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Location services and applications, based on network data or global positioning systems, are greatly influencing and changing the way people use mobile phone networks by improving not only user-applications but also the network management part. These applications and services can be further developed by introducing location prediction. We design a system that logs cell id and timestamp data from the users' mobile device, detects the significance of the location to the user, such as home and workplace, and predicts future locations over a chosen time period using artificial neural networks. A novel method is designed for location detection that automatically determines the significance of the location to the user, by spatial and temporal analysis. In our approach, the neural network is automatically adapted, with the help of the location detection algorithm, to the period of the week for which a prediction is desired, achieving accurate weekday and weekend location prediction.

Wi-Fi fingerprinting positioning systems have been deployed for a long time in location-based services for indoor environments. Combining mobile crowdsensing and Wi-Fi fingerprinting systems could reduce the high cost of collecting the necessary data, enabling the deployment of the resulting system for outdoor positioning in areas with dense Wi-Fi coverage. In this paper, we present the results attained in the design and evaluation of an urban fingerprinting positioning system based on crowdsensed Wi-Fi measurements. We first assess the quality of the collected measurements, highlighting the influence of received signal strength on data collection. We then evaluate the proposed system by comparing the influence of the crowdsensed fingerprints on the overall positioning accuracy for different scenarios. This evaluation helps gain valuable insight into the design and deployment of urban Wi-Fi positioning systems while also allowing the proposed system to match GPS-like accuracy in similar conditions.

Elliptic curve cryptosystems have gained increase attention and have become an intense area of research, mainly because of their shorter key length when compared to other public key cryptosystems such as RSA. Shorter key length brings advantages such as reduced computation effort, power consumption and storage requirements, making it possible to increase the available security for portable devices, smartcards and other power strained devices. ECC manages to cover all the significant cryptographic operations such as key exchange and agreement or digital signature with greater efficiency than previous systems. These operations rely heavily on point multiplication which is also the most time-consuming operation. This paper evaluates point operations (doubling, tripling, quadrupling, and addition) and proposes an algorithm for combining the operations in order to achieve faster scalar multiplication when compared to the standard algorithm for scalar multiplication of double and add.

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