As repetition of activities can establish habits and exploration of new ones can provide a healthy variety, we investigate how a recommender system for physical activities can optimally balance these two approaches. We conducted an eight-week user study with 62 physically inactive participants who receive personalized repetition and exploration recommendations in a random order. We distinguish between location, workout, and general activities, and collect participants’ subjective perceptions. Our findings indicate that participants initially preferred exploring general activities, but rated repeating recommendations higher after two weeks. By exploring the optimal transition point from exploration to repetition in personalized recommendations, this study contributes to designing more effective recommender systems for health improvement and healthy habit formation.

Most spectrum surveys conducted worldwide demonstrate that the radio-electric spectrum in use at any given location and instant of time is below 25%. Current spectrum management policies and spectrum utilization inefficiency is becoming unsustainable for future development of radio technologies and services. In this context, dynamic spectrum access is a promising technique for improving spectrum utilization efficiency. A key scientific gap is identifying inaccurate spectrum data from hidden nodes that is not homogeneously distributed in the spatial domain and dynamically vary in time and frequency. For bridging this gap, our paper presents the research results of a spectrum feature extraction algorithm based on multi-correlation and Random Forest. Our algorithm is capable of estimating the spectrum utilization pattern in the spatial and frequency domain with a minimum reliability of 92% for a real heterogeneous networking scenario.

As personalization has great potential to improve mobile health apps, analyzing the effect of different recommender algorithms in the health domain is still in its infancy. As such, this paper investigates whether more accurate recommendations from a content-based recommender or more diverse recommendations from a user-based collaborative filtering recommender will lead to more motivation to move. An eight-week longitudinal between-subject user study is being conducted with an Android app in which participants receive personalized recommendations for physical activities and tips to reduce sedentary behavior. The objective manipulation check confirmed that the group with collaborative filtering received significantly more diverse recommendations. The subjective manipulation check showed that the content-based group assigned more positive feedback for perceived accuracy and star rating to the recommendations they chose and executed. However, perceived diversity and inspiringness was significantly higher in the content-based group, suggesting that users might experience the recommendations differently. Lastly, momentary motivation for the executed activities and tips was significantly higher in the content-based group. As such, the preliminary results of this longitudinal study suggest that more accurate and less diverse recommendations have better effects on motivating users to move more.

Facing the climate change and the energy crisis, plenty of challenges remain to achieving the carbon neutrality in the energy intensive industry. This work investigates a flow-shop production scheduling problem minimizing the energy cost without delaying any jobs. The research problem comes from a Belgium printing company with solar panels as its own energy source. Two sub-problems, the job sequence determination and the job-machine allocation, are tackled using a memetic algorithm. An energy-efficient local search heuristic is also designed to make best use of the self-generated electricity. Validated using realistic production data of 12 jobs in a 2 day planning horizon, the proposed method outperforms a standard binary-encoded genetic algorithm in both solution quality (having 27.17% lower energy cost) and the calculation time (saving 69.89%).

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve decimeter-level accuracy. However, the performance can degrade significantly under Non-Line-of-Sight (NLoS) conditions. Detection and mitigation of NLoS conditions is a complex problem, and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is an important cause of NLoS. In this paper, we propose an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors by attaching an inertial measurement unit to the UWB tag. Two algorithms are designed and implemented, of which the second algorithm is designed for robustness against errors in the IMU's estimated heading. The proposed algorithms are validated by UWB Two Way Ranging (TWR) measurements, performed in two environments. Two more algorithms are implemented as a benchmark, of which one is based on the estimated first path power, and the other is based on range residuals. The proposed algorithm outperforms the other algorithms in the higher error statistics, achieving a 49% reduction of the p90 error depending on the environment.

This paper presents a fast, memory-efficient, and worldwide map matching algorithm based on raw geographic coordinates and enriched open map data with support for trajectories on foot, by bike, and motorized vehicles. The proposed algorithm combines the Markovian behavior and the shortest path aspect while taking into account the type and direction of all road segments, information about one-way traffic, maximum allowed speed per road segment, and driving behavior. Furthermore, a self-adapting lane detection algorithm based solely on accelerometer readings is added on top of the map matching algorithm. An experimental validation consisting of 30 trajectories on foot, by bike, and by car, showed the efficiency and accuracy of the proposed algorithms, with an average F1-score and median error of 99.5% and 1.89 m for the map matching algorithm and an average F1-score of 86.7% for the lane detection algorithm, which resulted in the correctly estimated lane 93.0% of the time. Moreover, the proposed technique outperforms existing state of the art techniques with accuracy improvements up to 45.2%.

