Advances in Internet of Things with Symmetric/Asymmetric Structure, Computing and Interaction

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (30 October 2022) | Viewed by 17914

Special Issue Editors


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Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing, China
Interests: cryptography and security protocols; internet of things security; big data security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China
Interests: pattern recognition; computer vision; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science, University of Copenhagen, København, Denmark
Interests: data security; privacy protection; mobile crowdsourcing; blockchain

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) systems involve interacting, heterogeneous, distributed, and intelligent things, both from the digital and physical world and digital world. IoTs research and application often accompany heterogeneous networks, data, and diverse security and service demands coexisting, and there are many IoT systems with symmetric or asymmetric structure, computing, and interaction. For example, in “Cloud-Edge-Terminal” cooperative IoT computing architecture, the storage, communication, and computing capabilities on each side are asymmetric. Integrating blockchain into IoT can enable symmetric information replicated in blockchain nodes. For security and privacy concerns, either symmetric or asymmetric cryptography can be employed to achieve authentication, access control, secure data sharing, and computation, etc. Moreover, heterogeneous interconnected things such as terminal devices and edge nodes make interactions between entities asymmetric. Symmetry interactions may only occur between isomorphic parties. Some research concerns are popular, such as asymmetric load matching of IoT task nodes, the asymmetric communication protocol of IoT, symmetric or asymmetric key encryption of IoT, and symmetric digital twinning based on IoT. However, the array of challenges include better understanding of IoT applications in terms of symmetry/asymmetry, scalability, and heterogeneity; reliable communication in constantly changing networks and traffic scenarios with symmetry or asymmetry; ability to mask high-latency with the symmetric or asymmetric structure of IoT; secure information handling and privacy preservation with symmetric or asymmetric key; integrating and mediating things within intelligent systems, where IoT technologies are potential catalysts; addressing impending social and societal impacts vital to the success of the IoT, etc.

The purpose of this Special Issue is to provide academic and industrial communities a platform to discuss the symmetry and asymmetry issues in IoT and share their research results. We will address the aforementioned challenges and foster the dissemination of the latest technologies, solutions, case studies, and prototypes regarding symmetric or asymmetric computing and interaction of IoT. Only high-quality articles describing previously unpublished, original, state-of-the-art research, and not currently under review by a conference or journal will be considered.

Dr. Liangmin Wang
Dr. Keyang Cheng
Dr. Haiqin Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetric or asymmetric technologies for IoT
  • IoT edge and cloud architectures with symmetry/asymmetry
  • AI for IoT with symmetry/asymmetry interaction
  • IoT communication technologies
  • energy efficiency and sustainability in IoT
  • sensing, signal processing, actuation and analytics with symmetric/asymmetric interaction
  • security and privacy in IoT with cryptology
  • symmetric/asymmetric interaction with human and IoT
  • distributed ledger technologies for IoT
  • formal methods for symmetry technologies with IoT
  • intrusion detection and prevention techniques for IoT
  • symmetry/asymmetry technology for edge computing

Published Papers (9 papers)

