Philip Leroux

City of things: An integrated and multi-technology testbed for IoT smart city experiments

While smart cities have the potential to monitor and control the city in real-time through sensors and actuators, there is still an important road ahead to evolve from isolated smart city experiments to real large-scale deployments. Important research questions remain on how and which wireless technologies should be setup for connecting the city, how the data should be analysed and how the acceptance by users of applications can be assessed. In this paper we present the City of Things testbed, which is a smart city testbed located in the city of Antwerp, Belgium to address these questions. It allows the setup and validation of new smart city experiments both at a technology and user level. City of Things consists of a multi-wireless technology network infrastructure, the capacity to easily perform data experiments on top and a living lab approach to validate the experiments. In comparison to other smart city testbeds, City of Things consists of an integrated approach, allowing experimentation on three different layers: networks, data and living lab while supporting a wide range of wireless technologies. We give an overview of the City of Things architecture, explain how researchers can perform smart city experiments and illustrate this by a case study on air quality.

Mobile TV services through IP Datacast over DVB-H: Dependability of the quality of experience on the IP-based distribution network quality of service

The Digital Video Broadcast-Handheld (DVB-H) is an international standard which offers broadcast data services to mobile handheld devices. Its main enhancements to conventional Digital Video Broadcast-Terrestrial (DVB-T) systems include the addition of time-slicing and an extra stage of error correction, referred to as Multi-Protocol Encapsulation-Forward Error Correction (MPE-FEC) at the link layer. The essential components required to deploy an IP-based mobile TV service on top of DVB-H are described by the DVB standard for IP DataCast specifications (DVB-IPDC). Since mobile TV services mostly consist of real-time video data, very stringent demands in terms of quality of service (QoS) are imposed to the broadcast operators primary distribution network. In this article, the impact of packet loss, introduced in a IP-based distribution network, on the quality of experience (QoE), the video quality as perceived by the end user, is characterised. In recent work, a first analysis of the impact of random packet loss was reported upon. This paper describes a more profound analysis of both random and burst packet loss by taking into account several objective video quality metrics. It is shown that, although MPE-FEC is introduced for better reception between antenna and receiver, its use has a major impact on the effects of packet loss in the distribution network. The use of error-correction techniques in the distribution network only has a beneficial impact on the QoE when lower MPE-FEC code rates are used. Moreover, the impact of action scenes or scene switches on the perceived video quality is also characterised in detail. Based on the experimental results, an analytical model is presented which allows to predict the QoE detoriation as a function of the burst loss probability in the distribution network.

Design and evaluation of a DASH-compliant second screen video player for live events in mobile scenarios

The huge diffusion of mobile devices is rapidly changing the way multimedia content is consumed. Mobile devices are often used as a second screen, providing complementary information on the content shown on the primary screen, as different camera angles in case of a sport event. The introduction of multiple camera angles poses many challenges with respect to guaranteeing a high Quality of Experience to the end user, especially when the live aspect, different devices and highly variable network conditions typical of mobile environments come into play. Due to the ability of HTTP Adaptive Streaming (HAS) protocols to dynamically adapt to bandwidth fluctuations, they are especially suited for the delivery of multimedia content in mobile environments. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Recently, a standardized solution has been proposed by the MPEG consortium, called Dynamic Adaptive Streaming over HTTP (DASH). We present in this paper a DASH-compliant iOS video player designed to support research on rate adaptation heuristics for live second screen scenarios in mobile environments. The video player allows to monitor the battery consumption and CPU usage of the mobile device and to provide this information to the heuristic. Live and Video-on-Demand streaming scenarios and real-time multi-video switching are supported as well. Quantitative results based on real 3G traces are reported on how the developed prototype has been used to benchmark two existing heuristics and to analyse the main aspects affecting battery lifetime in mobile video streaming.