This paper proposes a genetic algorithm (GA) combined with ray tracer to generate a cell-free topology of massive MIMO (mMIMO) for the optimal focusing performance serving multiple users. The realistic hardware impairment, for instance the non-ideal power amplifier, is taken into account of the system modeling and topology optimization. To the best of our knowledge, this is the first attempt to apply GA in optimizing the hardware-impaired multi-user cell-free mMIMO. Although the demonstrated numerical analysis is for indoor scenario, the proposed approach is transferable for generic scenarios. In GA, the base station (BS) antennas’ placement is encoded with an adjusted binary matrix representation, which is straightforward for the subsequent genetic operations. The explored candidates by GA can evolve beyond the parents, where the fitness of individuals is evaluated dynamically via a ray tracer radio channel simulator. Compared to the traditional GA, our proposed GA can find better solutions with a faster convergence speed. The algorithm provides near-optimal results in experiments, applicable to generic environment with multiple mobile users and different signal-to-interference-plus-noise ratios.

We present a new floor number detection algorithm for use in smartphone-based indoor localisation systems. It is designed to complement any pedestrian dead reckoning (PDR) algorithm able to detect steps and estimate a 2D trajectory from data of the smartphone’s inertial measurement unit.Our proposed method is based on the Viterbi algorithm, fusing data from an off-the-shelf smartphone’s accelerometer, barometer and wifi received signal strength (RSS) measurements. The accelerometer is used to detect accelerating elevators, while the barometer is used to detect stair climbing. This is combined with model-based wifi RSS fingerprinting, enabling accurate floor number detection. Our system is tested in an office environment with 7 41 m x 27 m floors, each of which has 2 pre-existing wifi access points. The algorithm is evaluated with a total of 116 minutes of recorded data, in which the floor number changed 76 times and a distance of 4.8 km was travelled. Since the Viterbi algorithm allows to easily correct past states (i.e. floor numbers) based on new information, it is evaluated in real-time and batch mode. Our proposed algorithm achieves a floor number detection accuracy of 99.1% (real-time) and 99.7% (batch), while using only RSS measurements resulted in 91% accuracy.

This work assesses the applicability of the well-known SAGE algorithm for time-of-arrival estimation on ultra-wideband (UWB) measurements taken with cheap COTS hardware. Performance is comparable with a simple leading-edge detection (LDE) algorithm, establishing a general precision of approximately 30 cm/60 cm. SAGE performance is slightly worse in general (33 cm/71 cm), but is more stable in non-line-of-sight (NLOS) caused by human body presence. A more detailed breakdown of the effect of incidence angle on one-dimensional ranging accuracy is studied in relationship to human body shadowing effects. Within a cone of 135 degrees in front of the UWB device (pointing away from the body), the azimuthal incidence angle has no influence on the ranging performance of either algorithm.

The fading process in vehicular communications is inherently non-stationary. In this paper, vehicle-to-infrastructure (V2I) radio channel measurements are performed inside a tunnel for low and medium traffic conditions to estimate the stationarity time, in addition to the time-varying RMS delay and Doppler spreads. Furthermore, we show the good fit of the spreads to a lognormal distribution, as well as for the Rician K-factor of the fading amplitude. From our analysis we conclude that the traffic density has an impact on the large-scale parameters as it increases delay and Doppler spreads, while reducing the correlation between them as well as the average K-factor. Larger traffic densities may be required to impact the stationarity time.

The article describes a numerical approach for Massive MIMO channel modeling that accounts for the effects of electromagnetic coupling between a user and the receiving device. The modeling is performed by a combination of the Finite-Difference Time-Domain and the Ray-Tracing methods, supplemented with a stochastic geometry model of the propagation environment. The influence of user-coupling on the channel properties was studied statistically using the singular value spread and matrix power ratio metrics of the channel correlation matrix. The time-averaged Poynting vector distribution in the near-field of the receiver was evaluated using a realistic human phantom model and the Maximum Ratio Transmission precoding scheme in the downlink. The average enhancement of the time-average Poynting vector magnitude at the receiver location, compared to the surrounding area, was found to be around 10 dB when using 36 antenna elements at the base station. The electromagnetic field exposure of the phantom was assessed in terms of the 10g-average peak-spatial Specific Absorption Rate and compared with the existing public guidelines. Comparison of the EMF and exposure results provides a new perspective on the future regulatory procedures.

In this paper, we investigate the coexistence of cognitive radio networks on TV white spaces for rural and suburban connectivity. Although experimental models and laboratory measurements defined the maximum interference threshold for TV white space technologies for general use cases, our research found that in real wireless rural and suburban scenarios, severe interference to the broadcasting services might occur. This is particularly relevant when the traffic load of the telecom base stations (BSs) exceeds 80% of their maximum capacity. We propose a dynamic management algorithm for minimizing the interference, based on a centralized access control architecture for cognitive radio wireless networks. In an experimental emulation for assessing the impact of cognitive radio interference on the broadcasting service’s QoE, our method reduced the perceived video distortion by the broadcasting users by at least 50% and 27.5% in a rural and suburban scenario, respectively, while the spectrum usage is increased by just 8%.

