The Internet of Things (IoT) connects physical objects such as baby monitors, cars, tablet computers, fridges through the internet and they are equipped with capabilities to communicate with each other. They exchange information about themselves and their surroundings and provide improved efficiencies for the benefit of users. The future Internet is an emerging world of highly networked smart items that will be able to independently communicate with each other with little or no human intervention as the world moves into the era of smart phones, smart homes, smart offices, smart vehicles, smart classrooms, smart factories to smart everything. As the Internet of Things (IoT) continues to grow security including new attack vectors, new vulnerabilities, and perhaps most concerning of all, a vastly increased ability to use remote access to cause physical destruction becomes a major concern. In this paper we seek to explain what the Internet of Things is, its future impact, challenges and how Digital Forensics Technology can be used to get evidence to prosecute offenders in the law court.
Biometric recognition refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. It is used to confirm an individual's identity rather than using an identification card. Unique identifiers of humans include fingerprints, hand geometry, earlobe geometry and retina. Fingerprint verification is one of the most reliable personal identification methods. The performance of Automatic fingerprint identification systems relies heavily on the quality of the captured fingerprint images. Automatic Fingerprint identification systems are currently being implemented in Ghana. In the 2012 elections, fingerprint biometric verification was implemented and the opposition went to court challenging the results. One of their petitions is that some voters did not go through biometric verification, but the electoral commission argued that even though the verification machines rejected them, their names were in the electoral register and they had identification cards. This research studied categories of workers who do not protect their fingers while at work and established that quite a number of them cannot be verified with fingerprint methods successfully because they had poor fingerprint images. A multimodal approach is proposed where multiple traits can be used, but if it is found to be expensive parties should develop a framework to enable those who cannot be verified due to poor fingerprint images to vote.
Forecasting electricity consumption is vital, it guides policy makers and electricity distribution companies in formulating policies to manage production and curb pilfering. Accurately forecasting electricity consumption is a challenging task. Relying on a single model to forecast electricity consumption data which comprises both linear and nonlinear components produces inaccurate results. In this paper, a hybrid model using autoregressive integrated moving average (ARIMA) and deep long short-term memory (DLSTM) model based on discrete fourier transform (DFT) decomposition is presented. Aided by its superior decomposition capability, filtering using DFT can efficiently decompose the data into linear and nonlinear components. ARIMA is employed to model the linear component, while DLSTM is applied on the nonlinear component; the two predictions are then combined to obtain the final predicted consumption. The proposed techniques are applied on the household electricity consumption data of France to obtain forecasts for one day, one week and ten days ahead consumption. The results reveal that the proposed model outperforms other benchmark models considered in this investigation as it attained lower error values. The proposed model could accurately decompose time series data without exhibiting a performance degradation, thereby enhancing prediction accuracy.
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