Abstract. Real-time indoor positioning systems based on Wi-Fi signal strength are studied. This study is about how to decide specific three target APs based on Wi-Fi RSSI signals from several APs(access point), and calculate the distance from a mobile object to the selected reference APs by ChipconCO formula. Calculated three distances are used to position the mobile object by using the triangulation method. To overcome the positioning errors, a topology which has five Aps in a square area is proposed and analyzed.
Sophisticated traffic analytics, such as the encrypted traffic analytics and unknown malware detection, emphasizes the need for advanced methods to analyze the network traffic. Traditional methods of using fixed patterns, signature matching, and rules to detect known patterns in network traffic are being replaced with AI (Artificial Intelligence) driven algorithms. However, the absence of a high-performance AI networking-specific framework makes deploying real-time AI-based processing within networking workloads impossible. In this paper, we describe the design of Traffic Analytics Development Kits (TADK), an industry-standard framework specific for AI-based networking workloads processing. TADK can provide real-time AI-based networking workload processing in networking equipment from the data center out to the edge without the need for specialized hardware (e.g., GPUs, Neural Processing Unit, and so on). We have deployed TADK in commodity WAF and 5G UPF, and the evaluation result shows that TADK can achieve a throughput up to 35.3Gbps per core on traffic feature extraction, 6.5Gbps per core on traffic classification, and can decrease SQLi/XSS detection down to 4.5µs per request with higher accuracy than fixed pattern solution.
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