Continuous manufacturing is playing an increasingly important role in modern industry, while research on production scheduling mainly focuses on traditional batch processing scenarios. This paper provides an efficient genetic method to minimize energy cost, failure cost, conversion cost and tardiness cost involved in the continuous manufacturing. With the help of Industrial Internet of Things, a multi-objective optimization model is built based on acquired production and environment data. Compared with a conventional genetic algorithm, non-random initialization and elitist selection were applied in the proposed algorithm for better convergence speed. Problem specific constraints such as due date and precedence are evaluated in each generation. This method was demonstrated in the plant of a pasta manufacturer. In experiments of 71 jobs in a one-month window, near-optimal schedules were found with significant reductions in costs in comparison to the existing original schedule.

This paper assesses the accuracy of a three-dimensional Visible Light Positioning (VLP) algorithm for two different Light Emitting Diode (LED) configurations using the same four LEDs, but mounted at different locations on the ceiling. The two configurations are both simulated and measured at 22801 test points. It is observed that a classic square LED configuration results in position ambiguities, causing errors up to several meters. Alternatively, a star-shaped LED configuration is able to uniquely reconstruct the photodiode's location. For LEDs at a height of approximately 3 m above the receiver, median errors of 12.7 cm and maximal errors of 21.1 cm are experimentally obtained, showcasing the applicability of 3D VLP for drone navigation.

When a disaster occurs, the land-based cellular network could go offline for some days. Using an Unmanned Aerial Base Station (UABS) network is a promising solution to serve unconnected ground users. In this article, we propose a multifrequency backhaul architecture, which considers power and capacity constraints, to support the UABS network in a realistic 3D scenario in the city of Ghent, Belgium. Simulations results show that at the optimal flight height (80 m), up to 87% of the users could be supported using the multifrequency scenario compared with single frequency scenarios where coverage is about 70%.

Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices, so that a rise in energy cost is prevented without affecting production on the shop floor. This paper introduces a multiobjective optimization (MOO) model that jointly schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to a common tradeoff between energy cost and labor cost. An adaptive multiobjective memetic algorithm (AMOMA) is proposed to fast converge toward the Pareto front without loss in diversity. It leverages feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. In this way, adaptive balance remains between exploration of the nondominated sorting genetic algorithm II and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer and benchmarks demonstrate the MOO effectiveness and efficiency of AMOMA. The impacts of production-prohibited periods and relative portion of energy and labor costs on MOO are further analyzed, respectively. The generalization of this method was further demonstrated in a multimachine experiment. The common tradeoff relations between the energy and labor costs as well as between the makespan and the sum of the two cost parts were quantitatively revealed.

The precise (location) tracking of automated guided vehicles will be key in enlarging the productivity, efficiency and safety in the connected warehouse or production infrastructure. Combining the modest price tag, the adequate coverage and the potential centimetre accuracy makes Visible Light Positioning (VLP) systems appealing as replacements for the current, high-cost, tracking systems. Model-fingerprinting-based received signal strength (RSS) VLP enables the required accuracy. It requires an elaborate optical channel model fingerprinted in a fine-grained, and predefined positioning grid. Depending on the grid's granularity, constructing the fingerprint database demands a significant computation and storage effort. This paper employs response adaptive or sequential experimental design to form sparse channel models, vastly reducing the storage and computation. It is shown that model-fingerprinting-based RSS only requires modelling less than 1 percent of the grid points, in an elementary positioning cell. The sparse model can be re-evaluated as a way to cope with environment changeover. Model recomputation as a way of compensating for LED ageing is also studied.

This paper experimentally compares the positioning accuracy of TDoA-based and RSS-based localization in a public outdoor LoRa network in the Netherlands. The performance of different Received Signal Strength (RSS)-based approaches (proximity, centroid, map matching,...) is compared with Time-Difference-of-Arrival (TDoA) performance. The number of RSS and TDoA location updates and the positioning accuracy per spreading factor (SF) is assessed, allowing to select the optimal SF choice for the network. A road mapping filter is applied to the raw location estimates for the best algorithms and SFs. RSS-based approaches have median and maximal errors that are limited to 1000 m and 2000 m respectively, using a road mapping filter. Using the same filter, TDoA-based approaches deliver median and maximal errors in the order of 150 m and 350 m respectively. However, the number of location updates per time unit using SF7 is around 10 times higher for RSS algorithms than for the TDoA algorithm.

We develop and benchmark four RSS localisation algorithms where different a priori knowledge is required. The selection of the best algorithm depends on the availability of additional information on path loss exponent and/or transmit power. We compare our algorithms with centroid localization and show that the algorithms provide better results for shadowing on the values not exceeding 6dB. We perform experiments and simulations with Bluetooth Low Energy and LoRaWAN technologies and select the best technology and algorithm for localisation in large open industrial environments.

