Fog-enabled Internet Of Things Research Articles

Lightweight blockchain-based remote user authentication for fog-enabled IoT deployment

The Internet of Things (IoT) seeks to enhance human life by embedding everyday objects with intelligence. As deployed in diverse environments, IoT devices exchange substantial data to offer autonomously smart and innovative services. Cloud computing serves as an effective solution for processing and storing data from IoT devices, enabling remote access for end-users. However, authenticating remote users poses a critical challenge. Although several existing schemes tackle this challenge, they have notable security and performance weaknesses. This paper introduces a lightweight, distributed multi-factor authentication scheme for remote users in IoT. The proposed approach, named Lightchain, integrates blockchain technology and fog computing while incorporating a lightweight cryptographic hash function. The security of Lightchain is rigorously confirmed through formal verification using the well-recognized Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Additionally, we develop and assess Lightchain’s functionality using the solidity language across diverse use cases. A comparative analysis is presented to evaluate performance costs and security requirements against recent methods. The proposed scheme demonstrates efficiency and robustness, making it well-suited for a range of IoT applications.

SPMAC: Secure and privacy-preserving multi-authority access control for fog-enabled IoT cloud storage

The fog computing paradigm extends the storage, network and computing facilities of the Internet of Things (IoT) to the edge of the network and provides increased convenience in real life. However, IoT still faces great security challenges, such as unauthorized access, collusion attacks between users, cloud attack, and privacy leaks. Most recently, Xiong et al. proposed a scheme to attempt to solve these issues (Xiong et al., 2020). However, we found that their scheme still could not resist the collusion attacks between non-revoked users and revoked users. This study performs the security analysis of SEM-ACSIT and proposes a secure and privacy-preserving multi-authority access control scheme named SPMAC. The proposed scheme can realize flexible user and attribute revocation. The proposed scheme achieves forward security and backward security. For the resource-limited devices, This scheme csn achieve lightweight computing using online–offline encryption technique, outsourced decryption method, and fog computing. It is worth noting that the scheme proposes a ciphertext update check algorithm, allowing users to confirm whether the ciphertext from the untrusted cloud and the user secret key have been updated. The security analysis shows that the proposed scheme can resist multiple collusion attacks, such as revoked and non-revoked users attack, cloud-users attack, and cloud attack. In addition, we realize the hidden access policy to protect users’ privacy. Finally, the results of simulation experiments indicate that the proposed scheme has great practical significance in the access control of IoT.

Delay and energy aware task scheduling mechanism for fog-enabled IoT applications: A reinforcement learning approach

With the expansion of the internet of things (IoT) devices and their applications, the demand for executing complex and deadline-aware tasks is growing rapidly. Fog-enabled IoT architecture has evolved to accomplish these tasks at the fog layer. However, fog computing devices have limited power supply and computation resources compared to cloud devices. In delay-sensitive applications of fog-enabled IoT architecture, executing tasks with stringent deadlines while reducing the service latency and energy usage of fog resources is a difficult challenge. This paper presents an effective task scheduling strategy to allocate fog computing resources for IoT requests to meet the deadline of the requests and resource availability. Initially, the scheduling problem is formulated as mixed-integer nonlinear programming (MINLP) to reduce the energy consumption of the fog resources and service time of the tasks subject to the deadline and resource availability constraints. To address the high dimensionality issue of the tasks in a dynamic environment, a fuzzy-based reinforcement learning (FRL) mechanism is employed to reduce the service delay of the tasks and energy usage of the fog nodes. Initially, the tasks are prioritized using fuzzy logic. Then the prioritized tasks are scheduled using the on-policy reinforcement learning technique, which enhances the long-term reward compared to the Q-learning approach. Further, the evaluation outcomes reflect that the proposed task scheduling technique outperforms the existing algorithms with an improvement of up to 23% and 18% regarding service latency and energy consumption, respectively.

A secure and efficient access control scheme with attribute revocation and merging capabilities for fog-enabled IoT

The current era’s technological innovation has accelerated the expansion of the Internet of things (IoT), leading to an exponential growth of the number of connected devices and the produced data. This inspired the emergence of fog computing paradigm that enables data processing and analysis near end-users. The cloud co-exists with fog to offer services like processing resources, huge storage, etc. But sending the data through multiple levels for analysis and storage poses a severe threat to data security in cloud-fog-IoT framework. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a well-known cryptographic mechanism for providing fine-grained access control. However, the existing CP-ABE schemes are not suitable as they fail to address the issues of attribute merging, attribute revocation, outsourcing operations, and facilitating privileged access altogether. Hence, this paper proposes a CP-ABE scheme addressing all the aforementioned issues. Further, security and performance analyses show that the proposed scheme is secure and suitable for IoT devices.

