Construction and analysis of cluster algorithmwith application in defining behavioural risk factors in Serbian adult population

Klaster analiza ima dugu istoriju i mada se primenjuje u mnogim oblastima i dalje ostaju značajni izazovi. U disertaciji je prikazan uvod u neglatki optimizacioni pristup u klasterovanju, sa osvrtom na problem klasterovanja velikih skupova podataka. Međutim, ovi optimizacioni algoritmi bolje funkcionišu u radu sa neprekidnim podacima. Jedan od glavnih izazova u klaster analizi je rad sa velikim skupovima podataka sa kategorijalnim i kombinovanim (numerički i kategorijalni) tipovima promenljivih. Rad sa velikim brojem instanci (objekata) i velikim brojem dimenzija (promenljivih), može predstavljati problem u klaster analizi, zbog vremenske složenosti. Jedan od načina rešavanja ovog problema je redukovanje broja instanci, bez gubitka informacija. Prvi cilj disertacije je bio upoređivanje rezultata klasterovanja na celom skupu i prostim slučajnim uzorcima sa kategorijalnim i kombinovanim podacima, za različite veličine uzorka i različit broj klastera. Nije utvrđena značajna razlika (p>0.05) u rezultatima klasterovanja na uzorcima obima 0.03m,0.05m,0.1m,0.3m (gde je m obim posmatranog skupa) i celom skupu. Drugi cilj disertacije je bio konstrukcija efikasnog postupka klasterovanja velikih skupova podataka sa kategorijalnim i kombinovanim tipovima promenljivih. Predloženi postupak se sastoji iz sledećih koraka: 1. klasterovanje na prostim slučajnim uzorcima određene kardinalnosti; 2. određivanje najboljeg klasterskog rešenja na uzorku, primenom odgovarajućeg kriterijuma validnosti; 3. dobijeni centri klastera iz ovog uzorka služe za klasterovanje ostatka skupa. Treći cilj disertacije predstavlja primenu klaster analize u definisanju klastera bihejvioralnih faktora rizika u populaciji odraslog stanovništva Srbije, kao i analizu sociodemografskih karakteristika dobijenih klastera. Klaster analiza je primenjena na velikom reprezentativnom uzorku odraslog stanovništva Srbije, starosti 20 i više godina. Izdvojeno je pet jasno odvojenih klastera sa karakterističnim kombinacijama bihejvioralnih faktora rizika: Bez rizičnih faktora, Štetna upotreba alkohola i druge rizične navike, Nepravilna ishrana i druge rizične navike, Nedovoljna fizička aktivnost, Pušenje. Rezultati multinomnog logističkog regresionog modela ukazuju da ispitanici koji nisu u braku, lošijeg su materijalnog stanja, nižeg obrazovanja i žive u Vojvodini imaju veću šansu za prisustvo višestrukih bihejvioralnih faktora rizika.The cluster analysis has a long history and a large number of clustering techniques have been developed in many areas, however, significant challenges still remain. In this thesis we have provided a introduction to nonsmooth optimization approach to clustering with reference to clustering large datasets. Nevertheless, these optimization clustering algorithms work much better when a dataset contains only vectors with continuous features. One of the main challenges is clustering of large datasets with categorical and mixed (numerical and categorical) data. Clustering deals with a large number of instances (objects) and a large number of dimensions (variables) can be problematic because of time complexity. One of the ways to solve this problem is by reducing the number of instances, without the loss of information. The first aim of this thesis was to compare the results of cluster algorithms on the whole dataset and on simple random samples with categorical and mixed data, in terms of validity, for different number of clusters and for different sample sizes. There were no significant differences (p>0.05) between the obtained results on the samples of the size of 0.03m,0.05m,0.1m,0.3m (where m is the size of the dataset) and the whole dataset. The second aim of this thesis was to develop an efficient clustering procedure for large datasets with categorical and mixed (numeric and categorical) values. The proposed procedure consists of the following steps: 1. clustering on simple random samples of a given cardinality; 2. finding the best cluster solution on a sample (by appropriate validity measure); 3. using cluster centers from this sample for clustering of the remaining data. The third aim of this thesis was to examine clustering of four lifestyle risk factors and to examine the variation across different socio-demographic groups in a Serbian adult population. Cluster analysis was carried out on a large representative sample of Serbian adults aged 20 and over. We identified five homogenous health behaviour clusters with specific combination of risk factors: 'No Risk Behaviours', 'Drinkers with Risk Behaviours', 'Unhealthy diet with Risk Behaviours', 'Smoking'. Results of multinomial logistic regression indicated that single adults, less educated, with low socio-economic status and living in the region of Vojvodina are most likely to be a part of the clusters with a high-risk profile