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Research

17 pages, 2383 KiB  
Article
Firewall Anomaly Detection Based on Double Decision Tree
by Zhiming Lin and Zhiqiang Yao
Symmetry 2022, 14(12), 2668; https://doi.org/10.3390/sym14122668 - 16 Dec 2022
Cited by 1 | Viewed by 1896
Abstract
To solve the problems regarding how to detect anomalous rules with an asymmetric structure, which leads to the firewall not being able to control the packets in and out according to the administrator’s idea, and how to carry out an incremental detection efficiently [...] Read more.
To solve the problems regarding how to detect anomalous rules with an asymmetric structure, which leads to the firewall not being able to control the packets in and out according to the administrator’s idea, and how to carry out an incremental detection efficiently when the new rules are added, anomaly detection algorithms based on an asymmetric double decision tree were considered. We considered the packet filter, the most common and used type of First Matching Rule, for the practical decision space of each rule and the whole policy. We adopted, based on the asymmetric double decision tree detection model, the policy equivalent decision tree and the policy decision tree of anomalies. Therefore, we can separate the policy’s effective decision space and the anomalous decision space. Using the separated decision trees can realize the optimization of the original policy and the faster incremental detection when adding new rules and generating a detailed report. The simulation results demonstrate that the proposed algorithms are superior to the other decision tree algorithms in detection speed and can achieve incremental detection. The results demonstrate that our approach can save about 33% of the time for complete detection compared with the other approaches, and the time of incremental anomaly detection compared to complete detection is about 90% of the time saved in a complex policy. Full article
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19 pages, 711 KiB  
Article
SDSM: Secure Data Sharing for Multilevel Partnerships in IoT Based Supply Chain
by Chuntang Yu, Yongzhao Zhan and Muhammad Sohail
Symmetry 2022, 14(12), 2656; https://doi.org/10.3390/sym14122656 - 15 Dec 2022
Cited by 8 | Viewed by 1871
Abstract
Symmetric encryption algorithms enable rapid encryption of data in IoT based supply chains, which helps to alleviate the concerns of supply chain participants about privacy disclosure when sharing data. However, in supply chain management where multilevel partnerships exist universally, a pure symmetric encryption [...] Read more.
Symmetric encryption algorithms enable rapid encryption of data in IoT based supply chains, which helps to alleviate the concerns of supply chain participants about privacy disclosure when sharing data. However, in supply chain management where multilevel partnerships exist universally, a pure symmetric encryption scheme cannot provide efficient data sharing and fine-grained access control. To overcome these problems, this paper proposes a secure data sharing scheme (SDSM) for IoT based supply chains by combining blockchain and ciphertext-based attribute cryptography. This scheme supports the enforcement of fine-grained access control for different levels of partnerships. In addition, to identify partnerships, we propose a metric based on the historical transaction facts on the blockchain, where the level of partnerships among participants is automatically calculated by smart contracts. Finally, we introduce personalized attributes of participants in the ciphertext-based attribute encryption algorithm to support the construction of access policies that include partnerships, allowing for more fine-grained access control. Security analyses and simulation experiments show that our proposed scheme is secure, effective, and practical. Full article
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14 pages, 1447 KiB  
Article
An Energy-Efficient Method for Recurrent Neural Network Inference in Edge Cloud Computing
by Chao Chen, Weiyu Guo, Zheng Wang, Yongkui Yang, Zhuoyu Wu and Guannan Li
Symmetry 2022, 14(12), 2524; https://doi.org/10.3390/sym14122524 - 29 Nov 2022
Cited by 1 | Viewed by 1476
Abstract
Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such as natural language processing. Edge cloud computing systems are in an asymmetric structure, where task managers allocate tasks to the asymmetric edge and cloud computing systems based on computation requirements. In [...] Read more.
Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such as natural language processing. Edge cloud computing systems are in an asymmetric structure, where task managers allocate tasks to the asymmetric edge and cloud computing systems based on computation requirements. In such a computing system, cloud servers have no energy limitations, since they have unlimited energy resources. Edge computing systems, however, are resource-constrained, and the energy consumption is thus expensive, which requires an energy-efficient method for RNN job processing. In this paper, we propose a low-overhead, energy-aware runtime manager to process tasks in edge cloud computing. The RNN task latency is defined as the quality of service (QoS) requirement. Based on the QoS requirements, the runtime manager dynamically assigns RNN inference tasks to edge and cloud computing systems and performs energy optimization on edge systems using dynamic voltage and frequency scaling (DVFS) techniques. Experimental results on a real edge cloud system indicate that in edge systems, our method can reduce the energy up to 45% compared with the state-of-the-art approach. Full article
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21 pages, 828 KiB  
Article
MDS2-C3PF: A Medical Data Sharing Scheme with Cloud-Chain Cooperation and Policy Fusion in IoT
by Heng Pan, Yaoyao Zhang, Xueming Si, Zhongyuan Yao and Liang Zhao
Symmetry 2022, 14(12), 2479; https://doi.org/10.3390/sym14122479 - 23 Nov 2022
Cited by 2 | Viewed by 1691
Abstract
The Internet of Things (IoT) and cloud technologies have significantly facilitated healthcare. In such a context, medical data are collected by the terminals from the patients, manipulated, and stored on the cloud by hospitals (doctors). This brings asymmetry problems in medical data access [...] Read more.
