Business Process Decomposition and Distribution for Adaptive Internet of Things

Asjade internet (IoT) pakub suurt potentsiaali mitmetes erinevates valdkondades. Selleks, et seda potentsiaali realiseerida, on vaja veel palju takistusi ületada. Üheks takistusteks on see, kuidas integreerida IoT süsteemid äriportsesside juhtimiseks mõeldud standarditega. Käesoleva magistritöö esimene osa annab ülevaate erinevate äriprotsesside standardite sobivusest IoT platvormiga. Teises osas võetakse luubi alla esimeses osas sobivaimaks tunnistatud äriprotsesside juhtimise modelleerimisstandard ja keskendutakse IoT platvormi probleemidele, mis on seotud koduautomaatikaga. Esimene väljakutse on selles, et kuna turule tuleb üha uusi IoT andureid ja seadmeid, mis võimaldavad suuremaid ja keerukamaid lahendusi nagu näiteks näotuvastusel toimiv tark ukselukk, siis need seadmed nõuavad ka suuremat arvutusvõimsust. Kodused kontrollerid on aga sageli piiratud ressursiga seadmed ning on seetõttu pudelikaelaks sellistes targa kodu süsteemides. Teine väljakutse on see, et erinevad tootjad tulevad turule endakoduautomaatika süsteemidega, mis omavahel suhelda ei suuda. Seega läheb vaja mitut rakendust, et tarka kodu juhtida. Nende probleemide lahendamiseks esitan kontseptuaalse IoT terviksüsteemi, mis võimaldab protsessid lõhkuda osadeks ja need osad käivitada teistes süsteemides, et vähendada koduse kontrolleri jõudlusvajadust. IoT seadmed selles terviksüsteemis on ühendatud kasutades platvormi, mis võimaldab erinevad koduautomaatika süsteemid üheks luua. Magistritöö raames valmis laiendus Camunda töövoo juhtimise platvormile, mis realiseerib esitatud terviksüsteemi. Esitan selle laienduse nõuded ja piirangud ning ilmestan neid kasutades targa kodu protsesse.The Internet of Things (IoT) offers a great potential in many different application areas such as health care and home automation. However, in order to realize this potential, significant hurdles still have to be overcome. One of the hurdles is how to integrate IoT systems into business processes in the context of using standards such as BPMN2.0. In this thesis I present the results of a state of the art synthesis of Business Process Driven Internet of Things approaches. Based on the results, a business process modelling standard was chosen and used to overcome challenges which are related with IoT and home automation. The first challenge is the adaptiveness of the current IoT systems. For example, while new and more capable sensors enable complex applications such as smart door locks with face recognition that require significant computing resources, home controllers are often resource-constrained devices and therefore considered as a bottleneck for these complex systems. The second challenge is the interoperability of the current home automation systems because systems provided by different vendors cannot be controlled by the same application. To address these issues, this thesis proposes a software framework that enhances the adaptiveness and performance of IoT solutions. This is achieved through a novel approach where the process model is decomposed and the decomposed subparts are offloaded to an external entity. A prototype of this framework has been implemented using Camunda workflow engine. The framework supports IoT by integrating with the OpenHAB smart home system. The performance and scalability of the system is evaluated through a series of experimental case studies. Results showed that offloading can help in case of compute intensive tasks, a face recognition task performed three times faster for example

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

An SOA-Based Framework of Computational Offloading for Mobile Cloud Computing

Mobile Computing is a technology that allows transmission of audio, video, and other types of data via a computer or any other wireless-enabled device without having to be connected to a fixed physical link. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, connection instability, and limited computational power. In particular, the advent of connecting mobile devices to the internet offers the possibility of offloading computation and data intensive tasks from mobile devices to remote cloud servers for efficient execution. This proposed thesis develops an algorithm that uses an objective function to adaptively decide strategies for computational offloading according to changing context information. By following the style of Service-Oriented Architecture (SOA), the proposed framework brings cloud computing to mobile devices for mobile applications to benefit from remote execution of tasks in the cloud. This research discusses the algorithm and framework, along with the results of the experiments with a newly developed system for self-driving vehicles and points out the anticipated advantages of Adaptive Computational Offloading

IOT future in Edge Computing

With the advent of Internet of Things (IoT) and data convergence using rich cloud services, data computing has been pushed to new horizons. However, much of the data generated at the edge of the network leading to the requirement of high response time. A new computing paradigm, edge computing, processing the data at the edge of the network is the need of the time. In this paper, we discuss the IoT architecture, predominant application protocols, definition of edge computing and its research opportunities

Internet of Things future in Edge Computing

With the advent of Internet of Things (IoT) and data convergence using rich cloud services, data computing has been pushed to new horizons. However, much of the data generated at the edge of the network leading to the requirement of high response time. A new computing paradigm, edge computing, processing the data at the edge of the network is the need of the time. In this paper, we discuss the IoT architecture, predominant application protocols, definition of edge computing and its research opportunitie

Towards Distributed Mobile Computing

In the latest years, we observed an exponential growth of the market of the mobile devices. In this scenario, it assumes a particular relevance the rate at which mobile devices are replaced. According to the International Telecommunicaton Union in fact, smart-phone owners replace their device every 20 months, on average. The side effect of this trend is to deal with the disposal of an increasing amount of electronic devices which, in many cases, arestill working. We believe that it is feasible to recover such an unexploited computational power. Through a change of paradigm in fact, it is possible to achieve a two-fold objective: 1) extend the mobile devices lifetime, 2) enable a new opportunity to speed up mobile applications. In this paper we aim at providing a survey of state-of-art solutions aim at going in the direction of a Distributed Mobile Computing paradigm. We put in evidence the challenges to be addressed in order to implement this paradigm and we propose some possible future improvements