Spatial dependency in edge-native artificial intelligence

Abstract Edge computing augments cloud computing. While cloud computing is based on far away computing centres, edge computing acknowledges the computing resources in the continuum between local devices and the cloud. The computing resources in edge computing are often heterogeneous, with varying capacity, intermittent connectivity, and opportunistic availability. In contrast, modern artificial intelligence and especially machine learning methods are often deployed in the cloud, and assume the computing resources are homogeneous, abundant, centralized, easily scalable, and always available. This thesis studies edge AI, a nascent field of research combining edge computing and artificial intelligence. A particular focus in the thesis is on spatial dependencies, which quantify the similarity of observations in the spatial dimension. Spatial dependencies are prominent in edge AI due to the local nature of edge service users, the computational resources, as well as many of the observed data-generating processes. The thesis asks three research questions. The first one looks for a method to explicitly consider spatial dependencies in edge AI, while the second and third ones apply the method for edge server placement and environmental sensing. As result, the thesis first proposes a novel spatial clustering method, named PACK, which partitions a set of spatial data points according to configurable attributes and constraints. PACK then provides a basis for edge server placement and workload allocation, where a large-scale edge deployment can be optimized such that user quality of experience and deployment quality of service are maximised. Furthermore, PACK serves a crucial function in environmental sensing with a massive fleet of mobile sensors, providing grounds for distributing computations and data for a novel, edge-native method for interpolation. In both edge server placement and environmental sensing, the proposed methods outperform state-of-the-art. Finally, the thesis looks at the limitations of the proposed methods, their significance, and maps potential avenues for future research.Tiivistelmä Reunalaskenta täydentää pilvilaskentaa. Siinä missä pilvilaskenta perustuu kaukaisiin datakeskuksiin, ottaa reunalaskenta huomioon myös laskentaresurssit matkalla paikallisista laitteista pilveen. Reunalaskennan resurssit ovat ominaisuuksiltaan kirjavia: niiden kapasiteetit saattavat olla hyvin erilaisia, niiden yhteydet saattavat katkeilla ja ne saattavat olla saatavilla vain toisinaan. Toisaalta nykyaikaisia tekoäly- ja koneoppimismenetelmiä käytetään tavallisesti pilvipalveluissa, ja ne olettavat, että laskentaresurssit ovat homogeenisia ja ne ovat saatavilla keskitetysti, skaalautuvasti ja jatkuvasti. Tämä väitöstyö tutkii reunalaskennan tekoälyä. Reunalaskennan tekoäly on uusi tieteenala, jossa tutkitaan, kuinka reunalaskenta sekä tekoäly- ja koneoppimismenetelmät voidaan yhdistää. Erityisesti väitös pureutuu paikkariippuvuuksiin, jotka kuvaavat havaintojen paikallisia samankaltaisuuksia. Paikkariippuvuudet ovat reunalaskennan tekoälyssä tärkeitä, sillä reunapalveluiden käyttäjät, niiden laskentaresurssit, ja myös dataa tuottavat prosessit ovat usein luonteeltaan paikallisia. Väitös kysyy kolme tutkimuskysymystä. Ensimmäinen hakee menetelmää, jolla paikkariippuvuudet voisi valjastaa käyttöön reunalaskennan tekoälyssä. Toinen ja kolmas tutkimuskysymys ovat luonteeltaan soveltavia, ja etsivät menetelmää reunalaskennan palvelimien sijoitteluun ja ympäristön anturointiin. Väitöksen tuloksena on, ensinnä, uusi spatiaalinen klusterointimenetelmä, nimeltään PACK, jonka avulla paikkapisteiden joukko voidaan jakaa osiin erilaisten muokattavien ominaisuuksien ja rajoitteiden mukaisesti. Toiseksi, PACK toimii pohjaratkaisuna reunalaskennan palvelimien sijoittelulle ja kuormanjaolle, kun kaupungin laajuinen reunalaskennan asennus halutaan optimoida siten, että käyttäjäkokemus ja palveluntaso saadaan parhaaksi mahdolliseksi. Kolmanneksi, PACK on jälleen kriittisessä roolissa reunatekoälysovelluksessa, jossa massiivinen määrä antureita mittaa ympäristöään, ja antureiden tuottama data sekä datan interpoloinnin aiheuttama laskentakuorma hajautetaan reunalaskennan avulla. Sekä sijoittelu- että anturointisovellus tuottavat parempia tuloksia kuin nykyiset ratkaisut. Lopuksi väitös avaa esiteltyjen menetelmien rajoituksia sekä merkitystä laajemmassa mittakaavassa, ja kartoittaa mahdollisia tulevaisuuden tutkimussuuntia aiheesta

