عرض تفاصيل البحث

ادناه جميع التفاصيل الخاصة بالبحث المطلوب عرضه. توفر هذه المنصة معلومات اساسية حول البحث, لمزيد من التفاصيل يرجى متابعة التصفح من خلال الضغط على الرابط الاصلي للبحث او رابط DOI.

عنوان البحث
Two-Level Energy-Efficient Data Reduction Strategies Based On Sax-Lzw And Hierarchical Clustering For Minimizing The Huge Data Conveyed On The Internet Of Things Networks
عنوان المجلة
The Journal Of Supercomputing
ISSN-15730484
تفاصيل النشر
سنة النشر - 2022 / الفهرس الاصلي للمجلة - 78 : 0 (عدد الصفحات 46)
تصنيف البحث
تكنولوجيا المعلومات - المجموعة العلمية
رابط DOI
https://doi.org/10.1007/s11227-022-04548-7
رابط البحث
فتح في موقع المجلة الرسمي
البحث والاستدامة
الهدف 9– الصناعة والابتكار والبنية التحتية   المزيد حول هذا الهدف
البحث والمجتمع
نعم , يدعم
الكلمات المفتاحية
Data compression,Data reduction,IoT,LZW,Network lifetime,SAX quantization,Sensor networks

اسم الباحثجهة الانتساببلد الباحث
علي كاظم جامعة بابل العراق
سها عبد الحسين عبد الزهره جامعة بابل العراق
علي كاظم ادريس جامعة بابل العراق

The Internet of things (IoT) is an omnipresent system that can be accessed from a long distance, linking a variety of devices (things), including wireless sensor networks (WSNs). Cyber-physical systems monitor things from a distance and control them. Because of its widespread usage in a variety of applications, WSN is among the most essential contributors to the IoT and plays a key part in the daily lives of people. The battery’s energy is a vital source in the sensor node, impacting the lifespan of the WSN. Energy scarcity is a serious concern in WSN, as a large volume of redundant data is gathered and transferred on a regular basis. As a result, efficient energy consumption is the fundamental approach to maximizing network lifetime. This article proposes a two-level data reduction approach for use at two network levels: sensor nodes and gateways (GWs). A novel Compression-Based Data Reduction (CBDR) technology and an effective transmitting data strategy derived from data correlation are being developed at the sensor node level. These strategies are designed to more efficiently compress data readings from IoT devices. CBDR compresses data in two stages: lossy SAX quantization and lossless LZW compression. The suggested approaches function as filtering at the GW level, allowing the GW to discover and subsequently delete groups of data that are duplicated and provided by surrounding nodes. At this level, two strategies are advised: the first is based on the data compression concept, and the second is to identify all couples of member nodes that produce duplicated sets so that redundancy may be eliminated before they are delivered to the sink. The proposed solutions are evaluated using extensive simulation tests made available by the network’s OMNeT++ simulator. The proposed methodologies’ efficiency is tested using four related works: the PFF protocol, the ATP protocol, the AVMDA protocol, and the PIP-DA protocol. The proposed solution uses up to 79%, 80%, 90%, and 6% less for each of the remaining data, transmitted data, energy, and data loss, respectively, depending on the results.