Partially Observable Markov Decision Process (POMDP) is widely used to model sequential decision making process under uncertainty and incomplete knowledge of the environment. It requires strong computation capability and is thus usually deployed on powerful machine. However, as mobile platforms become more advanced and more popular, the potential has been studied to combine POMDP and mobile in order to provide a broader range of services. And yet a question comes with this trend: how should we implement POMDP on mobile platform so that we can take advantages of mobile features while at the same time avoid being restricted by mobile limitations, such as short battery life, weak CPU, unstable networking connection, and other limited resources. In response to the above question, we first point out that the cases vary by problem nature, accuracy requirements and mobile device models. Rather than pure mathematical analysis, our approach is to run experiments on a mobile device and concentrate on a more specific question: which POMDP implementation is the ``best'' for a particular problem on a particular kind of device. Second, we propose and justify a POMDP implementation criterion mainly based on battery consumption that quantifies ``goodness'' of POMDP implementations in terms of mobile battery depletion rate. Then, we present a mobile battery consumption model that translates CPU and WIFI usage into part of the battery depletion rate in order to greatly accelerate the experiment process. With our mobile battery consumption model, we combine a set of simple benchmark experiments with CPU and WIFI usage data from each POMDP implementation candidate to generate estimated battery depletion rates, as opposed to conducting hours of real battery experiments for each implementation individually. The final result is a ranking of POMDP implementations based on their estimated battery depletion rates. It serves as a guidance for on POMDP implementation selection for mobile developers. We develop a mobile software toolkit to automate the above process. Given basic POMDP problem specifications, a set of POMDP implementation candidates and a simple press on the ``start'' button, the toolkit automatically performs benchmark experiments on the target device on which it is installed, and records CPU and WIFI statistics for each POMDP implementation candidate. It then feeds the data to its embedded mobile battery consumption model and produces an estimated battery depletion rate for each candidate. Finally, the toolkit visualizes the ranking of POMDP implementations for mobile developers' reference. Evaluation is assessed through comparsion between the ranking from estimated battery depletion rate and that from real experimental battery depletion rate. We observe the same ranking out of both, which is also our expectation. What's more, the similarity between estimated battery depletion rate and experimental battery depletion rate measured by cosine-similarity is almost 0.999 where 1 indicates they are exactly the same.

COSTA, Philipp Bernardino. Uma abordagem para offloading em múltiplas plataformas móveis. 2014. 104 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2014. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-12T15:14:02Z No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-20T13:52:31Z (GMT) No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) / Made available in DSpace on 2016-07-20T13:52:31Z (GMT). No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) Previous issue date: 2014 / The mobile devices, like smartphones and tablets, have evolved considerably in last years in computational terms. Despite advances in their hardware, these devices have energy constraints regarded to their poor computing performance. Therefore, on this context, a new paradigm called Mobile Cloud Computing (MCC) has emerged. MCC studies new ways to extend the computational and energy resources, on mobile devices using the offloading techniques. A literature survey about MCC, has shown that there is no support heterogeneity on reported studies. In response, we propose a framework called MpOS (Multi-platform Offloading System), which supports the offloading technique in mobile application development, for two mobile platforms (Android and Windows Phone). Two case studies were developed with MpOS solution in order to evaluate the framework for each mobile platform. These case studies show how the offloading technique works on several perspectives. In BenchImage experiment, the offloading performance was analyzed, concerning to its execution on a remote execution site (a cloudlet on local network and public cloud in the Internet). The Collision application promotes the analysis of the offloading technique performance on real-time application, also using different serialization systems. In both experiments, results show some situations where it was better to run locally on smarphone, than performing the offloading operation and vice versa. / Os dispositivos móveis, especificamente os smartphones e os tablets, evoluíram bastante em termos computacionais nos últimos anos, e estão cada vez mais presentes no cotidiano das pessoas. Apesar dos avanços tecnológicos, a principal limitação desses dispositivos está relacionada com a questão energética e com seu baixo desempenho computacional, quando comparado com um notebook ou computador de mesa. Com base nesse contexto, surgiu o paradigma do Mobile Cloud Computing (MCC), o qual estuda formas de estender os recursos computacionais e energéticos dos dispositivos móveis através da utilização das técnicas de offloading. A partir do levantamento bibliográfico dos frameworks em MCC verificou-se, para o problema da heterogeneidade em plataformas móveis, ausência de soluções de offloading. Diante deste problema, esta dissertação apresenta um framework denominado de MpOS (Multiplataform Offloading System), que suporta a técnica de offloading, em relação ao desenvolvimento de aplicações para diferentes plataformas móveis, sendo desenvolvido inicialmente para as plataformas Android e Windows Phone. Para validação foram desenvolvidas para cada plataforma móvel, duas aplicações móveis, denominadas de BenchImage e Collision, que demonstram o funcionamento da técnica de offloading em diversos cenários. No caso do experimento realizado com BenchImage foi analisado o desempenho da aplicação móvel, em relação à execução local, no cloudlet server e em uma nuvem pública na Internet, enquanto no experimento do Collision (um aplicativo de tempo real) foi analisado o desempenho do offloading, utilizando também diferentes sistemas de serialização de dados. Em ambos os experimentos houve situações que era mais vantajoso executar localmente no smartphone, do que realizar a operação de offloading e vice-versa, por causa de diversos fatores associados com a qualidade da rede e com volume de processamento exigido nesta operação.

