HIP based mobility for Cloudlets

Computation offloading can be used to leverage the resources of nearby computers to ease the computational burden of mobile devices. Cloudlets are an approach, where the client's tasks are executed inside a virtual machine (VM) on a nearby computing element, while the client orchestrates the deployment of the VM and the remote execution in it. Mobile devices tend to move, and while moving between networks, their address is prone to change. Should a user bring their device close to a better performing Cloudlet host, migration of the original Cloudlet VM might also be desired, but their address is then prone to change as well. Communication with Cloudlets relies on the TCP/IP networking stack, which resolves address changes by terminating connections, and this seriously impairs the usefulness of Cloudlets in presence of mobility events. We surveyed a number of mobility management protocols, and decided to focus on Host Identity Protocol (HIP). We ported an implementation, HIP for Linux (HIPL), to the Android operating system, and assessed its performance by benchmarking throughput and delay for connection recovery during network migration scenarios. We found that as long as the HIPL hipfw-module, and especially the Local Scope Identifier (LSI) support was not used, the implementation performed adequately in terms of throughput. On the average, the connection recovery delays were tolerable, with an average recovery time of about 8 seconds when roaming between networks. We also found that with highly optimized VM synthesis methods, the recovery time of 8 seconds alone does not make live migration favourable over synthesizing a new VM. We found HIP to be an adequate protocol to support both client mobility and server migration with Cloudlets. Our survey suggests that HIP avoids some of the limitations found in competing protocols. We also found that the HIPL implementation could benefit from architectural changes, for improving the performance of the LSI support.Liikkuvassa tietojenkäsittelyssä laskennan ulkoistaminen on menetelmä, jolla voidaan käyttää ympäristössä olevien tietokoneiden resursseja keventämään mobiililaitteeseen kohdistuvaa laskennallista rasitusta. Cloudletit ovat eräs ratkaisu mobiililaskennan ulkoistamiseen, jossa laitteessa suoritettavia tehtäviä siirretään suoritettavaksi tietokoneessa ajettavaan virtuaalikoneeseen. Mobiililaite ohjaa virtuaalikoneen luomista ja siinä tapahtuvaa laskentaa verkon yli. Mobiililaitteen taipumus liikkua käyttäjänsä mukana aiheuttaa haasteita nykyisen TCP/IP protokollapinon joustavuudelle. Mobiililaitteen siirtyessä verkosta toiseen, on tyypillistä että sen IP-osoite vaihtuu. Mikäli mobiililaite siirtyy lähelle Cloudlet-isäntäkonetta, joka olisi resurssiensa ja tietoliikenneyhteyksiensä puolesta suotuisampi käyttäjän tarpeisiin, voi käyttäjän Cloudlet-virtuaalikoneen siirtäminen olla toivottavaa. Tällöin kuitenkin myös virtuaalikoneen osoite voi vaihtua. TCP/IP ratkaisee osoitteen vaihtumisen katkaisemalla yhteyden, mikä käyttäjien liikkuvuutta rajoittavana tekijänä tekee Cloudlet-ratkaisun käytöstä vähemmän houkuttelevaa. Tässä tutkielmassa tutustuimme joukkoon sopivaksi arvioimiamme liikkuvuutta tukevia protokollia, ja valitsimme niistä HIP -protokollan lähempää tarkastelua varten. Teimme HIP for Linux -protokollaohjelmistosta sovituksen Android-käyttöjärjestelmälle ja tutkimme sen soveltuvuutta liikkuvuuden tukemiseen mittaamalla sen avulla muodostetuilla yhteyksillä saavutettavia siirtonopeuksia sekä yhteyden palautumiseen kuluvaa aikaa osoitteenvaihdosten yhteydessä. Mikäli HIPL:in hipfw-moduuli, ja erityisesti sen LSI-tuki (IPv4-sovellusrajapinta) ei ollut käytössä, mittaustemme mukaan protokollatoteutus suoriutui Cloudlet-käyttöön riittävän hyvin siirtonopeuksien suhteen. Lisäksi yhteyksien palauttaminen osoitteenvaihdosten yhteydessä sujui siedettävässä ajassa, keskimäärin noin kahdeksassa sekunnissa. Hyvin optimoitujen Cloudlet-virtuaalikoneiden synteesimenetelmien vuoksi kahdeksan sekunnin toipumisaika yksinään ei tarjoa virtuaalikoneen siirtämisestä merkittävää etua uuden luomiseen nähden. HIP protokolla soveltuu yhteydenpitoon sekä mobiililaitteesta Cloudlet-isäntäkoneille, että Cloudlet-virtuaalikoneeseen; pienehkön kirjallisuuskatsauksen perusteella muita oleellisia protokollia hieman paremmin. Tunnistimme myös uudistamistarpeen HIPL-toteutuksen arkkitehtuurissa LSI-tuen suorituskyvyn parantamiseksi

DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification

Copresence verification based on context can improve usability and strengthen security of many authentication and access control systems. By sensing and comparing their surroundings, two or more devices can tell whether they are copresent and use this information to make access control decisions. To the best of our knowledge, all context-based copresence verification mechanisms to date are susceptible to context-manipulation attacks. In such attacks, a distributed adversary replicates the same context at the (different) locations of the victim devices, and induces them to believe that they are copresent. In this paper we propose DoubleEcho, a context-based copresence verification technique that leverages acoustic Room Impulse Response (RIR) to mitigate context-manipulation attacks. In DoubleEcho, one device emits a wide-band audible chirp and all participating devices record reflections of the chirp from the surrounding environment. Since RIR is, by its very nature, dependent on the physical surroundings, it constitutes a unique location signature that is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR data with various mobile devices and in a range of different locations. We show that DoubleEcho mitigates context-manipulation attacks whereas all other approaches to date are entirely vulnerable to such attacks. DoubleEcho detects copresence (or lack thereof) in roughly 2 seconds and works on commodity devices

Towards Data-Driven Generation of Visualizations for Automatically Generated News Articles

Peer reviewe

Software Newsroom – an approach to automation of news search and editing

We have developed tools and applied methods for automated identification of potential news from textual data for an automated news search system called Software Newsroom. The purpose of the tools is to analyze data collected from the internet and to identify information that has a high probability of containing new information. The identified information is summarized in order to help understanding the semantic contents of the data, and to assist the news editing process. It has been demonstrated that words with a certain set of syntactic and semantic properties are effective when building topic models for English. We demonstrate that words with the same properties in Finnish are useful as well. Extracting such words requires knowledge about the special characteristics of the Finnish language, which are taken into account in our analysis. Two different methodological approaches have been applied for the news search. One of the methods is based on topic analysis and it applies Multinomial Principal Component Analysis (MPCA) for topic model creation and data profiling. The second method is based on word association analysis and applies the log-likelihood ratio (LLR). For the topic mining, we have created English and Finnish language corpora from Wikipedia and Finnish corpora from several Finnish news archives and we have used bag-of-words presentations of these corpora as training data for the topic model. We have performed topic analysis experiments with both the training data itself and with arbitrary text parsed from internet sources. The results suggest that the effectiveness of news search strongly depends on the quality of the training data and its linguistic analysis. In the association analysis, we use a combined methodology for detecting novel word associations in the text. For detecting novel associations we use the background corpus from which we extract common word associations. In parallel, we collect the statistics of word co-occurrences from the documents of interest and search for associations with larger likelihood in these documents than in the background. We have demonstrated the applicability of these methods for Software Newsroom. The results indicate that the background-foreground model has significant potential in news search. The experiments also indicate great promise in employing background-foreground word associations for other applications. A combined application of the two methods is planned as well as the application of the methods on social media using a pre-translator of social media language.Peer reviewe

Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota—Implementation and Computational Analysis

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof-of-concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratory assembled microbial mixtures as well as human fecal samples. </p

InDEx – Industrial Data Excellence

InDEx, the Industrial Data Excellence program, was created to investigate what industrial data can be collected, shared, and utilized for new intelligent services in high-performing, reliable and secure ways, and how to accomplish that in practice in the Finnish manufacturing industry.InDEx produced several insights into data in an industrial environment, collecting data, sharing data in the value chain and in the factory environment, and utilizing and manipulating data with artificial intelligence. Data has an important role in the future in an industrial context, but data sources and utilization mechanisms are more diverse than in cases related to consumer data. Experiences in the InDEx cases showed that there is great potential in data utili zation.Currently, successful business cases built on data sharing are either company-internal or utilize an existing value chain. The data market has not yet matured, and third-party offerings based on public and private data sources are rare. In this program, we tried out a framework that aimed to securely and in a controlled manner share data between organizations. We also worked to improve the contractual framework needed to support new business based on shared data, and we conducted a study of applicable business models. Based on this, we searched for new data-based opportunities within the project consortium. The vision of data as a tradeable good or of sharing with external partners is still to come true, but we believe that we have taken steps in the right direction.The program started in fall 2019 and ended in April 2022. The program faced restrictions caused by COVID-19, which had an effect on the intensity of the work during 2020 and 2021, and the program was extended by one year. Because of meeting restrictions, InDEx collaboration was realized through online meetings. We learned to work and collaborate using digital tools and environments. Despite the mentioned hindrances, and thanks to Business Finland’s flexibility, the extension time made it possible for most of the planned goals to be achieved.This report gives insights in the outcomes of the companies’ work within the InDEx program. DIMECC InDEx is the first finalized program by the members of the Finnish Advanced Manufacturing Network (FAMN, www.famn.fi).</p