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Security and Privacy for Edge, Fog, and Cloud Computing; the Internet of Things and Mobile Crowdsensing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 18875

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Special Issue Editors

Dr. Tao Peng

E-Mail Website
Guest Editor

School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Interests: network security and privacy protection; cloud computing security; blockchain technology

Dr. Ke Gu

E-Mail Website
Co-Guest Editor

School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: network and information security; artificial intelligence security; edge computing; fog computing; Internet of Vehicles
Special Issues, Collections and Topics in MDPI journals

Dr. Wei Zhou

E-Mail Website
Co-Guest Editor

School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3000, Australia
Interests: computer networks; network security; fingerprint biometric

Special Issue Information

Dear Colleagues,

Mobile crowdsensing (MCS), a powerful technology in smart cities, has recently received significant research attention and has become an appealing paradigm in the field of urban sensing. The mobility and intelligence of humans guarantee a higher coverage and better contextual awareness compared to traditional sensor networks. As sensing data become increasingly fine-grained and complicated, there is a tendency to enhance MCS with an edge computing paradigm to reduce time delays and high bandwidth costs, while individuals may be reluctant to share data for privacy concerns. Despite the growing interest in mobile crowdsensing and smart cities, solutions require deeper investigations and research on many aspects, ranging from sensing and communication to data security and the preservation of privacy.

This Special Issue aims to gather and share research achievements, emerging ideas and trends regarding security and the preservation of privacy in edge, fog, and cloud computing, as well as the Internet of Things and mobile crowdsensing.

Dr. Tao Peng
Dr. Ke Gu
Dr. Wei Zhou
Guest Editors

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Keywords

Cyber Security
Data Security
Privacy Preservation
Mobile Crowdsensing
Smart City
Internet of Things
Edge Computing
Cloud Computing
Fog Computing
Blockchain

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Published Papers (7 papers)

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Research

16 pages, 2532 KiB

Open AccessArticle

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

by Xiaoxue He, Yubo Wang, Xu Zhao, Tiancong Huang and Yantao Yu

Sensors 2024, 24(2), 651; https://doi.org/10.3390/s24020651 - 19 Jan 2024

Viewed by 1165

Abstract

Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user’s participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user’s profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user’s income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms. Full article

(This article belongs to the Special Issue Security and Privacy for Edge, Fog, and Cloud Computing; the Internet of Things and Mobile Crowdsensing)

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22 pages, 572 KiB

Open AccessArticle

Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords

by Kamila Lis, Ewa Niewiadomska-Szynkiewicz and Katarzyna Dziewulska

Sensors 2023, 23(15), 6685; https://doi.org/10.3390/s23156685 - 26 Jul 2023

Cited by 3 | Viewed by 1608

Abstract

The paper addresses issues concerning secure authentication in computer systems. We focus on multi-factor authentication methods using two or more independent mechanisms to identify a user. User-specific behavioral biometrics is widely used to increase login security. The usage of behavioral biometrics can support verification without bothering the user with a requirement of an additional interaction. Our research aimed to check whether using information about how partial passwords are typed is possible to strengthen user authentication security. The partial password is a query of a subset of characters from a full password. The use of partial passwords makes it difficult for attackers who can observe password entry to acquire sensitive information. In this paper, we use a Siamese neural network and n-shot classification using past recent logins to verify user identity based on keystroke dynamics obtained from the static text. The experimental results on real data demonstrate that keystroke dynamics authentication can be successfully used for partial password typing patterns. Our method can support the basic authentication process and increase users’ confidence. Full article

(This article belongs to the Special Issue Security and Privacy for Edge, Fog, and Cloud Computing; the Internet of Things and Mobile Crowdsensing)

