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Load balancing routing and quality of transmission (QoT) aware routing have been increasingly studied in mesh wireless networks (WMN) to improve their performance. For the load balancing routing, the traffic bottleneck in the network can be resolved. However, it can decrease QoT because the routes may pass through multiple hops. On the other hand, the QoT aware routing often improves the QoT of the routes, but it can increase the traffic bottleneck due to the unbalanced traffic load in the network. Therefore, the investigation of load balancing routing taking into account QoT is very essential, especially in the case of a wide and ultra-high speed WMN. In this paper, we propose a load balancing routing algorithm under the constraints of QoT for WMN. Our method uses the principle of the software defined networking (SDN) to choose the load balancing routes satisfying the constraints of QoT. Our performance evaluations using OMNeT++ have shown the effectiveness of the proposed algorithm in improving QoT of the data transmission routes, increasing the packet delivery ratio and the network throughput, decreasing the end-to-end delay.

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Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI), which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on.

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