Verification of road surface temperature forecasts assimilating data from mobile sensors

Abstract The advances in communication technologies have made it possible to gather road condition information from moving vehicles in real time. However, data quality must be assessed and its effects on the road weather forecasts analyzed before using the new data as input in forecasting systems. Road surface temperature forecasts assimilating mobile observations in the initialization were verified in this study. In addition to using measured values directly, different statistical corrections were applied to the mobile observations before using them in the road weather model. The verification results are compared to a control run without surface temperature measurements and to a control run that utilized interpolated values from surrounding road weather stations. Simulations were done for the period 12 October 2017–30 April 2018 for stationary road weather station points in southern Finland. Road surface temperature observations from the stations were used in the forecast verification. According to the results, the mobile observations improved the accuracy of road surface temperature forecasts when compared to the first control run. The statistical correction methods had a positive effect on forecast accuracy during the winter, but the effect varied during spring when the daily temperature variation was strong. In the winter season, the forecasts based on the interpolated road surface temperature values and the forecasts utilizing mobile observations with statistical correction had comparable accuracy. However, the tested area has high road weather station density and not much elevation variation, so results might have been different in more varying terrain

A on spam filtering classification:a majority voting like approach

Abstract Despite the improvement in filtering tools and informatics security, spam still cause substantial damage to public and private organizations. In this paper, we present a majority-voting based approach in order to identify spam messages. A new methodology for building majority voting classifier is presented and tested. The results using SpamAssassin dataset indicates non-negligible improvement over state of art, which paves the way for further development and applications

Cyber personalities as a target audience

Abstract Target audience analysis (TAA) is an essential part of any psychological operation. In order to convey a change in behaviour, the overall population is systematically segmented into target audiences (TAs) according to their expected responsiveness to different types of influence and messages, as well as their expected ability to behave in a desired way. The cyber domain poses a challenge to traditional TAA methods. Firstly, it is vast and complex, requiring effective algorithms to filter out relevant information within a meaningful timeframe. Secondly, it is constantly changing, representing a meshwork in formation, rather than a stable collection of TAA-specific data. The third challenge is that the target audience (TA) consists not of people, but of digital representations of people, whose true identity and characteristics cannot usually be verified. To address these challenges, the authors of this article suggest that the concept of TAA has to be revised for use in the cyber domain. Instead of trying to analyse physical people through the cyber interface, the authors have conceptualized an abstract entity whose physical identity might not be known, but whose behavioural patterns can be observed in the cyber environment. These cyber personalities, some of which are more or less intelligent algorithms, construct and share their interpretation of reality as well as carefully planned narratives in the digital environment. From the viewpoint of TAA, the only relevant quality of these entities is their potential ability to contribute to the objectives of a psychological operation. As a first step, this article examines the cyber domain through a five-layer structure and looks at what TAA-relevant data is available for analysis. The authors also present ways of analysing cyber personalities and their networks, in order to conduct a TAA that effectively supports psychological influence in the cyber domain. As a way of better utilizing the digital nature of cyber personalities, a concept of dynamic TAs is also introduced

Wellbeing in smart environments:definition, measurement, prediction and control

Abstract What does well-being mean in the context of smart environments? What restrictions are set, how can well-being be measured and predicted? Can smart environments control or influence individual well-being? We seek to answer these questions by aggregating existing research on well-being and identifying the concepts relevant for smart environments. As a result, we provide a falsifiable definition candidate for well-being in smart environments and outline the experiments necessary for verifying the validity of the definition

