International Journal of Network Management最新文献

Multi-access edge computing (MEC) enables users to exploit the resources of cloud computing at a base station (BS) in proximity to the users where an MEC server is hosted. While we have advantage of being able to communicate with low latency and small network load in MEC networks, the resources in BSs are limited. One challenge is where to provide users with services from to make efficient use of resources. Furthermore, to enhance the reliability of MEC system, the case that a BS fails needs to be considered. This paper proposes a service placement and user assignment model with preventive start-time optimization against a single BS failure in MEC networks. The proposed model preventively determines the service placement and user assignment in each BS failure pattern to minimize the worst-case penalty which is the largest penalty among all failure patterns. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP-hard. When the problem size becomes large, it may not be solved in a practical computation time. To solve larger size problems, we introduce two algorithms: one is the greedy algorithm with allocation upgrade and the other is with allocation upgrade and preemption. The results show that the introduced algorithms obtain solutions with smaller worst-case penalty than the benchmark in a practical time.

In blockchain networks, topology discovery is a prerequisite when investigating the network characteristics (e.g., performance and robustness), which can provide a deeper comprehension of the behavior of the nodes and topology dynamicity. In this paper, we conduct a longitudinal study on the Bitcoin topology by collecting network snapshots from 2018 to 2022 with Node-Probe, our topology discovery technique that uses recursive scanning to find all reachable nodes in the Bitcoin network. Using Node-Probe, we have collected 5-week-long snapshots (36-day-long snapshots) of the Bitcoin main network and analyzed the network properties, community structure, and topology dynamicity. We confirm that our approach achieves a precision of 99% with a recall of 98% in inferring the topology. Analytical results on community structure show that the Bitcoin network has $�\times �4$ more communities than what should be expected from a random network. Meanwhile, analytical results on dynamicity indicate that the topology stands firmly on heavy and long-running nodes. Improving the propagation mechanism using master nodes could improve the propagation delay by proximity $�\times �25$ compared with the Bitcoin default protocol. Considering a K-anonymity attack, any transaction from one of the autonomous systems containing only a single Bitcoin node can easily be linked to real users' IP information.

Dynamic vehicle detection requires the transmission of large amounts of data collected by different types of sensors to the edge computing nodes. This is likely to cause network delays and congestion, affecting the computation of the edge computing nodes and thus posing serious security risks. Therefore, optimizing data transmission between vehicles and edge computing nodes is a new challenge to be addressed in the practical application of edge computing-based vehicle dynamic detection architectures. The data requirements of VDT for vehicle detection dynamic detection in different environments are considered, the optimization objectives and constraints are analysed, and a deviation detection and greedy algorithm is proposed in this paper to address the problems of long mixed-integer linear programme solution time and insufficient practical applications, and the performance of the algorithm is evaluated through simulation experiments conducted by simulation of urban mobility, a traffic flow simulation tool, and PreScan, a vehicle simulation test software. The results show that compared with the deviation detection algorithm, the greedy algorithm can reduce the communication overhead by 82.6%–86.2% in all cases and improve the performance by 13.6%–19.5%, which is more suitable for practical applications. The results of this paper contribute to the automation and modernization of vehicle technology management and information transfer.

Internet of Things (IoT) and related applications have revolutionized most of our societal activities, enhancing the quality of human life. This study presents an IoT-based model that enables optimized parking space utilization. The paper implements a Hybrid Deep DenseNet Optimization (HDDNO) algorithm for predicting parking spot availability involving Machine Learning (ML) and deep learning techniques. The HDDNO-based ML model uses secondary data from the National Research Council Park (CNRPark) in Pisa, Italy. Different regression algorithms are employed to forecast parking lot availability for a given time as part of the prediction process. The DenseNet technique has generated promising results, whereas the HDDNO model yielded better accuracy. The use of five optimizers, namely, Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop), Adaptive Gradient (AdaGrad), AdaDelta, and Stochastic Gradient Descent (SGD), have played significant roles in minimizing the loss of the model. The part of Adam has enabled the HDDNO model to generate predictions with high accuracy 99.19% and low loss 0.0306%. This proposed methodology would significantly improve environmental safety and act as an initiative toward developing smart cities.

