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您的检索式：作者名="Mahantesh Mathapati"
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| 1 | Two-Tier Clustering with Routing Protocol for IoT Assisted WSN显示文摘In recent times,Internet of Things(IoT)has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking applications.Wireless Sensor Network(WSN)is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation,smart buildings,intelligent transportation,disaster management,and other such IoT-based applications.Clustering methods are widely-employed energy efficient techniques with a primary purpose i.e.,to balance the energy among sensor nodes.Clustering and routing processes are considered as Non-Polynomial(NP)hard problems whereas bio-inspired techniques have been employed for a known time to resolve such problems.The current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol(EETTC-MRP)for IoT networks.The presented EETTC-MRP technique operates on different stages namely,tentative Cluster Head(CH)selection,final CH selection,and routing.In first stage of the proposed EETTC-MRP technique,a type II fuzzy logic-based tentative CH(T2FL-TCH)selection is used.Subsequently,Quantum Group Teaching Optimization Algorithm-based Final CH selection(QGTOA-FCH)technique is deployed to derive an optimum group of CHs in the network.Besides,Political Optimizer based Multihop Routing(PO-MHR)technique is also employed to derive an optimal selection of routes between CHs in the network.In order to validate the efficacy of EETTC-MRP method,a series of experiments was conducted and the outcomes were examined under distinct measures.The experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures. | AArokiaraj Jovith Mahantesh Mathapati M.Sundarrajan N.Gnanasankaran Seifedine Kadry Maytham N.Meqdad Shabnam Mohamed Aslam | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 2 | Intelligent DoS Attack Detection with Congestion Control Technique for VANETs显示文摘VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation Systems(ITS)in today’s life.VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world.VANET is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in location.So,effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET.At the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a traveler.With this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for VANET.The presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network.TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization.TLBO is applied to avoid congestion on roadways.Besides,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network.The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network. | R.Gopi Mahantesh Mathapati B.Prasad Sultan Ahmad Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal | 2022 | Computers, Materials & Continua2022，，7： | 0 |
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