abstract: Heterogeneous multiprocessor systems-on-chip (MPSoCs) powering mobile platforms integrate multiple asymmetric CPU cores, a GPU, and many specialized processors. When the MPSoC operates close to its peak performance, power dissipation easily increases the temperature, hence adversely impacts reliability. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This abstract presents a DTPM algorithm based on a practical temperature prediction methodology using system identification. The DTPM algorithm dynamically computes a power budget using the predicted temperature, and controls the types and number of active processors as well as their frequencies. Experiments on an octa-core big.LITTLE processor and common Android apps demonstrate that the proposed technique predicts temperature within 3% accuracy, while the DTPM algorithm provides around 6x reduction in temperature variance, and as large as 16% reduction in total platform power compared to using a fan. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Os dispositivos mÃveis, especificamente os smartphones e os tablets, evoluÃram bastante em termos computacionais nos Ãltimos anos, e estÃo cada vez mais presentes no cotidiano das pessoas. Apesar dos avanÃos tecnolÃgicos, a principal limitaÃÃo desses dispositivos està relacionada com a questÃo energÃtica e com seu baixo desempenho computacional, quando comparado com um notebook ou computador de mesa. Com base nesse contexto, surgiu o paradigma do Mobile Cloud Computing (MCC), o qual estuda formas de estender os recursos computacionais e energÃticos dos dispositivos mÃveis atravÃs da utilizaÃÃo das tÃcnicas de offloading. A partir do levantamento bibliogrÃfico dos frameworks em MCC verificou-se, para o problema da heterogeneidade em plataformas mÃveis, ausÃncia de soluÃÃes de offloading. Diante deste problema, esta dissertaÃÃo apresenta um framework denominado de MpOS (Multiplataform Offloading System), que suporta a tÃcnica de offloading, em relaÃÃo ao desenvolvimento de aplicaÃÃes para diferentes plataformas mÃveis, sendo desenvolvido inicialmente para as plataformas Android e Windows Phone. Para validaÃÃo foram desenvolvidas para cada plataforma mÃvel, duas aplicaÃÃes mÃveis, denominadas de BenchImage e Collision, que demonstram o funcionamento da tÃcnica de offloading em diversos cenÃrios. No caso do experimento realizado com BenchImage foi analisado o desempenho da aplicaÃÃo mÃvel, em relaÃÃo à execuÃÃo local, no cloudlet server e em uma nuvem pÃblica na Internet, enquanto no experimento do Collision (um aplicativo de tempo real) foi analisado o desempenho do offloading, utilizando tambÃm diferentes sistemas de serializaÃÃo de dados. Em ambos os experimentos houve situaÃÃes que era mais vantajoso executar localmente no smartphone, do que realizar a operaÃÃo de offloading e vice-versa, por causa de diversos fatores associados com a qualidade da rede e com volume de processamento exigido nesta operaÃÃo. / The mobile devices, like smartphones and tablets, have evolved considerably in last years in computational terms. Despite advances in their hardware, these devices have energy constraints regarded to their poor computing performance. Therefore, on this context, a new paradigm called Mobile Cloud Computing (MCC) has emerged. MCC studies new ways to extend the computational and energy resources, on mobile devices using the offloading techniques. A literature survey about MCC, has shown that there is no support heterogeneity on reported studies. In response, we propose a framework called MpOS (Multi-platform Offloading System), which supports the offloading technique in mobile application development, for two mobile platforms (Android and Windows Phone). Two case studies were developed with MpOS solution in order to evaluate the framework for each mobile platform. These case studies show how the offloading technique works on several perspectives. In BenchImage experiment, the offloading performance was analyzed, concerning to its execution on a remote execution site (a cloudlet on local network and public cloud in the Internet). The Collision application promotes the analysis of the offloading technique performance on real-time application, also using different serialization systems. In both experiments, results show some situations where it was better to run locally on smarphone, than performing the offloading operation and vice versa.