Weathering the reallocation storm:large-scale analysis of edge server workload

Abstract Efficient service placement and workload allocation methods are necessary enablers for the actively studied topic of edge computing. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks — a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections in 2013—2014, with more than 47M connections over ca. 800 access points. We identify the conditions for avoiding the reallocation storm for three common edge-based reallocation strategies, and study the latency-workload trade-off related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of top ES workload. Further, while a reallocation strategy aiming to minimize reallocation distance consistently resulted in the worst reallocation storms, the two other strategies, namely, a random reallocation strategy, and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments

The intersection of blockchain and 6G technologies

Abstract The fifth generation (5G) wireless networks are on the way to be deployed around the world. The 5G technologies target to support diverse vertical applications by connecting heterogeneous devices and machines with drastic improvements in terms of high quality of service, increased network capacity and enhanced system throughput. However, 5G systems still remain a number of security challenges that have been mentioned by researchers and organizations, including decentralization, transparency, risks of data interoperability, and network privacy vulnerabilities. Furthermore, the conventional techniques may not be sufficient to deal with the security requirements of 5G. As 5G is generally deployed in heterogeneous networks with massive ubiquitous devices, it is quite necessary to provide secure and decentralized solutions. Motivated from these facts, in this paper we provide a state-of-the-art survey on the integration of blockchain with 5G networks and beyond. In this detailed survey, our primary focus is on the extensive discussions on the potential of blockchain for enabling key 5G technologies, including cloud computing, edge computing, Network Function Virtualization, Network Slicing, and D2D communications. We then explore and analyse the opportunities that blockchain potentially empowers important 5G services, ranging from spectrum management, data sharing, network virtualization, resource management to interference management, federated learning, privacy and security provision. The recent advances in the applications of blockchain in 5G Internet of Things are also surveyed in a wide range of popular use-case domains, such as smart healthcare, smart city, smart transportation, smart grid and UAVs. The main findings derived from the comprehensive survey on the cooperated blockchain-5G networks and services are then summarized, and possible research challenges with open issues are also identified. Lastly, we complete this survey by shedding new light on future directions of research on this newly emerging area

A dark and stormy night:reallocation storms in edge computing

Abstract Efficient resource usage in edge computing requires clever allocation of the workload of application components. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks—a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections, with more than 47M connections over ca. 560 access points. We study the occurrence of reallocation storms in three common edge-based reallocation strategies and compare the latency–workload trade-offs related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of the peak ES workload. Further, while a reallocation strategy aiming to minimize latency consistently resulted in the worst reallocation storms, the two other strategies, namely a random reallocation strategy and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments. Moreover, we study the conditions associated with reallocation storms. We discover that edge servers with the very highest workloads are best associated with reallocation storms, with other servers around the few busy nodes thus mirroring their workload. Further, we identify circumstances associated with an elevated risk of reallocation storms, such as summertime (ca. 4 times the risk than on average) and on weekends (ca. 1.5 times the risk). Furthermore, mass events such as popular sports games incurred a high risk (nearly 10 times that of the average) of a reallocation storm in a MEC-based scenario

On the use of URLs and hashtags in age prediction of Twitter users

Abstract Social media data represent an important resource for behavioral analysis of the ageing population. This paper addresses the problem of age prediction from Twitter dataset, where the prediction issue is viewed as a classification task. For this purpose, an innovative model based on Convolutional Neural Network is devised. To this end, we rely on language-related features and social media specific metadata. More specifically, we introduce two features that have not been previously considered in the literature: the content of URLs and hashtags appearing in tweets. We also employ distributed representations of words and phrases present in tweets, hashtags and URLs, pre-trained on appropriate corpora in order to exploit their semantic information in age prediction. We show that our CNN-based classifier, when compared with an SVM baseline model, yields an improvement of 12.3% and 6.6% in the micro-averaged F1 score on the Dutch and English datasets, respectively

Towards measuring well-being in smart environments

Abstract This study discusses measurement of well-being in the context of smart environments. We propose an experimental design which induces variation in an individual’s flow, stress, and affect for testing different measurement methods. Both qualitative and quantitative measuring methods are applied, with a variety of wearable sensors (EEG sensor, smart ring, heart rate monitor) and video monitoring. Preliminary results show significant agreement with the test structure in the readings of wearable stress and heart rate sensors. Self-assessments, on the contrary, fail to show significant evidence of the experiment structure, reflecting the difficulty of subjective estimation of short-term stress, flow and affect