Experimental characterisation of the impact of IP-based distribution network QoS on the QoE of DVB-H mobile broadcast video services

DVB-H is an international broadcasting standard, which offers reliable high data reception to mobile handheld devices. Its main enhancements to conventional DVB-T systems include the addition of time-slicing and an extra stage of error correction called MPE-FEC at the link layer. Time-slicing means that each service will be sent in bursts while MPE-FEC increases the robustness of reception for mobile terminals. DVB-H is mostly used to offer real-time mobile video services, which have very stringent demands in terms of QoS. When deploying such a DVBH mobile television service, the broadcast operator has to feed all the DVB-H antennas through its primary distribution network. This is typically done by using a satellite uplink but in more dense arias existing fixed network infrastructure may be used. In this paper we present the effects of packet loss, introduced in an IP based distribution network, on the Quality of Experience (QoE), the quality of the mobile video service as perceived by the end user measured through PSNR, the most commonly used objective video quality metric. It is shown that, although MPE-FEC is introduced for better reception between antenna and receiver, that its use also has a major impact on the effects of packet loss that occurs in the distribution network. In order to reduce the impact of packet loss in the distribution link, additional error correction may be introduced in the distribution link. This paper also evaluates the benefit of such an additional error correction technique with respect to the QoE of the DVB-H service, with a given MPE-FEC rate.

Designing a smart city playground: Real-time air quality measurements and visualization in the City of Things testbed

While smart cities have the potential to monitor and control the city in real-time through sensors and actuators, there is still an important road ahead to evolve from isolated smart city experiments to real large-scale deployments. The City of Things testbed, which is a smart city testbed located in the city of Antwerp, Belgium wants to address the underlying research challenges by means of a multi-technology network infrastructure, the capacity to easily perform data experiments on top and a living lab approach to validate the experiments. The demo described in this paper showcases a first use case scenario, visualizing live air quality data that is monitored via sensors installed on top of mobile post vans, driving around in the City of Antwerp. By means of this test demo we will further detail how our generic test bed enables the dynamic integration and support of multiple wireless technologies, novel data processing use cases scenarios as well as the active user panel involvement.

Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites

Since years, online social networks have evolved from profile and communication websites to online portals where people interact with each other, share and consume multimedia-enriched data and play different types of games. Due to the immense popularity of these online games and their huge revenue potential, the number of these games increases every day, resulting in a current offering of thousands of online social games. In this paper, the applicability of neighborhood-based collaborative filtering (CF) algorithms for the recommendation of online social games is evaluated. This evaluation is based on a large dataset of an online social gaming platform containing game ratings (explicit data) and online gaming behavior (implicit data) of millions of active users. Several similarity metrics were implemented and evaluated on the explicit data, implicit data and a combination thereof. It is shown that the neighborhood-based CF algorithms greatly outperform the content-based algorithm, currently often used on online social gaming websites. The results also show that a combined approach, i.e., taking into account both implicit and explicit data at the same time, yields overall good results on all evaluation metrics for all scenarios, while only slightly performing worse compared to the strengths of the explicit or implicit only approaches. The best performing algorithms have been implemented in a live setup of the online game platform.

Modeling and predicting the popularity of online news based on temporal and content-related features

As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest importance to publishers. A prerequisite for being able to optimize any online strategy, is to have trustworthy predictions of how popular new online content may become. This paper presents a novel methodology to model and predict the popularity of online news. We first introduce a new strategy and mathematical model to capture view patterns of online news. After a thorough analysis of such view patterns, we show that well-chosen base functions lead to suitable models, and show how the influence of day versus night on the total view patterns can be taken into account to further increase the accuracy, without leading to more complex models. Second, we turn to the prediction of future popularity, given recently published content. By means of a new real-world dataset, we show that the combination of features related to content, meta-data, and the temporal behavior leads to significantly improved predictions, compared to existing approaches which only consider features based on the historical popularity of the considered articles. Whereas traditionally linear regression is used for the application under study, we show that the more expressive gradient tree boosting method proves beneficial for predicting news popularity.