Truth Discovery Technology for Mobile Crowd Sensing in Water Quality MonitoringRead 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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Using an Efficient Detection Method to Prevent Personal Data Leakage for Web-Based Smart City Platforms
Many Internet of Things and information exchange technologies bring convenience, cost-efficiency, and sustainability to smart city solutions. These changes have improved our day-to-day quality of life, with impacts on: (a) lifestyle (e.g., automation and robotic reaction), (b) infrastructure (efficient energy consumption), and (c) data-driven management (data sensing, collection, and investigation). It is common to integrate Web-based interfaces and such solutions for developing platforms. When software and hardware components store, retrieve, and transfer such information, people may suffer from personal data leakage. This paper introduces a privacy information detection method, using a data weighting mechanism to save time and cost in finding personal information leaks over Web services. According to an initial evaluation, the proposed method can reduce time by 62.19% when processing 8,000 crawled files, and roll-back verification shows that it maintains 90.08% accuracy for finding marked content.
Research on 5G Network Slicing Strategy for Urban Complex Environment
Focusing on the layout of the 5G mobile communication base station in the city center, we design a 5G city network slicing strategy for the three typical application scenarios with enhanced mobile broadband (eMBB), ultrareliable low-latency communications (URLLC), and massive machine type communications (mMTC). The strategy considers multiple important network performance indicators, including user guaranteed bandwidth, maximum bandwidth limit, QoS (quality of service), link delay tolerance, and slicing throughput. The slicing strategy can greatly increase the connections of base station clients and the utilization of network resources, and effectively reduce block radio and handover radio. The simulation experiments adopt the 5G base station dataset of a coastal city layout in Zhejiang province. Our tests show that the 5G network slicing strategy has certain advantages in network transmission performance in urban complex environment. The research can provide an effective reference for 5G infrastructure construction in other cities.
RF Sign: Signature Anticounterfeiting Real-Time Monitoring System Based on Single Tag
Signatures are one of the most important means to ensure the authenticity of documents and are commonly used in life and work. In identifying imitation handwriting, it is easy to make mistakes that cannot correctly identify and evaluate different writing characteristics. In this paper, from the perspective of dynamic handwriting detection, we propose RF sign, a signature anticounterfeiting real-time monitoring model, which achieves passive recognition of signature behavior using only a single antenna with a single tag. The RF sign identifies different users by extracting fine-grained reflection features from the original RF signal. We introduced a dynamic time regularization and neural network technique for similarity calculation and signature recognition matching to achieve template matching and classification. We compiled a real-time signature handwriting detection system. The system effectively identifies the person’s signature by checking real-time spatial and temporal information. Comprehensive experiments show that the recognition accuracy of my signature can reach over 93% and is robust to input location, environmental changes, and user diversity.
DDoS Attack Detection and Classification Using Hybrid Model for Multicontroller SDN
A software-defined network (SDN) brings a lot of advantages to the world of networking through flexibility and centralized management; however, this centralized control makes it susceptible to different types of attacks. Distributed denial of service (DDoS) is one of the most dangerous attacks that are frequently launched against the controller to put it out of service. This work takes the special ability of SDN to propose a solution that is an implementation run at the multicontroller to detect a DDoS attack at the early stage. This method not only detects the attacks but also identifies the attacking paths and starts a mitigation process to provide protection for the network devices. This method is based on the entropy variation of the destination host targeted with its IP address and can detect the attack within the first 250 packets of malicious traffic attacking a particular host. Then, fine-grained packet-based detection is performed using a deep-learning model to classify the attack into different types of attack categories. Lastly, the controller sends the updated traffic information to neighbor controllers. The chi-squared () test feature selection algorithm was also employed to reveal the most relevant features that scored the highest in the provided data set. The experiment result demonstrated that the proposed Long Short-Term Memory (LSTM) model achieved an accuracy of up to 99.42% using the data set CICDDoS2019, which has the potential to detect and classify the DDoS attack traffic effectively in the multicontroller SDN environment. In this regard, it has an enhanced accuracy level to 0.42% compared with the RNN-AE model with data set CICDDoS2019, while it has improved up to 0.44% in comparison with the CNN model with the different data set ICICDDoS2017.
Relay Node-Based Routing Algorithm for Reducing Latency in Industrial Mobile Communication Network
Mobile network nodes perform time transfer during data packet performance in an asynchronous manner. Network nodes get packets for transmission from node sensors that reject the demands of node attackers. When packet loss occurs unexpectedly due to a network removal, it is exceedingly difficult to detect assaults and time forwarding. Attack detection accuracy deteriorates, and packet loss rates rise. A strategy for node mobile intermediate that gets over attacks that identify the forward choosing is provided for improved data forwarding. Network performance is completely damaged by specific attacks, such as communication processes that degrade or lose packets. The algorithm’s architecture ensures that the best nodes are chosen for relaying and that transmission packets that do not drop are declined. To enable the node and create an effective path routing, a process is executed. Decreasing the amount of packet loss and increasing attack efficiency are being identified.
Distributed Space Shift Keying in Cooperative Wireless Networks: Capacity Analysis and Error Performance
In this study, a distributed fashion of space shift keying as an efficient cooperative framework is proposed and studied to address the main challenges in future cellular networks by providing high energy efficiency and meeting the demand growth with the lowest complexity and the highest performance. A comprehensive mathematical analysis has been performed to find a closed form for the channel capacity of distributed space shift keying and to depict the influence of the number of distributed antennas on the achievable channel capacity. In addition, a simulation study of the error performance is presented using two detection methods: maximum-likelihood detection based on channel state information and blind detection. It was shown that as the number of cooperating relays increases, the error performance of the detectors converges, although they incur different costs and computational complexities. A comparison study in terms of error performance and capacity is applied with other cooperative spatial modulation to portray the outperformance of the system presented in this work.