The world is becoming increasingly connected, from Internet of Things (IoT) and social networks to big data and cloud computing. Networked systems are not just relevant to information technology but have been already integrated with the engineering and cyber-physical systems domains. The agents of networked systems (e.g., sensor networks) can sense, compute, and interact based on a given task. However, networked systems suffer from many security and trust issues, as these systems are mostly designed based on traditional IT infrastructure. For example, attackers can compromise one internal sensor nodes and infect other nodes afterwards. There was an increase in malware attacks on IoT/Connected Devices of 77% in the first half of 2022, according to a recent report by Sonic Wall. This special issue focuses on how to build a trust and secure networked systems, and identifies new issues and directions for future research and development work.

In the first contribution entitled “Privacy-preserving and efficient user matching based on attribute encryption in mobile social networks,” Wu et al. aimed to protect users' privacy and introduced an attribute-based encryption scheme based on the defined policy. Then the server does not need to decrypt the attribute matching file frequently for dating users. The proposed scheme can provide several benefits: (1) it allows two-way matching to support suitable publishers recommended to requesters with dating interests, (2) our scheme protects the privacy of users by encrypting the tagged keywords of the interest information collected from requesters and the personal attribute information of publishers, and (3) the scheme can reduce the computational cost by transferring most of the decryption work to the matching server and dividing the encryption into preparation and online stages.

In the second contribution entitled “Privacy Preserving distributed smart grid system based on Hyperledger Fabric and Wireguard,” Yao et al. focused on the security issues of Smart Grid and designed a secure and decentralized energy trading platform in edge area of smart grid system by means of Hyperledger Fabric and WireGuard VPN. WireGuard can customize network gateway and controls traffic by WireGuard Interface, which includes an exclusive private key generated by the elliptic curve, a User Datagram Protocol (UDP) listening port, and a group of peer nodes. The proposed architecture was composed of four main layers, including application, blockchain platform, network structure, and physical infrastructure. In the experiment, the authors tested the bandwidth in WireGuard network and transactions throughput capacity of HyperLedger Fabric blockchain. It showed the feasibility of the proposed architecture, even with the WireGuard communication latency.

In the third contribution entitled “Intelligent detection of vulnerable functions in software through neural embedding-based code analysis,” Zeng et al. found that conv

This paper concentrates on the issue of detecting abnormal flows in distributed monitoring systems, which has many network management applications such as anomaly detection and traffic engineering. Collecting massive network traffic in real-time remains a large challenge due to the limited system resource. Most existing approaches perform abnormal flow detection at one measurement point, while they cause large computation and memory overhead for recovering abnormal flows. In this paper, we propose a novel data streaming method that supports accurate abnormal flow detection with a low memory requirement. The key idea of our method is that each monitor compresses flow information to summary data structure, sends the generated data structure to the controller; then the controller aggregates the received data structures, recovers candidates of abnormal flows and estimates their size and change to find abnormal flows on the basis of the aggregated data structure. The experimental results based on real network traffic show that the proposed approach can detect up to 97% of abnormal flows with low memory and update requirements in comparison with related approaches.

A decentralized application runs on the blockchain network without the intervention of a central authority. Transparency in transactions and security in vehicular networks are the issues for central systems. The proposed system uses blockchain-based smart contracts, which eliminate the requirement for any third-party verification. Additionally, with signature verification and reduced overhead, smart contracts also help in a fast and secure transaction. This study suggests a trust-based system paradigm where certificate authority (CA) is employed for vehicle registration. We also propose a blockchain-based system that provides efficient two-way authentication and key agreement through encryption and digital signatures. The analysis of the proposed model reveals that it is an efficient way of establishing distributed trust management, which helps in preserving vehicle privacy. The proposed scheme is tested in Automated Validation of Internet Security-sensitive Protocols (AVISPA), and security parameters verification in Network Simulator 2(NS2) also shows that the proposed scheme is more effective in comparison with existing schemes in terms of authentication cost, storage cost, and overhead.