.

Internet of Things (IoT) of Smart Homes: Privacy and Security

The Internet of Things (IoT) constitutes a sophisticated network that interconnects devices, optimizing functionality across various domains of human activity. Recent literature projections anticipate a significant increase, with estimates exceeding 50 billion connected devices by 2025. Despite its transformative potential, the IoT landscape confronts formidable privacy and security challenges, encompassing intricate issues such as data acquisition, anonymization, retention, sharing practices, and behavioural profiling. Effectively addressing these challenges mandates the development of scalable solutions, innovative management strategies, and adaptable policy frameworks. In this paper, we conduct an exhaustive examination of major IoT applications, alongside associated privacy and security concerns. We systematically categorize prevalent privacy, security, and interoperability issues within the context of the IoT layered architecture. The review highlights current research initiatives focused on developing energy-efficient devices, optimizing microprocessors, and fostering interdisciplinary collaborations to address the challenges in the IoT landscape. To efficaciously manage risks in this dynamic landscape, stakeholders must implement comprehensive strategies that span stringent data protection legislation, extensive user education initiatives, and the deployment of robust authorization and authentication frameworks. This paper aims to empower industry leaders, policymakers, and researchers by providing actionable solutions, not just insights, to navigate the complexities of the IoT landscape effectively. Future research initiatives should prioritize the fortification of security measures for large-scale IoT deployments, the formulation of user-centric privacy solutions, and the standardization of interoperability protocols. By establishing a robust foundational framework, our paper endeavours to spearhead the discourse on IoT applications, privacy paradigms, and security frameworks, paving the way towards a resilient and interconnected future.

Internet of Things security and privacy in Higher Education Institutions in Developing Countries

Technological breakthroughs like the Internet of Things (IoT) technology have tremendously impacted today's world and have led to changes in many spheres of life, including education. With the pervasiveness of IoT devices, learning and academic institutions are already integrating IoT into educational activities. Nonetheless, despite the numerous technological advancements and the demand for high-quality education for everyone, IoT's adoption and impact on education are still in their infancy. On the other hand, with the number of connected items having reached billions and still growing, there is cause for concern regarding the Internet of Things' potential for serious privacy violations, considering it is one of the most potent technologies for creating, modifying, and sharing data. Therefore, to emphasise the thematic areas and prospects for additional research related to the security and privacy of the Internet of Things (IoT) and within Higher Education Institutions (HEIs), this study explores the security and privacy of IoT in the context of HEIs within developing countries.

Blockchain for Enhancing IoT Privacy and Security

The technological advancements in the network technologies that deal with interconnected computing devices has led to the dramatic increase in the number of Internet of Things (IoT) devices. It promises to improve the convenience of our lives by leveraging to modernize the way day to day items are connected to various elements of existence by converting it to smart object. However, with the unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years, and it must be addressed properly. Due to the massive scale and distributed nature of IoT networks, overlooking these privacy and security issues will negatively impact many elements of our existence that involves our surroundings, and these effects will influence our own masses too. The national or jurisdictional boundaries are not restricted to the centralized cloud infrastructure that provides interconnectivity of IoT items, hence maintaining robustness, authentication and security becomes crucial. Recently, the development of blockchain technology is a plausible solution to offer such features. Security, authentication, and maintenance issues can be solved by the secure decentralization of blockchain and overcome the current IoT ecosystem's drawbacks. This paper highlights the IoT features and presents a IoT security and privacy problems are examined and classified in accordance with the IoT layered architecture. Further, we explore the IoT security and privacy challenges and examine blockchain technology towards a solution that help solve many of the IoT privacy and security issues

Cyber Security Challenges and Effective Security Measures for IOT-based Intelligent Healthcare Systems

Aims and Background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Objectives and Methodology: However, the Internet-of-Things(IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results and Conclusions: This study also contributes in two ways to the ongoing effort to investigate security-privacy concerns in the circumstance of smart cities for healthcare purposes. Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented.

