Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

Abstract

Wireless Sensor Networks (WSNs) are a major element of Internet of Things (IoT) networks which offer seamless sensing and wireless connectivity. Disaster management in smart cities can be considered as a safety critical application. Therefore, it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN. Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks. This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management (EAMCR-RTDM). The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region. To achieve this, EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering (YSGF-C) technique to elect cluster heads (CHs) and organize clusters. In addition, enhanced cockroach swarm optimization (ECSO) based multihop routing (ECSO-MHR) approach was derived for optimal route selection. The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime. The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work. For examining the improved outcomes of the EAMCR-RTDM system, a wide range of simulations were performed and the extensive results are assessed in terms of different measures. The comparative outcomes highlighted the enhanced outcomes of the EAMCR-RTDM algorithm over the existing approaches.

Archimedes optimisation algorithm quantum dilated convolutional neural network for road extraction in remote sensing images

“…For instance, on the Massachusetts Road dataset, the AOA-QDCNNRE method gains a lower CT of 0.55s, whereas the CNN, U-Net, GL-Dense-U-Net, RDRCNN, and RDRCNN + post-process models obtain higher CT of 1.13s, 1.23s, 1.07s, 1.20s, and 0.98s respectively [ [26] , [27] , [28] ]. At the same time, in the GF-2 Road repository, the AOA-QDCNNRE method obtains a lesser CT of 0.17s, whereas the CNN, U-Net, GL-Dense-U-Net, RDRCNN, and RDRCNN + post-process approaches achieve superior CT of 0.95s, 0.88s, 1.02s, 1.08s, and 1.02s correspondingly [ [34] , [35] , [36] , [37] ].…”

An intelligent air quality monitoring system using quality indicators and Transfer learning based Lightweight recurrent network with skip connection

“…These spatial-temporal elements can be effectively captured by deep learning algorithms. Santhanaraj R. K et al (2023) introduced an integrated LSTM network (LSTM-FC) to predict PM2.5 contamination using previous air quality information, climatic data, weather prediction data and the day of the week. The two parts of this predictive model are as follows: modelling the local discrepancy of PM2.5 contamination utilizing an LSTM-based temporal simulant; and capturing the spatial dependences among the PM2.5 contamination of the primary station and neighboring stations by means of a neural network-based spatial collaborative model.…”

Yellow Saddle Goatfish Optimization Algorithm based Energy Aware Multihop Routing Protocol for Wireless Sensor Networks

“…Thus, energy utilization of such SNs must be to have a longer network lifespan. Such SNs even inhibited in terms of storage, energy, transmission range, and computational power but from them [6]; the energy of SNs was the major constraint while devising WSNs. A great deal of work was made in this domain for the past few years to solve overcome this problem and it is noted that cluster related routing was the method by which energy consumption of SNs is proficiently minimized and provided greater network lifetime as compared to other techniques such as direct communication [7].…”