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| Title | Data Reduction Techniques For Wireless Multimedia Sensor Networks: A Systematic Literature Review |
|---|---|
| ID_Doc | 17328 |
| Authors | Abbood I.K.; Idrees A.K. |
| Year | 2024 |
| Published | Journal of Supercomputing, 80, 7 |
| DOI | http://dx.doi.org/10.1007/s11227-023-05842-8 |
| Abstract | The potential of Internet of Things and wireless sensor networks technologies can be used to build a picture of a future intelligent surveillance system. Because of the small size of the sensor nodes and their ability to transmit data remotely, they can be deployed at locations that are difficult or impossible to access. Wireless multimedia sensor network represents a distinct subdomain within the broader scope of wireless sensor networks. It has found diverse applications in the context of future smart cities, particularly in areas such as healthcare monitoring, home automation, transportation systems, and vehicular networks. A wireless multimedia sensor network is a resource-constrained network in which the nodes are small battery-powered devices. In addition, sending a large amount of collected data by wireless multimedia sensor network across the Internet of Things network imposes important challenges in terms of bandwidth, storage, processing, energy consumption, and wireless multimedia sensor network lifespan. One of the solutions to these kinds of problems is data transmission reduction. Therefore, this systematic literature review will review various techniques used in data transmission reduction, ranging from data transmission redundancy reduction to machine learning algorithms. Additionally, this review investigates the range of applications and the challenges encountered within the domain of wireless multimedia sensor networks. This work can serve as a basic strategy and a road map for scholars interested in data reduction techniques for intelligent surveillance systems using WMSN in Internet of Things networks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. |
| Author Keywords | Compressive sensing; Data transmission reduction; Energy saving; IoT; Machine and deep learning; Systematic literature review; WMSN |
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