IoT Complexity: Security, Vulnerabilities and Risks

IoT privacy highlights the critical significance of tailored threat models to address the evolving challenges in the IoT landscape. This research paper presents an in-depth examination of privacy threat modeling in the context of the Internet of Things (IoT), and aims to develop threat models tailored to different IoT device categories, identifying vulnerabilities and potential privacy risks associated with each. This analysis seeks to provide insights into the diverse privacy challenges posed by IoT devices, ranging from wearables to healthcare IoT and smart home, which exhibit unique vulnerabilities and privacy risks. By developing threat models specific to each device category, this research elucidates the diversity of privacy concerns, such as data breaches, unauthorized access, and data tracking the applicability of privacy regulations varies across these categories, emphasizing the need for tailored regulatory frameworks. The research underscores the importance of user education and responsible device design, advocating for privacy literacy and transparency, as it ensures that privacy is an integral part of the development process, fostering a safer, more secure, and privacy-conscious IoT ecosystem where innovation and privacy coexist for the benefit of all.

Pioneering Security: A Hybrid Logic Framework for the Future of IoT Protection

In a complicated situation, the Internet of Things simplifies the process of connecting a wide range of things. Because of its openness and lack of human control, the Internet of Things is open to assaults like denial of service (DoS) and man-in-the-middle attacks. Furthermore, any device that can connect to the internet may be hacked. These attacks put the network connections and the actual equipment at danger. IoT security and privacy will so be compromised. Due to its limited power, bandwidth, and storage, the Internet of Things requires a security solution that does not overload it. This study aims to preserve consumers' trust in the Internet of Things (IoT) by safeguarding their data against unauthorised access and maintaining the privacy of their personal information. With the ultimate objective of presenting a unique hybrid and optimised lightweight logical security architecture to ensure data privacy and integrity while making effective use of available resources, all research are carried out with this purpose in mind. The Hybrid Lightweight Security Framework (HLSF), which this study suggests, offers secrecy and integrity in addition to authentication. The structure consists of three distinct steps. The first step is registration, the second is authentication, and the third is transit security, which protects data while it is being transported. Compared to other current frameworks, the results reveal that HLSF performs better in terms of precision, recall, and accuracy when applied to an IoT situation.

Survey on IoT Privacy, Data Protection and Security Concerns

Abstract: The proliferation of Internet of Things (IoT) devices has transformed the way we interact with the digital world, offering unprecedented connectivity and convenience. However, this rapid expansion brings forth profound challenges concerning privacy, data protection, and security. This survey paper comprehensively explores the multifaceted landscape of IoT, dissecting its architecture, and delving into the intricate web of concerns surrounding user privacy, data safeguarding, and system security.Beginning with an examination of the IoT architecture, we dissect the interconnected layers, ranging from sensors to cloud platforms, illustrating the intricate pathways of data flow. The survey meticulously unfolds the privacy challenges embedded in the constant data collection, tracking, and profiling mechanisms intrinsic to IoT devices. Drawing on real-world examples, we underscore the tangible consequences of privacy breaches and the potential erosion of user trust.In the realm of data protection, we delve into the complexities of safeguarding data integrity, confidentiality, and availability within the IoT ecosystem. Regulatory frameworks, notably the General Data Protection Regulation (GDPR), are scrutinized for their impact on shaping data protection practices. Concurrently, the paper uncovers common security threats in IoT, emphasizing vulnerabilities in devices and the looming risks associated with unauthorized access and data breaches.Navigating through the regulatory landscape, the survey provides an overview of existing standards and regulations governing IoT. Case studies spotlight instances of privacy breaches, data protection lapses, and security vulnerabilities, offering practical insights into the implications of these incidents. Mitigation strategies, encompassing encryption, authentication, and access control, are explored to address the identified concerns.As the paper concludes, it synthesizes the key findings, emphasizing the urgency of continued efforts to fortify IoT against privacy, data protection, and security challenges. The survey anticipates future trends and challenges, offering a roadmap for researchers, policymakers, and industry stakeholders to collectively forge a secure and privacy-aware IoT landscape.

