Cloud-enabled Internet Of Things Research Articles

Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) enables fine-grained access control on ciphertexts, making it a promising approach for managing data stored in the cloud-enabled Internet of Things. But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content. Additionally, resource-constrained IoT devices, especially those adopting wireless communication, frequently encounter affordability issues regarding decryption costs. In this paper, we propose an efficient and fine-grained access control scheme with fully hidden policies (named FHAC). FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them. A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy. Dictionary attacks are thwarted by providing unauthorized users with invalid values. The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers. Additionally, users can verify the correctness of multiple outsourced decryption results simultaneously. Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain.

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

IAKA-CIoT: An Improved Authentication and Key Agreement Scheme for Cloud Enabled Internet of Things Using Physical Unclonable Function.

The Internet of Things (IoT) with cloud services are important functionalities in the latest IoT systems for providing various convenient services. These cloud-enabled IoT environments collect, analyze, and monitor surrounding data, resulting in the most effective handling of large amounts of heterogeneous data. In these environments, secure authentication with a key agreement mechanism is essential to ensure user and data privacy when transmitting data between the cloud server and IoT nodes. In this study, we prove that the previous scheme contains various security threats, and hence cannot guarantee essential security requirements. To overcome these security threats, we propose an improved authentication and key agreement scheme for cloud-enabled IoT using PUF. Furthermore, we evaluate its security by performing informal, formal (mathematical), and simulation analyses using the AVISPA tool and ROR model. The performance and security properties of our scheme are subsequently compared with those of other related schemes. The comparison confirms that our scheme is suitable for a practical cloud-enabled IoT environment because it provides a superior security level and is more efficient than contemporary schemes.

Towards a Compressive-Sensing-Based Lightweight Encryption Scheme for the Internet of Things

Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the Cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this work, we propose Kryptein, a compressive-sensing-based lightweight encryption scheme for Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Kryptein supports random compressed encryption, statistical computation over cipher, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices. After comparing with other state-of-the-art lightweight ciphers on IoT (Simon and Speck), IoT system with Kryptein is expected to have a much more longevity with about 35 percent extended lifetime. Further, experiments illustrated IoT data variance will not affect Kryptein's accuracy in a long term usage, and Krpytein is also able to support basic analytics tasks like machine learning (e.g., classification).

Design of a secure file storage and access protocol for cloud-enabled Internet of Things environment

The so-called cloud-enabled Internet of Things (CIoT) systems interconnect numerous devices to render robust data storage, efficient analysis, and message exchange features. However, it confronts the confidentiality of data from the owner as the access control of data is performed by honest-but-curious service providers. Although the encryption strategy ensures data security, it has adverse data deliverance due to its inherent all-or-nothing decryption. Thus, a robust protocol is proposed to achieve data sharing among geographically dispersed physical devices and efficient access to cloud data for clients. The proposed protocol resists the chosen-ciphertext attack (CCA) based on the hardness of the Decisional Diffie–Hellman Inversion (DDHI) assumption. The comprehensive analysis and simulation results show that our protocol is efficient in computation, communication, and storage costs.

The utilization of algorithms for cloud internet of things application domains: a review

Cloud internet of things (IoT) is an emerging technology that is already impelling the daily activities of our lives. However, the enormous resources (data and physical features of things) generated from Cloud-enabled IoT sensing devices are lacking suitable managerial approaches. Existing research surveys on Cloud IoT mainly focused on its fundamentals, definitions and layered architecture as well as security challenges. Going by the current literature, none of the existing researches is yet to provide a detailed analysis on the approaches deployed to manage the heterogeneous and dynamic resource data generated by sensor devices in the cloud-enabled IoT paradigm. Hence, to bridge this gap, the existing algorithms designed to manage resource data on various CloudIoT application domains are investigated and analyzed. The emergence of CloudIoT, followed by previous related survey articles in this field, which motivated the current study is presented. Furthermore, the utilization of simulation environment, highlighting the programming languages and a brief description of the simulation packages adopted to design and evaluate the performance of the algorithms are examined. The utilization of diverse network communication protocols and gateways to aid resource dissemination in the cloud-enabled IoT network infrastructure are also discussed. The future work as discussed in previous researches, which pave the way for future research directions in this field is also presented, and ends with concluding remarks.

