Fog-assisted Internet Of Things Research Articles

Efficient and Secure Blockchain-Based Access Control for Fog-Assisted IoT Cloud in Electronic Medical Records Sharing

Fog-assisted IoT Cloud environments have emerged as a result of the rapid spread of Internet of Things (IoT) devices and the requirement for seamless data sharing in the healthcare industry. The effective and safe management of access control to sensitive data, such as Electronic Medical Records (EMRs), becomes crucial in such configurations. The Efficient and Secure Blockchain-Based Access Control (ESBAC) framework, designed specifically for Fog-assisted IoT Cloud scenarios in EMR sharing, is our creative approach to this problem. The ESBAC architecture makes use of the blockchain's openness and immutability to provide reliable access control and auditability. The implementation of a consortium blockchain includes participants including healthcare providers, patients, and authorised entities. Each EMR has a smart contract attached to it that specifies access restrictions and permissions to make sure that only authorised parties are able to access and edit the data. Because of the decentralisation provided by the blockchain, security and privacy are improved because no single party has overwhelming control. Proposed framework uses attribute-based access control and lightweight encryption methods to provide a fair trade-off between security and efficiency. As a result, there can be fine-grained access control policies with the least amount of computing overhead. Comprehensive simulations are used to evaluate the suggested solution and compare it to current methods. The outcomes show that ESBAC improves security in EMR sharing and greatly lowers access latency.

On the Design of an Algorithm for Mobility Management Over Fog Geographical Allocation for Healthcare Applications

Healthcare applications need an immediate response with minimal latency. Fog computing provides real-time monitoring and computation at the network edge so the data collected by Internet of Things (IoT) devices can be analyzed with minimal cost in real-time. However, dealing with end-user mobility becomes even more challenging with fog-assisted IoT healthcare frameworks because low latency in mobility support and fog node locating is a vital prerequisite. In this paper, we propose a fog node (FN) allocation and mobility-adapted fog computing architecture. The architecture is based on an algorithm that offers low latency for location-sensitive operations and service stability with high data broadcast quality for user mobility in fog computing. In our proposed architecture, it is no longer obligatory to either carry out a job on, or redirect it to, the cloud but instead connect with other FNs to continue with a request for a new job. The proposed architecture minimizes average network lateness by 40%–50%, bandwidth by 30%–40%, and end-to-end communication by 35%–55%. Additionally, the utilization of the FN computational resources is maximized.

Privacy-preserved learning from non-i.i.d data in fog-assisted IoT: A federated learning approach

With the prevalence of the Internet of Things (IoT) systems, smart cities comprise complex networks, including sensors, actuators, appliances, and cyber services. The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks, especially privacy-related attacks such as inference and data poisoning ones. Federated Learning (FL) has been regarded as a hopeful method to enable distributed learning with privacy-preserved intelligence in IoT applications. Even though the significance of developing privacy-preserving FL has drawn as a great research interest, the current research only concentrates on FL with independent identically distributed (i.i.d) data and few studies have addressed the non-i. i.d setting. FL is known to be vulnerable to Generative Adversarial Network (GAN) attacks, where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors. This paper proposes an innovative Privacy Protection-based Federated Deep Learning (PP-FDL) framework, which accomplishes data protection against privacy-related GAN attacks, along with high classification rates from non-i. i.d data. PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other, where class probabilities are protected utilizing a private identifier generated for each class. The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets. The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8% as accuracy improvements.