IoT Security in the Era of Ubiquitous Computing: A Multidisciplinary Approach to Addressing Vulnerabilities and Promoting Resilience

The Internet of Things (IoT) has rapidly become a pivotal, transformative force, seamlessly integrating billions of physical devices through sophisticated networks of embedded sensors, software, and internet connectivity. This expansive and interconnected ecosystem offers a broad spectrum of applications, significantly benefiting urban infrastructure with innovative solutions, enhancing industrial operations through optimization, and enriching consumer experiences with smart devices for safety and convenience. Despite the numerous benefits, the widespread adoption of IoT technologies has challenges, particularly in security and privacy. The proliferation of IoT devices has opened up new avenues for potential cyber threats, posing risks of data breaches and privacy violations. An in-depth analysis of notable IoT security incidents, such as the 2015 Jeep Hack, the Owlet WiFi Baby Heart Monitor Hack, and the TRENDnet Webcam Hack, highlights the critical vulnerabilities inherent in many IoT systems.
 Organizations must adopt comprehensive and robust security measures to address these security concerns, including implementing advanced encryption protocols, deploying effective firewalls stringent access control mechanisms, and conducting regular security audits. A multi-layered security architecture becomes essential in mitigating such threats and ensuring the integrity of IoT networks. Furthermore, integrating blockchain technology presents a promising enhancement to IoT security and privacy protocols. Blockchain's inherent features of decentralization, transparency, and immutability offer an additional layer of security, making it more difficult for unauthorized entities to compromise IoT systems.
 Equally crucial is the need to elevate IoT security awareness among organizations; this can be achieved through persistent research, fostering collaborations with security experts, and promoting best practices in IoT security. By actively addressing these security challenges, organizations can not only harness the full potential of IoT but also protect their reputations, build trust with stakeholders, and ensure the privacy and safety of their data. Therefore, while IoT presents an array of opportunities for innovation and efficiency, the importance of vigilance in security cannot be overstated. Balancing the benefits of IoT with robust security measures will be vital to realizing its full potential safely and reliably.

Smart Contract-Based Access Control Framework for Internet of Things Devices

The Internet of Things (IoT) has recently attracted much interest from researchers due to its diverse IoT applications. However, IoT systems encounter additional security and privacy threats. Developing an efficient IoT system is challenging because of its sophisticated network topology. Effective access control is required to ensure user privacy in the Internet of Things. Traditional access control methods are inappropriate for IoT systems because most conventional access control approaches are designed for centralized systems. This paper proposes a decentralized access control framework based on smart contracts with three parts: initialization, an access control protocol, and an inspection. Smart contracts are used in the proposed framework to store access control policies safely on the blockchain. The framework also penalizes users for attempting unauthorized access to the IoT resources. The smart contract was developed using Remix and deployed on the Ropsten Ethereum testnet. We analyze the performance of the smart contract-based access policies based on the gas consumption of blockchain transactions. Further, we analyze the system’s security, usability, scalability, and interoperability performance.

Knowledge elicitation methodology for evaluation of Internet of Things privacy characteristics in smart cities

AbstractOne of the impediments to transforming urban cities into smart cities is the security and privacy concerns that arise due to use of Internet of Things (IoT) devices in various smart city applications. While IoT device vendors publish their security and privacy policies, manual evaluation of these policies is tedious and prone to misinterpretation as there is a lot of variability in the language used across IoT vendors. Local administrations and policy analysts are faced with understanding the implications of integrating IoT devices with differing security and privacy characteristics but lack methods that support them in analysis of privacy characteristics from a holistic perspective. In this paper, a methodology for knowledge elicitation from textual information is outlined to evaluate privacy characteristics of IoT devices. The methodology includes natural language processing and deep learning techniques to evaluate the relevance of IoT privacy policies to the National Institute of Standards and Technology (NIST) security and privacy framework 5. Based on the analysis, text similarity scores are calculated for each IoT privacy policy document and each section of the policy document is labeled to NIST categories and functions. Analysis of these resulting labels and scores helps analysts to gain insights on each privacy policy as well as provide a holistic perspective of the privacy characteristics of IoT devices used in smart city applications. For example, all the policy documents used in the study talk about Protect domain and half of the documents cover Detect domain. However, most of the policies contain gaps regarding the Identify, Respond, and Recover domains. The study has implications for policy analysts, IoT vendors, and smart city administrators in terms of understanding the privacy gaps in IoT devices with respect to the NIST framework which can ultimately support policy alignment to address privacy concerns for smart cities.