Network attacks detection based on traffic flows analysis using hybrid machine learning algorithms

Razvoj savremenih mrežnih okruženja se zasniva na primeni različitih tehnologija, povezivanju sa drugim tehnološki drugačijim konceptima i obezbeđivanju njihove interoperabilnosti. Tako složeno mrežno okruženje je neprekidno izloženo različitim izazovima, pri čemu je obezbeđivanje sigurnosti servisa i podataka jedan od najvažnijih zadataka. Novi zahtevi za sisteme zaštite se zasnivaju na potrebi za efikasnim praćenjem i razumevanju karakteristika mrežnog saobraćaja, a uslovljeni su stalnim porastom broja korisnika i razvojem novih aplikacija. Razvoj rešenja u oblasti detekcije anomalija i napada je postao svojevrsni imperativ, imajući u vidu da se paralelno odvija intenzivni razvoj u oblasti sajber napada. Osim toga, promene mrežnog saobraćaja su postale sve dinamičnije, a kao poseban problem se izdvaja velika heterogenost primenjenih tehnologija i korisničkih uređaja. Iako dostupna literatura prepoznaje veliki broj radova koji se bave analizom tokova mrežnog saobraćaja za potrebe praćenja performansi i sigurnosnih aspekata mreža, mali je broj istraživanja koja se zasnivaju na procedurama generisanja i analize profila ponašanja mrežnog saobraćaja, odnosno specifičnih komunikacionih obrazaca. U tom smislu, analiza ponašanja mreže se u sve većoj meri oslanja na razumevanje normalnih ili prihvatljivih obrazaca ponašanja na osnovu kojih je moguće efikasno otkrivanje obrazaca anomalija. Za razliku od sistema za otkrivanje napada koji se zasnivaju na analizi sadržaja svakog pojedinačnog paketa (signature-based), ovaj pristup je izuzetno koristan za identifikaciju nepoznatih pretnji, napada nultog dana, sumnjivog ponašanja i za sveopšte poboljšavanje performansi mrežnih okruženja...The development of the modern network environments, their application, and the dynamics of their interoperability with other technologically different concepts, is based on the application and compatibility of different heterogeneous technologies. Such a complex network environment is constantly exposed to various operational challenges, where ensuring the security and safety of services and data represents one of the most important tasks. The constant increase in the number of users and the intensive development of new applications that require high bandwidth has defined new requirements for security systems, which are based on monitoring and effectively understanding network traffic characteristics. In the light of the increasingly intensive development in the field of cyberattacks, persistent dynamic changes in network traffic, as well as the increased heterogeneity of the used technologies and devices, the development of solutions in the field of anomaly and attack detection has become a kind of imperative. Although the available literature recognizes a large number of papers dealing with the analysis of network traffic flows for the needs of the monitoring of the performance and security aspects of networks, just a few studies are based on the procedures for generating network traffic behavior profiles, or specific communication patterns. In this sense, network behavior analysis relies on an understanding of normal or acceptable behavior patterns, which would allow for the effective detection of unusual, anomalous behavior patterns. Unlike the intrusion detection systems that are based on the packet payload or signature (signature-based), this approach is extremely useful not only for the identification of unknown threats, zero-day attacks, and suspicious behavior, but also for the improvement of the overall network performance..

Prepoznavanje prekida u procesu struganja primenom linearnog klasifikatora

Klasa pojava koje se manifestuju kao nagli prekid u procesu rezanja (kao što su različiti tipovi loma alata, defekti u materijalu obratka i sl.) mogu dovesti do neželjenih posledica u procesu obrade. Stoga je prepoznavanje ovakvih pojava izuzetno značajno sa aspekta upravljanja obradnim sistemom. U ovom radu se izlaže koncept mašine za prepoznavanje prekida u procesu rezanja. Predložena mašina za prepoznavanje oblika je zasnovana na linearnom klasifikatoru. Proces obučavanja linearnog klasifikatora izvršen je na osnovu rezultata klasterovanja dobijenih ISODATA algoritmom. Za ekstrakciju obeležja relevantnih za klasifikaciju upotrebljena je diskretna vejvlet transformacija. Predložena metodologija je eksperimentalno verifikovana

