No abstract

No abstract

This work is focused to improve the military communication by introducing Channel Load basedAverage Link Interference Estimation (CLAIE) which will assess the channel load level of every route path, thus the interference reduced communication can be guaranteed. Channel load metric is capable of identifying interference superior to the previous metrics specified in IEEE 802.11 networks. This routing metric attains greater performance, when measured in the environment affected with interference, which contains inadequate radio channel resources such as wireless mesh network. This research offers a sequence of formulas in order to calculate link-interference, path-interference, and node-interference and, in addition, to choose the paths containing the least average link-interference amid the source and target, and an interference-aware routing protocol is presented. Paths identified by this protocol are likely to evade the places containing greater interferences, decreasing the amount of retransmissions of packet and collisions, bringing about greater network throughput and increased lifespan. We present this average link interference-aware routing. The overall methodology is deployed in military communication environment. In the NS2 simulation environment, the complete assessment of the research technique is implemented and proved that the presented research technique gives improved result. KEYWORDSaverage link, channel load, destination node, interference, packet transmission, throughput INTRODUCTIONMobile ad hoc networks (MANETs) are typically a multi-hop temporary independent system consisting of mobile nodes having wireless transmitters and receivers deprived of the support of pre-defined network infrastructure. 1The effectiveness of the previous routing protocols is a serious and challenging problem owing to the changing nature of the network structure and less amount of resources, and their performance will have a big influence on the complete performance of the network. 2The related research recommended numerous tries to deal with various routing scenarios that concentrated on designing multipath routing protocols for distributing the traffic load on diverse node-disjoint routes. Normally, these research studies are intended to improve the previous routing protocols. Such kind of protocols differs in their improvement conditions, for instance, load balancing, power saving, or improving the delivery ratio and throughput, for instance, LS-AOMDV 3 and NDM _ AODV. 4 On the other hand, the presence of interference amid numerous node-disjoint paths considerably has impact on the complete performance of MANETs (for example, with regard to conflict, data loss, retransmission, and channel share). 5 So, interference is one among the most significant aspects that must be considered while designing a multiple path disjoint approach.While designing a novel routing protocol, the quality of the chosen paths is a significant problem that has to be taken. The selection of the chosen path could have emotional impact on the...

Machine learning is a crucial decision-support tool for forecasting agricultural yields, enabling judgments about which crops to cultivate and what to do when in the growing seasonFor this study,we performed a Systematic Literature Review(SLR) to find and combine the methods and components that are employed in agricultural prediction research. Using inclusion and exclusion criteria from six internet databases, we chose 50 publications out of a total of 567 that met our search criteria for relevancy.We thoroughly examined the chosen publications, applied, and offeredrecommendations for additional studies. Our data show that temperature, rainfall, and soil type are the most often used characteristics in these models, and artificial neural networks are the most frequently used methodology. This observation was based on an examination of 50 publications, and we next looked for studies employing deep learning in additional electronic databases. We gathered the deep learning algorithms from 30 of these publications that we discovered. Convolution Neural Networks(CNN),Long-Short Term Memory(LSTM), and Deep Neural Networks are the three deep learning algorithms that are used in these investigations, according to this additional analysis(DNN).