Abstract
Known for excellent convenience and abundant facilities, smart cities offer CCTV, delivery robots, security robots, and so on to its residents. Along with the collaboration of IoT (Internet Of Things), the innovation of smart city has gained immense attraction at present. Besides, the risks and challenging in the field of telecommunication still persists as the implemented wireless networks results in traffic and anomaly behaviour. Such issues become critical in case of large-scale infrastructure networks like WSN’s. As such circumstances, to perform efficient health and environment monitoring, the need for a next generation networked system raises. As the traditional anomaly detection schemes doesn’t work out for delay-sensitive environments due to increased latency, we propose a scalable, hybrid spatiotemporal anomaly detection approach that can effectively detect potential anomalies in the network. With the use of real-time stream processing, and other methodologies like Software-Defined Networking (SDN), a Fog Computing-based 5G low-power Wide Area Network (LPWAN) solution is developed and tested on a Antwerp’s City of Things testbed. The proposed approach is found to be beneficial when deployed in a real network environment with nearly 1800 sensor nodes.
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