The Internet of Things (IoT) and cloud technologies have significantly facilitated healthcare. In such a context, medical data are collected by the terminals from the patients, manipulated, and stored on the cloud by hospitals (doctors). This brings asymmetry problems in medical data access control, processing, and storage between doctors and patients, which results in medical data sharing face many challenges such as privacy leakage and malicious feedback from cloud servers on queries. To solve these asymmetry problems, this paper proposes a medical data sharing scheme with cloud-chain cooperation and policy fusion in the IoT. Regarding asymmetrical access control rights, a conflict resolution and fusion algorithm that enables co-authorization of medical data by the doctor and the patient is introduced. To balance the symmetry of medical data storage and processing, a cloud-chain cooperation ciphertext retrieval method is proposed by means of two-stage joint searching from cloud servers and the blockchain, which can not only detect malicious medical data feedback from cloud servers, but also improve the data search efficiency. The security analysis showed that this scheme satisfies the confidentiality and verifiability of the retrieved information, and the feasibility of the proposed scheme was demonstrated through experiments. Full article
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18 pages, 3283 KiB  
Article
Bidirectional Statistical Feature Extraction Based on Time Window for Tor Flow Classification
by Hongping Yan, Liukun He, Xiangmei Song, Wang Yao, Chang Li and Qiang Zhou
Symmetry 2022, 14(10), 2002; https://doi.org/10.3390/sym14102002 - 24 Sep 2022
Cited by 2 | Viewed by 1686
Abstract
The anonymous system Tor uses an asymmetric algorithm to protect the content of communications, allowing criminals to conceal their identities and hide their tracks. This malicious usage brings serious security threats to public security and social stability. Statistical analysis of traffic flows can [...] Read more.
The anonymous system Tor uses an asymmetric algorithm to protect the content of communications, allowing criminals to conceal their identities and hide their tracks. This malicious usage brings serious security threats to public security and social stability. Statistical analysis of traffic flows can effectively identify and classify Tor flow. However, few features can be extracted from Tor traffic, which have a weak representational ability, making it challenging to combat cybercrime in real-time effectively. Extracting and utilizing more accurate features is the key point to improving the real-time detection performance of Tor traffic. In this paper, we design an efficient and real-time identification scheme for Tor traffic based on the time window method and bidirectional statistical characteristics. In this paper, we divide the network traffic by sliding the time window and then calculate the relative entropy of the flows in the time window to identify Tor traffic. We adopt a sequential pattern mining method to extract bidirectional statistical features and classify the application types in the Tor traffic. Finally, extensive experiments are carried out on the UNB public dataset (ISCXTor2016) to validate our proposal’s effectiveness and real-time property. The experiment results show that the proposed method can detect Tor flow and classify Tor flow types with an accuracy of 93.5% and 91%, respectively, and the speed of processing and classifying a single flow is 0.05 s, which is superior to the state-of-the-art methods. Full article
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21 pages, 1201 KiB  
Article
Effective Consensus-Based Distributed Auction Scheme for Secure Data Sharing in Internet of Things
by Xuedan Jia, Xiangmei Song and Muhammad Sohail
Symmetry 2022, 14(8), 1664; https://doi.org/10.3390/sym14081664 - 11 Aug 2022
Cited by 5 | Viewed by 2039
Abstract
In a traditional electronic auction, the centralized auctioneer and decentralized bidders are in an asymmetric structure, where the auctioneer has more ability to decide the auction result. This asymmetric auction structure is not fair to the participants and not suitable for data auctions [...] Read more.
In a traditional electronic auction, the centralized auctioneer and decentralized bidders are in an asymmetric structure, where the auctioneer has more ability to decide the auction result. This asymmetric auction structure is not fair to the participants and not suitable for data auctions in the Internet of Things (IoT). The blockchain-based auction system, with participant equality and fairness, is typically symmetrical and particularly suitable for IoT data sharing. However, when applied to IoT data sharing in reality, it faces privacy and efficiency problems. In this context, how to guarantee privacy and break the inherent performance bottleneck of blockchain is still a major challenge. In this paper, a consensus-based distributed auction scheme is proposed for data sharing, which enforces privacy preservation and collusion resistance. A reverse auction-based decentralized data trading model is introduced to solve the trust problem without a centralized auctioneer, where bidders reach consensus on the auction result. Specifically, we devise a differentially private auction mechanism to incentivize data owners to participate in data sharing. An effective hybrid consensus algorithm is constructed among bidders to reach consensus on the auction result with improved security and efficiency. Theoretical analysis shows that the proposed scheme ensures the properties of privacy preservation, incentive compatibility and collusion resistance. Experimental results reveal that the proposed mechanism guarantees the data sharing efficiency and has certain scalability. Full article
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16 pages, 2560 KiB  
Article
Collision Forgery Attack on the AES-OTR Algorithm under Quantum Computing
by Lipeng Chang, Yuechuan Wei, Xiangru Wang and Xiaozhong Pan
Symmetry 2022, 14(7), 1434; https://doi.org/10.3390/sym14071434 - 13 Jul 2022
Cited by 2 | Viewed by 1732
Abstract
In recent years, some general cryptographic technologies have been widely used in network platforms related to the national economy and people’s livelihood, effectively curbing network security risks and maintaining the orderly operation and normal order of society. However, due to the fast development [...] Read more.
