Central Aggregator Intrusion Detection System for Denial of Service Attacks

Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles (EVs) to be used by the smart grid through the central aggregator. Since the central aggregator is connected to the smart grid through a wireless network, it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system. However, existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network. In this paper, the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated and an intrusion detection system for the vehicle-to-grid network is proposed. The proposed system, central aggregator–intrusion detection system (CA-IDS), works as a security gateway for EVs to analyze and monitor incoming traffic for possible DoS attacks. EVs are registered with a Central Aggregator (CAG) to exchange authenticated messages, and malicious EVs are added to a blacklist for violating a set of predefined policies to limit their interaction with the CAG. A denial of service (DoS) attack is simulated at CAG in a vehicle-to-grid (V2G) network manipulating various network parameters such as transmission overhead, receiving capacity of destination, average packet size, and channel availability. The proposed system is compared with existing intrusion detection systems using different parameters such as throughput, jitter, and accuracy. The analysis shows that the proposed system has a higher throughput, lower jitter, and higher accuracy as compared to the existing schemes.     

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.     

Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm⁻¹. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm⁻¹, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R² value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology.     

Harmonic modification and data adaptive filtering based approach to robust pitch estimation

A novel and robust pitch estimation method is presented in this paper. The basic idea is to reshape the speech signal using a combination of the dominant harmonic modification (DHM) and data adaptive time domain filtering techniques. The noisy speech signal is filtered within the ranges of fundamental frequencies to obtain the pre-filtered signal (PFS). The dominant harmonic (DH) of the PFS is determined and enhanced its amplitude. Normalized autocorrelation function (NACF) is applied to that modified signal. Then empirical mode decomposition (EMD) based data adaptive time domain filtering is applied to the NACF signal. Partial reconstruction is performed in EMD domain. The pitch period is determined from the partially reconstructed signal. The experimental results show that the proposed method performs better than the other recently developed methods for noisy and clean speech signals in terms of gross and fine pitch errors.     

Intelligently tuned wavelet parameters for GPS/INS error estimation

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of th...     

The performance of multiwavelets based OFDM system under different channel conditions

In wireless communication reception, the reliability of orthogonal frequency division multiplexing (OFDM) is limited because of the time-varying nature of the channel. This causes inter-carrier interference (ICI) and increases inaccuracies in channel tracking. This can effectively be avoided at the cost of power loss and bandwidth expansion by inserting a cyclic prefix guard interval before each block of parallel data symbols. However, this guard interval decreases the spectral efficiency of the OFDM system as the corresponding amount. Recently, it was found that based on Haar-orthonormal wavelets, discrete wavelet-based OFDM (DWT-OFDM) is capable of reducing the inter symbol interference (ISI) and ICI, which are caused by the loss in orthogonality between the carriers. DWT-OFDM can also support much higher spectrum efficiency than discrete Fourier-based OFDM (DFT-OFDM). In this paper the DFT-OFDM is replaced by Multiwavelets OFDM (DMWT-OFDM) in order to further reduce the level of interference and increase spectral efficiency. It is found that proposed Multiwavelet design achieves much lower bit error rates, increases signal to noise power ratio (SNR), and can be used as an alternative to the conventional OFDM. The proposed OFDM system was modeled tested, and its performance was found under different channel conditions.     

A new mathematical programming approach to multi-group classification problems

In this paper we introduce a goal programming formulation for the multi-group classification problem. Although a great number of mathematical programming models for two-group classification problems have been proposed in the literature, there are few mathematical programming models for multi-group classification problems. Newly proposed multi-group mathematical programming model is compared with other conventional multi-group methods by using different real data sets taken from the literature and simulation data. A comparative analysis on the real data sets and simulation data shows that our goal programming formulation may suggest efficient alternative to traditional statistical methods and mathematical programming formulations for the multi-group classification problem.     

Structure and reversibility of 2D hexagonal cellular automata

Cellular automata are used to model dynamical phenomena by focusing on their local behavior which depends on the neighboring cells in order to express their global behavior. The geometrical structure of the models suggests the algebraic structure of cellular automata. After modeling the dynamical phenomena, it is sometimes an important problem to be able to move backwards in order to understand it better. This is only possible if cellular automata is reversible. In this paper, 2D finite cellular automata defined by local rules based on hexagonal cell structure are studied. Rule matrix of the hexagonal finite cellular automaton is obtained. The rank of rule matrices representing the 2D hexagonal finite cellular automata via an algorithm is computed. It is a well known fact that determining the reversibility of a 2D cellular automata is a very difficult problem in general. Here, the reversibility problem of this family of 2D hexagonal cellular automata is also resolved completely.     

Customized kernel execution on reconfigurable hardware for embedded applications

To conserve space and power as well as to harness high performance in embedded systems, high utilization of the hardware is required. This can be facilitated through dynamic adaptation of the silicon resources in reconfigurable systems in order to realize various customized kernels as execution proceeds. Fortunately, the encountered reconfiguration overheads can be estimated. Therefore, if the scheduling of time-consuming kernels considers also the reconfiguration overheads, an overall performance gain can be obtained. We present our policy, experiments, and performance results of customizing and reconfiguring Field-Programmable Gate Arrays (FPGAs) for embedded kernels. Experiments involving EEMBC (EDN Embedded Microprocessor Benchmarking Consortium) and MiBench embedded benchmark kernels show high performance using our main policy, when considering reconfiguration overheads. Our policy reduces the required reconfigurations by more than 50% as compared to brute-force solutions, and performs within 25% of the ideal execution time while conserving 60% of the FPGA resources. Alternative strategies to reduce the reconfiguration overhead are also presented and evaluated.     

Decentralised controller design based on structured singular values

In this study, structured singular values are used in a different way from those commonly used in the robust control literature. It is shown that subject to conditions based on structured singular values, each local area controller can be designed independently. A MATLAB? program is developed to plot inverse structured singular values of multi input multi output (MIMO) system relative error matrix. This plot can be used to predict the stability of the global system with decentralised controller. Therefore decentralised controller design problem can be translated into an equivalent problem of decentralized controller design for a MIMO control system.