A State-of-the-Art Survey for IoT Security and Energy Management based on Hashing Algorithms

The Internet of Things (IoT) has developed as a disruptive technology with wide-ranging applications across several sectors, enabling the connecting of devices and the acquisition of substantial volumes of data. Nevertheless, the rapid expansion of networked gadgets has generated substantial apprehensions pertaining to security and energy administration. This survey paper offers a detailed examination of the present state of research and advancements in the field of Internet of Things (IoT) security and energy management. The work places special emphasis on the use of hashing algorithms in this context. The security of the Internet of Things (IoT) is a crucial element in safeguarding the confidentiality, integrity, and availability of data inside IoT environments. Hashing algorithms have gained prominence as a fundamental tool for enhancing IoT security. This survey reviews the state of the art in cryptographic hashing techniques and their application in securing IoT devices, data, and communication. Furthermore, the efficient management of energy resources is essential to prolong the operational lifespan of IoT devices and reduce their environmental impact. Hashing algorithms are also instrumental in optimizing energy consumption through data compression, encryption, and authentication. This survey explores the latest advancements in energy-efficient IoT systems and how hashing algorithms contribute to energy management strategies. Through a comprehensive analysis of recent research findings and technological advancements, this survey identifies key challenges and open research questions in the fields of IoT security and energy management based on hashing algorithms. It provides valuable insights for researchers, practitioners, and policymakers to further advance the state of the art in these critical IoT domains

Exploring the Integration of Blockchain in IoT Use Cases: Challenges and Opportunities

Blockchain and The Internet of Things (IoT) is a significant paradigm which has gained traction in today’s digital age as two complimentary technologies. The combination of IoT\u27s connectivity with blockchain\u27s security creates new opportunities and solves problems associated with centralized systems. This culminating project aims to delve deeper into the integration of blockchain technology in IoT applications based on select use cases to uncover potential benefits and significant challenges of blockchain integration across different sectors. The research objectives to be addressed are: (RO1) How emerging vulnerabilities manifest in the implementation of blockchain within current IoT ecosystems. (RO2) How current opportunities and challenges are influencing the successful integration of blockchain in IoT ecosystems. The findings from the case studies are: (RO1) Significant vulnerabilities exist within core blockchain features such as smart contracts which could lead to cascading failures and widespread system disruption within an IoT ecosystem. Additionally, difficulty in quickly patching smart contract vulnerabilities due to blockchain immutability further exacerbates this risk. (RO2) The successful integration of blockchain in IoT has the potential to provide enhanced trust, performance and security however significant bottlenecks such as interoperability challenges between various IoT devices and blockchain protocols, effective consensus mechanisms suited for resource constrained IoT devices and scalability challenges must be navigated to achieve a seamless integration of the technologies The conclusions are: (RO1) IoT-blockchain convergence introduces new potential attack vectors that must be analyzed and secured, especially at the intersection of resource-constrained IoT devices and computationally intensive blockchain protocols. (RO2) Blockchain integration in IoT requires specific considerations related to the heterogenous nature of IoT devices, resource limitations for traditional IoT ecosystems, scalability of blockchain solutions in the diverse nature of IoT ecosystems and standardization and compatibility of different blockchain platforms across IoT applications. Areas of further study include researching minimum security requirements in software development of IoT devices which can support complex computational requirements needed for successful integration of blockchain in IoT applications and improving blockchain security against quantum attacks. Public-key cryptography, a cornerstone of blockchain security, is particularly susceptible to such threats

Demystifying Quantum Blockchain for Healthcare

The application of blockchain technology can be beneficial in the field of healthcare as well as in the fight against the COVID-19 epidemic. In this work, the importance of blockchain is analyzed and it is observed that blockchain technology and the processes associated with it will be utilised in the healthcare systems of the future for data acquisition from sensors, automatic patient monitoring, and secure data storage. This technology substantially simplifies the process of carrying out operations because it can store a substantial quantity of data in a dispersed and secure manner, as well as enable access whenever and wherever it is required to do so. With the assistance of quantum blockchain, the benefits of quantum computing, such as the capability to acquire thermal imaging based on quantum computing and the speed with which patients may be located and monitored, can all be exploited to their full potential. Quantum blockchain is another tool that can be utilised to maintain the confidentiality, authenticity, and accessibility of data records. The processing of medical records could potentially benefit from greater speed and privacy if it combines quantum computing and blockchain technology. The authors of this paper investigate the possible benefits and applications of blockchain and quantum technologies in the field of medicine, pharmacy and healthcare systems. In this context, this work explored and compared quantum technologies and blockchain-based technologies in conjunction with other cutting-edge information and communications technologies such as ratification intelligence, machine learning, drones, and so on

