Development of near real-time wireless image sequence streaming cloud using Apache Kafka for road traffic monitoring application

Abstract

In this paper, the authors have designed and implemented the prototype for a near real-time wireless image sequence streaming cloud with two-layered restoration for a road traffic monitoring application of a small-scale network. Since the proposed design is targeted to implement outdoors where the link or node failure could occur, the fault-tolerant capability must be considered. Having only one layer restoration may not provide a good quality of service. Therefore, a two-layer restoration framework is designed in the proposed system by restoring the network layer with the underlying software-defined wireless mesh network capability and at the local broker selection over the Apache Kafka framework. The monitoring application performance has been investigated for the end-to-end average latency and image loss percentage by outdoor testing for 13 hours from 5:40 P.M. 17th November 2020 to 6:40 A.M. 18th November 2020. The end-to-end average latency and image loss percentage have been found to be within the acceptable condition i.e. less than 5 seconds on average with approximated 10% image losses. The proposed system has also been compared with the traditional ad-hoc network, running the OLSR-based network layer, in terms of the rerouting time, restoration time and end-to-end average latency. Based on the emulated wireless network in controllable laboratory environments, the proposed SDWMN-based system outperforms the conventional OLSR-based system with potentially faster rerouting/restoration time due to SDN central controllability and with only marginally increased end-to-end average latency after re-routing/restoration completion. Algorithm complexity analysis has also been given for both the systems. Both the experimental and complexity analysis results thus suggest the practical applicability of the proposed system. Given this promising result, it is therefore recommended as the future research in further developing from the prototype design into the actual deployment for daily traffic monitoring operations.

Network Traffic Monitoring and Real-time Risk Warning based on Static Baseline Algorithm

“…Figure 3.3 shows the actual traffic and calculated baseline values for TCP port 135 on a certain working day from 16:00 to 17:35. This port is used for shock waves, with a byte count of 48 [7]. From the graph, we can see that at 16:45, the traffic value of Port 135 suddenly increased, and then returned to normal.…”