Blockchain-based identity management systems: A review

Multi-standard mobile terminals (MTs) are the trend of current and future mobile devices for taking advantage of heterogeneous integration of wireless access networks and providing ubiquitous connectivity and better quality of service. Holding multiple active interfaces, however, incurs significant power consumption burdens to MTs. This not only increases the carbon footprint of MTs but also makes the batteries of MTs deplete rapidly. Consequently, mobile users may have to relentlessly look for power outlets to charge their devices, which may threaten their true mobility freedom. To this end, in this article, we propose a promising approach based on coalition formation game and inter-terminal cooperation. Our innovative approach motivates MTs to cooperate, while addressing the issue of isolating selfish players. MTs assess radio channels and disseminate the acquired information as well as their available resources to sketch a global view of the radio environment. Based on this view, coalitions are formed whenever energy saving is foreseeable. Within a coalition, MTs pool their resources and perform their tasks cooperatively to maximize their energy efficiency. Simulation results validate that the proposed approach can effectively double the battery lifetimes of MTs, while successfully eliminating selfish players from cooperative groups.

Coalition formation game toward green mobile terminals in heterogeneous wireless networks

IEEE 802.11 WLANs are a very important technology to provide high speed wireless Internet access. Especially at airports, university campuses or in city centers, WLAN coverage is becoming ubiquitous leading to a deployment of hundreds or thousands of Access Points (AP). Managing and configuring such large WLAN deployments is a challenge. Current WLAN management protocols such as CAPWAP are hard to extend with new functionality. In this paper, we present CloudMAC, a novel architecture for enterprise or carrier grade WLAN systems. By partially offloading the MAC layer processing to virtual machines provided by cloud services and by integrating our architecture with OpenFlow, a software defined networking approach, we achieve a new level of flexibility and reconfigurability. In Cloud-MAC APs just forward MAC frames between virtual APs and IEEE 802.11 stations. The processing of MAC layer frames as well as the creation of management frames is handled at the virtual APs while the binding between the virtual APs and the physical APs is managed using OpenFlow. The testbed evaluation shows that CloudMAC achieves similar performance as normal WLANs, but allows novel services to be implemented easily in high level programming languages. The paper presents a case study which shows that dynamically switching off APs to save energy can be performed seamlessly with CloudMAC, while a traditional WLAN architecture causes large interruptions for users.

CloudMAC — An OpenFlow based architecture for 802.11 MAC layer processing in the cloud

This paper investigates advanced energy-efficient wireless systems in orthogonal frequency-division multiple access (OFDMA) downlink networks using coordinated multipoint (CoMP) transmissions between the base stations (BSs) in a heterogeneous network (HetNet), which is adopted by Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE)-Advanced to meet International Mobile Telecommunications-Advanced targets HetNet CoMP has received significant attention as a way of achieving spectral efficiency (SE) and energy efficiency (EE) Usually, in the literature, the total network power consumption is restricted to the sum of the power consumption of all BSs The significance of the power consumption of the backhaul links in wireless networks is normally omitted for its trivial effect with respect to that of the radio BSs For SE and EE analysis of HetNet CoMP, the energy and bandwidth consumption of the backhaul is considered, without which, the investigation remains incomplete However, SE and EE are design criteria in conflict with each other, and a careful study of their tradeoff is mandatory for designing future wireless communication systems The EE is measured as “throughput (bits) per joule,” whereas the power consumption model includes RF transmit (radiated), circuit, and backhaul power Furthermore, a nonideal backhaul model such as a microwave link is also investigated within intra-HetNet-CoMP (inside one cell), where an implementing fiber is not feasible An intercell interference (ICI) coordination method is also studied to mitigate ICI At the end, a novel resource allocation algorithm is proposed—modeled as an optimization problem—which takes into account the total power consumption, including radiated, circuit, and backhaul power, and the minimum required data rate to maximize EE Given an SE requirement, the EE optimization problem is formulated as a constrained optimization problem The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using cubic inequality, which results in an efficient iterative resource allocation algorithm In each iteration, the transformed problem is solved by using dual decomposition with a projected gradient method Simulations results demonstrate how backhaul has a significant impact on total power consumption and the effectiveness of the studied schemes In addition, the results demonstrate that the proposed iterative resource allocation algorithm converges within a small number of iterations and illustrate the fundamental tradeoffs between SE and EE Our analytical results shed light on future “green” network planning in advanced OFDMA wireless systems like those envisioned for a fifth-generation (5G) system

Green HetNet CoMP: Energy Efficiency Analysis and Optimization

Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user’s personality. As filling out personality questionnaires is tedious, we propose the prediction of the user’s personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app track your daily routine (TYDR), which tracks and records smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than many other existing apps, including metadata on notifications, photos taken, and music played back by the user. Based on the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user’s different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we developed the Privacy Model for Mobile Data Collection Applications (PM-MoDaC) specifically tailored for apps that are related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Our experimental evaluation is based on data collected with TYDR during a two-month period. We find evidence that our users accept our proposed privacy model. Based on data about granting TYDR all or no Android system permissions, we find evidence that younger users tend to be less willing to share their data (average age of 30 years compared to 35 years). We also observe that female users tend to be less willing to share data compared to male users. We did not find any evidence that education or personality traits are a factor related to data sharing. TYDR users score higher on the personality trait openness to experience than the average of the population, which we assume to be evidence that the type of app influences the user base it attracts in terms of average personality traits.

