Abstract
Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature.
Highlights
The Internet migrates from linking people to linking things, moving to the modern Internet of Things (IoT) concept
Compression-Based Data Reduction (CBDR) includes two stages of compression, a lossy Symbolic Aggregate approXimation (SAX) Quantization stage that reduces the dynamic range of the sensor data readings and increases the amount of reoccurring data patterns, followed by a lossless LZW compression to compress lossy quantization output
Work: For a vast amount of data created by IoT sensor networks, data compression is very beneficial to save energy and provide important information to the end-user
Summary
The Internet migrates from linking people to linking things, moving to the modern Internet of Things (IoT) concept. The modern concept brings objects or things into the Web and produces new business and applications. Such things, from interior wearable devices to exterior environmental sensors, become new sources, produce data on the Internet, and together make the entities on the Internet more conscious of the real world [1,2]. In IoT one of the most important contributors is wireless sensor networks (WSNs). WSN includes a large number of dispersed sensors interconnected wirelessly for environmental and physical surveillance applications. As an IoT branch, wireless sensor networks (WSNs) have been commonly used in a number of smart technologies and services, like smart building, smart home, smart cities, smart industrial automation, smart transport, smart grids, and smart healthcare [3]. The sensing devices contain restricted-energy resources (power of battery), storage and processing capability, range of radio communication and reliability, etc., and still, their deployment should be covering a wide range area [4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
