Monetizing Personal Data: A Two-Sided Market Approach

© 2016 The Authors. Mobile phone-based sensing is a new paradigm that aims at using smartpohnes to answer sensing requests and collect useful data. Nowadays, a wide variety of domains ranging from health-care applications to pollution monitoring are benefiting from such collected data. However, despite its increasing popularity and the huge amount of data provided by users, there is no platform where mobile phone owners can effectively sell their data. In this paper, we propose the idea of a data monetization platform using two-sided market theory. In this platform, the data is viewed as an economic good and the data sharing activity is considered as an economic transaction. The proposed platform considers the case of abundant data. An experimental analysis is conducted to compare our approach against the peer-to-peer model using a real case study from the health care domain. We show that our proposed platform has the potential to generate higher profit for both data providers and data consumers

Life is short. The impact of power states on base station lifetime

We study the impact of power state transitions on the lifetime of base stations (BSs) in mobile networks. In particular, we propose a model to estimate the lifetime decrease/increase as a consequence of the application of power state changes. The model takes into account both hardware (HW) parameters, which depend on the materials used to build the device, and power state parameters, that instead depend on how and when power state transitions take place. More in depth, we consider the impact of different power states when a BS is active, and one sleep mode state when a BS is powered off. When a BS reduces the power consumption, its lifetime tends to increase. However, when a BS changes the power state, its lifetime tends to be decreased. Thus, there is a tradeoff between these two effects. Our results, obtained over universal mobile telecommunication system (UMTS) and long term evolution (LTE) case studies, indicate the need of a careful management of the power state transitions in order to not deteriorate the BS lifetime, and consequently to not increase the associated reparation/replacement costs

Towards Intelligent Energy-Aware Self-Organised Cellular Networks (iSONs)

This thesis investigates the application of intelligent energy-aware resource management techniques for current and future wireless broadband deployments. Energy-aware topology management is firstly studied aiming at dynamically managing the network topology by fine tuning the status of network entities (dormant / active) to scale the energy consumption with traffic demands. This is studied through an analytical model based on queueing theory and through simulation to help understand its operational capabilities under a range of traffic conditions. Advanced radio resource management is also investigated. This reduces the number of nodes engaged in the service whenever possible reducing the energy consumption at low and medium traffic loads while enhancing system capacity and QoS when the traffic load is high. As an enabling technology for self-awareness and adaptability, Reinforcement Learning (RL) is applied to manage network resources in an intelligent, self-aware, and adaptable manner. This is complemented with a range of novel cognitive learning and reasoning algorithms which are capable of translating past experience into valuable sets of information in order to optimise decisions taken as part of the radio resource and topology management functionalities. Dependencies between the proposed techniques are also addressed formulating an intelligent self-adaptable approach, which is capable of dynamically deactivating redundant nodes and redirecting traffic appropriately while enhancing system capacity and QoS

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words