A Survey of COVID-19 in Public Transportation: Transmission Risk, Mitigation and Prevention

The COVID-19 pandemic is posing significant challenges to public transport operators by drastically reducing demand while also requiring them to implement measures that minimize risks to the health of the passengers. While the collective scientific understanding of the SARS-CoV-2 virus and COVID-19 pandemic are rapidly increasing, currently there is a lack of understanding of how the COVID-19 relates to public transport operations. This article presents a comprehensive survey of the current research on COVID-19 transmission mechanisms and how they relate to public transport. We critically assess literature through a lens of disaster management and survey the main transmission mechanisms, forecasting, risks, mitigation, and prevention mechanisms. Social distancing and control on passenger density are found to be the most effective mechanisms. Computing and digital technology can support risk control. Based on our survey, we draw guidelines for public transport operators and highlight open research challenges to establish a research roadmap for the path forward.Peer reviewe

A Multi-tier Communication Schemefor Drone-assisted Disaster Recovery Scenarios

International audienceDisaster scenarios are particularly devastating in urban environments, which are generally very densely populated. Disasters not only endanger the life of people, but also affect the existing communication infrastructure. In fact, such an infrastructure could be completely destroyed or damaged; even when it continues working, it suffers from high access demand to its resources within a short period of time, thereby compromising the efficiency of rescue operations. This work leverages the ubiquitous presence of wireless devices (e.g., smartphones) in urban scenarios to assist search and rescue activities following a disaster. It considers multi-interface wireless devices and drones to collect emergency messages in areas affected by natural disasters. Specifically, it proposes a collaborative data collection protocol that organizes wireless devices in multiple tiers by targeting a fair energy consumption in the whole network, thereby extending the network lifetime. Moreover, it introduces a scheme to control the path of drones so as to collect data in a short time. Simulation results in realistic settings show that the proposed solution balances the energy consumption in the network by means of efficient drone routes, thereby effectively assisting search and rescue operations

Federated split GANs for collaborative training with heterogeneous devices[Formula presented]

Publisher Copyright: © 2022 The Author(s)Applications based on machine learning (ML) are greatly facilitated by mobile devices and their enormous volume and variety of data. To better safeguard the privacy of user data, traditional ML techniques have transitioned toward new paradigms like federated learning (FL) and split learning (SL). However, existing frameworks have overlooked device heterogeneity, greatly hindering their applicability in practice. In order to address such limitations, we developed a framework based on both FL and SL to share the training load of the discriminative part of a GAN to different client devices. We make our framework available as open-source software1.Peer reviewe

AICP: Augmented Informative Cooperative Perception

Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, require human driver supervision and are currently constrained to visual information in their line-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception range. Existing solutions focus on improving perspective transformation and fast information collection. However, such solutions fail to filter out large amounts of less relevant data and thus impose significant network and computation load. Moreover, presenting all this less relevant data can overwhelm the driver and thus actually hinder them. To address such issues, we present Augmented Informative Cooperative Perception (AICP), the first fast-filtering system which optimizes the informativeness of shared data at vehicles to improve the fused presentation. To this end, an informativeness maximization problem is presented for vehicles to select a subset of data to display to their drivers. Specifically, we propose (i) a dedicated system design with custom data structure and lightweight routing protocol for convenient data encapsulation, fast interpretation and transmission, and (ii) a comprehensive problem formulation and efficient fitness-based sorting algorithm to select the most valuable data to display at the application layer. We implement a proof-of-concept prototype of AICP with a bandwidth-hungry, latency-constrained real-life augmented reality application. The prototype adds only 12.6 milliseconds of latency to a current informativeness-unaware system. Next, we test the networking performance of AICP at scale and show that AICP effectively filters out less relevant packets and decreases the channel busy time.Peer reviewe

Air Pollution Exposure Monitoring using Portable Low-cost Air Quality Sensors

Urban environments with a high degree of industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital not only to citizens, warning them on the health risks of air pollutants, but also to policy-makers,assisting them on drafting regulations and laws that aim at minimizing those health risks. Currently,air pollution monitoring predominantly relies on expensive high-end static sensor stations. These stations produce only aggregated information about air pollutants, and are unable to capture variations in individual’s air pollution exposure. As an alternative, this article develops a citizen-based air pollution monitoring system that captures individual exposure levels to air pollutants during daily indoor and outdoor activities. We present a low-cost portable sensor and carry out a measurement campaign using the sensors to demonstrate the validity and benefits of citizen-based pollution measurements. Specifically, we (i) successfully classify the data into indoor and outdoor, and (ii) validate the consistency and accuracy of our outdoor-classified data to the measurements of a high-end reference monitoring station. Our experimental results further prove the effectiveness of our campaign by (i) providing fine-grained air pollution insights over a wide geographical area, (ii) identifying probable causes of air pollution dependent on the area, and (iii) providing citizens with personalized insights about air pollutants in their daily commute.Peer reviewe