Algorithms for task assignment in wireless networks of microcontroller sensor nodes and autonomous robots

U bežičnoj mreži senzora i robota, senzorski moduli vrše nadzor fizičkih veličina od značaja, a roboti imaju ulogu izvršilaca zadataka koji im se dodeljuju primenom odgovarajućeg algoritma. Nakon detekcije događaja od strane statičkih senzorskih čvorova i prosleđivanja informacija o događajima robotima, potrebno je dodeliti zadatke robotima na efikasan način. Dodela zadataka vrši se u skladu sa prirodom različitih scenarija koji se mogu javiti u praksi. U okviru disertacije razmatran je slučaj kada se konkurentno javlja više događaja kojima je potrebno dodeliti izvršioce. U pogledu energetske efikasnosti, u ovakvim sistemima kao ključni problemi javljaju se minimizacija ukupne dužine kretanja robota i optimizacija komunikacije u mreži. Od komunikacinih protokola za otkrivanje izvršilaca, u ovoj disertaciji predstavljena su poboljšanja postojećeg iMesh protokola i uveden je novi vCell protokol zasnovan na lokalizovanom formiranju ćelija Voronoi dijagrama. Takođe, upoređene su performanse novog protokola sa postojećim (pravougaoni kvorum i iMesh) u gustim mrežama, retkim mrežama i mrežama sa rupama u topologiji. Uz to, uvedeni su algoritmi za ažuriranje lokacije kojima mreža reaguje na kretanje robota. Rezultati simulacija pokazuju da vCell postiže efikasnost blizu 100% u nalaženju najbližeg robota u gustim mrežama. U retkim mrežama, efikasnost mu je do 40% bolja u odnosu na ostala rešenja. Kao glavni rezultat u disertaciji prikazani su novi algoritmi za dodelu robota kao izvršilaca zadataka događajima, čime su prevaziđni nedostaci više do sada poznatih rešenja ovog problema. Za zadati skup događaja i skup robota, svakom događaju dodeljen je po jedan robot koji je zadužen za obilazak lokacije događaja. Tokom pojedinačnih rundi, robotima je dozvoljen obilazak jednog događaja kada se vrši uparivanje, ili više događaja, kada se vrši sekvencijalna dodela. U distribuiranom slučaju, statički senzorski uređaji detektuju događaje i prijavljuju ih obližnjim robotima. Algoritam PDM koji se odnosi na unapređeno uparivanje sa mogućnošću razmene partnera, eliminiše dugačke ivice koje se mogu javiti prilikom uparivanja. Algoritam SQD za sekvencijalnu dodelu događaja robotima iterativno pronalazi par robot-događaj sa najmanjim međusobnim rastojanjem, uvrštava izabrani događaj u listu za oblazak izabranog robota i ažurira poziciju robota. Takođe su predložene generalizacije koje omogućavaju da događaji budu posećeni od strane više robota i koje uzimaju u obzir vremenska ograničenja. Distribuirani algoritam MAD, koji je zasnovan na iMesh informacionoj strukturi i lokalnim aukcijama u robotskoj mreži, vrši dodelu robota događajima na lokalizovan i energetski efikasan način. Rezultati simulacija potvrđuju prednosti predloženih algoritama u odnosu na postojeća rešenja, kako u pogledu skraćivanja dužina putanja robota, tako i u produženju životnog vremena sistema.In a typical wireless sensor and robot network, sensor nodes monitor physical values of interest, while robots perform some automated tasks. The tasks are assigned to robots by means of an appropriate algorithm. Upon the occurrence of events which are detected by sensor nodes, the information about the events needs to be delivered to robots. Afterwards, it is necessary to assign tasks to robots in an efficient way. Task assignment is performed according to the nature of different scenarios which might occur in practice. This thesis is focused on the case when multiple events, all of which require to be visited by robots, happen simultaneously. Regarding energy efficiency, the key issues which arise in such systems are minimization of robot travel paths, and optimization of the network traffic. In this thesis, the following service discovery protocols are presented: improvements of the existing iMesh protocol, and the novel vCell protocol, which is based on localized formation of an information structure which resembles Voronoi diagram. Furthermore, the performaces of new vCell protocol is compared with the existing protocols (Quorum and iMesh) in dense networks, sparse networks, and networks with holes in topology. Also, location update algorithms are introduced, which deal with robot mobility. The simulations show that vCell achieves nearly 100% success rate in finding the nearest robot in dense networks. In sparse networks, it outperforms the other existing solutions by up to 40%. As a key contributtion, the novel dispatch lgorithms have been introduced. Given a set of events and a set of robots, the dispatch problem is to allocate one robot for each event to visit it. In a single round, each robot may be allowed to visit only one event (matching dispatch), or several events in a sequence (sequence dispatch). In a distributed setting, each event is discovered by a sensor and reported to a robot. In this thesis, novel algorithms are presented, whichh are aimed at overcoming the shortcomings of several existing solutions. Pairwise distance based matching algorithm (PDM) eliminates long edges by pairwise exchanges between matching pairs. Sequence dispatch algorithm (SQD) iteratively finds the closest event-robot pair, includes the event in dispatch schedule of the selected robot and updates its position accordingly. When event-robot distances are multiplied by robot resistance (inverse of the remaining energy), the corresponding energybalanced variants are obtained. Also, generalizations are introduced which handle multiple visits and timing constraints. Distributed algorithm MAD is based on information mesh infrastructure and local auctions within the robot network for obtaining the optimal dispatch schedule for each robot. The simulations conducted confirm the advantages of our algorithms over other existing solutions in terms of average robot-event distance and lifetime