In recent years, some general cryptographic technologies have been widely used in network platforms related to the national economy and people’s livelihood, effectively curbing network security risks and maintaining the orderly operation and normal order of society. However, due to the fast development and considerable benefits of quantum computing, the classical cryptosystem faces serious security threats, so it is crucial to analyze and assess the anti-quantum computing ability of cryptographic algorithms under the quantum security model, to enhance or perfect the design defects of related algorithms. However, the current design and research of anti-quantum cryptography primarily focus on the cryptographic structure or working mode under the quantum security model, and there is a lack of quantum security analysis on instantiated cryptographic algorithms. This paper investigates the security of AES-OTR, one of the third-round algorithms in the CAESAR competition, under the Q2 model. The periodic functions of the associated data were constructed by forging the associated data according to the parallel and serial structure characteristics of the AES-OTR algorithm in processing the associated data, and the periodic functions of the associated data were constructed multiple times based on the Simon quantum algorithm. By using the collision pair, two collision forgery attacks on the AES-OTR algorithm can be successfully implemented, and the period s is obtained by solving with a probability close to 1. The attacks in this paper caused a significant threat to the security of the AES-OTR algorithm. Full article
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22 pages, 1689 KiB  
Article
EBAS: An Efficient Blockchain-Based Authentication Scheme for Secure Communication in Vehicular Ad Hoc Network
by Xia Feng, Kaiping Cui, Haobin Jiang and Ze Li
Symmetry 2022, 14(6), 1230; https://doi.org/10.3390/sym14061230 - 14 Jun 2022
Cited by 6 | Viewed by 1963
Abstract
A vehicular ad hoc network (VANET) is essential in building an intelligent transportation system that optimizes traffic conditions and makes traffic information conveniently accessible. However, malicious vehicles may disrupt the traffic order via propagating forged traffic/road information. Therefore, using digital certificates based on [...] Read more.
A vehicular ad hoc network (VANET) is essential in building an intelligent transportation system that optimizes traffic conditions and makes traffic information conveniently accessible. However, malicious vehicles may disrupt the traffic order via propagating forged traffic/road information. Therefore, using digital certificates based on cryptography, some existing authentication schemes were proposed to manage vehicles’ identities. At first glance, these schemes can effectively identify malicious vehicles. However, these schemes require more computation and storage resources to maintain certificates. This is because the data storage of the database increases in a near-linear trend as the number of certificates grows. In this paper, we propose an efficient blockchain-based authentication scheme for secure communication in VANET (EBAS) to address the aforementioned issues. In EBAS, the regional trusted authority (RTA) receives traffic messages uploaded by the vehicle, together with transactions constructed via the unspent transaction output (UTXO) model. The verifier checks the legitimacy of the single input contained in the uploaded transaction to verify the legitimacy of the message sender’s identity. In terms of privacy preservation, a asymmetric key encryption technique, elliptic curve cryptography (ECC), is applied for constructing the transaction pseudonym, and users participate in the authentication process anonymously. In addition, our scheme guarantees the scalability of EBAS by proposing a transaction update mechanism, which can keep data storage at a stable level rather than near-linear growth. Under the simulation, the retrieving overhead remains at approximately 0.32 ms while the storage cost is stable at around 32.7 M for the blockchain state database. In terms of authentication efficiency, the average overhead of the proposed scheme is around 0.942 ms, which outperforms the existing schemes. Full article
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22 pages, 2276 KiB  
Article
Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression
by Xiangwen Liu, Xia Feng and Yuquan Zhu
Symmetry 2022, 14(3), 472; https://doi.org/10.3390/sym14030472 - 25 Feb 2022
Cited by 3 | Viewed by 2110
Abstract
Ubiquitous devices in IoT-based environments create a large amount of transactional data on daily personal behaviors. Releasing these data across various platforms and applications for data mining can create tremendous opportunities for knowledge-based decision making. However, solid guarantees on the risk of re-identification [...] Read more.
Ubiquitous devices in IoT-based environments create a large amount of transactional data on daily personal behaviors. Releasing these data across various platforms and applications for data mining can create tremendous opportunities for knowledge-based decision making. However, solid guarantees on the risk of re-identification are required to make these data broadly available. Disassociation is a popular method for transactional data anonymization against re-identification attacks in privacy-preserving data publishing. The anonymization algorithm of disassociation is performed in parallel, suitable for the asymmetric paralleled data process in IoT where the nodes have limited computation power and storage space. However, the anonymization algorithm of disassociation is based on the global recoding mode to achieve transactional data km -anonymization, which leads to a loss of combinations of items in transactional datasets, thus decreasing the data quality of the published transactions. To address the issue, we propose a novel vertical partition strategy in this paper. By employing local suppression and global partition, we first eliminate the itemsets which violate km-anonymity to construct the first km-anonymous record chunk. Then, by the processes of itemset creating and reducing, we recombine the globally partitioned items from the first record chunk to construct remaining km-anonymous record chunks. The experiments illustrate that our scheme can retain more association between items in the dataset, which improves the utility of published data. Full article
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