Enabling Internet of Things Applications: An End-to-end Approach

The public defense on 10th June 2020 at 12:00 will be available via remote technology. Link: https://aalto.zoom.us/j/67819394930 Zoom Quick Guide: https://www.aalto.fi/en/services/zoom-quick-guide Electronic online display version of the doctoral thesis is available by email by request from [email protected] is a massive amount of smart objects around us that interact with each other through Internet-based communication standards, forming the so-called Internet of Things (IoT). The scope of the IoT is quite wide and the related applications have diverse requirements in terms of security, data quality, and reliability. We consider different aspects of the IoT: taking an IoT device securely into use, establishing communication with an application server, and collecting as well as transmitting sensory data to remote data storage facilities (e.g., servers in the cloud). In fact, the IoT ecosystem and its immense device-generated data have given rise to several computing paradigms (e.g., cloud, edge, and fog) with different potential and means of sustaining the ever-growing IoT. This dissertation addresses the requirements of a secure and dependable IoT by taking an end-to-end approach. First, it proposes a light-weight mechanism for the initial configuration of network and security parameters to ensure secure bootstrapping of IoT devices. We specifically target a secure as well as a user-friendly IoT: in fact, our solution requires neither human intervention nor physical access to the device, and it incurs low power expenditure. Second, this dissertation addresses challenges in data collection due to the constrained resources available on IoT devices and limited availability of wireless bandwidth. To this end, we consider people- and agent-based data collection with different types of mobility (e.g., uncontrolled, semi-controlled, and fully-controlled). In particular, we leverage the fog computing paradigm and propose a protocol to offload data opportunistically from IoT end-devices to mobile gateways with unknown and uncontrolled mobility. Moreover, we investigate the impact of incentive mechanisms to ensure user participation in the collection of sensory data. To this end, we leverage the mobile edge computing paradigm and design a smart incentive mechanism for participatory crowdsourcing systems that increases the amount of collected data and maximizes the social welfare of the system. Additionally, we propose a communication protocol for ubiquitous wireless devices to disseminate data to mobile agents with fully-controlled mobility to assist search and rescue teams during disaster scenarios. We characterize the impact of our proposed protocol in extending the battery life of the devices and thus increasing the chances of assisting the survivors. Finally, this dissertation presents a light-weight data reduction mechanism that operates at gateways and edge tiers, supporting data-intensive IoT applications. Specifically, it performs filtering and fusion on time series data, thereby reducing the amount of data transmitted to a remote data center while retaining a high recovery accuracy with respect to the original data stream

A Multi-tier Communication Schemefor Drone-assisted Disaster Recovery Scenarios

International audienceDisaster scenarios are particularly devastating in urban environments, which are generally very densely populated. Disasters not only endanger the life of people, but also affect the existing communication infrastructure. In fact, such an infrastructure could be completely destroyed or damaged; even when it continues working, it suffers from high access demand to its resources within a short period of time, thereby compromising the efficiency of rescue operations. This work leverages the ubiquitous presence of wireless devices (e.g., smartphones) in urban scenarios to assist search and rescue activities following a disaster. It considers multi-interface wireless devices and drones to collect emergency messages in areas affected by natural disasters. Specifically, it proposes a collaborative data collection protocol that organizes wireless devices in multiple tiers by targeting a fair energy consumption in the whole network, thereby extending the network lifetime. Moreover, it introduces a scheme to control the path of drones so as to collect data in a short time. Simulation results in realistic settings show that the proposed solution balances the energy consumption in the network by means of efficient drone routes, thereby effectively assisting search and rescue operations

Incentivizing Opportunistic Data Collection for Time-Sensitive IoT Applications

Funding Information: ACKNOWLEDGMENT This work was partially supported by: the Academy of Finland under grants 299222, 319710, and 326346; and the US National Science Foundation under grant CNS-1751075. Publisher Copyright: © 2021 IEEE.Urban environments are the most prevalent application scenario for the Internet of Things (IoT). In this context, effective data collection and forwarding to a cloud (or edge) server are particularly important. This work leverages opportunistic data collection based on the mobile crowd sourcing (MCS) paradigm for time-sensitive IoT applications. Specifically, it introduces an incentive mechanism for the crowd to collect data that are valuable to data consumers in terms of regions of interest and time constraints. The proposed approach successfully incorporates the willingness of the crowd to participate in the data collection as part of the related incentives. It also ensures collection of valuable data via selective user incentivization. Accordingly, a weighted social welfare maximization problem is defined for users to decide which sensors to visit subject to deadline constraints. Following the NP-hardness of the problem, an online heuristic algorithm is proposed for sensors to dynamically incentivize mobile users with a low message and time complexity. The proposed solution is shown to be effective for time-sensitive quality data collection through extensive simulations on realistic mobility traces. It significantly increases the overall social welfare as well as the amount of collected data compared to other approaches.Peer reviewe

Fog-based Data Offloading in Urban IoT Scenarios

Urban environments are a particularly important application scenario for the Internet of Things (IoT). These environments are usually dense and dynamic; in contrast, IoT devices are resource-constrained, thus making reliable data collection and scalable coordination a challenge. This work leverages the fog networking paradigm to devise a multi-tier data offloading protocol suitable for diverse data-centric applications in urban IoT scenarios. Specifically, it takes advantage of heterogeneity in the network so that sensors can collaboratively offload data to each other or to mobile gateways. Second, it evaluates the performance of this offloading process through the amount of data successfully reported to the cloud. In detail, it provides an analytical characterization of data drop-off rates as a random process and derives a light-weight yet efficient method for collaborative data offloading. Finally, it shows that the proposed fog-based solution significantly decreases the data drop-off rate through both analysis and extensive trace-driven simulations based on human mobility data from real urban settings.Peer reviewe

Federated split GANs for collaborative training with heterogeneous devices[Formula presented]

Publisher Copyright: © 2022 The Author(s)Applications based on machine learning (ML) are greatly facilitated by mobile devices and their enormous volume and variety of data. To better safeguard the privacy of user data, traditional ML techniques have transitioned toward new paradigms like federated learning (FL) and split learning (SL). However, existing frameworks have overlooked device heterogeneity, greatly hindering their applicability in practice. In order to address such limitations, we developed a framework based on both FL and SL to share the training load of the discriminative part of a GAN to different client devices. We make our framework available as open-source software1.Peer reviewe