ArticleSensors (Basel, Switzerland)2019
Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed, 59 citations in OpenAlex.
- Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors.Sensors (Basel, Switzerland) · 2022Article
- Collision Prevention for Duty-Cycle Receiver-Initiation MAC Protocol via Multiple Access Reservation (MAR-RiMAC).Sensors (Basel, Switzerland) · 2020Article
- CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things.Sensors (Basel, Switzerland) · 2020Article
- Analysis for Joint Delay-Power Tradeoff with Buffer/Channel-Aware and its FPGA Implementation in Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL's performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.