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EN
Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In order to better evaluate the performance of the probabilistic offloading strategy in a MEC system, we give a modeling method to capture the stochastic behavior of tasks based on a multi-source fluid queue. Considering multiple mobile devices (MDs) in a MEC system, we build a multi-source fluid queue to model the tasks offloaded to the MEC server. We give an approach to analyze the fluid queue driven by multiple independent heterogeneous finite-state birth-and-death processes (BDPs) and present the cumulative distribution function (CDF) of the edge buffer content. Then, we evaluate the performance measures in terms of the utilization of the MEC server, the expected edge buffer content and the average response time of a task. Finally, we provide numerical results with some analysis to illustrate the feasibility of the stochastic model built in this paper.
EN
Embedded platforms with GPU acceleration, designed for performing machine learning on the edge, enabled the creation of inexpensive and pervasive computer vision systems. Smartphones are nowadays widely used for profiling and tracking in marketing, based on WiFi data or beacon-based positioning systems. We present the Arahub system, which aims at integrating world of computer vision systems with smartphone tracking for delivering data useful in interactive applications, such as interactive advertisements. In this paper we present the architecture of the Arahub system and provide insight about its particular elements. Our preliminary results, obtained from real-life test environments and scenarios, show that the Arahub system is able to accurately assign smartphones to their owners, based on visual and WiFi/Bluetooth positioning data. We show the commercial value of such system and its potential applications.
EN
Cooperative adaptive cruise control (CACC) for human and autonomous self-driving aims to achieve active safe driving that avoids vehicle accidents or traffic jam by exchanging the road traffic information (e.g., traffic flow, traffic density, velocity variation, etc.) among neighbor vehicles. However, in CACC, the butterfly effect is encountered while exhibiting asynchronous brakes that easily lead to backward shock-waves and are difficult to remove. Several critical issues should be addressed in CACC, including (i) difficulties with adaptive steering of the inter-vehicle distances among neighbor vehicles and the vehicle speed, (ii) the butterfly effect, (iii) unstable vehicle traffic flow, etc. To address the above issues in CACC, this paper proposes the mobile edge computing-based vehicular cloud of the cooperative adaptive driving (CAD) approach to avoid shock-waves efficiently in platoon driving. Numerical results demonstrate that the CAD approach outperforms the compared techniques in the number of shock-waves, average vehicle velocity, average travel time and time to collision (TTC). Additionally, the adaptive platoon length is determined according to the traffic information gathered from the global and local clouds.
EN
This paper presents an application of built-in sensors of a mobile device – a more robust version of authors’ own motion type detection method introduced in previous work. Use of accelerometer and magnetometer for recording acceleration in the World’s coordinate system is explained. The original results and identification criteria are briefly described. New tests and their results are presented. An improved version of the motion type identification method is introduced.
PL
Artykuł przedstawia zastosowanie sensorów urządzeń mobilnych – bardziej rozbudowaną wersję autorskiej metody identyfikacji rodzaju ruchu. Metoda ta została zaproponowana we wcześniejszej pracy. Krótko omówione zostało zastosowanie akcelerometru i magnetometru do pomiaru przyspieszenia we współrzędnych świata. Przedstawiono wyniki wstępnych pomiarów razem z oryginalnymi kryteriami rozpoznawania ruchu. Opisano nowe przeprowadzone eksperymenty i ich wyniki. Zaproponowano ulepszoną wersję metody rozpoznawania rodzaju ruchu.
5
Content available remote Parallel Code Generation for Mobile Devices
EN
Mobile computing is driven by pursuit of ever increasing performance. Multicore processing is recognized as a key component for continued performance improvements. This paper presents the Iteration Space Slicing (ISS) framework aimed at automatic parallelization of code for Mobile Internet Devices (MID). ISS algorithms permit us to extract coarse-grained parallelism available in arbitrarily nested parameterized loops. The loops are parallelized and transformed to multi-threaded application for the Android OS. Experimental results are carried out by means of the benchmark suites (UTDSP and NPB) using the ARM dual core processor. The related parallelization techniques are discussed, in particular for embedded systems. The future work is outlined.
PL
Przetwarzanie obliczeń za pomocą urządzeń mobilnych wiąże się z rosnącym zapotrzebowaniem na moc ich procesorów. Artykuł przedstawia zastosowanie narzędzia ISS (podziału przestrzeni iteracji pętli programowych) do wyznaczenia równoległego kodu dedykowanego dla urządzeń mobilnych (MID). Algorytmy pozwalają na wyznaczenie równoległości gruboziarnistej dla dowolnie zagnieżdżonych pętli i wygenerowanie wielowątkowego kodu dla systemu Android. Wyniki eksperymentalna dla zestawów pętli testowych NAS i UTDSP przeprowadzono wykorzystując dwurdzeniowy procesor ARM. Prace pokrewne i przyszłe zadania przedstawiono na końcu artykułu.
PL
W niniejszym artykule dokonano porównania wydajności podstawowych metod całkowania zaimplementowanych w środowisku App Inventor oraz Java dla platformy Android. Wybrane metody (prostokątów, trapezów i Simpsona) zastosowano dla funkcji liniowej, sześciennej oraz sinusoidy. Rezultaty eksperymentu wykazały, że działanie algorytmów zaimplementowanych w App Inventor jest wielokrotnie wolniejsze niż w Java dla Android, co znacząco ogranicza przydatność środowiska App Inventor do tworzenia aplikacji realizujących obliczenia matematyczne.
EN
This paper presents comparison of efficiency of basic integration methods implemented in App Inventor and Java for Android environment. Chosen methods (rectangle, trapezoidal and Simpson’s rules) were applied for linear, cubic and sine functions. Conducted experiment revealed that applications developed in App Inventor were significantly slower than in case of Java, which makes App Inventor unsuitable for applications involving intensive calculations.
EN
In the paper the concept, main design problems, applied solutions and some implementation details of a hospital information system based on handheld computers are presented. Increasing power and low prices of handheld computers make it very convenient tool for mobile information systems in medicine and health care. Permanent availability of patients data to nursing personnel and physicians at the patient’s bed make medical decision making easier, more accurate, reduces the risk of mistakes, eliminates unnecessary paper works and provides more up-to-date information. The purpose of the system being described here is to utilize all these advantages of mobile computing at the relatively low costs of hardware and software.
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