Big Data Challenges and Trade-offs in Energy Efficient Internet of Things systems

Gambiroza, Jelena Culic; Masteli, Tonic

doi:10.23919/softcom.2018.8555770

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Over the last decade, a tremendous literature has been dealing with the energy efficiency for IoT and WSN [14,15]. In energy-constrained WSN, nodes are either powered by limited batteries, which embody the only source of energy [16], or equipped with energy-harvesting (EH) system to provide energy neutrality [17].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Joint Lossy Source-Channel Coding for Multihop IoT Networks

Ez‐zazi

Arioua

Oualkadi

2020

Wireless Communications and Mobile Computing

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We consider monitoring applications in multihop wireless sensor networks (WSNs), where nodes rely on limited batteries so that energy efficiency and reliability are of paramount importance. Typically, lossy compression is aimed at saving transmission energy, yet affects the quality of transmitted data over lossy channels. Accordingly, using error correction coding (ECC) along with compression is required to guarantee both energy efficiency and high-fidelity reconstruction. In this paper, we analyze the energy efficiency of the joint use of lossy compression along with ECC, with the twofold objective of extending the network lifetime and assuring reliability. Specifically, we consider an adaptive joint lossy source-channel coding (JLSCC) system, where the energy efficiency and reliability performances depend on both the compression and the coding rates. Therein, we first carry out a performance analysis of JLSCC, considering realistic models of communication and computational energies, when the communication is performed over a Rayleigh fading channel. Then, we evaluate the performance of the JLSCC system compared to lossy compression and ECC systems in both end-to-end and multihop communications. Our results reveal that an adaptive JLSCC results in substantial energy saving while guaranteeing the required reliability performance, compared to both lossy compression and channel coding systems, that cannot be efficient for both energy and reliability. Instead, the JLSCC system is proved to be energy efficient for small distance end-to-end communication and large-scale multihop network, while leading to satisfactory reliability performance.

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Section: Related Workmentioning

confidence: 99%

Adaptive Joint Lossy Source-Channel Coding for Multihop IoT Networks

Ez‐zazi

Arioua

Oualkadi

2020

Wireless Communications and Mobile Computing

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“…(2) IoT network with large number of battery powered sensors requires IoT tasks to be carefully planned due to a limited lifetime of sensor batteries. Therefore, data collection is commonly optimized [13]. On the one hand, in cases with predefined structures, data optimization can be done by excluding timestamps, information on sensor types and variable names which are later recovered in the cloud where the configuration metadata is stored.…”

Section: Introductionmentioning

confidence: 99%

Lost in data: recognizing type of time series sensor data using signal pattern classification

Gambiroza

Mastelić

Kosović

et al. 2023

Int J Data Sci Anal

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With the increase in number and size of Internet of Things systems, there is an ever-growing risk of (meta)data loss, as well as the maintenance overhead to mitigate such risks. The experts recognize three main challenges in this area that need to be tackled, namely (1) downsizing the manual work required for configuring sensor networks, (2) recovering metadata, such as sensor type, in case of connection issues, malfunctions or malicious actions in sensor networks, (3) rebuilding metadata lost due to unexpected problems within a data storage. Fortunately, all three challenges can be tackled with a uniform solution, namely the signal type classification approach, which is able to match raw signal to an appropriate data type. In this research, we evaluate and compare different approaches for signal type classification that can be used to recognize a signal type being read from an IoT sensor. This is done by using machine learning methods for modelling a signal represented as raw time series data. Three machine learning classification approaches are taken into a consideration, namely one class, two class and multi-class. According to the results of the evaluation, the most accurate multi-class random forest algorithm can correctly classify unknown signals in $$\sim {75}\%$$ ∼ 75 % of the cases based on only 20 consecutive sensor readings. Moreover, multi-class random forest can detect two most probable classes of monitored signal with the accuracy of 95%.

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“…Internet of Things (IoT) has attracted much research interest in recent years. It is predicted that there will be around 50 billion IoT devices 2020 [1]. In IoT applications, it is common to collect a large amount of information from distributed sensors and perform a complex task, e.g., executing a Machine Learning (ML) inference model over the data collected.…”

Section: Introductionmentioning

confidence: 99%

SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge

Khandel,

Rassafi,

Pourahmadi

et al. 2019

Preprint

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In IoT solutions, it is usually desirable to collect data from a large number of distributed IoT sensors at a central node in the cloud for further processing. One of the main design challenges of such solutions is the high communication overhead between the sensors and the central node (especially for multimedia data). In this paper, we aim to reduce the communication overhead and propose a method that is able to determine which sensors should send their data to the central node and which to drop data. The idea is that some sensors may have data which are correlated with others and some may have data that are not essential for the operation to be performed at the central node. As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node. The proposed approach has been evaluated on a multi-view multi-camera dataset, and we observe a significant reduction in communication overhead with marginal degradation in object classification accuracy.

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Big Data Challenges and Trade-offs in Energy Efficient Internet of Things systems

Cited by 6 publications

References 24 publications

Adaptive Joint Lossy Source-Channel Coding for Multihop IoT Networks

Adaptive Joint Lossy Source-Channel Coding for Multihop IoT Networks

Lost in data: recognizing type of time series sensor data using signal pattern classification

SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge

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