A Secure Data Sharing Based on Key Aggregate Searchable Encryption in Fog-Enabled IoT Environment

Cloud server and fog computing have been utilized to manage enormous volumes of data with low service transmission time in Internet of Things (IoT) environment. However, untrustworthy relationships between network entities and cloud server cause a lot of security concerns. Many researches have been studied based on traditional cryptosystems for data sharing systems. But they lead to key management difficulty and do not ensure the data owner's authority by depending on Trusted Third Party (TTP). Therefore, we use key aggregate searchable encryption (KASE), which enables owners to exercise their rights in data management and sharing without the help of TTP and resolves the key management problem. In this paper, we propose a secure data sharing system based on KASE in fog-enabled IoT environment using blockchain. We prove the resistance of the proposed scheme against various attacks through informal analysis and Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. We prove the guarantee of secure mutual authentcation using Burrows-Abadi-Needham (BAN) logic. Furthermore, we compare security features and performance of the proposed scheme with existing schemes through Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL).

RMA-CPABE : A multi-authority CPABE scheme with reduced ciphertext size for IoT devices

Internet of Things (IoT) generates a massive amount of data, which can be prone to huge data and privacy leaks unless given a proper access control. Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic mechanism that provides confidentiality and fine-grained access to stored data. In CP-ABE, a single trusted authority primarily handles attribute management and its revocation. However, in an environment like IoT, due to its heterogeneous nature, the attributes from multiple domains are evidently inherited by IoT devices and hence develop a need for a multi-authority scheme. Therefore, this paper proposes a novel multi-authority CP-ABE scheme with reduced-size ciphertext (RMA-CPABE) to provide secure access in fog-enabled IoT applications with attributes from multiple domains. This scheme considers the resource-constrained nature of IoT devices and reduces the ciphertext size in the sense that the ciphertext size does not increase linearly with the number of attributes; instead, it increases linearly with the number of multi-domain attribute authorities involved in the policy building. RMA-CPABE supports highly required functionalities like attribute update and outsourced decryption to reduce the computation load on the end devices. Further, the proposed scheme is the first to eliminate the most expensive ciphertext update operation during attribute revocation and addition, which is the biggest drawback of most of the existing schemes. The end-user in RMA-CPABE requires holding only a constant size key to access the ciphertexts securely. The security analysis proves that the proposed scheme is secure against Chosen-Ciphertext Attack (CCA) under multiple augmented Multi-Sequence of Exponents Decisional Diffie Hellman (maMSE-DDH) assumptions. The performance analysis shows that RMA-CPABE is suitable for IoT applications.

Security and Makespan Trade-Off Strategy in Fog-Enabled IoT Networks

Fog computing relieves the Internet of Things (IoT) from a heavy burden of workload computation, consequently minimizes workload makespan, and reduces the considerable energy consumption of IoT devices. However, the fog-enabled IoT poses a risk to data security due to the intrinsic open and distributed structure of the fog. Hence, it is imperative to search for an optimal scheduling strategy on fog nodes to minimize workload makespan, while guaranteeing high data security. In this study, we investigate the trade-off between security and makespan and propose a multiobjective optimization model to optimize them simultaneously. To achieve this goal, we design an evolutionary algorithm together with two effective measures to handle large-scale partitionable workloads: a conflict-free multi-installment scheduling (ConfMIS) strategy as well as a security criterion based on Eigenvector centrality measure. Experimental results show that the proposed algorithm can generate a set of widely spread and uniformly distributed solutions in the decision space. Among these representative solutions, system designers/decision-makers can choose their favorite one based on specific IoT application demands.

Soft-Health: Software-Defined Fog Architecture for IoT Applications in Healthcare

In this article, we propose a software-defined fog architecture, named as Soft-Health, to serve various Internet-of-Things (IoT)-based healthcare applications. The health conditions of the patients fluctuate over time. Further, specialized medical care may not always be available in all healthcare facilities. The use of wireless body area network (WBAN) for continuous patient monitoring addresses the issue to a certain extent. However, as the physiological parameters of a patient are time-critical in nature, any delay, packet loss, and network overhead, may result in deterioration of the patient’s health conditions. Considering this, we design a Software-defined fog-enabled IoT platform for various healthcare applications. We consider that the fog layer comprises SDN switches that allocate the packet to the appropriate fog/cloud depending upon the criticality index (CI) of the data packets originating from patients. We mathematically formulate the CI, based on the physiological parameters sensed and transmitted to the switches. Further, we design an optimization function to obtain the maximum utility of a fog node, for an optimal number of processes executed by that node. We apply the Lagrangian method to simplify the optimization function and solve it using Karush–Kuhn–Tucker (KKT) conditions. We apply the auto-regression model to predict the total delay incurred and the total energy consumed by the proposed scheme. Exhaustive analysis of our proposed scheme, Soft-Health, demonstrates that the delay incurred decreases by 24.57% and 40.1% approximately, compared to the existing schemes, Mobi-Flow, and CARE, respectively.