For the drivers of heavy trucks, performing some maneuvers with high precision could be a challenging task even for experienced ones. Volvo has a system which helps drivers in reversing the truck. Developing a human machine interface on a mobile platform with high usability for this system could help drivers to decrease both the stress level and spent time on maneuvering and will result in performing the task easier. This thesis introduces a new area in safety critical systems by combining automation with a mobile platform. An iterative and user centered design process utilized and three main iterations performed. In first iteration a low-fidelity prototype was created and evaluated by performing user tests. The output of usability test used to implement the software prototype for the second iteration. Evaluation of software prototype was done by desktop testing. In third iteration, second version of software prototype evaluated by performing field testing. Android and Google maps were used to implement three tasks: Destination, Rewind and Saved point. In all these iterations usability and safety were two main concerns and considered by looking into guidelines and performing evaluations. In the final test, the prototype was evaluated considering four usability factors: satisfaction, learnability, safety and achievement. After analyzing these factors prototype showed strong potential for a future product.

Mobile platforms for archaeological purposes have increased in use over the last 20 years with many of the developments coming from Continental Europe. Mobile platform developments have mainly focused on one type of instrumentation, offering multiple sensors, depths of detection or frequencies. This development of mobile platforms has focused on data acquisition rates but has not considered the physical impact on the soil. The Geoscan Research Mobile Sensor Platform (MSP40) was intended to improve survey efficiency and remain a lightweight system. The platform can collect two earth resistance configurations that show directional variation of the current flow through soil. Additional sensors were integrated on to the square frame of the hand-pulled cart to record simultaneous fluxgate gradiometer data and a microtopographic surveys. Ground based geophysical investigation will always have a physical impact on a site. The MSP40 is no exception but careful selection of wheel types and the lightweight frame limit the damage compared to many mobile arrays. The MSP40 has been tested on a number of different soils at various times of the year with encouraging results; however issues with overcoming the contact resistance of electrodes remain. The continuous collection rate and combination of techniques means a slight drop in data quality is inevitable. However the increased data density, multiple-sensors and improved rate of collection offset reductions in data quality. The research has shown that the MSP40 can perform low impact rapid site assessments on ¿vulnerable¿ sites, whilst maximising the information gained from a single traverse. / AHRC, Geoscan Research