Attribute-Based Access Control for AWS Internet of Things and Secure Industries of the Future

Internet of Things (IoT) is revolutionizing and enhancing the quality of human lives in every aspect. With a disruption of IoT devices and applications, attackers are leveraging weak authentication and access control mechanisms on these IoT devices and applications to gain unauthorized access on user devices and data and cause them harm. Access control is a critical security mechanism to secure the IoT ecosystem which comprises cloud computing and edge computing services along with smart devices. Today major cloud and IoT service providers including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure utilize some customized forms of Role-Based Access Control (RBAC) model along with specific authorization policies enabled by policy-based access control models. To enable fine-grained access control and overcome limitations of existing access control models, there is an imminent need to develop a flexible and dynamic access control model for securing smart devices, data and resources in the cloud-enabled IoT architecture. In this paper, we develop a formal attribute-based access control (ABAC) model for AWS IoT by building upon and extending previously developed access control model for AWS IoT, known as AWS-IoTAC model. We demonstrate the applicability of our proposed model through an industrial IoT use case and its implementation in the AWS IoT platform. Our proposed fine grained model for AWS IoT incorporates its existing capabilities and introduces new attributes for IoT entities and attribute-based policies for enabling expressive access control in AWS IoT. We also evaluate the performance of our model on the AWS cloud and IoT platform with the future smart industries use-case to depict the feasibility of our model in a real-world platform.

Future Smart Connected Communities to Fight COVID-19 Outbreak

Internet of Things (IoT) has grown rapidly in the last decade and continues to develop in terms of dimension and complexity, offering a wide range of devices to support a diverse set of applications. With ubiquitous Internet, connected sensors and actuators, networking and communication technology along with artificial intelligence (AI), smart cyber-physical systems (CPS) provide services rendering assistance and convenience to humans in their daily lives. However, the recent outbreak of COVID-19 (also known as coronavirus) pandemic has exposed and highlighted the limitations of contemporary technological deployments especially to contain the widespread of this disease. IoT and smart connected technologies together with data-driven applications can play a crucial role not only in the prevention, mitigation, or continuous remote monitoring of patients, but also enable prompt enforcement of guidelines, rules, and administrative orders to contain such future outbreaks.In this paper, we envision an IoT and data-supported connected ecosystem designed for intelligent monitoring, pro-active prevention and control, and mitigation of COVID-19 and similar epidemics. We propose a gamut of synergistic applications and technology systems for various smart infrastructures including E-Health, smart home, supply chain management, transportation, and city, which will work in convergence to develop ‘pandemic-proof’ future smart communities. We also present a generalized cloud-enabled IoT implementation framework along with scientific solutions, which can be adapted and extended to deploy smart connected ecosystem scenarios using widely used Amazon Web Services (AWS) cloud infrastructures. In addition, we also implement an E-Health RPM use case scenario to demonstrate the need and practicality for smart connected communities. Finally, we highlight challenges and research directions that need thoughtful consideration and across the board cooperation among stakeholders to build resilient communities against future pandemics.

A Decentralized Blockchain-based Architecture for a Secure Cloud-Enabled IoT

The integration of cloud computing and Internet of Things (IoT) offers a promising, rich platform for data collection and analysis in smart healthcare. In such a model, IoT devices collect data about patient health status through multiple intelligent sensors, whereas cloud offers scalable resources to quickly meet workload demands. Despite these remarkable improvements, the current architectures do not sufficiently address the security needs for patient medical records. In this perspective, and bearing in mind the specific characteristics of each technology, we propose a distributed security mechanism in a way that fits with IoT and cloud constraints. Our contribution to secure cloud-enabled IoT is twofold. First, we rely on OM-AM (Objective, Model, Architecture and Mechanism) for modeling and analysing the security and privacy requirements of smart healthcare. Second, we use blockchain architecture along with Attribute-Based Access Control (ABAC) model as a decentralized flexible system to support access control decisions. In particular, we rely on XACML (eXtensible Access Control Markup Language) to easily build and implement robust policies required for maintaining a secure IoT-based environment. The novelty of the proposed framework lies at smartly leveraging the recent technologies to keep health information confidential. In fact, putting blockchain and IoT together would undoubtedly create a totally new solution for remote patient monitoring. The simulation results show that the proposal is an efficient way of implementing ubiquitous and cognitive tools for smart healthcare systems.