Which Attributes Should be Considered in Regulating the Internet of Things? Evidence From Conjoint Analysis

With rapid development in the Internet infrastructure, new and innovative services using the Internet of Things (IoT) have emerged. Despite the wide range of topics regarding IoT services that have been discussed, regulatory concerns solely concentrate on personal information and privacy protection in academic research and legislation. In other words, a comprehensive regulatory framework for IoT services should be developed with potential consumers in mind; however, relevant studies are lacking. To bridge this research gap, this study estimates consumer preferences to build an appropriate regulation for IoT service deployment. The study uses conjoint analysis and a layered model to investigate the importance of attributes for IoT services as part of the consideration supporting IoT regulations. Based on the estimation results, all attributes of the IoT services are relevant to potential consumers, and as an example of IoT applications, the Big Data analysis application is more critical to consumers in terms of marginal willingness-to-pay and relative importance. Furthermore, a new type of vertical integration is appealing to potential customers. Policymakers can benefit from the development of various IoT policies, simultaneous pursuits of IoT application innovation and privacy concerns, and nondiscriminatory regulation principles.

Stochastic gradient descent classifier-based lightweight intrusion detection systems using the efficient feature subsets of datasets

The Internet of Things (IoT) has become an essential part of our daily lives. However, with the increasing use of IoT, the number of botnet attacks targeting resource-constrained IoT devices is also on the rise. To mitigate these threats, intrusion detection systems (IDSs) have been developed. However, traditional IDSs based on heavyweight deep/machine learning, fuzzy logic, rough set theories, or data mining techniques, often lack in detection accuracy and energy efficiency. Therefore, there is a crucial need for more lightweight, accurate, and energy-efficient IDSs capable of detecting a broad spectrum of cyber attacks. This paper presents a solution to these challenges by introducing lightweight and accurate IDSs that use a stochastic gradient descent classifier (SGDC) and four feature-selection algorithms based on a ridge regressor. The hyperparameters of the SGDC algorithm and ridge regressor model were fine-tuned to enhance the accuracy of IDSs while reducing computational complexity. Moreover, the fine-tuned feature selectors were used to decrease the dataset’s dimensionality and improve the accuracy of IDSs. To evaluate the proposed IDSs, three network traffic datasets (KDD-CUP-1999, BotIoT-2018, and N-BaIoT-2021) were used. The systems achieved an average accuracy of 92.69%, and the number of features was reduced by an average of 79.93%. The results demonstrate that the proposed systems can be utilized for lightweight IDSs on resource-constrained IoT devices. Overall, this paper presents a significant contribution to the field of IDSs for IoT devices by offering an efficient and accurate solution. The proposed lightweight IDSs have the potential to enhance IoT security and privacy, safeguarding sensitive IoT data.

Interactive Privacy Management: Toward Enhancing Privacy Awareness and Control in the Internet of Things

The balance between protecting user privacy while providing cost-effective devices that are functional and usable is a key challenge in the burgeoning Internet of Things (IoT). In traditional desktop and mobile contexts, the primary user interface is a screen; however, in IoT devices, screens are rare or very small, invalidating many existing approaches to protecting user privacy. Privacy visualizations are a common approach for assisting users in understanding the privacy implications of web and mobile services. To gain a thorough understanding of IoT privacy, we examine existing web, mobile, and IoT visualization approaches. Following that, we define five major privacy factors in the IoT context: type, usage, storage, retention period, and access. We then describe notification methods used in various contexts as reported in the literature. We aim to highlight key approaches that developers and researchers can use for creating effective IoT privacy notices that improve user privacy management (awareness and control). Using a toolkit, a use case scenario, and two examples from the literature, we demonstrate how privacy visualization approaches can be supported in practice.