Mobile platforms for archaeological purposes have increased in use over the last 20 years with many of the developments coming from Continental Europe. Mobile platform developments have mainly focused on one type of instrumentation, offering multiple sensors, depths of detection or frequencies. This development of mobile platforms has focused on data acquisition rates but has not considered the physical impact on the soil. The Geoscan Research Mobile Sensor Platform (MSP40) was intended to improve survey efficiency and remain a lightweight system. The platform can collect two earth resistance configurations that show directional variation of the current flow through soil. Additional sensors were integrated on to the square frame of the hand-pulled cart to record simultaneous fluxgate gradiometer data and a microtopographic surveys. Ground based geophysical investigation will always have a physical impact on a site. The MSP40 is no exception but careful selection of wheel types and the lightweight frame limit the damage compared to many mobile arrays. The MSP40 has been tested on a number of different soils at various times of the year with encouraging results; however issues with overcoming the contact resistance of electrodes remain. The continuous collection rate and combination of techniques means a slight drop in data quality is inevitable. However the increased data density, multiple-sensors and improved rate of collection offset reductions in data quality. The research has shown that the MSP40 can perform low impact rapid site assessments on 'vulnerable' sites, whilst maximising the information gained from a single traverse.

THE SHARING ECONOMY AND DIGITALIZATION: CHALLENGE FOR COMPLEX MODIFICATION OF LABOR LAW? ABSTRACT This diploma thesis discusses the phenomenon of last decade called sharing economy, which has many different forms and names. In its purest meaning, the sharing economy concerns behavior of economically active entities that, in order to reduce their own costs or to use their spare capacity, share free resources. As the best examples of sharing economy, we can name capital platform Airbnb, where people share their unused immovable in order to generate profit or work platform BlaBlaCar trough which people reduce their car costs while travelling one-off long-distance trips. However, as mentioned above, sharing economy has many different forms. One of them, which cannot be recognized as its pure form, is provided by Uber. Uber, as well as BlaBlaCar, created mobile platform trough which providers of transport services can get in touch with users of these services. Despite this similarity, there are several significant differences from which one can conclude that Uber is not a classic provider of information technology services, such as BlaBlaCar. That was also borne out by foreign courts, including European Court of Justice. On that basis, a question arises, whether Uber drivers really carry on the activity of...

Mobilių technologijų populiarėjimas tarp vartotojų ir jų platformų įvairovė skatina mobilių programėlių kūrėjus užimti vis didesnę rinkos dalį. Kiekviena mobili platforma turi savo specifiką, todėl kūrėjams reikia vis daugiau žinių arba specialistų kuriant mobilias aplikacijas, tam reikalinga papildomų resursų, apmokymų,kaštų ir laiko. Vienas iš galimų problemos sprendimų, sukurti tam tikrus įrankius, kurie mobilių programėlių projektavimo ir kūrimo bei testavimo etape, leistų automatiškai suprojektuoti, suprogramuoti mobilias aplikacijas, nepriklausomai kokiai platformai išlaikant tos programėlės logiką. Tam pakaktų mobilių programų kūrėjams turėti vienai mobiliai platformai aprašytą modelį arba programėlę, ir iš jų remiantis MDA (Model Dirven Architecture) metodologijomis arba aprašytais karkasais atliktų transformacijas į reikiamą platformą. Šiame darbe pateikimas MDA principais paremtos sukurtos priemonės , kurios, atlieka programų transformacijas iš Android į Windows Phone. Įrodant transformacijų svarbą, atliktas transformavimo priemonių tyrimas, įvedant tam tikras metrikas ir jų palyginimą tarp atskirai realizuotų programų, šių priemonių transformuotų programų ir naudojant universalias priemones kaip JavaScript arba žiniatinklio principu veikiančių programų. / There is growth of mobile technologies and platforms providing for users so and developers of mobile applications need to take a larger market. There is some specificity of platforms, therefore developer needs a more knowledge or experts of mobile application developing where require a more resources, training, costs and it takes a time. One of the possible solutions to the problem, to make the tools which allow design and create mobile applications independent by platform keep the logic in design and development or testing phase. This is sufficient for developers to design or creates one mobile applications and using methods of Mobile Driven Architecture (MDA) and frameworks create transformations more applications many platforms. In this research paper representing the tools developed based MDA to carry out transformations from Android to Windows Phone. To prove the importance of transformations performed research of transformation tools with certain comparison of metrics between the programs of separated implementation, these tools transformed programs and used universal tools like JavaScript or web-based software implementation.

Master of Science