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IoT streamed data handling model using delta encoding
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AI
DOI：10.1002/dac.5243.png)
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Abstract
En 中文
Internet of Thing (IoT) is a trending internet connectivity extension to connect physical devices on the heterogeneous networks to meet application requirements on various grounds, that is, health, agriculture, industry, and so forth. The amount of streamed data is generated by the IoT devices and processed via IoT device itself or external resources such as Cloud/Fog by considering real-time constraints. The problem arises in real-time data transmission when a huge amount of stream data is generated from IoT devices, and it travels through the network and needs to be processed on low-speed IoT infrastructure. Considering this scenario, there is an extremely requirement for designing a model relying on real-time stream data handling for speedy transmission considering IoT device real-time communication constraint. On the other hand, IoT devices are also very sensitive towards energy consumption during the streamed data transmission, and low battery devices become a barrier in data handling in real-time. To overcome these limitations, a model is proposed to compress and encrypt the data stream during the IoT network transmission. In this model, delta encoding facilitates the compression in form of deltas. To ensure security requirements, a light weighted stream cipher encryption technique is proposed. For the experimentation, a test-bed setup is prepared to utilize Raspberry Pi (IoT device), CPU, and collectors to collect data stream. The proposed approach is compared with the baseline model, that is, LDPC encoding on temperature sensor datasets. As an outcome, the data are compressed up to 37.59%, and the average transmission time and the average power consumption are reduced by 72.57% and 68.86%, respectively.
Keywords:
data compression
delta encoding
IoT
power consumption
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