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21 pages, 2586 KiB

Open AccessArticle

Enhancing Microservices Security with Token-Based Access Control Method

by Algimantas Venčkauskas, Donatas Kukta, Šarūnas Grigaliūnas and Rasa Brūzgienė

Sensors 2023, 23(6), 3363; https://doi.org/10.3390/s23063363 - 22 Mar 2023

Cited by 2 | Viewed by 5677

Abstract

Microservices are compact, independent services that work together with other microservices to support a single application function. Organizations may quickly deliver high-quality applications using the effective design pattern of the application function. Microservices allow for the alteration of one service in an application without affecting the other services. Containers and serverless functions, two cloud-native technologies, are frequently used to create microservices applications. A distributed, multi-component program has a number of advantages, but it also introduces new security risks that are not present in more conventional monolithic applications. The objective is to propose a method for access control that ensures the enhanced security of microservices. The proposed method was experimentally tested and validated in comparison to the centralized and decentralized architectures of the microservices. The obtained results showed that the proposed method enhanced the security of decentralized microservices by distributing the access control responsibility across multiple microservices within the external authentication and internal authorization processes. This allows for easy management of permissions between microservices and can help prevent unauthorized access to sensitive data and resources, as well as reduce the risk of attacks on microservices. Full article

(This article belongs to the Special Issue Security and Privacy for Edge, Fog, and Cloud Computing; the Internet of Things and Mobile Crowdsensing)

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20 pages, 6233 KiB

Open AccessArticle

Multi-Objective Path Optimization in Fog Architectures Using the Particle Swarm Optimization Approach

by Nerijus Morkevičius, Agnius Liutkevičius and Algimantas Venčkauskas

Sensors 2023, 23(6), 3110; https://doi.org/10.3390/s23063110 - 14 Mar 2023

Cited by 5 | Viewed by 1737

Abstract

IoT systems can successfully employ wireless sensor networks (WSNs) for data gathering and fog/edge computing for processing collected data and providing services. The proximity of edge devices to sensors improves latency, whereas cloud assets provide higher computational power when needed. Fog networks include various heterogeneous fog nodes and end-devices, some of which are mobile, such as vehicles, smartwatches, and cell phones, while others are static, such as traffic cameras. Therefore, some nodes in the fog network can be randomly organized, forming a self-organizing ad hoc structure. Moreover, fog nodes can have different resource constraints, such as energy, security, computational power, and latency. Therefore, two major problems arise in fog networks: ensuring optimal service (application) placement and determining the optimal path between the user end-device and the fog node that provides the services. Both problems require a simple and lightweight method that can rapidly identify a good solution using the constrained resources available in the fog nodes. In this paper, a novel two-stage multi-objective path optimization method is proposed that optimizes the data routing path between the end-device and fog node(s). A particle swarm optimization (PSO) method is used to determine the Pareto Frontier of alternative data paths, and then the analytical hierarchy process (AHP) is used to choose the best path alternative according to the application-specific preference matrix. The results show that the proposed method works with a wide range of objective functions that can be easily expanded. Moreover, the proposed method provides a whole set of alternative solutions and evaluates each of them, allowing us to choose the second- or third-best alternative if the first one is not suitable for some reason. Full article

(This article belongs to the Special Issue Security and Privacy for Edge, Fog, and Cloud Computing; the Internet of Things and Mobile Crowdsensing)

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25 pages, 832 KiB

Open AccessArticle

Joint Optimization of Energy Consumption and Data Transmission in Smart Body Area Networks

by Limiao Li, Junyao Long, Wei Zhou, Alireza Jolfaei and Mohammad Sayad Haghighi

Sensors 2022, 22(22), 9023; https://doi.org/10.3390/s22229023 - 21 Nov 2022

Cited by 2 | Viewed by 2139

Abstract

In Wireless Body Area Networks (BAN), energy consumption, energy harvesting, and data communication are the three most important issues. In this paper, we develop an optimal allocation algorithm (OAA) for sensor devices, which are carried by or implanted in human body, harvest energy from their surroundings, and are powered by batteries. Based on the optimal allocation algorithm that uses a two-timescale Lyapunov optimization approach, we design a framework for joint optimization of network service cost and network utility to study energy, communication, and allocation management at the network edge. Then, we formulate the utility maximization problem of network service cost management based on the framework. Specifically, we use OAA, which does not require prior knowledge of energy harvesting to decompose the problem into three subproblems: battery management, data collection amount control and transmission energy consumption control. We solve these through OAA to achieve three main goals: (1) balancing the cost of energy consumption and the cost of data transmission on the premise of minimizing the service cost of the devices; (2) keeping the balance of energy consumption and energy collection under the condition of stable queue; and (3) maximizing network utility of the device. The simulation results show that the proposed algorithm can actually optimize the network performance. Full article