Detection of intentionally made changes in image content

Digital images and video signals represent the most frequently transmitted contents. Namely, with the development of modern digital cameras and smartphones, the use of multimedia content increases every day. They are used in everyday life, for getting information and also as authenticated proofs or corroboratory evidence in different areas like: forensic studies, law enforcement, journalism and others...Multifraktalna analiza se pokazala kao dobar alat za analizu postojećih slika, kao i segmentaciju određenih regiona, izdvajanje ivica, uglova slike i slično. Kako kopirani i nalepljeni delovi imaju sličnu strukturu, može se primeniti multifraktalna analiza, koja u osnovi analizira samosličnost. Multifraktalni spektar daje globalni opis slike (ili, opštije, fenomena koji se ispituje). Vrednost Hölder-ovog eksponenta zavisi od položaja u strukturi i opisuje lokalnu regularnost signala. Naime, različiti objekti na slici imaju različite spektre, različite pozicije maksimuma, minimuma, prve nule itd, što se pokazalo kao interesantan skup različitih parametara pomoću kojih se mogu detektovati namerne promene na slikama..

Prepoznavanje prekida u procesu rezanja primenom Voronoi dijagrama

Prepoznavanje klase pojava koje se manifestuju kao nagli prekid u procesu rezanja izuzetno je znacajno sa aspekta upravljanja obradnim sistemom. U ovom radu se izlaže koncept mašine za prepoznavanje prekida u procesu rezanja koja je zasnovana na Voronoi dijagramu Proces obucavanja izvršen je na osnovu rezultata klasterovanja dobijenih FSD (Feature Space Deformation – deformacija prostora obeležja) algoritmom. Predložena metodologija je eksperimentalno verifikovana

Improvement of model of decision-making by system of association rules

Osnovni cilj istraživanja Doktorske disertacije je definisanje okvira za sprovođenje celovitog istraživačkog poduhvata unapređenja modela poslovnog odlučivanja i otkrivanja zakonitosti u podacima za potrebe brojnih analiza: pre svega otkrivanja asocijativnih pravila i predviđanja, kao i upotrebe rezultata radi donošenja ispravnih upravljačkih poslovnih odluka. Dakle, cilj je analiza i primena sistema asocijativnih pravila radi unapređenja modela poslovnog odlučivanja menadžera najvišeg nivoa poslovnog sistema, radi donošenja efektivnih i efikasnih odluka. U istraživanju su korišćene savremene naučne metode iz oblasti poslovne inteligencije. Glavna hipoteza: “Moguće je unaprediti model poslovnog odlučivanja sistemom asocijativnih pravila“ je potvrđena u istraživanju. Ukazano je na značaj poslovne inteligencije za stvaranje modela koji može povećati efektivnost procesa menadžerskog odlučivanja. Primena asocijativnih pravila u svrhe istraživanja ima izuzetan potencijal u oblasti poslovanja. Razvijen je i prikazan model poslovnog odlučivanja pomoću sistema asocijativnih pravila. Dokazano je da je ova oblast poslovne inteligencije veoma aktuelna i sa velikim potencijalom. Izvedeni zu zaključci i date su smernice za buduća istraživanja kao izazov da se pruže značajan naučni i stručni doprinos sa ciljem unapređenja poslovnog odlučivanja.Main goal of research of the Doctoral Dissertation is defining the framework for integral research project of improvement of model of decisionmaking and data mining for numerous analysis: mining of association rules and prediction, as well as the use of results in order to gain effective management decisions. The goal of the research is analysis and application of system of association rules in order to improve the model of business decision-making of top-level managers of business system, in order to get the most effective decisions. Modern scientific methods from the field of business intelligence have been used during the research. The main hypothesis: “It is possible to improve the model of business decicion-making by system of association rules” has been confirmed during the research. The importance of business intelligence for creation of model that can increase the effectiveness of managers’ decision making is highlighted. The application of association rules with the purposes of research has an immense potential in the business. The model of business decision-making by association rules has been developed and presented. It is proven that this field of business intelligence is very popular and has big potential. Concluding remarks, as well as the recommendations for future research have been given in order to provide significant scientific and professional contribution with goal of improving business decision- making