A Comparison of IoT Security Threats and their Preventative Methods

The applicationof IoTnetworks in daily life attempts to simplify and minimize the complexity of our lives. However, it has also raised the number of brand-new security risks to our data and information. Our personal lives are also threatened by various kinds of security dangers. As a result, it is now necessary to be aware of many security risks that IoT networks may face. As our daily interactions with Internet of Things (IoT) devices grow, various security and privacy concerns—such as linkability, unauthorized chats, and side-channel attacks—are brought up. These problems could endanger the IoT if they are not addressed. They come about for two basic causes. One is that the limited computational, memory, and bandwidth capabilities of IoT gadgets make it difficult to directly deploy established Internet security approaches. The absence of generally regarded IoT security and privacy principles, as well as suitable implementation methods, is the second factor. Additionally, we identify the IoT stakeholders—including consumers andmanufacturers—who stand to gain the most from these rules. Additionally, we define a set of implementation methods for carrying out such directives and mitigating any threats to the levels stated before. Thirdly, we talk about the difficulties with IoT security and privacy regulations and briefly touch on IoT digital rights management. Finally, through this survey, we identify several unresolved concerns that demand future research. The authors' knowledge indicates that this work is the first survey to address the aforementioned goals. This paper investigates several IoT security risks and how they affect daily living. This paper highlights the strategies used to counter some attacks in an IoT network.

Three-tier Storage Framework Based on TBchain and IPFS for Protecting IoT Security and Privacy

Recently, most of the Internet of things (IoT) infrastructures are highly centralized with single points of failure, which results in serious security and privacy issues of IoT data. Fortunately, blockchain technique can provide a decentralized and secure IoT framework to deal with security issues based on the characteristics of decentralization, non-tampering, openness, transparency, and traceability. However, the blockchain consensus protocol guarantees the safety and reliability of data, but it also brings problems such as scalability limitations and poor storage extensibility, resulting in the inability to directly integrate blockchain and the IoT in existing conditions. In this article, a private three-tier local blockchain, Three-tier architecture Blockchain (TBchain), is proposed to solve the problem by splitting part of the transactions in the public blockchain and locking them in a higher-level blockchain TBchain. Additionally, the private blockchain TBchain is connected to the public blockchain to build a hierarchical blockchain network to provide privacy protection for the IoT data stored on the blockchain. Finally, we implement an IoT framework based on TBchain and the InterPlanetary File System (IPFS) to realize the decentralized IoT, which guarantees the user’s access control right to personal data. Experimental results show that the IoT framework based on TBchain and IPFS realizes the user’s access control right to personal data by verifying in advance to ensure the confidentiality and security of shared data, and improves the security and privacy of IoT data and transactions. Moreover, we prove that the scalability and storage extensibility of the blockchain is positively correlated with the number of data blocks in TBchain.

