Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

Understanding building and urban environment interactions: An integrated framework for building occupancy modelling

Improving building energy efficiency requires accurate modelling and a comprehensive understanding of how occupants use building space. This thesis focuses on modelling building occupancy to enhance the predictive accuracy of occupancy patterns and gain a better understanding of the causal reasons for occupancy behaviour. A conceptual framework is proposed to relax the restriction of isolated building analysis, which accounts for interactions between buildings, its occupants, and other urban systems, such as the effects of transport incidents on occupancy and circulation in buildings. This thesis also presents a counterpart mapping of the framework that elaborates the links between modelling of transport and building systems. To operationalise the proposed framework, a novel modelling approach which has not been used in the current context, called the hazard-based model, is applied to model occupancy from a single building up to a district area. The proposed framework is further adapted to integrate more readily with transport models, to ensure that arrivals and departures to and from the building are consistent with the situation of the surrounding transport systems. The proposed framework and occupancy models are calibrated and validated using Wi-Fi data and other variables, such as transport and weather parameters, harvested from the South Kensington campus of Imperial College London. In addition to calibrating the occupancy model, integrating a travel simulator produces synthetic arrivals into or around the campus, which are further distributed over campus buildings via an adapted technique and feed the occupancy simulations. The model estimation results reveal the causal reasons for or exogenous effects on individual occupancy states. The validation results confirm the ability of the proposed models to predict building occupancy accurately both on average and day by day across the future dataset. Finally, evaluating occupancy simulations for various hypothetical scenarios provides valuable suggestions for efficient building design and facility operation.Open Acces

Public entities driven robotic innovation in urban areas

Cities present new challenges and needs to satisfy and improve lifestyle for their citizens under the concept “Smart City”. In order to achieve this goal in a global manner, new technologies are required as the robotic one. But Public entities unknown the possibilities offered by this technology to get solutions to their needs. In this paper the development of the Innovative Public Procurement instruments is explained, specifically the process PDTI (Public end Users Driven Technological Innovation) as a driving force of robotic research and development and offering a list of robotic urban challenges proposed by European cities that have participated in such a process. In the next phases of the procedure, this fact will provide novel robotic solutions addressed to public demand that are an example to be followed by other Smart Cities.Peer ReviewedPostprint (author's final draft

On the traffic offloading in Wi-Fi supported heterogeneous wireless networks

Heterogeneous small cell networks (HetSNet) comprise several low power, low cost (SBSa), (D2D) enabled links wireless-fidelity (Wi-Fi) access points (APs) to support the existing macrocell infrastructure, decrease over the air signaling and energy consumption, and increase network capacity, data rate and coverage. This paper presents an active user dependent path loss (PL) based traffic offloading (TO) strategy for HetSNets and a comparative study on two techniques to offload the traffic from macrocell to (SBSs) for indoor environments: PL and signal-to-interference ratio (SIR) based strategies. To quantify the improvements, the PL based strategy against the SIR based strategy is compared while considering various macrocell and (SBS) coverage areas and traffic–types. On the other hand, offloading in a dense urban setting may result in overcrowding the (SBSs). Therefore, hybrid traffic–type driven offloading technologies such as (WiFi) and (D2D) were proposed to en route the delay tolerant applications through (WiFi) (APs) and (D2D) links. It is necessary to illustrate the impact of daily user traffic profile, (SBSs) access schemes and traffic–type while deciding how much of the traffic should be offloaded to (SBSs). In this context, (AUPF) is introduced to account for the population of active small cells which depends on the variable traffic load due to the active users

Building occupancy modelling at the district level: A combined copula-nested hazard-based approach

Planning and managing an energy system in a district require a comprehensive understanding and accurate modelling of people's occupancy and circulation among multiple buildings. Due to the lack of occupancy modelling tools for district scale analysis, energy models still use simplified occupancy patterns provided in building codes and standards. However, the simplified information restricts the reflection of complex occupancy patterns driven by urban heterogeneity. This paper fills this research gap and presents a hazard-based model combined with nested copula dependence to describe the complex occupants' interactions between buildings in a district, enabling the characterisation of irregular occupancy patterns in special cases. The proposed model is calibrated using Wi-Fi authentication data from the Imperial College London (UK) South Kensington campus and is validated using the following days of the same data by evaluating the performance of predicted occupancy patterns both on average and day by day. The validation results demonstrate that the model can accurately capture the effects of the urban environment on occupancy duration and choice of transition within a district. Mean Absolute Percentage Errors (MAPEs) of average-pattern predictions are between 7% and 16% for most buildings, though a bit lower in accuracy for the Library and Food Hall predictions with MAPEs of 32%–36%. We also discuss the contributions of the proposed occupancy model to potential future applications, including efficient building space use, local energy planning and management