The internet of things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi-dimensional data, and low computational cost. Prediction in energy consumption is essential for the enhancement of sustainable cities and urban planning, as buildings are the world's largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting prediction performance, best N window size, and model uncertainty estimation. Deep learning models for household energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty.
Abstract-Vehicular ad hoc networks (VANETs) can provide ascendable results for dynamic route planning and condition-aware advertisement using short-range wireless interaction. This paper shows routing mechanisms dealing with performance characteristics in their plan, such as big solidity and constrained strength. It can provide good production for a large spectrum of applications. The objective of this paper is to focus on the performance comparison of the dynamic routing protocols Geographical Junction Information Based Routing (GJIBR) and Ad Hoc On-Demand Distance Vector (AODV) in terms of (i) Throughput (ii) Delay time (iii) Packet delivery Ratio and (iv) Control Overhead using NS-2 simulator.Keywords -VANET, DSDV, DSR, AODV and GJIBR I. INTRODUCTION VANET (Vehicular Ad Hoc Network) is an emerging technology to achieve intelligent inter vehicle communications, it is the specialized derivation of pure multi hop ad hoc networking and are already going through industrial prototyping; the dreamed idea of general purpose vehicular ad hoc network is still away from reality [1]. VANETS are spontaneously formed between moving vehicles equipped with wireless interfaces that could have similar or different radio interface technologies, employing short range to medium range communication system [2]. The geographic routing protocols are more efficient and scalable when there is a dynamic change in the network topology and when the mobility is high [3]. On-demand reactive routing protocols namely AODV and Dynamic Source Routing (DSR) which works on gateway discovery algorithms and a geographical routing protocol namely Greedy Perimeter Stateless Routing (GPSR) which works on an algorithm constantly geographical based updates network topology information available to all nodes in VANETs for different scenarios [4]. Features of AODV routing protocol are loop free routing and notification to be sent to affected nodes or link breakage [5]. In geography based routing, the metaheuristic method provides the optimal route to use to transmit packets in with the least geometric distance from the source to the destination [6]. AODV protocol is a reactive routing protocol is basically a modification of Destination Sequenced Distance Vector (DSDV) protocol in which routes are defined only when it required [7]. The route discovery mechanism in AODV includes routing tables, one route per destination, sequence number to maintain route [8]. The Multicast Ad-hoc On-demand Distance Vector (MAODV) routing protocol builds directly upon their previous work on AODV by adding support for multicast operation to the protocol [9]. AODV is probably more suitable for cognitive wireless networks compared to Dynamic Source Routing (DSR). One of the reasons is because DSR route discovery may lead to unpredictable packet length, which is not suitable for intermittent connectivity environment of cognitive radio networks [10]. Classification of Ad hoc Routing protocols: A. Ad hoc routing mechanism with or without network topology information Proactive rout...
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