Distributed privacy-preserving nested compressed sensing for multiclass data collection with identity authentication

With the increase of new sensing devices in Internet of things (IoT), data dimensions and types have risen dramatically. The traditional data collection structure cannot satisfy the requirements for multiclass data and access control. By integrating fog computing, we design a layer-aware fog computing model which supports a distributed privacy-preserving compressed sensing (CS) for multiclass data with identity authentication in fog-assisted IoT. In our model, a novel distributed nested CS scheme samples and compresses the encrypted multiclass data in the sensing layer. The encrypted sampling data is transmitted to the fog node. Subsequently, the fog node embeds the identity watermark for later VIP user authentication in the cloud. Then, the fog uploads the watermarked data to the cloud for storage and reconstruction. When receiving the request, all results returned by the cloud are encrypted versions. In particular, the cloud performs the designed reconstruction transformation task on the sensing data for security. Specifically, the cloud only reconstructs sensing data for general users, but returns all multiclass data to the authenticated VIP users. In the experiment, we analyze the security of our scheme, discuss the impact of parameters on the reconstruction performance of different data, and summarize the recommended parameter settings.

Sustainable and Efficient Fog-Assisted IoT Cloud Based Data Collection and Delivery for Smart Cities

The Internet of Things (IoT) aims to enhance the quality of human life by sensing, processing and delivering data from the physical world. The Internet of Things Cloud (IoTC) is able to enhance sensing capabilities of IoT devices while fog computing can overcome computation and storage limitations of IoT devices. Based on this observation, we propose a fog-assisted IoTC data collection and delivery framework to reduce IoTC-based data communication costs and enhance success rates. The framework exploits the named data networking to deliver IoTC-based data so that multiple IoT devices can retrieve data from the nearest provider via one data delivery process. Moreover, the framework achieves mobility support for IoT devices to guarantee successful receipt of data. The experimental results show that this framework reduces data communication costs by nearly 37.15% and improves success rates by nearly 9.25%.

Lightweight ID-based broadcast signcryption for cloud–fog-assisted IoT

Balancing the security and efficiency of the internet of things (IoT) is difficult due to its numerous heterogeneous devices, different trust levels of stakeholders, and asymmetric resource distribution among layers. Identity-based broadcast encryption (IBBE) is a promising technique to ensure data confidentiality, simplify the certification management issue, and achieve one-to-many data sharing while reducing overheads simultaneously. However, broadcast encryption (BE) schemes without source authentication are not applicable in the real world as they cannot prevent invalid messages from malicious users. Meanwhile, the computation cost of IBBE is heavy and not suitable for constrained devices. This paper proposes a lightweight ID-based broadcast signcryption scheme (LIBBSC), which takes advantage of fog nodes to temporarily store ciphertext and to partially designcrypt for end users. A fog node transforms the ciphertext based on the specific user’s public identity key and then sends the outdesigncrypted ciphertext to the end-user (this step transforms the broadcast to the one-to-one communication). Finally, only the designated end-user with the associated secret key can rapidly designcrypt with minimal computational cost. Our scheme is selective-ID secure against chosen-ciphertext attacks in the random oracle model. And the implementation and evaluation of the proposed scheme show that it is efficient and suitable for IoT devices.

Revocable and Privacy-Preserving Decentralized Data Sharing Framework for Fog-Assisted Internet of Things

Fog-assisted Internet of Things (IoT) can outsource the massive data of resource-constraint IoT devices to cloud and fog nodes (FNs). Meanwhile, it enables convenient and low time-delay data-sharing services, which relies heavily on high security of data confidentiality and fine-grained access control. Many efforts have been focused on this urgent requirement by leveraging ciphertext-policy attribute-based encryption (CP-ABE). However, when deployed in fog-assisted IoT systems for secure data sharing, it remains a challenging problem of how to preserve attribute privacy of access policy, and trace-then-revoke traitors (i.e., malicious users intending to leak decryption keys for illegal profits) efficiently and securely in such a large scale and decentralized environment with resource-constraint user devices, especially in consideration of misbehaving cloud and FNs. Therefore, in this article, we propose a revocable and privacy-preserving decentralized data-sharing framework (RPDDSF) by designing a large universe and multiauthority CP-ABE scheme with fully hidden access policy for secure data sharing in IoT systems to achieve user attribute privacy preserving with unbounded attribute universe and key escrow resistance suitable for large scale and decentralized environment. Based on this, with RPDDSF, anyone can efficiently expose the traitors and punish them by forward/backward secure revocation. Besides, RPDDSF is able to guarantee data integrity for both data owners (DOs) and users to resist misbehaving cloud and FNs, alongwith low computation overhead for resource-constraint devices. Finally, RPDDSF is proven to be secure with detailed security proofs, and its high efficiency and feasibility are demonstrated by extensive performance evaluations.

