Kris Vanhecke

Coverage prediction and optimization algorithms for indoor environments

A heuristic algorithm is developed for the prediction of indoor coverage. Measurements on one floor of an office building are performed to investigate propagation characteristics and validations with very limited additional tuning are performed on another floor of the same building and in three other buildings. The prediction method relies on the free-space loss model for every environment, this way intending to reduce the dependency of the model on the environment upon which the model is based, as is the case with many other models. The applicability of the algorithm to a wireless testbed network with fixed WiFi 802.11b/g nodes is discussed based on a site survey. The prediction algorithm can easily be implemented in network planning algorithms, as will be illustrated with a network reduction and a network optimization algorithm. We aim to provide an physically intuitive, yet accurate prediction of the path loss for different building types.

Exposure Optimization in Indoor Wireless Networks by Heuristic Network Planning

Due to the increased use of indoor wireless networks and the concern about human exposure to radio-frequency sources, exposure awareness has increased during recent years. However, current-day network planners rarely take into account electric-fleld strengths when designing networks. Therefore, in this paper, a heuristic indoor network planner for exposure calculation and optimization of wireless networks is developed, jointly optimizing coverage and exposure, for homogeneous or heterogeneous networks. The implemented exposure models are validated by simulations and measurements. As a flrst novel optimization feature, networks are designed that do not exceed a user-deflned electric-fleld strength value in the building. The in∞uence of the maximally allowed fleld strength, based on norms in difierent countries, and the assumed minimal separation between the access point and the human are investigated for a typical o-ce building. As a second feature, a novel heuristic exposure minimization algorithm is presented and applied to a wireless homogeneous WiFi and a heterogeneous WiFi-LTE femtocell network, using a new metric that is simple but accurate. Field strength reductions of a factor 3 to 6 compared to traditional network deployments are achieved and a more homogeneous distribution of the observed fleld values on the building ∞oor is obtained. Also, the in∞uence of the throughput requirement on the fleld strength distribution on the building ∞oor is assessed. Moreover, it is shown that exposure minimization is more efiective for high than for low throughput requirements and that high fleld values are more reduced than low fleld values.

Proposed architecture and algorithm for personalized advertising on iDTV and mobile devices

The advances in digital media entail an increase in the number of interactive advertising channels: Internet, interactive digital television (iDTV) and mobile applications are gaining importance in the advertisement sector. New interactive advertisement formats can complement the traditional commercial breaks and increase the revenues. Personalization is a promising technique to reach the target audience precisely, get a greater response and increase the return on investment. In this article we present an architecture that offers personalized commercials for iDTV, internet, and mobile devices. By logging the user activity on the three platforms, the system constructs a detailed profile usable for commercial targeting. This personal profile, supplemented with the community behavior and the metadata about the commercials, makes up the data source for the personalization algorithm.

Simple Indoor Path Loss Prediction Algorithm and Validation in Living Lab Setting

A simple heuristic algorithm has been developed for an accurate prediction of indoor wireless coverage, aiming to improve existing models upon multiple aspects. Extensive measurements on several floors in four buildings are used as validation cases and show an excellent agreement with the predictions. As the prediction is based on the free-space loss model for every environment, it is generally applicable, while other propagation models are often too dependent on the environment upon which it is based. The applicability of the algorithm to a wireless testbed network in a living lab setting with WLAN 802.11b/g nodes is investigated by a site survey. The results can be extremely useful for the rollout of indoor wireless networks.

Privacy Aspects of Recommender Systems

The popularity of online recommender systems has soared; they are deployed in numerous websites and gather tremendous amounts of user data that are necessary for recommendation purposes. This data, however, may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their users. In this chapter we overview the state of the art in privacy enhanced recommendations. We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of recommenders. We conclude that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users.

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.

Joint Minimization of Uplink and Downlink Whole-Body Exposure Dose in Indoor Wireless Networks

The total whole-body exposure dose in indoor wireless networks is minimized. For the first time, indoor wireless networks are designed and simulated for a minimal exposure dose, where both uplink and downlink are considered. The impact of the minimization is numerically assessed for four scenarios: two WiFi configurations with different throughputs, a Universal Mobile Telecommunications System (UMTS) configuration for phone call traffic, and a Long-Term Evolution (LTE) configuration with a high data rate. Also, the influence of the uplink usage on the total absorbed dose is characterized. Downlink dose reductions of at least 75% are observed when adding more base stations with a lower transmit power. Total dose reductions decrease with increasing uplink usage for WiFi due to the lack of uplink power control but are maintained for LTE and UMTS. Uplink doses become dominant over downlink doses for usages of only a few seconds for WiFi. For UMTS and LTE, an almost continuous uplink usage is required to have a significant effect on the total dose, thanks to the power control mechanism.

Calculation tool for optimal wireless design and minimal installation cost of indoor wireless LANs

An algorithm is presented for the optimal placement of access points, followed by a minimization of the cost for connecting WLAN networks to the power and ethernet network. The algorithm is described and applied to a simple building layout. The algorithm outputs an overview of the different costs and allows WLAN installers to build the cheapest solution.

Prediction and comparison of downlink electric-field and uplink localised SAR values for realistic indoor wireless planning

In this paper, for the first time a heuristic network calculator for both whole-body exposure due to indoor base station antennas or access points (downlink exposure) and localised exposure due to the mobile device (uplink exposure) in indoor wireless networks is presented. As an application, three phone call scenarios are investigated (Universal Mobile Telecommunications System (UMTS) macrocell, UMTS femtocell and WiFi voice-over-IP) and compared with respect to the electric-field strength and localised specific absorption rate (SAR) distribution. Prediction models are created and successfully validated with an accuracy of 3 dB. The benefits of the UMTS power control mechanisms are demonstrated. However, dependent on the macrocell connection quality and on the users average phone call connection time, also the macrocell solution might be preferential from an exposure point of view for the considered scenario.