A statistical approach to sensitivity zone definition in remote sensing methods

Osnovni predmet istraživanja u disertaciji je analiza metodologije daljinskog uzorkovanja s aspekta prikupljanja i pripreme podataka u formu pogodnu za obradu, i posebno obrade podataka statističkim metodama u svrhu klasifikacije, sa ciljem identifikacije određenih pojava. Poseban akcenat istraživanja je predlaganje metodologije za definisanje zone osetljivosti pri klasifikaciji pojava, primarmo pomoću statističkih metoda multivarijacione analize. Daljinsko uzorkovanje se najčešće opisuje kao naučna oblast i tehnika za prikupljanje informacija o objektu (najčešće Zemljinoj površini) bez dolaženja u kontakt sa njim. Sprovodi se uzorkovanjem (očitavanjem) putem beleženja reflektovane ili emitovane energije (elektromagnetno zračenje, akustičnost, itd.) objekta, procesiranjem, analiziranjem i primenom informacija. Danas, daljinsko uzorkovanje je prepoznata interdisciplinarna oblast širom sveta. Često je uparena sa disciplinama obrade slika i geografskog informacionog sistema (GIS) za široku oblast geospatijalne nauke i tehnologije, te se u disertaciji čini osvrt na povezanost GIS-a i daljinskog uzorkovanja, gde je GIS nezaobilazan alat u analizi prostornih podataka. Svaka digitalna slika je sastavljena od piksela, koji čine najsitnije komponente jedne slike i koji imaju svoju osvetljenost i zabeležni spektar boja, a koje možemo smatrati pojedinačnim entitetima statističkog uzorka koji predstavlja sama slika. U disertaciji je od posebnog značaja klasifikacija daljinski uzorkovanih podataka i koristi se da identifikuje i klasifikuje delove ili piksele slike. Klasifikacija se izvodi na višestrukim skupovima podataka a cilj je dodeljivanje svakog piksela slike određenoj klasi na osnovu statističkih karakteristika intenziteta i obojenosti piksela. Termin multivarijaciona analiza se koristi da predstavi višedimenzionalni aspekt analize podataka. Mnogobrojne pojave i fenomeni opisani su većim brojem različitih promenljivih, a to se svakako odnosi i na podatke dobijene daljinskim uzorkovanjem gde je svako piksel tipično predstavljen u tri ili više različitih opsega svetlosnog spektra. Multivarijaciona analiza se opisuje kao skup statističkih metoda koje simultano analiziraju višedimenziona merenja dobijena za svaku jedinicu posmatranja iz skupa objekata koji ispitujemo. U disertaciji su naročito opisane tehnike Analize grupisanja, koja je od manjeg značaja za kasnije predloženi metod, i Diskriminacione analize, koja je u srži predloženog metoda. Posebno je opisana Bajesova teorija odlučivanja kao fundamentalan statistički pristup problemu klasifikacije. Pristup bazira na kvantifikaciji kompromisa između različitih odluka klasifikacije pomoću verovatnoće i cene ili napora koji se javljaju tokom odlučivanja. Bajesova teorija odlučivanja pretpostavlja da je problem odlučivanja postavljen u probabilističkom kontekstu. Diskriminaciona analiza se bavi problemom razdvajanja grupa i alokacijom opservacija u ranije definisane grupe. U disertaciji je od posebnog značaja cilj diskriminacione analize koji se naziva klasifikacija a koji se odnosi na utvrđivanje postupka za klasifikaciju opservacija na osnovu vrednosti nekoliko promenljivih u dve ili više razdvojenih, unapred definisanih grupa...The main topic of the research in this dissertation is the analysis of remote sensing methodology from the aspect of collecting and preparing data in a form suitable for processing, and in particular data processing by the means of statistical methods for the purpose of classification, to identify certain phenomena. A particular emphasis of the research is to propose a methodology for defining the sensitivity zone in the classification of phenomena, primarily using statistical methods of multivariate analysis. Remote sensing is typically described as a scientific field and technique for collecting information about an object (usually the Earth's surface) without being in direct contact with the object. It is carried out by sampling through recording reflected or emitted energy (electromagnetic radiation, acoustics, and others) of the object, processing, analysing, and applying the information. Today, remote sensing is recognized as an interdisciplinary field all over the world. It is often paired with image processing disciplines and a geographic information system (GIS) for a vast area of geospatial science and technology, hence, in this dissertation a link between GIS and remote sensing is described, where GIS is a necessary tool in spatial data analysis. Each digital image is composed of pixels, which are the smallest components of a single image and have their brightness and a color spectrum recorded, which we can consider as individual entities of a statistical sample representing the image. In this dissertation, the classification of remotely-sensed data is of particular importance and is used to identify and classify parts or the pixels of the image. The classification is performed on multiple data sets, and the goal is to assign each pixel of an image to a particular class based on the statistical characteristics of the intensity and color of the pixels. The term multivariate analysis is used to present a multidimensional aspect of data analysis. Numerous phenomena are described by many different variables, and this certainly applies to data obtained by remote sensing where each pixel is typically represented in three or more different light spectrum bandwidths. Multivariate analysis is described as a set of statistical methods that simultaneously analyze multidimensional measurements obtained for each observation unit from a set of objects that we are examining. This dissertation describes the Data clustering techniques, which are, though, of less importance to the proposed method, and the Discriminant Analysis, which is at the core of the proposed method. Bayesian decision theory is described as a fundamental statistical approach to the problem of classification. The approach is based on the quantification of the compromise between different classification decisions using the probability and cost or effort that arise during decision-making. Bayesian decision theory assumes that the decision-making problem is set in a probabilistic context. Discriminant analysis deals with the problem of group separation and the allocation of observations in previously defined groups. In this dissertation, it is of particular importance the objective of discriminant analysis called classification, which refers to the establishment of a procedure for classifying observations based on the value of several variables in two or more separated, predefined a group..