(This article belongs to the Special Issue Security and Privacy for Edge, Fog, and Cloud Computing; the Internet of Things and Mobile Crowdsensing)

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20 pages, 1632 KiB

Open AccessArticle

Recover User’s Private Training Image Data by Gradient in Federated Learning

by Haimei Gong, Liangjun Jiang, Xiaoyang Liu, Yuanqi Wang, Lei Wang and Ke Zhang

Sensors 2022, 22(19), 7157; https://doi.org/10.3390/s22197157 - 21 Sep 2022

Cited by 5 | Viewed by 2052

Abstract

Exchanging gradient is a widely used method in modern multinode machine learning system (e.g., distributed training, Federated Learning). Gradients and weights of model has been presumed to be safe to delivery. However, some studies have shown that gradient inversion technique can reconstruct the input images on the pixel level. In this study, we review the research work of data leakage by gradient inversion technique and categorize existing works into three groups: (i) Bias Attacks, (ii) Optimization-Based Attacks, and (iii) Linear Equation Solver Attacks. According to the characteristics of these algorithms, we propose one privacy attack system, i.e., Single-Sample Reconstruction Attack System (SSRAS). This system can carry out image reconstruction regardless of whether the label can be determined. It can extends gradient inversion attack from a fully connected layer with bias terms to attack a fully connected layer and convolutional neural network with or without bias terms. We also propose Improved R-GAP Alogrithm, which can utlize DLG algorithm to derive ground truth. Furthermore, we introduce Rank Analysis Index (RA-I) to measure the possible of whether the user’s raw image data can be reconstructed. This rank analysis derive virtual constraints

V_{i}

from weights. Compared with the most representative attack algorithms, this reconstruction attack system can recover a user’s private training image with high fidelity and attack success rate. Experimental results also show the superiority of the attack system over some other state-of-the-art attack algorithms. Full article

(This article belongs to the Special Issue Security and Privacy for Edge, Fog, and Cloud Computing; the Internet of Things and Mobile Crowdsensing)

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19 pages, 1052 KiB

Open AccessArticle

Optimization of PBFT Algorithm Based on QoS-Aware Trust Service Evaluation

by Wei Liu, Xuhao Zhang, Wenlong Feng, Mengxing Huang and Yun Xu

Sensors 2022, 22(12), 4590; https://doi.org/10.3390/s22124590 - 17 Jun 2022

Cited by 10 | Viewed by 2086

Abstract

In service-transaction scenarios, blockchain technology is widely used as an effective tool for establishing trust between service providers and consumers. The consensus algorithm is the core technology of blockchain. However, existing consensus algorithms, such as the practical Byzantine fault tolerance (PBFT) algorithm, still suffer from high resource consumption and latency. To solve this problem, in this study, we propose an improved PBFT blockchain consensus algorithm based on quality of service (QoS)-aware trust service evaluation for secure and efficient service transactions. The proposed algorithm, called the QoS-aware trust practical Byzantine fault tolerance (QTPBFT) algorithm, efficiently achieves consensus, significantly reduces resource consumption, and enhances consensus efficiency. QTPBFT incorporates a QoS-aware trust service global evaluation mechanism that implements service reliability ranking by conducting a dynamic evaluation according to the real-time performance of the services. To reduce the traffic of the blockchain, it uses a mechanism that selects nodes with higher values to form a consensus group that votes for consensus according to the global evaluation result of the trust service. A practical protocol is also constructed for the proposed algorithm. The results of extensive simulations and comparison with other schemes verify the efficacy and efficiency of the proposed scheme. Full article

(This article belongs to the Special Issue Security and Privacy for Edge, Fog, and Cloud Computing; the Internet of Things and Mobile Crowdsensing)

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