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Smart Home Automation Security
Arun Cyril Jose,Reza Malekian 한국산학기술학회 2015 SmartCR Vol.5 No.4
This paper presents a comprehensive description about different home automation systems and technologies from a security standpoint. The work highlights various security flaws in existing home automation systems. In our work, we address how the concept of security and the meaning of the word “intruder” have evolved over time. We examine the challenges in home automation security from the point of view of both the homeowner and security engineer. The work goes on to explain why home automation systems are such attractive targets for an attacker. We point out the role of user interfaces in security. Various home automation technologies considered in our work include context-aware home automation systems, central controller-based home automation systems, Bluetooth-based home automation systems, Global System for Mobile communication or mobile-based home automation systems, Short Messaging Service-based home automation systems, General Packet Radio Service-based home automation systems, Dual Tone Multi Frequency-based home automation systems, and Internet-based home automation systems. The work concludes by explaining future directions home automation Security Research could take.
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Vehicle trajectory prediction based on Hidden Markov Model
( Ning Ye ),( Yingya Zhang ),( Ruchuan Wang ),( Reza Malekian ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.7
In Intelligent Transportation Systems (ITS), logistics distribution and mobile e-commerce, the real-time, accurate and reliable vehicle trajectory prediction has significant application value. Vehicle trajectory prediction can not only provide accurate location-based services, but also can monitor and predict traffic situation in advance, and then further recommend the optimal route for users. In this paper, firstly, we mine the double layers of hidden states of vehicle historical trajectories, and then determine the parameters of HMM (hidden Markov model) by historical data. Secondly, we adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory. Finally, we propose a new algorithm (DHMTP) for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states, and predict the nearest neighbor unit of location information of the next k stages. The experimental results demonstrate that the prediction accuracy of the proposed algorithm is increased by 18.3% compared with TPMO algorithm and increased by 23.1% compared with Naive algorithm in aspect of predicting the next k phases` trajectories, especially when traffic flow is greater, such as this time from weekday morning to evening. Moreover, the time performance of DHMTP algorithm is also clearly improved compared with TPMO algorithm.
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