Current (theoretical) Visible Light Positioning (VLP) systems assume the transmission of ideal waveforms. Existing VLP LED transmitters, however, transmit far from perfect signals, causing a plunge in the positioning accuracy. The impact of transmitter nonidealities, such as overshoot and rise time, remains unclear. The lack of quality (industry-ready) LED drivers presents a major hurdle in large-scale VLP adoption. The extent to which transmitter nonidealities are tolerable, is valuable information for LED driver designers. This paper provides specifications for the latter. It charts the potentially detrimental impact of transmitter nonidealities on the positioning performance. The influence of both overshoot and rise/fall time is studied.

In the next generation of wireless communication networks, Massive MIMO remains the appropriate technology to provide higher throughput to the users with incredible gains in terms of power consumption and energy efficiency. However, current research rarely considers the electromagnetic field exposure during the network design phase. In this paper, we propose a simulation-based method that enables an optimal design of the massive MIMO 5G networks with respect to both power consumption and electromagnetic field exposure. The results of the simulations show that the massive MIMO network achieves the same performance in terms of users coverage with 10 times less power consumption and an electric field strength 17 times weaker in comparison with the traditional 4G network.

Beamforming is one of the key features enabled in the fifth generation (5G) of wireless communications networks to accommodate the higher throughput demanded by the users for their data-intensive applications. This paper simulates energy-efficient 5G networks with beamforming capabilities deployed on a realistic area in Ghent, Belgium to respond to the instantaneous bit rate needed by the users. Various beamforming architectures have been investigated and the results are compared with the 4G reference network. When beamforming is enabled, the results of the simulations show that under the same coverage performance, 5G networks require 15% more base stations to provide more capacity to the users and are 3 times more energy-efficient than the 4G reference network. Moreover, the hybrid beamforming architecture provides good trade-off between the higher capacity and the low-power consumption requirements and needs to be considered when designing 5G cellular networks.

We investigate the feasibility of Internet of Things (IoT) technology to monitor and improve the energy efficiency and spectrum usage efficiency of broadcasting networks in the Ultra‐High Frequency (UHF) band. Traditional broadcasting networks are designed with a fixed radiated power to guarantee a certain service availability. However, excessive fading margins often lead to inefficient spectrum usage, higher interference, and power consumption. We present an IoT‐based management platform capable of dynamically adjusting the broadcasting network radiated power according to the current propagation conditions. We assess the performance and benchmark two IoT solutions (i.e., LoRa and NB‐IoT). By means of the IoT management platform the broadcasting network with adaptive radiated power reduces the power consumption by 15% to 16.3% and increases the spectrum usage efficiency by 32% to 35% (depending on the IoT platform). The IoT feedback loop power consumption represents less than 2% of the system power consumption. In addition, white space spectrum availability for secondary wireless telecommunications services is increased by 34% during 90% of the time.

Massive MIMO techniques are expected to deliver significant performance gains for the future wireless communication networks by improving the spectral and the energy efficiencies. In this paper, we propose a method to optimize the positions, the coverage, and the energy consumption of the massive MIMO base stations within a suburban area in Ghent, Belgium, while meeting the low power requirements. The results reveal that massive MIMO provides better performances for the crowded scenario where users’ mobility is limited. With 256 antennas, a massive MIMO base station can simultaneously multiplex 18 users at the same time‐frequency resource while consuming 8 times less power and providing 200 times more capacity than a 4G reference network for the same coverage. Moreover, a pilot reuse pattern of 3 is recommended in a multiuser multicell environment to obtain a good tradeoff between the high spectral efficiency and the low power requirement.

The performance of LoRa geolocation for outdoor tracking purposes has been investigated on a public LoRaWAN network. Time Difference of Arrival (TDoA) localization accuracy, update probability, and update frequency were evaluated for different trajectories (walking, cycling, and driving) and LoRa spreading factors. A median accuracy of 200 m was obtained for the raw TDoA output data. In 90% of the cases, the error was less than 480 m. Taking into account the road map and movement speed significantly improves accuracy to a median of 75 m and a 90th percentile error of less than 180 m.

For the first time, a wearable personal distributed exposimeter (WPDE) is designed and calibrated for the Long-Term Evolution (LTE) 800 and 2600 MHz downlink bands. The proposed WPDE has a 68% confidence interval of 4.8–5.6 dB for different number of antennas and polarizations. Measurements of the WPDE are compared and validated with a commercial exposimeter in a real environment.

Using an Unmanned Aerial Base Station (UABS) i.e., a base station carried by a UAV (Unmanned Aerial Vehicle) or drone, is a promising approach to offer coverage and capacity to those users that are not being served by the base stations of the terrestrial network. In this paper, we propose an approach to the design of the drone's trajectory to account for the quickly varying user traffic and pattern. This approach is based on the identification of clusters made of nearby users to be served. The decision on which cluster to visit next by the UABS depends on a cost-function considering the distance to the next cluster, the user density and spread in the cluster, and the direction compared to the previously visited cluster. Furthermore, we propose a radio resource assignment algorithm to minimize the interference from the UABS to the terrestrial network when both are operating in the same frequency band. The potential improvements in terms of network capacity (sum throughput) and user satisfaction are estimated in this study.