IoT Privacy and Security: Challenges and Solutions

Privacy and security are among the significant challenges of the Internet of Things (IoT). Improper device updates, lack of efficient and robust security protocols, user unawareness, and famous active device monitoring are among the challenges that IoT is facing. In this work, we are exploring the background of IoT systems and security measures, and identifying (a) different security and privacy issues, (b) approaches used to secure the components of IoT-based environments and systems, (c) existing security solutions, and (d) the best privacy models necessary and suitable for different layers of IoT driven applications. In this work, we proposed a new IoT layered model: generic and stretched with the privacy and security components and layers identification. The proposed cloud/edge supported IoT system is implemented and evaluated. The lower layer represented by the IoT nodes generated from the Amazon Web Service (AWS) as Virtual Machines. The middle layer (edge) implemented as a Raspberry Pi 4 hardware kit with support of the Greengrass Edge Environment in AWS. We used the cloud-enabled IoT environment in AWS to implement the top layer (the cloud). The security protocols and critical management sessions were between each of these layers to ensure the privacy of the users’ information. We implemented security certificates to allow data transfer between the layers of the proposed cloud/edge enabled IoT model. Not only is the proposed system model eliminating possible security vulnerabilities, but it also can be used along with the best security techniques to countermeasure the cybersecurity threats facing each one of the layers; cloud, edge, and IoT.

LVPDA: A Lightweight and Verifiable Privacy-Preserving Data Aggregation Scheme for Edge-Enabled IoT

Edge computing is envisioned to be a powerful platform that provides efficient data storage and computation services in the smart Internet-of-Things (IoT) systems. In this data-intensive architecture, protecting user-side data privacy is one of the most critical concerns to prevent privacy leakage from any other untrusted entities. Aiming to resist this concern, many privacy-preserving data aggregation (PPDA) schemes have been proposed for various cloud-enabled IoT applications. However, due to the resource-constrained nature of the smart IoT devices, the conventional PPDA solutions, in terms of both privacy and performance requirements, are unsuitable in edge computing. To address this challenge, we propose a lightweight and verifiable PPDA scheme, named LVPDA, for the edge-computing-enabled IoT system, where the Paillier homomorphic encryption method and an online/offline signature technique are combined to ensure the privacy preserving and integrity verification during the data aggregation process. A detailed security analysis indicates that LVPDA is existentially unforgeable under the chosen message attack (EU-CMA) and the data integrity can be guaranteed with formal proof under $q$ -strong Diffie–Hellman ( $q$ -SDH) assumptions. Compared with other PPDA methods, our scheme can achieve lightweight PPDA in terms of less computational complexity and communication overhead.

Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography

Organizations share an evolving interest in adopting a cloud computing approach for Internet of Things (IoT) applications. Integrating IoT devices and cloud computing technology is considered as an effective approach to storing and managing the enormous amount of data generated by various devices. However, big data security of these organizations presents a challenge in the IoT-cloud architecture. To overcome security issues, we propose a cloud-enabled IoT environment supported by multifactor authentication and lightweight cryptography encryption schemes to protect big data system. The proposed hybrid cloud environment is aimed at protecting organizations' data in a highly secure manner. The hybrid cloud environment is a combination of private and public cloud. Our IoT devices are divided into sensitive and nonsensitive devices. Sensitive devices generate sensitive data, such as healthcare data; whereas nonsensitive devices generate nonsensitive data, such as home appliance data. IoT devices send their data to the cloud via a gateway device. Herein, sensitive data are split into two parts: one part of the data is encrypted using RC6, and the other part is encrypted using the Fiestel encryption scheme. Nonsensitive data are encrypted using the Advanced Encryption Standard (AES) encryption scheme. Sensitive and nonsensitive data are respectively stored in private and public cloud to ensure high security. The use of multifactor authentication to access the data stored in the cloud is also proposed. During login, data users send their registered credentials to the Trusted Authority (TA). The TA provides three levels of authentication to access the stored data: first-level authentication - read file, second-level authentication - download file, and third-level authentication - download file from the hybrid cloud. We implement the proposed cloud-IoT architecture in the NS3 network simulator. We evaluated the performance of the proposed architecture using metrics such as computational time, security strength, encryption time, and decryption time.

Forensic investigation of P2P cloud storage services and backbone for IoT networks: BitTorrent Sync as a case study

Cloud computing can be generally regarded as the technology enabler for Internet of Things (IoT). To ensure the most effective collection of evidence from cloud-enabled IoT infrastructure, it is vital for forensic practitioners to possess a contemporary understanding of the artefacts from different cloud services and applications. In this paper, we seek to determine the data remnants from the use of the newer BitTorrent Sync applications (version 2.x). Findings from our research using mobile and computer devices running Windows, Mac OS, Ubuntu, iOS, and Android devices suggested that artefacts relating to the installation, uninstallation, log-in, log-off, and file synchronisation could be recovered, which are potential sources of IoT forensics. We also extend the cloud forensics framework of Martini and Choo to provide a forensically sound investigation methodology for the newer BitTorrent Sync applications.