Secure Connectivity With Persistent Identities

In the current Internet the Internet Protocol address is burdened with two roles. It serves as the identifier and the locator for the host. As the host moves its identity changes with its locator. The research community thinks that the Future Internet will include identifier-locator split in some form. Identifier-locator split is seen as the solution to multiple problems. However, identifier-locator split introduces multiple new problems to the Internet. In this dissertation we concentrate on: the feasibility of using identifier-locator split with legacy applications, securing the resolution steps, using the persistent identity for access control, improving mobility in environments using multiple address families and so improving the disruption tolerance for connectivity. The proposed methods achieve theoretical and practical improvements over the earlier state of the art. To raise the overall awareness, our results have been published in interdisciplinary forums.Nykypäivän Internetissä IP-osoite on kuormitettu kahdella eri roolilla. IP toimii päätelaitteen osoitteena, mutta myös usein sen identiteetinä. Tällöin laitteen identiteetti muuttuu laitteen liikkuessa, koska laitteen osoite vaihtuu. Tutkimusyhteisön mielestä paikan ja identiteetin erottaminen on välttämätöntä tulevaisuuden Internetissä. Paikan ja identiteetin erottaminen tuo kuitenkin esiin joukon uusia ongelmia. Tässä väitöskirjassa keskitytään selvittämään paikan ja identiteetin erottamisen vaikutusta olemassa oleviin verkkoa käyttäviin sovelluksiin, turvaamaan nimien muuntaminen osoitteiksi, helpottamaan pitkäikäisten identiteettien käyttöä pääsyvalvonnassa ja parantamaan yhteyksien mahdollisuuksia selviytyä liikkumisesta usean osoiteperheen ympäristöissä. Väitöskirjassa ehdotetut menetelmät saavuttavat sekä teoreettisia että käytännön etuja verrattuna aiempiin kirjallisuudessa esitettyihin menetelmiin. Saavutetut tulokset on julkaistu eri osa-alojen foorumeilla

MegaSense: 5G and AI for Air Quality Monitoring

Air pollution has become a global challenge during the growth of megacities, which drives the deployment of air quality monitoring in order to understand and mitigate district level air pollution. Currently, air pollution monitoring mainly relies on high-end accurate reference stations, which are usually stationary and expensive. Thus, the air quality monitoring deployments are typically coarse grained with only a very small number of stations in a city. We propose scalable air quality monitoring by leveraging low-cost air pollution sensors, artificial intelligence methods, and versatile connectivity provided by 4G/5G. We describe pilot deployments for testing the developed sensing technologies in three different locations in Helsinki, Finland.Peer reviewe

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

Low-cost particulate matter (PM) sensors have been under investigation as it has been hypothesized that the use of low-cost and easy-to-use sensors could allow cost-efficient extension of the currently sparse measurement coverage. While the majority of the existing literature highlights that low-cost sensors can indeed be a valuable addition to the list of commonly used measurement tools, it often reiterates that the risk of sensor misuse is still high and that the data obtained from the sensors are only representative of the specific site and its ambient conditions. This implies that there are underlying reasons for inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to investigate the particle-size selectivity of low-cost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of size selectivity was carried out in the laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes (from similar to 0.55 to 8.4 mu m) on-line. The results of the study show that none of the low-cost sensors adhered to the detection ranges declared by the manufacturers; moreover, cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020) indicates that the sensors can only achieve independent responses for one or two size bins, whereas the spectrometer can sufficiently characterize particles with 15 different size bins. These observations provide insight into and evidence of the notion that particle-size selectivity has an essential role in the analysis of the sources of errors in sensors.Peer reviewe

MegaSense: Cyber-Physical System for Real-time Urban Air Quality Monitoring

Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacy-aware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a battery-powered, portable low-cost air quality sensor design for sampling PM2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying MegaSense with citizens carrying low-maintenance portable sensors, and using smart phone exposure apps. We demonstrate daily air pollution exposure profiles and the air pollution hot-spot profile of a district. Our contributions have applications in policy intervention management mechanisms and design of clean air routing and healthier navigation applications to reduce pollution exposure.Peer reviewe

Data Driven Analysis of Lithium-ion Battery Internal Resistance Towards Reliable State of Health Prediction

Accurately predicting the lifetime of lithium-ion batteries in the early stage is critical for faster battery production, tuning the production line, and predictive maintenance of energy storage systems and battery-powered devices. Diverse usage patterns, variability in the devices housing the batteries, and diversity in their operating conditions pose significant challenges for this task. The contributions of this paper are three-fold. First, a public dataset is used to characterize the behavior of battery internal resistance. Internal resistance has non-linear dynamics as the battery ages, making it an excellent candidate for reliable battery health prediction during early cycles. Second, using these findings, battery health prediction models for different operating conditions are developed. The best models are more than 95\% accurate in predicting battery health using the internal resistance dynamics of 100 cycles at room temperature. Thirdly, instantaneous voltage drops due to multiple pulse discharge loads are shown to be capable of characterizing battery heterogeneity in as few as five cycles. The results pave the way toward improved battery models and better efficiency within the production and use of lithium-ion batteries.Peer reviewe

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis

arXiv:1912.06384 [eess.SP]The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.Peer reviewe

Toward Massive Scale Air Quality Monitoring

Dangers associated with poor air quality are driving deployments of air quality monitoring technology. These deployments rely either on professional-grade measurement stations or a small number of low-cost sensors integrated into urban infrastructure. In this article, we present a research vision of real-time massive scale air quality sensing that integrates tens of thousands or even millions of air quality sensors to monitor air quality at fine spatial and temporal resolution. We highlight opportunities and challenges of our vision by discussing use cases, key requirements and reference technologies in order to establish a roadmap on how to realize this vision. We address the feasibility of our vision, introducing a testbed deployment in Helsinki, Finland, and carrying out controlled experiments that address collaborative and opportunistic sensor calibration, a key research challenge for our vision.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

Transit Pollution Exposure Monitoring using Low-Cost Wearable Sensors

Transit activities are a significant contributor to a person's daily exposure to pollutants. Currently obtaining accurate information about the personal exposure of a commuter is challenging as existing solutions either have a coarse monitoring resolution that omits subtle variations in pollutant concentrations or are laborious and costly to use. We contribute by systematically analysing the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters. Through extensive experiments carried out in the Helsinki metropolitan region, we demonstrate that low-cost sensors can capture the overall exposure with sufficient accuracy, while at the same time providing insights into variations within transport modalities. We also demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors. For example, we demonstrate that location within the vehicle carriage can result in up to 25% increase in daily pollution exposure -- a significant difference that existing solutions are unable to capture. Finally, we highlight the practical benefits of low-cost sensors as a pollution monitoring solution by introducing applications that are enabled by low-cost wearable sensors.Peer reviewe