Nowadays, the energy price is rising. The consciousness of environmental sustainability of governments and customers has been ever increasing. Consequently, manufacturing enterprises are increasingly motivated to reduce the energy cost involved in their production activities. This paper proposes a novel production scheduling method to minimize the energy cost involved in the production at the unit process level. Compared to the emerging energy-conscious production scheduling methods, this method builds the finite state machine based energy model from power data that are measured from the shop floor. By following the formulated mixed integer linear programming model, the power states and changeovers of a unit process can be additionally scheduled, and the potential multiple process idle modes can be optimally selected between two jobs. In addition, the process power consumption behavior can be predicted along with the optimal schedule. This method was demonstrated in an extrusion blow molding process in a Belgian plastic bottle manufacturer. Compared to two conventional schedules, i.e., “as-early-as-possible” and “as-late-as-possible”, the schedule given by the proposed method is able to reduce 21% and 11% of electricity cost for completing the same production task before a due date.

In this study, the potential of applying different strategies to reduce the energy consumption of a wireless access network, powered by a photovoltaic panel system, during energy shortages is investigated. The goal is to reduce the amount of energy that should be bought from the traditional energy grid during a renewable energy shortage. Three different strategies are compared and the results are looking very promising. Depending on the strategy, up to 72% less energy should be bought compared to the fully operational network for a worst case scenario and a time period of 1 week. However, applying such a strategy has also its influence on the network performance. The influence on the user coverage is limited, with a reduction of 3% at maximum, but the capacity offered by the network decreases significantly with 51% up to even 71%.

A mobile application is presented for real-time testing and optimization of path-loss models and network planning, based on the execution of validation measurements in the considered environment. The application is tested in three indoor scenarios and for three path-loss models. Without optimization, average absolute prediction errors of about 5 dB are obtained, with a simple free-space model performing best. Executing a limited set of ten additional measurements suffices to improve predictions by up to more than 40%. The application is particularly useful for very quick path-loss model tests in a certain environment or for easily obtaining more accurate network deployments, as a single measurement only takes a few seconds and optimization of the path-loss model is fully automated.

Recommender systems have proven their usefulness as a tool to cope with the information overload problem for many online services offering movies, books, or music.Recommender systems rely on identifying individual users and deducing their preferences from the feedback they provide on the content.To automate this user identification and feedback process for TV applications, we propose a solution based on face detection and recognition services.These services output useful information such as an estimation of the age, the gender, and the mood of the person.Demographic characteristics (age and gender) are used to classify the user and cope with the cold start problem.Detected smiles and emotions are used as an automatic feedback mechanism during content consumption.Accurate results are obtained in case of a frontal view of the face.Head poses deviating from a frontal view and suboptimal illumination conditions may hinder face detection and recognition, especially if parts of the face, such as eyes or mouth are not sufficiently visible.

In case of a large scale disaster, the wireless access network can become quickly saturated. This is of course undesirable because for this kind of situations we actually need a reliable wireless connectivity. In this study, the potential of mounting LTE femtocell base stations on drones to offer an alternative for the saturated existing wireless infrastructure is investigated. Our preliminary results show that this a very promising approach although a high amount of drones are needed to cover all users in the city center of Ghent, Belgium during a 1h intervention. The number of drones can be significantly reduced (up to 64%) by using a more advanced type of drone, by decreasing the user coverage requirement (11% less drones when requiring 80% instead of 90%) or by increasing the fly height of the drones (about 10% less drones needed when increasing the fly height by 10 m). This study shows that it is interesting to further investigate the use of drones to provide an emergency wireless access network.

This paper presents a future terminal antenna to enhance the performance of wireless container monitoring systems. It first introduces an optimized design that is experimentally validated for Wireless Personal Area Network (WPAN) technology taking into account the harsh environment of containers. The proposed antenna exhibits excellent performance next to the container in terms of return loss, gain, and bandwidth. Further, deterministic and stochastic path loss models for inter-container link budget calculations are used to illustrate the impact of the optimized antenna on path loss and attainable maximum ranges.

Rating datasets are of paramount importance in recommender systems research. They serve as input for recommendation algorithms, as simulation data, or for evaluation purposes. In the past, public accessible rating datasets were not abundantly available, leaving researchers no choice but to work with old and static datasets like MovieLens and Netflix. More recently, however, emerging trends as social media and smartphones are found to provide rich data sources which can be turned into valuable research datasets. While dataset availability is growing, a structured way for introducing and comparing new datasets is currently still lacking. In this work, we propose a five-step framework to introduce and benchmark new datasets in the recommender systems domain. We illustrate our framework on a new movie rating dataset—called MovieTweetings—collected from Twitter. Following our framework, we detail the origin of the dataset, provide basic descriptive statistics, investigate external validity, report the results of a number of reproducible benchmarks, and conclude by discussing some interesting advantages and appropriate research use cases.

