Purpose Routing protocol for low-power lossy network (RPL) being the de facto routing protocol used by low power lossy networks needs to provide adequate routing service to mobile nodes (MNs) in the network. As RPL is designed to work under constraint power requirements, its route updating frequency is not sufficient for MNs in the network. The purpose of this study is to ensure that MNs enjoy seamless connection throughout the network with minimal handover delay. Design/methodology/approach This study proposes a load balancing mobility aware secure hybrid – RPL in which static node (SN) identifies route using metrics like expected transmission count, and path delay and parent selection are further refined by working on remaining energy for identifying the primary route and queue availability for secondary route maintenance. MNs identify route with the help of smart timers and by using received signal strength indicator sampling of parent and neighbor nodes. In this work, MNs are also secured against rank attack in RPL. Findings This model produces favorable result in terms of packet delivery ratio, delay, energy consumption and number of living nodes in the network when compared with different RPL protocols with mobility support. The proposed model reduces packet retransmission in the network by a large margin by providing load balancing to SNs and seamless connection to MNs. Originality/value In this work, a novel algorithm was developed to provide seamless handover for MNs in network. Suitable technique was developed to provide load balancing to SNs in network by maintaining appropriate secondary route.
Abstract: Plant leaf diseases and ruinous insects are an important concern in the agricultural sector. The agriculture is dependent on the agricultural productivity by the country, the better is the agricultural productivity, the better is the economy, and hence better is the GDP. The most common and useful way of boosting this economy for any country is the identification of these diseases in the plant and agricultural product that has been obtained. Developments in Deep Learning have facilitated researchers to improve the performance and in exacting systems for object detection and recognition. In this paper, we propose an image processing and Convolutional Neural Network based approach to detect the diseases affecting plants. Our goal is to develop an Android application with a suitable algorithm that will help automate the process of monitoring and detecting plant health. The proposed android application can effectively detect and identify various types of diseases with the ability to handle complex plant-area scenarios.
Increase in mobile nodes has brought new challenges to IoT’s routing protocol-RPL. Mobile nodes (MN) bring new possibilities as well as challenges to the network. MN creates frequent route disruption, energy loss and increases end-to-end delay in the network. This could be solved by improving RPL to react faster to route failures through route prediction, while keeping energy expenditure for this process in reasonable limits. In this context a new Mobility Energy and Queue Aware-RPL (MEQA-RPL) is proposed that have the capability to sense route failure and to identify proactively the next possible route before the current route fails. While identifying the next route, MEQA-RPL employs constraint check on energy and queue availability to guarantee QoS for MN and better lifetime for the network. When compared to RPL with mobility support our model reduce average signaling cost by 31%, handover delay by 32% and improve packet delivery ratio by 17%. We run simulations with multiple mobile nodes which have also shown promising results on aforementioned parameters.
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