Privacy Preservation Using Machine Learning in the Internet of Things

The internet of things (IoT) has prepared the way for a highly linked world, in which everything is interconnected, and information exchange has become more easily accessible via the internet, making it feasible for various applications that enrich the quality of human life. Despite such a potential vision, users’ privacy on these IoT devices is a significant concern. IoT devices are subject to threats from hackers and malware due to the explosive expansion of IoT and its use in commerce and critical infrastructures. Malware poses a severe danger to the availability and reliability of IoT devices. If left uncontrolled, it can have profound implications, as IoT devices and smart services can collect personally identifiable information (PII) without the user’s knowledge or consent. These devices often transfer their data into the cloud, where they are stored and processed to provide the end users with specific services. However, many IoT devices do not meet the same security criteria as non-IoT devices; most used schemes do not provide privacy and anonymity to legitimate users. Because there are so many IoT devices, so much malware is produced every day, and IoT nodes have so little CPU power, so antivirus cannot shield these networks from infection. Because of this, establishing a secure and private environment can greatly benefit from having a system for detecting malware in IoT devices. In this paper, we will analyze studies that have used ML as an approach to solve IoT privacy challenges, and also investigate the advantages and drawbacks of leveraging data in ML-based IoT privacy approaches. Our focus is on using ML models for detecting malware in IoT devices, specifically spyware, ransomware, and Trojan horse malware. We propose using ML techniques as a solution for privacy attack detection and test pattern generation in the IoT. The ML model can be trained to predict behavioral architecture. We discuss our experiments and evaluation using the “MalMemAnalysis” datasets, which focus on simulating real-world privacy-related obfuscated malware. We simulate several ML algorithms to prove their capabilities in detecting malicious attacks against privacy. The experimental analysis showcases the high accuracy and effectiveness of the proposed approach in detecting obfuscated and concealed malware, outperforming state-of-the-art methods by 99.50%, and would be helpful in safeguarding an IoT network from malware. Experimental analysis and results are provided in detail.

ENHANCING SECURITY OF RFID-ENABLED IOT SUPPLY CHAIN

In addition to its benefits, the popular Internet of Things (IoT) technology has also opened the way to novel security and privacy issues. The basis of IoT security and privacy starts with trust in the IoT hardware and its supply chain. Counterfeiting, cloning, tampering of hardware, theft, and lost issues in the IoT supply chain have to be addressed, in order to ensure reliable IoT industry growth. In four previous works, radio-frequency identification (RFID)-enabled solutions have been proposed by the same authors, aimed to bring security to the entire IoT supply chain. The works propose a new RFID-traceable hardware architecture, device authentication, and supply chain tracing procedure. In each of these works, a variant of the same is proposed. However, the same variant of lightweight RFID authentication protocol coupled with the offline supply chain proposed in these works has such security vulnerabilities that make the whole supply chain unsafe. In our present work, an online supply chain hop-tracking procedure supported by a novel RFID mutual authentication protocol, based on the strong matching of the RFID readers-their operators-central database present at the transfer hops is proposed. Our proposed Strong RFID Authentication Protocol (STRAP) has been verified by two well-accepted formal protocol analyzers Scyther and AVISPA. The verification results demonstrate that STRAP overcomes the previous works’ vulnerabilities. Furthermore, our proposed novel online supply chain tracing procedure supporting STRAP removes the previous offline supply chain tracing procedure weaknesses.

Edge Trusted Sharing: Task-Driven Decentralized Resources Collaborate in IoT

6G is committed to providing a fully connected world. The deployment and application of 6G technology in the Internet of Things (IoT) can efficiently collaborate IoT resources and realize resource sharing, which mainly encourages IoT development. However, due to the lack of trust between IoT resources, security and privacy become the main challenges. As an emerging technology, blockchain can solve the trust-absence issues and provide more benefits, but the introduction of blockchain also brings problems for IoT resource collaboration and sharing. This article first proposes a blockchain-enabled edge resource sharing (BEERS) architecture by combining blockchain and edge computing technology. Then, based on a typical resource sharing and collaboration scenario, the joint optimization problem of resource scheduling and task assignment (JRSTA) is constructed. We decompose JRSTA into a primal problem and a master problem and design a greedy-based task assignment (GBTA) algorithm to solve the primal problem. Based on the GBTA algorithm, the resource scheduling and task assignment (RSTA) algorithm is developed. Next, we design a layered parallel edge resource scheduling and task assignment (LPRSTA) mechanism to improve practicability. Finally, we analyze the security and the performance of our proposed architecture and algorithms. The results show that the proposed architecture can support the secure collaboration of IoT resources, and the proposed algorithm can achieve effective resource scheduling and task assignment.