Wireless systems often need to optimize multiple conflicting objectives (low delay, high reliability, and low cost), which are difficult to fulfill simultaneously. In such cases, the wireless system exhibits multiple optimal operation points, referred to as the optimal Pareto front (OPF). However, due to the large number of parameter settings to be evaluated and the time-consuming nature of performing wireless experiments, it is typically not possible to identify the OPF by exhaustively evaluating all possible settings. Instead, for many use cases, an approximation is good enough. To this end, this paper applies a multi-objective surrogate-based optimization (MOSBO) toolbox to efficiently optimize wireless systems and approximate the OPF using a limited number of iterations. Moreover, a real Wi-Fi conferencing scenario is optimized that has two conflicting objectives (exposure and audio quality) and four configurable parameters (Tx-Power, Tx-Rate, Codec Bit-Rate, and Codec Frame-Length). The benefits of using the MOSBO approach for such a network problem is demonstrated by approximating the OPF using 94 iterations instead of requiring the exploration of 6528 different parameter combinations, while still dominating 96.58% of the complete design space.

The performance loss of 802.11 OFDM systems due to propagation delay spread has been analyzed as a function of OFDM parameters for a wide range of reverberation times. This analysis gives physical insight and solutions for the OFDM design to suppress the performance degradation.

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.However, in many applications, this contextual information may be relevant for the human decision process, and as a result, be important to incorporate into the recommendation process, which gave rise to context-aware recommender systems.However, the information value of contextual data for the recommendation process is highly dependent on the application domain and the users' consumption behavior in different contextual situations.This research aims to assess the information value of context for a recommender system of a mobile news service by analyzing user interactions and feedback.A large-scale user study shows that context-aware recommendations outperform traditional recommendations, but also indicates that the accuracy improvement might be limited in a real-life situation.Service usage takes place in a limited number of different contexts due to user habits and repetitive behavior, leaving little room for optimization based on the context.Data fragmentation over different contextual situations strengthens the sparsity problem, thereby limiting the user-perceived accuracy gain obtained by incorporating context in the recommender.These findings are important for news providers when considering to offer context-aware recommendations.

The performance of OFDM systems over a multipath channel can strongly degrade due to the propagation delay spread. The distortion of the received signal over the fast Fourier transform window is referred to as multipath noise. This work aims to analytically determine the performance loss due to multipath noise as a function of OFDM and channel parameters for narrowband OFDM systems. First, it is investigated whether it is possible to describe the multipath noise, varying over different OFDM packets due to the temporal variation of the channel, by an effective noise factor Fdelay, from which the loss factor is directly determined. Second, the theory of room electromagnetics is applied to develop a closed-form expression for Fdelayas a function of the OFDM and reverberation parameters. This analytical method is validated with excellent agreement. Finally, the loss factor is determined for IEEE 802.11 based on channel measurements in two large conference rooms, providing values up to 19 dB for an 800 ns cyclic prefix length.

Mobile Shopping Applications (MSAs) are rapidly gaining popularity. They enhance the shopping experience, by offering customized recommendations or incorporating customer loyalty programs. Although MSAs are quite effective at attracting new customers and binding existing ones to a retailer's services, existing MSAs have several shortcomings. The data collection practices involved in MSAs and the lack of transparency thereof are important concerns for many customers. This paper presents inShopnito, a privacy-preserving mobile shopping application. All transactions made in inShopnito are unlinkable and anonymous. However, the system still offers the expected features from a modern MSA. Customers can take part in loyalty programs and earn or spend loyalty points and electronic vouchers. Furthermore, the MSA can suggest personalized recommendations even though the retailer cannot construct rich customer profiles. These profiles are managed on the smartphone and can be partially disclosed in order to get better, customized recommendations. Finally, we present an implementation called inShopnito, of which the security and performance is analyzed. In doing so, we show that it is possible to have a privacy-preserving MSA without having to sacrifice practicality.

In this research, a food recommendation strategy for patients in a care facility is proposed. Since many of these patients cannot express their personal preferences, a recommender system can assist the caregivers in the selection of the menu items that match the patients' preferences. Recommendations are generated based on three information sources: explicit ratings for menu items, implicit feedback based on the patient's eating behavior and the amount of food that was eaten, and inferred preferences for the ingredients of the menu items. In addition, monitoring the amount of food that was eaten by each patient can provide insights into the optimal amount of each menu item that has to be served to each patient. Furthermore, monitoring food consumption allows to detect irregularities in the eating behavior of the patient, which can be a symptom of illness.

