Machine learning based efficient routing protocol in wireless sensor network

Abstract

Data loss and recovery are important factors that directly affect the efficiency of the wireless sensor network (WSN). The wireless channel characteristics have a significant impact on data transmission and reception. On the receiver side, the most difficult tasks are maximizing packet delivery ratio and recovering lost data. In some cases, cyclic redundancy check (CRC) based algorithms can provide better data recovery. The CRC method can be made adaptive by using channel characteristics to correct the error bits. This paper evaluates the performance of the proposed machine learning-based efficient routing protocol (ML-ERP). For data recovery, the CRC with channel impulse response (CIR) prediction based on sensor node location information was used. The data recovery capability of ML-ERP increased the network efficiency in terms of packet delivery ratio. Also, due to less data loss, the energy efficiency of the network was also improved by almost 6 % ove existing protocols.

Big Data Detection utilizing Cloud Networks with Video Vision Techniques

“…Several techniques have been presented in literature for object detection process have been brunched toward stream relied [10], LSTM-dependent [5], consideration relied [22], following relied [13] with various approaches [12]. The primary aim of the suggested study is to recognize the objects in a still image with along the visual utilizing OpenCV, Python, utilizing the better YOLOv3 technique foe such study.…”

“…In this manner, the wellsprings of data of the independent driving discernment framework [11] are basically pictures and point cloud information [12]. Video object detection includes identifying objects involving video information when contrasted with customary object detection utilizing passive pictures [13]. Dual utilizations which have assumed a significant part in the development of video data recognition are independent controlling [14] as well as video reconnaissance [15], [16].…”

Improving Network Layer Performance Using Artificial Intelligence: A Comprehensive Review

“…Addition of clustering and rendezvous nodes can further improve ML-ERP performance. Data recovery model proposed was critical for performance [3]. approaches.…”

“…[1]. The paper also highlights the application of ANN in signal capture and reconstruction, as well as data recovery from signals, showcasing the suitability of Compressed Sensing (CS) theory in such scenarios [3]. Additionally, the paper emphasizes the importance of open data-sets and implementations to foster replication and evolution of results in the AI and ML research community [6].…”

Global research on ubiquitous learning: A network and output approach

“…3 In this sense, the extension of the digital revolution has had an impact on education as a process based on knowledge, social interactions, and communication, causing modifications and transformations in the educational environment and the actors that compose it, such as teachers, students, and the educational institution. [4][5][6] On the other hand, the advantage of mobile devices over previous technologies for learning is important. Thanks to their connectivity, they can be used in formal and informal educational contexts, alone or in collaboration with others.…”