QoE Based Revenue Maximizing Dynamic Resource Allocation and Pricing for Fog-Enabled Mission-Critical IoT Applications

Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the cloud server. Fog nodes of varying response rates are available to the cloud service provider (CSP) and it is faced with a challenge of forwarding the sequentially received IoT data to one of the fog nodes for processing. Since the arrival times and nature of requests is random, it is important to optimally classify the requests in real-time and allocate available virtual machine instances (VMIs) at the fog nodes to provide a high QoE to the users and consequently generate higher revenues for the CSP. In this paper, we use a pricing policy based on the QoE of the applications as a result of the allocation and obtain an optimal dynamic allocation rule based on the statistical information of the computational requests. The developed solution is statistically optimal, dynamic, and implementable in real-time as opposed to other static matching schemes in the literature. The performance of the proposed framework has been evaluated using simulations and the results show significant improvement as compared with benchmark schemes.

A Survey on Privacy Preservation in Fog-Enabled Internet of Things

Despite the rapid growth and advancement in the Internet of Things (IoT ), there are critical challenges that need to be addressed before the full adoption of the IoT. Data privacy is one of the hurdles towards the adoption of IoT as there might be potential misuse of users’ data and their identity in IoT applications. Several researchers have proposed different approaches to reduce privacy risks. However, most of the existing solutions still suffer from various drawbacks, such as huge bandwidth utilization and network latency, heavyweight cryptosystems, and policies that are applied on sensor devices and in the cloud. To address these issues, fog computing has been introduced for IoT network edges providing low latency, computation, and storage services. In this survey, we comprehensively review and classify privacy requirements for an in-depth understanding of privacy implications in IoT applications. Based on the classification, we highlight ongoing research efforts and limitations of the existing privacy-preservation techniques and map the existing IoT schemes with Fog-enabled IoT schemes to elaborate on the benefits and improvements that Fog-enabled IoT can bring to preserve data privacy in IoT applications. Lastly, we enumerate key research challenges and point out future research directions.

Efficient privacy-preserving data replication in fog-enabled IoT

Internet of Things (IoT) devices generate a high volume of data that is processed and stored in traditional cloud computing. The processing including data replication in traditional cloud computing often results in excessive resource utilization, performance overhead, and long response time. Fog computing has been proposed to overcome the shortcomings of cloud computing. Fog computing alleviates the processing and storage burden during data replication from the cloud to the network edge closer to sensor devices. Numerous data replica schemes in fog computing have been proposed to improve the performance efficiency of the data, reduce the turnaround delays of data access, and minimize network latency. However, these schemes do not consider data replication privacy, which is essential for data protection, reliability, and authentication. Therefore, this paper proposes a data replica creation scheme and a data replica placement scheme for preserving the privacy of data in fog computing. Our proposed replica creation scheme is based on a Level of Privacy (LoP) defined by data owners and the service capacity of fog nodes. Our proposed replica placement scheme is based on the priority level of fog nodes. We have conducted a comprehensive experimental analysis to compare the performance of our scheme and the existing schemes. Our results demonstrate that the proposed scheme can significantly achieve efficient replicas privacy, prediction accuracy, as well as outperform the existing state-of-the-art schemes in terms of computational and memory costs.

Semi-Supervised Spatiotemporal Deep Learning for Intrusions Detection in IoT Networks

The rapid growth of the Internet of Things (IoT) technologies has generated a huge amount of traffic that can be exploited for detecting intrusions through IoT networks. Despite the great effort made in annotating IoT traffic records, the number of labeled records is still very small, increasing the difficulty in recognizing attacks and intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (SS-Deep-ID), in which we propose a multiscale residual temporal convolutional (MS-Res) module to finetune the network capability in learning spatiotemporal representations. An improved traffic attention (TA) mechanism is introduced to estimate the importance score that helps the model to concentrate on important information during learning. Furthermore, a hierarchical semi-supervised training method is introduced which takes into account the sequential characteristics of the IoT traffic data during training. The proposed SS-Deep-ID is easily integrated into a fog-enabled IoT network to offer efficient real-time intrusion detection. Finally, empirical evaluations on two recent data sets (CIC-IDS2017 and CIC-IDS2018) demonstrate that SS-Deep-ID improves the efficiency of intrusion detection and increases the robustness of performance while maintaining computational efficiency.

