Post-Quantum Privacy-Preserving Provable Data Possession Scheme Based on Smart ContractsRead the full article
Wireless Communications and Mobile Computing provides the R&D communities working in academia and the telecommunications and networking industries with a forum for sharing research and ideas in this fast moving field.
Chief Editor Dr Cai is an Associate Professor in the Department of Computer Science at Georgia State University, USA and an Associate Director at INSPIRE Center.
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Mobility-Aware Offloading and Resource Allocation Strategies in MEC Network Based on Game Theory
With the emergence and development of communication technology and new computing paradigm named mobile edge computing (MEC), fast response and ultralow latency are given higher requirements. Nevertheless, due to the low penetration and coverage of the MEC network, it is difficult to guarantee the large-scale connection needs of all user groups in industry 4.0. In addition, user mobility is closely related to the network connection between edge nodes (ENs) and mobile devices (MDs) in industry 4.0, the frequent mobility of MDs makes the computation offloading process not smooth and the channel unstable, which can reduce the network performance. Hence, this paper constructs an edge network environment for MEC-based industrial internet of things (IIoT), considering the combined benefits of energy consumption, time delay, and computing resource cost to tackle the aforementioned problem by maximizing the utility of the entire system. In order to solve this problem, this paper proposes a mobility-aware offloading and resource allocation scheme (MAORAS). This scheme first employs the Lagrange multiplier method to solve the problem of computing resource allocation; then, a noncooperative game between MDs is established and the existence of Nash equilibrium (NE) has been proven. Simulation results demonstrate that the practical performance of the MAORAS optimization scheme could improve the system utility significantly.
Improved Grey Wolf Optimization- (IGWO-) Based Feature Selection on Multiview Features and Enhanced Multimodal-Sequential Network Intrusion Detection Approach
The goal of the network intrusion detection system (NIDS) is to spot malicious activity in a network. It seeks to do that by examining the behavior of the traffic network. To find abnormalities, the NIDS heavily use machine learning (ML) and data mining techniques. The performance of NIDSs is significantly impacted by feature selection. This is due to the numerous characteristics that are used in anomaly identification, which take a lot of time. The time required to analyze traffic behavior and raise the accuracy level is thus influenced by the feature selection strategy. In the current work, the researcher’s goal was to provide a feature selection model for NIDSs. IGWO (improved grey wolf optimizations) for FSs (feature selections) was proposed to address these difficulties. The three primary processes in this proposed study are preprocessing, extractions and classifications of FSs, and evaluations of results. IGWOs are used to choose a subset of input variables by minimizing features to measure the accuracy in the search space and discover the best solution. A particular structure of HPNs (hierarchical progressive networks) is controlled by the MDAEs (multimodal deep autoencoders) and ABLSTMs (attention-based long short-term memories) for enhanced multimodal-sequential IDSs, i.e., AB-LSTMs. It is possible to understand relationships between neighboring network connections automatically and efficiently integrate information from many levels of characteristics inside a network connection using the EMS-DHPN technique simultaneously. This work’s suggested hybrid IDSs called IGWO-EMS-DHPN technique were evaluated using two intrusion datasets: UNSW-NB15 and CICIDS-2017 which is compared with other existing classifiers in terms of relative accuracies, precisions, recalls, and -scores in categorizations. While several classifiers have been developed, the suggested IGWO-EMS-DHPN classifier obtains maximum accuracy.
ZPA: A Smart Home Privacy Analysis System Based on ZigBee Encrypted Traffic
Currently, the ZigBee protocol is widely used in smart homes and provides convenience to people. However, smart home devices often carry a large amount of real physical world information, which may result in information leakage problems. In this paper, to reveal the privacy security issues existing in ZigBee-based smart home networks, we design a smart home privacy analysis system based on ZigBee-encrypted traffic, called ZPA. ZPA can extract ZigBee data features based on the device’s operating mode and time window and use state-of-the-art machine-learning models to identify the type and status of smart home devices that could leak users’ private information. Through the analysis of 20 different devices from 5 manufacturers, the results show that even if the ZigBee traffic is protected by encryption, the accuracy of the proposed method in device type identification and state inference can reach approximately 93% and 98%, respectively. The types and statuses of devices in smart homes will reveal the user’s activity information to a certain extent. The privacy security of ZigBee-based smart devices still needs to be further strengthened.
Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning
Wireless sensor networks are the core of the Internet of Things and are used in healthcare, locations, the military, and security. Threats to the security of wireless sensor networks built on the Internet of Things (IoT-WSNs) can come from a variety of sources. This study proposes secure attack localization and detection in IoT-WSNs to improve security and service delivery. The technique used blockchain-based cascade encryption and trust evaluation in a hierarchical design to generate blockchain trust values before beacon nodes broadcast data to the base station. Simulation results reveal that cascading encryption and feature assessment measure the trust value of nodes by rewarding each other for service provisioning and trust by removing malicious nodes that reduce localization accuracy and quality of service in the network. Federated machine learning improves data security and transmission by merging raw device data and placing malicious threats in the blockchain. Malicious nodes are classified through federated learning. Federated learning combines hybrid random forest, gradient boost, ensemble learning, -means clustering, and support vector machine approaches to classify harmful nodes via a feature assessment process. Comparing the proposed system to current ones shows an average detection and classification accuracy of 100% for binary and 99.95% for multiclass. This demonstrates that the suggested approach works well for large-scale IoT-WSNs, both in terms of performance and security, when utilizing heterogeneous wireless senor networks for the providing of secure services.
Signal Processing with Machine Learning for Context Awareness in 5G Communication Technology
To meet users’ expectations for speed and reliability, 5th Generation (5G) networks and other forms of mobile communication of the future will need to be highly efficient, flexible, and nimble. Because of the expected density and complexity of 5G networks, sophisticated network control across all layers is essential. In this context, self-organizing network (SON) is among the essential solutions for managing the next generation of mobile communication networks. Self-optimization, self-configuration, and self-healing (SH) are typical SON functions. This research creates a framework for analyzing SH by exploring the impact of recovery measures taken in precarious stages of health. For this reason, our suggested architecture takes into account both detection and compensating operations. The system is broken down into some faulty states and the “fuzzy c-means” (FCM) approach is used to conduct the classifying. In the compensation process, the network is characterized as the Markov decision model (MDM), and the linear programming (LP) technique is implemented to find the most effective strategy for reaching a goal. Numerical findings acquired from a variety of situations with varying objectives show that the suggested method with optimized operations in the compensation stage exceeds the approach with randomly chosen actions.
Stability Control of Position Flow Fuzzy Estimation in Swarm Intelligence Aware Privacy Protection
The group intelligence perception privacy protection model is a method to achieve the balance between user privacy and service requests through the cooperation between users using location services and has a good perception effect. In order to better protect the location privacy of network users and improve the stability control effect of fuzzy estimation of location flow, this paper designs a stability control method of fuzzy estimation of location flow in group intelligent perception privacy protection. This method uses the group intelligence aware privacy protection model to obtain the user network location coordinates in the group intelligence aware privacy protection. Taking the user’s network location coordinates as input, the location flow queue of multiple users in the group intelligence aware privacy protection network is obtained by the Lyapunov multiobjective location flow estimation queue model. After the fuzzy processing of the user location flow queue, the online control mechanism of location flow fuzzy estimation stability under different conditions is established. According to the online control mechanism, a stability control method based on access control and group intelligence aware task allocation is used to realize the stability control of location flow fuzzy estimation in group intelligence aware privacy protection. The experimental results show that the method can obtain 100% of the user location integrity in the group intelligence aware privacy protection, and the target location flow estimation queue is more accurate. It can effectively reduce the number of communication rounds of fuzzy estimation of location flow in the group intelligence aware privacy protection and has better stability control ability.