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With the increasing demand of energy, the energy production is not that much sufficient and that’s why it has become an important issue to make accurate prediction of energy consumption for efficient management of energy. Hence appropriate demand side forecasting has a great economical worth. Objective of our paper is to render representations of a suitable time series forecasting model using autoregressive integrated moving average (ARIMA) and Holt Winters model for the energy consumption of Ohio/Kentucky and also predict the accuracy considering different periods (daily, weekly, monthly). We apply these two models and observe that Holt Winters model outperforms ARIMA model in each (daily, weekly and monthly observations) of the cases. We also make a comparison among few other existing analyses of time series forecasting and find out that the mean absolute percentage error (MASE) of Holt Winters model is least considering the monthly data.

Smart meter is a unique metering system that provides users the capability to monitor power generation, transmission and consumption by enhancing the maintenance of real-time supervision and communication. By hacking and breaching the meter, faulty data can be transferred to the utility by the hacker. The power consumption data stored in a smart meter can be spoofed by two different hacking mechanisms. They are: Communication Interface Attack and Physical Memory Attack. In this paper, the malicious behaviour of a specific set of code is measured implementing fuzzy analytical hierarchy process. Analytical hierarchy process is engaged in the formation and inspecting of sophisticated issues by mathematical calculations. This scheme considers the relative priorities of factors or alternatives with a systematic and logical approach and serves the best alternative. Fuzzy theory has been incorporated into traditional AHP method for selecting and justifying alternatives in a systematic way.

In this paper, we introduce a robust authentication mechanism for identification in order to secure the wireless channel in IoT networks. Internet of Things is viewed as the most scaled down segment of nano sensor systems that have a significant potential to be embraced continuously for its large array of applications. However, when it comes to nanotechnology, smart identification technique with a secured communication channel is one of the main requirements. Operation with light computational complexity like XOR and Rotation have been used to enhance the security of the network channel. The proposed authentication mechanism has three levels of authentication. Moreover, ANFIS based authentication performance evaluation model has been developed and analyzed based on successful authentication rate and simulated with Fuzzy logic. Simulation has been conducted using MATLAB. It is being concluded after simulation that successful authentication should be 70% for any two levels and no less than 30% in any level. Finally, the proposed mechanism enhances the security in IoT networks which will indicate our mechanism as an effective one.

Smart Grid constitutes a network many lightweight devices. Through this network, the devices can exchange information. Compromisation of these devices can breach the security of the network. In this paper, we have proposed a trust management model based on fuzzy system to address the above-mentioned issue. we have successfully enhanced security of the smart online grid connected network system and eliminated compromised device. The model has been simulated in Xfuzzy-3.5. Simulation results depict that to be a trustworthy device, each device requires to maintain its trustworthiness behavior above the threshold, otherwise it will be treated as a malicious device. Simulation results show the maximum correctness and it is competent with the objective of this paper. Finally, the simulation results enhance the security in Smart Grid network through Trust management model which is satisfactory proven.

Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.