Exact greedy algorithm based split finding approach for intrusion detection in fog-enabled IoT environment

The essential role of intrusion detection is to manage the critical infrastructure to detect malicious activity competently concerning the Internet of Things (IoT). The IoT network is used to communicate and control information among various components composing a critical system. The essential inclination of infrastructure swerves to confront security issues and challenges as network systems are examined to expose cyber-related threats. Additionally, accessing real-time information from the cloud leads to a massive problem of latency. Fog computing is a novel archetype to encompass the cloud to the network edge with practical computation and critical infrastructure. The fog layer makes the device vulnerable to numerous attacks because of rapid access to resources. The practical way of addressing these issues is to detect intrusions effectively and trace the path leading to the attack source. The intent of this paper is to present a security mechanism and assure truthful operation of IoT networks with the intrusion detection system. A network intrusion detection system is proposed based on the conception of the Exact Greedy Boosting ensemble method for device implementation in the fog node because of protecting critical infrastructure from timely and accurate detection of malicious activities. The proposed model explores the traffic flow monitoring in novel IoT Intrusion Dataset 2020(IoTID20) network traffic by identifying and classifying the type of attack based on anomalies from normal behavior. Further, the paper estimates the complete experimentation performance and evaluations with competitive machine learning algorithms. The experimental observation of the simulation work is evident in the proposed model's efficiency and robustness in categorizing the attacks.

Privacy-Preserving Distributed Analytics in Fog-Enabled IoT Systems.

The Internet of Things (IoT) has evolved significantly with advances in gathering data that can be extracted to provide knowledge and facilitate decision-making processes. Currently, IoT data analytics encountered challenges such as growing data volumes collected by IoT devices and fast response requirements for time-sensitive applications in which traditional Cloud-based solution is unable to meet due to bandwidth and high latency limitations. In this paper, we develop a distributed analytics framework for fog-enabled IoT systems aiming to avoid raw data movement and reduce latency. The distributed framework leverages the computational capacities of all the participants such as edge devices and fog nodes and allows them to obtain the global optimal solution locally. To further enhance the privacy of data holders in the system, a privacy-preserving protocol is proposed using cryptographic schemes. Security analysis was conducted and it verified that exact private information about any edge device’s raw data would not be inferred by an honest-but-curious neighbor in the proposed secure protocol. In addition, the accuracy of solution is unaffected in the secure protocol comparing to the proposed distributed algorithm without encryption. We further conducted experiments on three case studies: seismic imaging, diabetes progression prediction, and Enron email classification. On seismic imaging problem, the proposed algorithm can be up to one order of magnitude faster than the benchmarks in reaching the optimal solution. The evaluation results validate the effectiveness of the proposed methodology and demonstrate its potential to be a promising solution for data analytics in fog-enabled IoT systems.

Distributed Resource Management for Blockchain in Fog-Enabled IoT Networks

Blockchain, an emerging decentralized but trusted system, has been applied in many applications, such as the Internet of Things (IoT), supply chains, and smart grid. However, due to the large amount of computing and storage resources blockchain typically demands, its wide deployment is faced with the sustainability issue. To resolve this issue, a viable solution is to empower the IoT system with fog computing that can offload the computation-demanding tasks. Due to varieties of mining tasks and heterogeneous resource capabilities at fog nodes (FNs), it is not an easy task to schedule mining tasks and manages resource allocation among FNs of conflicting interests and independent IoT devices in a distributed manner. In this article, under the framework of matching theory, we design a distributed matching mechanism to maximize the social welfare of resource-restricted FNs while guaranteeing various mining requirements of FNs. Besides, we also provide formal proof regarding the convergence and computational complexity of a distributed matching algorithm (DMA). Finally, we verify that DMA not only improves the social welfare of FNs but also reduces the mining latency compared with the existing algorithms through extensive simulations.