Horizontally distributed inference of deep neural networks for AI-enabled IoT

Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development of specific mechanisms for enabling a collaborative inference across edge devices towards the in situ execution of highly complex state-of-the-art deep neural networks (DNNs), despite the resource-constrained nature of such infrastructures. In particular, the review discusses the most salient approaches conceived along those lines, elaborating on the specificities of the partitioning schemes and the parallelism paradigms explored, providing an organized and schematic discussion of the underlying workflows and associated communication patterns, as well as the architectural aspects of the DNNs that have driven the design of such techniques, while also highlighting both the primary challenges encountered at the design and operational levels and the specific adjustments or enhancements explored in response to them.Agencia Estatal de Investigación | Ref. DPI2017-87494-RMinisterio de Ciencia e Innovación | Ref. PDC2021-121644-I00Xunta de Galicia | Ref. ED431C 2022/03-GR

Contributions to Securing Software Updates in IoT

The Internet of Things (IoT) is a large network of connected devices. In IoT, devices can communicate with each other or back-end systems to transfer data or perform assigned tasks. Communication protocols used in IoT depend on target applications but usually require low bandwidth. On the other hand, IoT devices are constrained, having limited resources, including memory, power, and computational resources. Considering these limitations in IoT environments, it is difficult to implement best security practices. Consequently, network attacks can threaten devices or the data they transfer. Thus it is crucial to react quickly to emerging vulnerabilities. These vulnerabilities should be mitigated by firmware updates or other necessary updates securely. Since IoT devices usually connect to the network wirelessly, such updates can be performed Over-The-Air (OTA). This dissertation presents contributions to enable secure OTA software updates in IoT. In order to perform secure updates, vulnerabilities must first be identified and assessed. In this dissertation, first, we present our contribution to designing a maturity model for vulnerability handling. Next, we analyze and compare common communication protocols and security practices regarding energy consumption. Finally, we describe our designed lightweight protocol for OTA updates targeting constrained IoT devices. IoT devices and back-end systems often use incompatible protocols that are unable to interoperate securely. This dissertation also includes our contribution to designing a secure protocol translator for IoT. This translation is performed inside a Trusted Execution Environment (TEE) with TLS interception. This dissertation also contains our contribution to key management and key distribution in IoT networks. In performing secure software updates, the IoT devices can be grouped since the updates target a large number of devices. Thus, prior to deploying updates, a group key needs to be established among group members. In this dissertation, we present our designed secure group key establishment scheme. Symmetric key cryptography can help to save IoT device resources at the cost of increased key management complexity. This trade-off can be improved by integrating IoT networks with cloud computing and Software Defined Networking (SDN).In this dissertation, we use SDN in cloud networks to provision symmetric keys efficiently and securely. These pieces together help software developers and maintainers identify vulnerabilities, provision secret keys, and perform lightweight secure OTA updates. Furthermore, they help devices and systems with incompatible protocols to be able to interoperate

Fortifying IoT against crimpling cyber-attacks: a systematic review

The rapid growth and increasing demand for Internet of Things (IoT) devices in our everyday lives create exciting opportunities for human involvement, data integration, and seamless automation. This fully interconnected ecosystem considerably impacts crucial aspects of our lives, such as transportation, healthcare, energy management, and urban infrastructure. However, alongside the immense benefits, the widespread adoption of IoT also brings a complex web of security threats that can influence society, policy, and infrastructure conditions. IoT devices are particularly vulnerable to security violations, and industrial routines face potentially damaging vulnerabilities. To ensure a trustworthy and robust security framework, it is crucial to tackle the diverse challenges involved. This survey paper aims to aid researchers by categorizing attacks and vulnerabilities based on their targets. It provides a detailed analysis of attack methods and proposes effective countermeasures for each attack category. The paper also highlights case studies of critical IoT applications, showcasing security solutions. In addition to traditional cryptographic approaches, this work explores emerging technologies like Quantum Crypto Physical Unclonable Functions (QC-PUFs) and blockchain, discussing their pros and cons in securing IoT environments. The research identifies and examines attacks, vulnerabilities, and security measures and endeavors to impact the overall understanding of IoT security. The insights and findings presented here will serve as a valuable resource for researchers, guiding the development of resilient security mechanisms to ensure the trustworthy and safe operation of IoT ecosystems

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.European CommissionNational Research Tomsk Polytechnic UniversityUpdate citation details during checkdate report - A