Akeem Olowolayemo

Mobile user location determination using extreme learning machine

There has been a rapid convergence to location based services for better resources management. This is made possible by rapid development and lower cost of mobile and handheld devices. Due to this widespread usage however, localization and positioning systems, especially indoor, have become increasingly important for resources management. This requires information devices to have context awareness and determination of current location of the users to adequately respond to the need at the time. There have been various approaches to location positioning to further improve mobile user location accuracy. In this work, we examine the location determination techniques by attempting to determine the location of mobile users taking advantage of signal strength (SS) and signal quality (SQ) history data and modeling the locations using extreme learning machine algorithm (ELM). The empirical results show that the proposed model based on the extreme learning algorithm outperforms k-Nearest Neighbor approaches.

Extreme learning machine for user location prediction in mobile environment

Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single‐hidden‐layer neural networks.Design/methodology/approach – Prediction approach to location determination based on historical data has attracted a lot of attention in recent studies, the reason being that it offers the convenience of using previously accumulated location data to subsequently determine locations using predictive algorithms. There have been various approaches to location positioning to further improve mobile user location determination accuracy. This work examines the location determination techniques by attempting to determine the location of mobile u...

Mobile user location prediction enabled services in ubiquitous computing: determination user location using MFN algorithm

The past decade has seen a rapid development of many applications in the field of pervasive or ubiquitous computing. This is made possible by rapid development in mobile and handheld devices. Due to this widespread usage however, localization and positioning systems, especially indoor, have become increasingly important for resources management. This entails information devices to have context awareness and prediction of current location of the users to adequately respond to the need at the time. In this work, we examine the location determination techniques by attempting to predict location of mobile users taking advantage of signal strength (SS) and signal quality (SQ).

Social networks privacy policy awareness among undergraduate students: The case of Twitter

People nowadays freely give out their personal information on social media without knowing the implications of such actions. The proliferation of social networking sites creates the illusion of fun that made people to overlook the downsides of using these media. This paper examines the privacy awareness and general concerns of undergraduate students who form the bulk of users of these media. An evaluation of students has been carried out to assess their level of concern for privacy and how conscientious they are regarding privacy policies. The work also examines whether based on the awareness, users are more careful or generally carefree. Furthermore, the research also sought to evaluate whether the privacy policies as currently presented is adequate. Another objective of the research is to examine better ways of engaging users if the composition and presentation of privacy policies are found to be contributive to the low level engagement.

Fuzzy Logic Based Compensated Wi-Fi Signal Strength for Indoor Positioning

Work in indoor positioning so far broadly relies on either signal propagation models or location fingerprinting. The former approach has inherent modelling complexity as a result of intervening walls and movement in the environment which, impacts the accuracy of such models. The latter approach on the other hand, is acclaimed to give better accuracy. However, for it to be used, an added overhead of surveying history data of a calibration of every indoor environment is required. Moreover, if any of the mobile Access Points (APs) included in the surveyed history data is down for any reason, the result of the location fingerprinting approach is impacted. This work proposes an indoor location determination approach that uses Fuzzy Weighted Aggregation of Received Signal Strengths (RSS) of Wi-Fi signals with Compensated Weighted Attenuation Factor (CWAF) in the form of fuzzy weighted signal quality and noise. The results are compared with locations away from APs with actual physical measurement in the environmental location to verify accuracy. The performance of the proposed algorithm shows that if the normalized weighted signal strength is properly compensated with weighted signal quality and noise, the approach offers a more computationally efficient positioning with adequate accuracy for indoor localization.

Sensitivity to online privacy in social networking sites

Privacy has been a subject of discussion long before the advent of computer networks, however, the notion of privacy has taken a dramatic turn due to the proliferation of information technology tools and applications, which is further aggravated by the social networking sites which allow users to display their profile information to be viewed and shared by millions of online visitors. This gives the potential of negative use of their information. Previous works have concentrated largely on privacy in online transaction with just a few on the higher institution of learning, which is regarded as one of the earliest adopters of information technology and the most unsecured environments. This work examines the perception of online privacy among university students with the intention of examining the relationship between the demography, level of awareness and online privacy. The results show that despite there is no significant difference based on gender as well as whether the respondent is in IT related field or not, there is a contradiction between the level of awareness and expected attitude to online privacy.

Security Failures in EMV Smart Card Payment Systems

New credit cards containing Europay, MasterCard and Visa (EMV) chips for enhanced security used in-store purchases rather than online purchases have been adopted considerably. EMV supposedly protects the payment cards in such a way that the computer chip in a card referred to as chip-and-pin cards generate a unique one time code each time the card is used. The one time code is designed such that if it is copied or stolen from the merchant system or from the system terminal cannot be used to create a counterfeit copy of that card or counterfeit chip of the transaction. However, in spite of this design, EMV technology is not entirely foolproof from failure. In this paper we discuss the issues, failures and fraudulent cases associated with EMV Chip-And-Card technology.