Though most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not for personal usage but rather for consumption in group. In this paper, we present a recommender system for audio-visual content that generates suggestions for groups of people (such as families or friends) in the home environment. In this context, different group recommendation strategies are evaluated for various algorithms and sizes of the group. An offline evaluation proves the assumption that for randomly composed groups the accuracy of all recommendation algorithms decreases if the group size grows. Besides, the results show that the group recommendation strategy which produces the most accurate results is depending on the algorithm that is used for generating individual recommendations. Consequently, if an existing recommender system for individuals is extended to a recommender system for groups, the group recommendation strategy has to be chosen based on the utilized recommendation algorithm in order to maximize the efficiency of the group recommendations.

Every year in Europe, about 1500 people die in traffic because they are not noticed by the truck driver. This problem could be solved by developing a wireless communication system where the truck driver and the cyclist are informed about each others presence. In this paper a test setup is presented in which the position of the cyclist is determined and displayed on a screen when being in the neighborhood of a truck. The cyclist gets an indication about notification by the truck. Because of the fast changing network, the cyclist must be added quickly to the network and the position must be updated very fast. For this reason a Zigbee communication system is used. The position of the cyclist is displayed in zones around the truck. The setup is experimentally tested and it is demonstrated that the proposed setup leads to a reliable and fast method to reduce the number of blind spot incidents.

The efficiency of personal suggestions generated by collaborative filtering techniques is highly dependent on the quality and quantity of the available consumption data. Extending data sets with additional consumption data (from the past) might enrich the user profiles and generally leads to more accurate recommendations. Although if a considerable amount of profile information is already available and detailed personal preferences can be derived, supplementary consumption data may not have any (or a very limited) added value for the recommendation algorithm. These additional consumption data increase the required storage capacity and the computational load to generate the personal recommendations. Moreover, since personal preferences and the relevance of content items may vary over time, older consumption data might be outdated and lead to inaccurate recommendations. Therefore, we investigate which consumption data are (the most) relevant to feed the conventional collaborative filtering algorithms. For provider-generated content systems, we demonstrate that the accuracy of collaborative filtering algorithms increases by extending user profiles with additional older consumption data. In contrast, we witness the opposite effect for user-generated content systems: involving older consumption data has a negative influence on the recommender accuracy. These results are important for website owners who intend to employ a recommendation system at a minimum storage and computation cost.

The goal of this paper is to investigate the performance of a mobile WiMAX system for various settings of its physical-layer parameters and for realistic propagation channels. For this, a physical layer model of IEEE 802.16e is developed in software. Different propagation channels are implemented, such as the Rayleigh channel and the SUI channel models. Moreover, a tapped delay line channel model is developed for a specific area in Ghent (Belgium) using raytracing software. For this area, the maximum achievable range of a realistic mobile WiMAX system is found to be 2.7 km. Additionally, the performance gain of MIMO systems with diversity is investigated. The diversity gain of a 2x2 MIMO Alamouti diversity scheme is found to vary between 3 and 5.5 dB.

For Mobile WiMAX, data rate and throughput figures are often presented in literature. However, the used calculation methods and the influence of the packet size on the data rate are often unclear. Therefore, we develop a new method in order to obtain more precise data rate figures at the MAC (Medium Access Control) layer. In this paper the common physical data rate and MAC data rate calculation method are first examined. Next, the new MAC data rate calculation method is presented. They are applied to the official set of Mobile WiMAX parameters. Our method is able to differentiate for different packet sizes and provides a prediction of the MAC layer throughput.

Wireless body area networks (WBANs) offer many promising new applications in the area of remote health monitoring. An important element in the development of a WBAN is the characterization of the physical layer of the network, including an estimation of the delay spread and the path loss between two nodes on the body. This paper discusses the propagation channel between two half-wavelength dipoles at 2.45 GHz, placed near a human body and presents an application for cross-layer design in order to optimize the energy consumption of different topologies. Propagation measurements are performed on real humans in a multipath environment, considering different parts of the body separately. In addition, path loss has been numerically investigated with an anatomically correct model of the human body in free space using a 3-D electromagnetic solver. Path loss parameters and time-domain channel characteristics are extracted from the measurement and simulation data. A semi-empirical path loss model is presented for an antenna height above the body of 5 mm and antenna separations from 5 cm up to 40 cm. A time-domain analysis is performed and models are presented for the mean excess delay and the delay spread. As a cross-layer application, the proposed path loss models are used to evaluate the energy efficiency of single-hop and multihop network topologies.

This paper investigates the actual measured performance of an 802.16-based system. A measurement methodology for evaluating the performance is proposed, which is then used for studying and comparing the results of different scenarios. More specifically, the influences of changing the modem height from 2.5 m to 6 m and base station height from 15 m to 45 m are analyzed and discussed in this paper, and it will be shown that only the latter one has a significant effect on the coverage and the performance. Furthermore, the relationship between the carrier-to-interference-noise ratio (CINR) and the wireless link throughput is analyzed, and a semi-empirical model will be proposed. The model remains identical for the different scenarios, emphasizing the usefulness of the obtained model. Indoor reception is also analyzed, and it will be shown that the reception at locations close to the window is approximately 7 dB higher than deeper inside the building. A profound statistical analysis is then performed to validate both the performance measurements and the model, and excellent correspondence is obtained. Finally, as the system supports link adaptive modulation and coding, the results of its effectiveness are discussed in this paper.

