Editorial

Citation

Khalil, I. (2011), "Editorial", International Journal of Pervasive Computing and Communications, Vol. 7 No. 2. https://doi.org/10.1108/ijpcc.2011.36107baa.001

Editorial

Article Type: Editorial From: International Journal of Pervasive Computing and Communications, Volume 7, Issue 2

In this second issue of IJPCC Volume 7, we have five outstanding papers. The first paper in this issue “Experiences with G2G: a location-aware mobile social networking system” by Konstantinos Mourtzoukos, Ioannis T. Christou, and Sofoklis Efremidis reports on lessons learnt from operating a location-aware mobile-social networking application, and critical functionalities that were deemed necessary in order to provide a pleasant user experience. As a result of user feedback, the authors enhanced a social networking system, called G2G, with functionalities such as login with Facebook credentials without the need to sign up to the system first, and a much improved localization system that works across different mobile operators.

The second paper “Enterprise network maintaining mobility – architectural model of services delivery” by Natalia Kryvinska, Christine Strauss, Bernhard Collini-Nocker, and Peter Zinterhof builds a framework to facilitate a continuous delivery of voice services. Authors also examined an architecture that traverses WLAN and LAN and proposed a mathematical model of the services delivery in order to analyze network behavior as a response to the new services.

This paper contributes to the development of seamless services delivery model for providing enhanced business services to the enterprise customers along with the ability to migrate more tightly.

The third paper “Automatic generation of mobile widgets” by Claudia Raibulet and Daniele Cammareri presents an approach to the automatic generation of widgets, which is based on the separation of concerns between the specification of their structural and functional characteristics, and their appearance. Mobile widgets represent applications exploiting web technologies and providing specific functionalities in an efficient and user-friendly way. Owing to the low or medium complexity of the mobile widgets, their development may be simplified and optimized through automatic mechanisms. The structural and functional features are expressed at a high-abstraction level through a Widget Markup Language, while their appearance through pre-defined or personalized templates. The automatic generator of mobile widgets translates the XML-based documents containing the widgets description based on the Widget Markup Language into functional widgets for various available technologies.

The fourth paper “An energy efficient pedestrian aware smart street lighting system” by Reinhard Müllner and Andreas Riener presents the SSL system, a framework developed for a dynamic switching of street lamps based on pedestrians’ locations and desired safety (or “fear”) zones. In the developed system prototype, each pedestrian is localized via his/her Smartphone, periodically sending location and configuration information to the SSL server. For street lamp control, each and every lamp post is equipped with a ZigBee-based radio device, receiving control information from the SSL server via multi-hop routing.

In this paper, the authors have confirmed that the application of the proposed SSL system has great potential to revolutionize street lighting, particularly in suburban areas with low-pedestrian frequency. More important, the broad utilization of SSL can easily help to overcome the regulatory requirement for CO₂ emission reduction by switching off lamp posts whenever not required.

The final paper in this issue “Extreme learning machine for user location prediction in mobile environment” by Teddy Mantoro, Akeem Olowolayemo, Sunday O. Olatunji, Media A. Ayu, and Abu Osman Md. Tap develops and implements a new computational intelligence modeling scheme, based on the extreme learning machine, as an efficient and more accurate predictive solution for determining position of mobile users based on location fingerprint data (signal strength and signal quality). Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work is an attempt to further device a better positioning accuracy based on location fingerprinting taken advantage of two important mobile fingerprints, namely signal strength and signal quality and subsequently building a model based on extreme learning machine, a new learning algorithm for single-hidden-layer neural networks.

Ismail KhalilEditor-in-Chief