توصیف تاثیر توپولوژی در پردازش جریان اینترنت اشیا
Abstract
1- INTRODUCTION
2- SYSTEM ARCHITECTURE & TOPOLOGIES
3- EXPERIMENTAL METHODOLOGY
4- OCCUPANCY PREDICTION & FORECASTING
5- RESULTS
6- RELATED WORK
7- CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES
Abstract
The Internet of Things (IoT) extends traditional cyber-physical systems by linking sensor based edge devices to network accessible services and resources. In most current IoT deployments, sensor data is streamed from edge devices to servers for storage. Analytical pipelines are then used to translate this raw sensor data into actionable information in real-time. As additional IoT devices are deployed, the volume and rate of data received on the server side can increase dramatically. This has a possibility of offsetting the response latencies beyond acceptable limits for IoT analytical systems. In this paper, we compare the impact of alternative serverside stream processing topologies for ingesting and analyzing IoT sensor data in real-time. We use real building sensor data with our real-time IoT platform called Namatad. We have characterized and analyzed the latency and QoS impact due to the different levels of granularity of the ingestion and routing process by which we transmit data into the analytical pipelines. Our results show that as IoT systems continue to scale in density, server-side topology management for IoT data streams is critical for latency-sensitive control and analysis applications.
INTRODUCTION
Internet of Things (IoT) systems consist of multiple compute platforms, notably edge devices and servers. Within IoT systems, most of the focus has been on the development and integration of novel new edge devices. For example, the recent proliferation of wearable devices designed to monitor personal health has increased significantly in recent years yielding unprecedented awareness of fitness. Similarly, new smart buildings are integrating new sensors with building control systems to improve energy efficiency, occupant comfort, and safety [1], [2]. The raw data obtained using IoT devices provides tremendous operational insight, which is driving the deployment of additional IoT devices. For deployed IoT devices, once sensor values are read the data generated is transmitted across the network and stored on server platforms for later analysis [3]. Once stored, this data is then analyzed, leveraging recent advances in machine learning. To date, most of these IoT analytics have been performed offline, using batch-oriented techniques. However, as IoT analytics transition to online, real-time pipelines that immediately translate raw data into actionable information, earlier approaches to manage streaming data becomes challenging. Additionally, as the number of deployed IoT devices increases, how IoT data is routed and processed must be handled judiciously to prevent overloading and ensure scalability.