• I have given an algorithm for BitTorrent engine in mobile environment. I have shown that the algorithm consider the limitations of the mobile devices.
• I have designed and implemented MobTorrent software package, which is the first BitTorrent client for feature phones and operates in real BitTorrent net-work. The designed MobTorrent engine is a platform independent BitTorrent library on mobile phones, I have successfully applied it on Android platform as well.
• I have proposed a model based on average measurements to calculate the total required energy for downloading a specific amount of data.
• I have provided measurements that show that memory requirement and en-ergy consumption of MobTorrent is low and remains on a relatively constant level during the download.
• I have shown how a BitTorrent based hybrid peer-to-peer based content dis-tribution architecture is applicable in a mobile related social network.
• I have proposed an analytical model to calculate the cost of the content distribution considering lower seed ratio for mobile phones. I have proven that the relative cost of the hybrid architecture decreases significantly when the seed relation increases, compared to the general client-server architecture.
• I have contributed in the implementation of a hybrid peer-to-peer content distribution solution at Nokia Research Center and Nokia Siemens Networks.
different popular social networks, e.g.: MySpace, iGoogle, iWiW, etc. Future work includes to create measurements on different type of large database that can help designing resource requirements and scalability of mobile related social networks further.
Efficient similarity detecting and handling is a key issue in mobile related social networks, we have proposed an algorithm for it and based on measurements more than 90% of the detected similarities and duplications were accepted by the users.
In this research we mainly focused on how to detect all similarities, but there are numerous researches for efficient document comparison. Further research should be done to examine and compare these methods from the perspective of person profile comparison and extend our current similarity detecting algorithm.
Mobile related social networks are based on the fact that the phonebook of the mobile phone represents the social relationships of its owner. However this approach can be extended further. Nowadays when we are using an online social network we can see all kind of social network related information which are sent by our friends to us (e.g.: picture comments, notes, messages, etc.). However even if mobile phones are involved in the social network we still cannot see our telco information related to our friends. We have been invited to participate in the Social Radar research and prototyping project at Nokia Siemens Networks, where the goal is to display telco information in online social networks, considering privacy issues as well. For example Social Radar is able to detect when we have a missed call from one of our friend and highlights this information inside the social networking application to us. Future work includes to propose and examine a model for such architecture and create measurements in order to analyze the overhead of the telco network.
In Chapter 6 we discussed about efficient content sharing on mobile phones.
We have shown that feature phones have limitations when considering peer-to-peer technologies. We have given a BitTorrent algorithm that considers the limitations.
The results were also demonstrated in the MobTorrent implementation. We have shown measurements to MobTorrent memory requirements and energy consump-tion. We have shown that energy consumption of the implementation remains on a constant level. However there are ongoing research [Kelényi and Nurminen, 2009]
related to energy consumption of mobile phones while using network
communica-tion, which should be examined in order to decrease the energy consumption in MobTorrent if possible. Therefore an energy efficient communication solution is still subject of further research.
We have proposed also in Chapter 6 a hybrid architecture for efficient content sharing, which is applicable in mobile related social networks. For the content distribution we have used MobTorrent, as the goal was to support as wide range of mobile devices as possible. Measurements, related to MobTorrent performance, were encouraging. However the performance of a BitTorrent client mainly depends from the applied extensions and protocol features. Currently MobTorrent supports multitracker feature and DHT, however there are numerous additional extensions and a new BitTorrent protocol is coming soon. This way improving MobTorrent further and analyze its behavior is a continuous research.
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