Smart-Cluster-Based Distributed Caching for Fog-IoT Networks

The idea of co-operative caching in a cache-enabled wireless network has gained much interest due to its services in terms of short service delay and improved transmission rate at the user end. In this article, we consider a co-operative caching mechanism for a fog-enabled Internet of Things (IoT) network. We propose a delay-minimizing policy for fog nodes (FNs), where the goal is to reduce the service delay for the IoT nodes, also known as terminal nodes (TNs). To this end, a novel smart clustering mechanism is proposed, aiming to efficiently assign FNs to the TNs while improving the network benefit by finding a tradeoff between the delay and the network’s energy consumption. We perform mathematical analysis and extensive simulations to highlight the potential gain and the proposed policy.

Achieving Democracy in Edge Intelligence: A Fog-Based Collaborative Learning Scheme

The emergence of fog computing has brought unprecedented opportunities to the Internet-of-Things (IoT) field, and it is now feasible to incorporate deep learning at the edge of the IoT network to provide a wide range of highly tailored services. In this article, we present a fog-based democratically collaborative learning scheme in which fog nodes collaborate on the model training process even without the support of the cloud, contributing to the advances of IoT in terms of realizing a more intelligent edge. To achieve that, we design a voting strategy so that a fog node could be elected as the coordinator node based on both distance and computational power metrics to coordinate the training process. Also, a collaborative learning algorithm is proposed to generalize the training of different deep learning models in the fog-enabled IoT environment. We then implement two popular use cases, including a user trajectory prediction and a distributed image recognition, to demonstrate the feasibility, practicality, and effectiveness of the scheme. More importantly, the experiments on both use cases are conducted through a real world, in-door fog deployment. The result shows that the scheme can utilize fog to obtain a well-performing deep learning model in the cloudless IoT environment while mitigating the data locality issue for each fog node.

A Reliable Data Loss Aware Algorithm for Fog-IoT Networks

In this paper, a reliable algorithm is proposed, which aims at providing the desired reliability, avoiding the data loss due to probable failures, and guaranteeing the energy consumption balancing among all fog nodes (FNs) in the fog-enabled Internet of Things (IoT) networks. To this end, first, a new optimization model is defined to find the best transmission power and the best transmission rate of each terminal node (TN) based on the channel conditions and the desired reliability of the network. Then, the best FN is assigned to each TN according to the topology potential between FNs and TNs, such that there exists an energy consumption balancing among all FNs. Simulation results reveal that the proposed algorithm improves the network performance in terms of reliability and loss ratio, while maintaining the energy consumption balancing among all FNs.

JOTE: Joint Offloading of Tasks and Energy in Fog-Enabled IoT Networks

Fog computing is a promising solution to enable delay-sensitive applications in the Internet of Things (IoT). In this article, based on the simultaneous wireless information and power transfer (SWIPT) technology, we investigate the joint offloading of tasks and energy (JOTE) in fog-enabled IoT networks. Specifically, the task node is allowed to offload energy and tasks to multiple neighboring helper nodes in a time-division multiple access (TDMA) manner. When there are no task queues in the nodes, the offloading decision for each task is independent. We first find the offloading strategy to minimize the task execution delay as well as the energy consumption for a specific task and then, analyze the condition under which the JOTE is beneficial. We show that it becomes more and more desirable to offload both the tasks and the energy from the task node as the number of helper nodes gets large. When there are task queues in the nodes, the offloading decision for each task becomes temporally correlated. We then characterize the optimal strategies to offload the tasks and energy jointly over multiple time slots. An online offloading policy based on the Lyapunov optimization is then proposed to minimize the time average expected delay while stabilizing the system operation. Comprehensive numerical results corroborate our theoretical results and demonstrate the superior performance of the proposed JOTE algorithms.

Efficient Attribute-Based Encryption Outsourcing Scheme With User and Attribute Revocation for Fog-Enabled IoT

With the rapid growth of Internet of Things (IoT) applications, fog computing enables the IoT to provide efficient services by extending the cloud computing paradigm to the edge of the network. However, the existing attribute-based encryption schemes rarely focus on user and attribute revocation in fog computing and most of them still impose high computational and storage overhead on resource-limited IoT devices. In this article, we propose an efficient attribute-based encryption outsourcing scheme with user and attribute revocation for fog-enabled IoT. The proposed scheme improves the existing encryption scheme that uses the concept of attribute groups to achieve attribute revocation, making it suitable for fog computing and improving the efficiency of ciphertext update. In addition, it implements a novel method of user revocation in fog computing based on the characteristics of fog computing. In order to reduce the computing and storage burden of IoT devices, the heavy computation operations of encryption and decryption are outsourced to fog nodes and part of secret keys are stored in fog nodes. The security analysis shows that the proposed scheme is secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. The performance analysis shows that the proposed scheme has high revocation efficiency and is efficient enough to be deployed in practical fog computing.