IoT Stream Data Compression Using LDPC Coding

Jindal, Rajni; Kumar, Neetesh; Patidar, Sanjay

doi:10.1007/978-981-15-5788-0_44

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Initially, the 8 bit data is passed from IoT device to an IoT border router. As input data is 8 bit, which represents 2 8 means 256 data points. The model compresses 52% of these data points, which means 133 out of 256 data points.…”

Section: Receiving Data In Data Stream Collectormentioning

confidence: 99%

“…When data streams are transmitted from the IoT devices to a data stream collector, it travels over the heterogeneous IoT network and the Internet. In this scenario, IoT devices have limited resources in terms of speed like Bluetooth Low Energy (BT‐LE), and some devices like computers, phones and laptops use fast networks such as Wi‐Fi or Ethernet 7,8 . Further, the size of transmitted data is also dependent on the number of fragmentation in terms of maximum transmission units.…”

Section: Introductionmentioning

confidence: 99%

“…In this scenario, IoT devices have limited resources in terms of speed like Bluetooth Low Energy (BT-LE), and some devices like computers, phones and laptops use fast networks such as Wi-Fi or Ethernet. 7,8 Further, the size of transmitted data is also dependent on the number of fragmentation in terms of maximum transmission units. Some important factors like power consumption and limited resources make it data size sensitive.…”

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confidence: 99%

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IoT streamed data handling model using delta encoding

Jindal

Kumar

Patidar

2022

Int J Communication

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Summary 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.

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Section: Receiving Data In Data Stream Collectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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IoT streamed data handling model using delta encoding

Jindal

Kumar

Patidar

2022

Int J Communication

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“…The underlying principle of the LEC-based scheme is to encode the difference in expected and original value and forward it. Similarly, authors in [15] have proposed a compression approach to reduce the data transmission. In their technique, data are encoded first with a key stream and then compression is performed using Low-density parity-check coding.…”

Section: Related Workmentioning

confidence: 99%

Using Dynamic Perceptually Important Points for Data Reduction in IoT

Hafeez

McArdle

2021

Proceedings of the 11th International Conference on the Internet of Things

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Massive amounts of data are generated from various Internet of Things (IoT) devices. Handling these data requires resources and as data volumes increase, the resource requirements increase too. For example, more storage and processing resources are required for downstream tasks such as predictive analytics in the cloud. Furthermore, the transmission of these data results in an increased energy consumption and congestion of network resources. IoT data reduction is one of the solutions to handle some of these resource issues. However, how to reduce data volume without affecting the integrity and pattern of the data for downstream monitoring tasks is a challenge. Moreover, investigating the effects of data reduction on the accuracy of prediction models is often ignored. Therefore, in this paper, a new data reduction algorithm has been proposed for delay-tolerant applications. It has three cases, namely best, good and worst. In the best, the approach forwards an average value. In the good case, the approach sends important data points using Perceptually Important Point (PIP) algorithm. All of the data in a window is forwarded in the worst case, preserving the data pattern when there are many important data points. In this paper, the performance of the approach is examined. The paper presents the trade-off between reduced data and the accuracy of the downstream machine learning and deep learning tasks as well as the time complexity of the approach. For comparison, three algorithms from the literature have also been implemented and tested on a NASA turbofan engine time-series dataset. The results show that the proposed algorithm reduces data size by almost half while maintaining the accuracy of the prediction task.

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