Network radar countermeasure systems4/5/2023 ![]() ![]() Results show that the spectrogram-based model classifies jamming with an accuracy of 99.79% and a false-alarm of 0.03%, in comparison to 92.20% and 1.35%, respectively, with the feature-based counterpart. The performance of both types of algorithms is analyzed quantitatively with metrics including detection and false alarm rates. Furthermore, spectrogram images collected following the same testing procedure are exploited to build aĬlassification model via state-of-the-art deep learning algorithms (i.e., convolutional neural networks). Numeric features that include signal-to-noise ratio (SNR), energy threshold, and key OFDM parameters are used to develop aĬlassification model via conventional ML algorithms. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Using software-defined radio (SDR), four types of jamming attacks namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). ![]() Our new framework was applied to a basketball game for validation and demonstrated greater effectiveness than the existing methods. The rewards derived from the quantitative human evaluation are designed to be updated quickly and easily in an adaptive manner. Considering the special problem of reinforcement learning in an environment in which multiple network topologies coexist, we propose a policy that properly computes and updates the rewards derived from quantitative human evaluation and computes together with the rewards of the system. Hence, to achieve this objective, this paper proposes a new method of learning complex network topologies that coexist and compete in the same environment and interfere with the learning objectives of the others. In previous studies, only binary numbers have been used for this purpose. In these cases, accurate human evaluations and diagnoses must be communicated to the system, which should be done using a series of real numbers. However, problem solving often involves human expertise and guidance. Recently, physical hardware- and software-based technologies have been utilized to support problem solving with computers. UAV support for clustering improved end-to-end connectivity by keeping the routing cost constant for intercluster communication in the same grid.Ĭomplex problems require considerable work, extensive computation, and the development of effective solution methods. A framework is also proposed with the support of a commercial Unmanned Aerial Vehicle (UAV) to improve routing by minimizing path creation overhead in VANETs. Experimental outcomes for IMOC consistently outperformed the state-of-the-art techniques for each scenario. A comparison was done with state-of-the-art clustering algorithms for routing such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Gray Wolf Optimization (GWO). These parameters were varied during simulations for each algorithm, and the results were recorded. Node density, grid size, and transmission ranges are the performance metrics used for comparative analysis. Delivering optimal route by providing end-to-end connectivity with minimum overhead is the core issue addressed in this article. This technique is used to provide maximum coverage for the vehicular node with minimum cluster heads (CHs) required for routing. In this work, an intelligent moth flame optimization-based clustering (IMOC) for a drone-assisted vehicular network is proposed. Evolutionary algorithm-based clustering techniques are used to solve such routing problems moth flame optimization is one of them. To scale complex routing problems, where static and dynamic routings do not work well, AI-based clustering techniques are introduced. Routing in VANETs is difficult as compared to mobile ad hoc networks (MANETs) topological constraints such as high mobility, node density, and frequent path failure make the VANET routing more challenging. Technology advancement in the field of vehicular ad hoc networks (VANETs) improves smart transportation along with its many other applications. ![]()
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