Detekcija i proaktivna isporuka informacija o saobraćajnim događajjima u mobilnim informacionim sistemima za podršku navigaciji i transportu

The research subject of this dissertation is using traffic participants (drivers, vehicles and mobile devices inside vehicles) as sources of traffic data further used in providing dynamic navigation service and traffic management. Motivation for this approach to detecting and disseminating traffic data is found in inefficiency of adequatelly covering large road network with traditional traffic sensors. Traditional traffic sensors include inductive loops, cameras etc. which are static and permanently integegrated with traffic infrastructure during construction. Participatory sensing concept is not new and is applied in the traffic domain form some years now. Widelly popular social network found their way into traffic domain too, and today there are manz commercially available social networks gathering drivers and allowing them to exchange information. This model of information exchange usually amounts to manual input of messages which are redistributed to other drivers by the system. Floating car Data (FCD) concept eliminates the driver from the information input loop. By anonymously collecting data about current speeds of a large number of vehicles information about traffic congestions can be constructed and used in various types of information systems. Extended Floating car Data (XFCD) concept, this dissertation deals with, expands dataset collected from traffic participants with data from various sensors inside vehicle. This dissertation especially focuses on one particular type of sensor commonly found today integrated with mobile devices used by drivers for navigation, accelerometer. Most important characteristic of this type of sensor is its capability to detect relevant vehicle maneuvers by recording force patterns that act on the vehicle (mobile device) during these maneuvers. GPS receiver traditionally used in FCD systems is typically not capable to detect these types of maneuvers. This dissertation demonstrates accelerometer data analysis methods localized on mobile device tasked with detecting relevant traffic events (vehicle maneuvers). This information collected from participating vehicles is further used in implemented prototype of proactive traffic information service that uses this data to timely warn drivers about hazardous traffic events and conditions existing on their navigation route. Localised analysis of accelerometer device on mobile devices used by the drivers is conditioned by the characteristics of accelerometer data, its sheer volume, and implemented prototype of proactive traffic information service acts preemptively by increasing drivers’ situational awareness of traffic conditions ahead of them