Cable operators offer today triple-play services (internet, video, and telephony) to their customers over Hybrid Fiber/Coax (HFC) networks. As the bandwidth in such an HFC network is shared by a service group of 2000 homes or less, and as the demand for bandwidth for the triple-play services heavily increases, redesign of this network may be needed to enhance the bandwidth per customer. In this short paper, two heuristic methods for reducing the number of homes per service group by moving the fiber closer to the homes at minimum cost are presented. These heuristic methods are investigated for three different areas: urban, suburban, and rural environments. The preliminary results show that nearly equal results are obtained for both heuristic methods and that the highest investment costs are generally obtained for rural areas.

This paper presents the experimental results and analysis of a link variability study for an indoor, stationary 802.11g network in a small office environment. Variability over time of throughput, jitter, and signal-to-noise ratio (SNR) is investigated. The network performance at different indoor locations is analyzed and the relationships of throughput and jitter to the SNR are obtained. The throughput prediction model is compared to other empirical models, showing the similarity between these models. A statistical distribution function of the SNR in this environment is determined, resulting in an SNR higher than 29.83 dB in 90% of the locations, corresponding with a throughput of about 18.25 Mbps and a jitter lower than 1.10 ms.

In this paper we apply the Belgian electronic identity card (elD) as a means for authentication and digital signing in an interactive digital television (iDTV) environment. Two different frameworks are shown to interface the elD with the multimedia home platform (MHP), which is an open standard for interactive television. The open card framework (OCF) is used in this study, as one of these platforms, for the implementation of a t-mail application scenario. Some application challenges are discussed briefly.

Users in todaypsilas mobile ICT environment are confronted with more and more innovations and an ever increasing technical quality, which makes them more demanding and harder to please. It is often hard to measure and to predict the user experience during service consumption. This is nevertheless a very important dimension that should be taken into account while developing applications or frameworks. In this paper we demonstrate a software tool that is integrated in a wireless living lab environment in order to validate and quantify actual user experience. The methodology to assess the user experience combines both technological and social assets. User experience of a Wineguide application on a PDA is related to signal strength, monitored during usage of the applications. Higher signal strengths correspond with a better experience (e.g. speed). Finally, difference in the experience among users will be discussed.

In this paper, large-scale fading and temporal fading characteristics of the industrial radio channel at 900, 2400, and 5200 MHz are determined. In contrast to measurements performed in houses and in office buildings, few attempts have been made until now to model propagation in industrial environments. In this paper, the industrial environment is categorized into different topographies. Industrial topographies are defined separately for large-scale and temporal fading, and their definition is based upon the specific physical characteristics of the local surroundings affecting both types of fading. Large-scale fading is well expressed by a one-slope path-loss model and excellent agreement with a lognormal distribution is obtained. Temporal fading is found to be Ricean and Ricean K-factors have been determined. Ricean K-factors are found to follow a lognormal distribution.

Instant Messaging (IM) has the potential to become one of the killer applications for interactive Digital Television (iDTV) [12]. However, several factors make it difficult to provide a good implementation of IM services, among which the limited resources of a set-top box and the different user experience compared to computer environments. This paper proposes the XMPP (Extensible Messaging and Presence Protocol) standard as a solution for implementing IM. When we compare XMPP with other technologies, it reveals itself to be very well adapted to the specific needs of iDTV middleware platforms like the Multimedia Home Platform (MHP) [5]. Moreover, the use of XMPP doesn't limit itself to IM The flexible architecture of XMPP opens a window of opportunities like the ease of adding new interactive services. To demonstrate the possibilities of XMPP on MHP, an IM client -IM4MHP- is presented in this paper.

In this paper, the performance of a system based on IEEE 802.16-2004 is analyzed for different conditions. The influence of different parameters of the system is investigated. Channel models are compared as well as the use of MIMO systems. The throughput and range are determined for different diversity schemes for a realistic scenario at 3500 MHz for business applications.

Fixed wireless access systems operating below 11 GHz have the potential to provide broadband wireless access for non line-of-sight operation. In this paper the performance of a typical broadband fixed wireless system based on the IEEE 802.16-2004 specifications is determined. A scenario for business applications with outdoor customer premises equipment is investigated in the 3.5 GHz frequency band. Different path loss models and terrain types are considered. Coverage and throughput in a sector are determined for this business scenario

In this paper we present a new approach for designing high-speed nonlinear integrated circuits with the aid of state of the art CAD tools. As an example, we discuss the design of a high-speed nonlinear optical switch driver that will be part of an optical Metropolitan Area Network (MAN). In the near future, this driver will be integrated with an optical switch using epitaxial lift-off (ELO).>