Load-Balancing Algorithm for Multiple Gateways in Fog-Based Internet of Things

This article investigates the performance of multicriteria-based load-balancing scheme among gateways in fog-assisted Internet of Things (IoT). We employ a queueing model of the IoT system to calculate the latency of data streams from the IoT devices to the applications. However, a gateway node may easily become congested since all the traffic to IP networks are directed toward the gateway. A congested point can affect system performance and may cause reliability problems in the system. Thus, we employ multiple gateways to alleviate the performance degradation of the network, along with a multicriteria decision-making (MCDM)-based load-balancing policy among the gateways to achieve a global load fairness. The proposed model is evaluated in single-hop IPv6 over low-power wireless personal area networks (6LoWPANs). The evaluation results show the effectiveness of the proposed load-balancing model in providing fast and reliable responses to user queries.

Information processing in Internet of Things using big data analytics

With innovation in persistent technologies, such as wearable sensor gadgets, sensor devices, and wireless ad-hoc communication networks connect everyday life things to the Internet, normally referred to as Internet of Things (IoT). IoT is observed as an active entity for design and development of smart and context awareness services and applications in the area of business, science and engineering discipline. These applications and services could vigorously respond to the surroundings transformation and users’ preference. Developing a scalable system for data analysis, processing and mining of enormous real world based datasets has turned into one of the demanding problems that faces both system research scholars and data management research scholars. Employing big data analytics with IoT technologies is one of the ways for handling the timely analyzing information (i.e., data, events) streams. In this paper, we propose an integrated approach that coalesce IoT systems with big data tools into a holistic platform for real-time and continuous data monitoring and processing. We propose Fog assisted IoT based Smart and real time healthcare information processing (SRHIP) system in which large amounts of data generated by IoT sensor devices are offloaded at Fog cloud form data analytics and processing with minimum delay. The processed data is then transferred to a centralized cloud system for further analysis and storage. In this work, we introduce a Fog-assisted model with big data environment for data analytic of real time data with remote monitoring and discuss our plan for evaluating its efficacy in terms of several performance metrics such as transmission cost, storage cost, accuracy, specificity, sensitivity and F-measure. The proposed SRHIP system needs less transmission cost of 40.10% in comparison to SPPDA, 100% fewer bytes are compromised in comparison to GCEDA. Our proposed system data size reduction of 60% reduction due to proposed compression scheme in comparison to other benchmark strategies that offer 40% of reduction.

Efficient Privacy Preserving Data Collection and Computation Offloading for Fog-Assisted IoT

The property of performing data processing near the source of data (i.e., at the edge of the network) enables fog computing that can effectively reduce computation latency, bandwidth and energy consumption, especially for big data network scenarios. For the sake of achieving efficient and secure big sensory data collection in fog-assisted Internet of Things (IoT), this paper proposes an efficient privacy preserving data collection and computation offloading scheme. In the proposed scheme, first, the designed layer-aware fog computing architecture provides effective support for efficient and secure data collection and fog computation offloading. Then the proposed sampling perturbation encryption method protects data privacy against eavesdroppers and active attackers without sacrificing data correlation, and it also facilitates the simultaneous execution of decrypting and decompressing operations on encrypted sampling data. Furthermore, the developed data processing method at fog nodes reduces the amount of redundant data transmissions significantly, and the formulated optimization model for the measurement matrix ensures the high precision of data reconstruction at the end user. Particularly, a completion time minimization problem is formulated for fog computation offloading, and an efficient offloading decision algorithm is developed to find the minimum completion time by determining the optimal offloading proportion with joint optimal allocation of local CPU, external CPU and channel bandwidth resources. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection and computation offloading scheme with a strong privacy preservation property. For example, when the temporal compression ratio is 0.5, the redundant data can be reduced by 65 percent at fog node with a low relative recovery error 0.0139. At the same time when the task size is 9 Mb, the completion time of compression computation task at fog node can be reduced by 14.6 percent compared with other computation offloading method.