What data are smartphone users willing to share with researchers

To be able to serve the traffic demand in today's mobile networks, more than 123.000 base stations (BSs) have been deployed by the different network providers in Germany. These BSs are working 24/7 and are dimensioned to serve capacity demand during busy hours. Currently no mechanisms for a load adaptive network reconfiguration are implemented. As a result, much energy is wasted during times in which the capacity demand is low. This paper describes an adaptive, context-aware, and technology-comprehensive system architecture for power management in modern radio networks. The results presented are developed within the project Communicate Green (ComGreen). A major goal of ComGreen is the development of a testbed in order to investigate and demonstrate the main achievements of the project. The aim of this paper is twofold: First, it highlights the ComGreen concepts for power management in heterogeneous radio networks. Second, a detailed description of the testbed is provided.

Energy optimisation in heterogeneous multi-RAT networks

Nowadays, mobile networks approach a steady growth in traffic demand. As a result, mobile network providers continuously expand their network infrastructure mainly by installing more base stations. Currently, there is a huge number of base stations serving mobile users all over the world, and this number is expected to double in the coming few years, which leads to a larger wastage of energy during low demand times. Exploiting the possibility of turning off base stations at low demand times is one of the promising approaches for saving energy and reducing CO2 emissions. Here, an early and accurate estimation of the traffic is crucial for managing resources proactively. Therefore, in this paper, we introduce a Power Management System that applies a global provisioning policy to base stations for enabling network reconfigurations in terms of power efficiency. This system is based on a Hybrid Traffic Prediction Model that forecasts the workload of base stations by utilizing historic traffic traces. A simulator is implemented to evaluate the proposed management system, which is fed with real data provided by the Open Mobile Network project. The experimental results show the possibility of turning off 49% of the base stations at some times of the day without degrading the QoS.

Optimizing the Power Consumption of Mobile Networks Based on Traffic Prediction

Online Social Networks have become one of the main tools for interpersonal online communication. In the age of the smartphone, mobile user scenarios become more and more important for Online Social Networks. Smartphones enable location-based and context-aware services, but bring the increased risk of privacy violations - at least in centralized OSN architectures. Decentralized Online Social Network architectures are promising as they inherently offer better privacy and less dependence on a single service provider, but they bring new challenges regarding core features of Online Social Networks. In this paper, we introduce a three-tiered view of the social graph and propose a new architecture for decentralized Online Social Networking applications, supporting the three-tiered view and focusing on location-based and context-aware user scenarios.

Towards a Three-tiered Social Graph in Decentralized Online Social Networks

Two of the most popular applications of smartphones are online social networking and playing back music. In this paper, we present the design and implementation of a prototype that combines those usages by creating an architecture that allows the generation and playback of group music playlists that are based on the musical taste of individual guests attending a meeting. In our architecture, we utilize automatically collected data on smartphones for the automatized generation of group music playlists. We follow the idea of utilizing context data in a preprocessing step to generate a group music profile for the recommendation process that generates a group music playlist. For designing such an architecture, we consider current discussions on privacy, data ownership, and data control.

Privacy-aware social music playlist generation

As of today, components of radio access networks are operated in an always-on manner. Since network providers need to fulfill the traffic demands of all customers even during peak times to stay competitive, a significantly higher amount of network resources (i.e., bandwidth) is provided. By the end of 2010, Germany's mobile network providers operated more than 123.000 base stations which are available 24/7 — even when the provided resources are not required at this time. The total energy consumption of these base stations sums up to approx. 1455 GWh per year, inflicting not only high costs to the network operators, but also contributes to worldwide carbon dioxide emissions. Context data, which is already present in the different network entities can be utilized to model the state of radio networks allowing network providers to identify idle components. These components can then be reconfigured dynamically according to the actual demands on connectivity in the network. In this paper, we propose a Context Management Architecture, which is able to acquire and consolidate context from various components in radio access networks as well as additional external sources. The raw context data is sensed by special Context Collection Agents, which are installed on the devices the context data is originating from. It is then published to Context Managers, which provide functionality for storage, reasoning mechanisms, and provisioning of the context data. Context-aware applications (e.g., an application capable of optimizing the configuration of radio networks) can access the refined context via appropriate interfaces for further processing.

Sebastian Göndör

Papers

Energy optimisation in heterogeneous multi-RAT networks

Optimizing the Power Consumption of Mobile Networks Based on Traffic Prediction

Towards a Three-tiered Social Graph in Decentralized Online Social Networks

Privacy-aware social music playlist generation

Towards a dynamic adaption of capacity in mobile telephony networks using context information