Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management

Abstract

In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The latter is combined with the Internet of Things (IoT) and it moves across the VANETs to analyze the traffic congestion status between the network nodes. The experimental analysis tests the effectiveness of DNN-IoT-BA in various machine or deep learning algorithms in VANETs. DNN-IoT-BA is validated through various network metrics, like packet delivery ratio, latency and packet error rate. The simulation results show that the proposed method provides lower energy consumption and latency than conventional methods to support real-time traffic conditions.

Increased Energy Conservation in Internet of Things (IoT) Related Wireless Networking

“…In several studies, the aim was to provide composite services through integration into wireless sensor networks (WSNs) [17][18][19][20][21]. A three-third party service-oriented architecture is suggested by Zhou et al [4]. As a service in a service class, the work of each sensor is abstracted.…”

“…In general, intelligent objects, since they are battery operated devices, wireless sensors and cell phones, have very little resources for calculation and storage. With the advent and speed of service-defined creation, intelligent objects are seen as services that match their cohosted functions [4][5][6]. In other words, a standard service can be used directly to provide each IoT smart entity with a feature.…”

Network Lifetime Analysis in IOT Environment in Healthcare Sectors Using Deep Learning Routing Approach

“…in particular In addition, the REPC, a current transmission device, collects and reveals additional information about the energy level in the nodes, which makes for a more efficient distribution of network resources. This research done by [8] authors compared routing protocols and IoT Medium Access Control (MAC), and their conclusions indicated that existing protocols do not provide for cooperation between routing strata and MAC layers; new requirements were therefore proposed as a consequence of this discovery. These requirements enable routing protocols to perform necessary modifications, such as to the MA and routing protocols, while also allowing adjustment of the parameters as needed.…”

A review of computational intelligence methods for traffic management systems

“…In some cases, ANN are combined with some other methods to increase efficiency of the hybridized model. For example, deep neural networks are hybridized with bat algorithm (BA) to provide a dynamic form of traffic control in vehicular ad-hoc networks (Kannan et al, 2021). The proposed model has shown precise traffic flow predictions in real-time conditions and, as a result, the main traffic parameters are improved, including decrease in delay, fuel consumption, and emissions.…”