Privacy-preserving data search with fine-grained dynamic search right management in fog-assisted Internet of Things

Fog computing, as an assisted method for cloud computing, collects Internet of Things (IoT) data to multiple fog nodes on the edge of IoT and outsources them to the cloud for data search, and it reduces the computation cost on IoT nodes and provides fine-grained search right management. However, to provide privacy-preserving IoT data search, the existing searchable encryptions are very inefficient as the computation cost is too high for the resource-constrained IoT ends. Moreover, to provide dynamic search right management, the users need to be online all the time in the existing schemes, which is impractical. In this paper, we first present a new fog-assisted privacy-preserving IoT data search framework, where the data from each IoT device is collected by a fog node, stored in a determined document and outsourced to the cloud, the users search the data through the fog nodes, and the fine-grained search right management is maintained at document level. Under this framework, two searchable encryption schemes are proposed, i.e., Credible Fog Nodes assisted Searchable Encryption (CFN-SE) and Semi-trusted Fog Nodes assisted Searchable Encryption (STFN-SE). In CFN-SE scheme, the indexes and trapdoors are generated by the fog nodes, which greatly reduce the computation costs at the IoT devices and user ends, and fog nodes are used to support offline users’ key update. In STFN-SE scheme, the semi-trusted fog nodes are used to provide storage of encrypted key update information to assist offline users’ search right update. In both schemes, no re-encryption of the keywords is needed in search right updates. The performance evaluations of our schemes demonstrate the feasibility and high efficiency of our system.

Treasure Collection on Foggy Islands: Building Secure Network Archives for Internet of Things

Fog computing has emerged as a promising paradigm in overcoming the growing challenges (e.g., low latency, location awareness, and geographic distribution) arising from many real-world Internet of Things (IoT) applications, by extending the cloud to the network edge. With the widespread deployment of fog-assisted IoT applications, unprecedentedly huge volumes of network traffic from massive IoT devices would continuously arrive at the fog nodes. Archiving the network traffic can be highly beneficial to fog computing, which forms the basis of forensic, monitoring, troubleshooting, and many other critical tasks. Such high value, however, constantly renders traffic archives the first-order target to experienced attackers. This mandates the traffic archives to be built in a trustworthy way and stayed encrypted at rest. Security aside, it is yet highly desirable to retain the utility of the encrypted traffic archives, in particular by making them privately queryable. In this paper, we take the first research attempt and explore a new design point to delicately bridge trusted hardware and searchable encryption for building trustworthy, encrypted, yet queryable network traffic archives for fog-assisted IoT applications. We take a systematic approach to address several key challenges, which are unsolvable by synthesizing out-of-box techniques, from ground up. Extensive evaluations show that our system can achieve stable archiving throughput of 350 Mb/s with one core, and saturate a 1 Gb/s link with four cores; for a real trace, it outperforms a baseline system without any of our designs by over $110 \times $ .

Performance evaluation of a Fog-assisted IoT solution for e-Health applications

Cloud Computing has been a predominant approach for the development of Internet of Things (IoT) solutions. However, to meet the requirements of real-time and latency-sensitive applications in healthcare, a new computing paradigm that follows a Cloud computing approach, called Fog Computing, demonstrates to be an effective tool by extending the Cloud resources to the edge of the network. This work studies the contribution of the Fog Computing paradigm applied to healthcare, highlighting its main benefits regarding latency, network usage, and power consumption. Based on these parameters, a Fog-assisted health monitoring system is proposed and its performance evaluation and demonstration is carried out. The results demonstrates the potential enhancement of this approach to minimise data traffic in the core of the network because data is analysed locally and, also, enhancing security on health information that can be kept locally, enhancing data security and providing better insights of patient’s health status.

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.