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Coverage and Connectivity in Mobile Wireless Sensor Networks
Published in Ankur Dumka, Sandip K. Chaurasiya, Arindam Biswas, Hardwari Lal Mandoria, A Complete Guide to Wireless Sensor Networks, 2019
Ankur Dumka, Sandip K. Chaurasiya, Arindam Biswas, Hardwari Lal Mandoria
With regard to the mobility, there comes two important notions: mobility pattern and mobility model. Mobility pattern deals with how people and things move, whereas mobility model deals with the mathematical generalization of the characteristics of mobility patterns. A mobility model defines how to compute location, speed, and acceleration of the sensor nodes in the network over time. As already explained in Chapter 12, mobility models can be broadly categorized as trace-based and synthetic models. Trace-based mobility models refer to the mobility patterns of real-life objects which are kept under constant observation over a long period of time. Though traces provide accurate information about the movement pattern of the objects, capturing the movement trajectory of mobile nodes is quite difficult even when adequate amount of previous historical data is available from the recurrent mobility patterns. Now, here comes the synthetic model into picture. Synthetic models aim for realistic mapping of the movement pattern of the mobile nodes without any use of traces (Camp et al., 2002).
Taxonomy of Mobility Models
Published in Khaleel Ahmad, Nur Izura Udzir, Ganesh Chandra Deka, Opportunistic Networks, 2018
Mobility is the prominent feature of opportunistic networks, thus considering the mobility characteristics; the formulation of the realistic mobility model is very important, as the unrealistic model can give unrealistic results during simulation and cannot be deployed in the real world when evaluating the performance of the systems. Communication traffic patterns and mobility models are the key parameters of the protocol simulation. Thus, the formulation of mobility models that accurately mimic the expected real-world scenario is necessary. Many mobility models for the MANETs are reconsidered by exploiting the real users’ movement behavior traces for the opportunistic networks.
Watchdog malicious node detection and isolation using deep learning for secured communication in MANET
Published in Automatika, 2023
Narmadha A. S., Maheswari S., Deepa S. N.
Simulation of the proposed DNLPPR-WMNDI approach and previous methods namely ODTMRP [1], and QASEC [2] are done in the NS2.34 simulator. WSN-DS dataset is utilized for secure data transmission in MANET. The dataset is taken from https://www.kaggle.com/datasets/bassamkasasbeh1/wsnds. This dataset includes 23 features such as Node ID, Time, Data Sent; Data received, Attack type and so on. The dataset size is 5MB. The data is collected from WSN-DS. Deep neural learning has been trained on the dataset to find different attacks. The various nodes are distributed in a square area of (1200 m * 1200 m). The Random Waypoint model is used as a mobility model to perform multicast routing in MANET. The no. of data packets varied from 15 to 150. Table 2 presents the parametric values employed during the simulation process. The simulation results of the developed DNLPPR-WMNDI approach and that of previous approaches ODTMRP [1], and QASEC [2] are considered for comparison (Table 3).
A Secured Zobrist Hash Symmetric Sentinel List Based Malicious Attack Detection in Vanet
Published in IETE Journal of Research, 2023
After evaluating our scheme, Secured Zobrist Hash Symmetric Attack Detection (SZ-HSAD) to secure VANET with the help of statistical testing and analytical modeling, we also evaluate our scheme with the help of the NS3 simulator. The simulation parameters used in the Table 1 is utilized. Here, we conduct our experiment for execution time, average traffic overhead and verification delay versus number of vehicles. In each scenario, we again took ten simulation runs with different numbers of vehicles. The mobility model is used as Random Waypoint model and the speed of the node is varied from 0 to 20m/sec. In the mobility model, the random waypoint mobility model is essential to use since a mobility model facilitates communication within the vehicular network. In Random waypoint mobility model, nodes can select the direction and speed randomly and individually to achieve the destination.
Generating Trips and Assigning Route to a SUMO Network Through the Origin–Destination Matrix: A Case Study of Mobility Routing Model for VANETs
Published in IETE Technical Review, 2022
Tarandeep Kaur Bhatia, Ramkumar Ketti Ramachandran, Robin Doss, Lei Pan
In this subsection, the simulation of VANETs is done for evaluating the performance of the presented methodology in various scenarios based on five parameters. The parameters selected for the evaluation purpose are PDR, Delay, PLR, Routing Overhead, and Throughput. The Network simulation parameters and Mobility model parameters are elaborated in Tables 1 and 2, respectively. In this research work, the region of Connaught Place, Delhi, India, is selected. This region is one of the congested regions of Delhi city. The most congested roads in this area are simulated. The principal purpose to conduct this simulation is to examine the execution of different routing protocols in the presented VANETs environment. The mentioned various scenarios describe the six distinct protocols, named as AODV, DSDV, DSR, GPSR, OLSR, and ZRP. The obtained outcomes show the impact and integration of our research approach with the six latest VANETs routing protocols. The analysis graphs for the five parameters are obtained by using the MATLAB tool.