Raamistik mobiilsete asjade veebile

Internet on oma arengus läbi aastate jõudnud järgmisse evolutsioonietappi - asjade internetti (ingl Internet of Things, lüh IoT). IoT ei tähista ühtainsat tehnoloogiat, see võimaldab eri seadmeil - arvutid, mobiiltelefonid, autod, kodumasinad, loomad, virtuaalsensorid, jne - omavahel üle Interneti suhelda, vajamata seejuures pidevat inimesepoolset seadistamist ja juhtimist. Mobiilseadmetest nagu näiteks nutitelefon ja tahvelarvuti on saanud meie igapäevased kaaslased ning oma mitmekülgse võimekusega on nad motiveerinud teadustegevust mobiilse IoT vallas. Nutitelefonid kätkevad endas võimekaid protsessoreid ja 3G/4G tehnoloogiatel põhinevaid internetiühendusi. Kuid kui kasutada seadmeid järjepanu täisvõimekusel, tühjeneb mobiili aku kiirelt. Doktoritöö esitleb energiasäästlikku, kergekaalulist mobiilsete veebiteenuste raamistikku anduriandmete kogumiseks, kasutades kergemaid, energiasäästlikumaid suhtlustprotokolle, mis on IoT keskkonnale sobilikumad. Doktoritöö käsitleb põhjalikult energia kokkuhoidu mobiilteenuste majutamisel. Töö käigus loodud raamistikud on kontseptsiooni tõestamiseks katsetatud mitmetes juhtumiuuringutes päris seadmetega.The Internet has evolved, over the years, from just being the Internet to become the Internet of Things (IoT), the next step in its evolution. IoT is not a single technology and it enables about everything from computers, mobile phones, cars, appliances, animals, virtual sensors, etc. that connect and interact with each other over the Internet to function free from human interaction. Mobile devices like the Smartphone and tablet PC have now become essential to everyday life and with extended capabilities have motivated research related to the mobile Internet of Things. Although, the recently developed Smartphones enjoy the high performance and high speed 3G/4G mobile Internet data transmission services, such high speed performances quickly drain the battery power of the mobile device. This thesis presents an energy efficient lightweight mobile Web service provisioning framework for mobile sensing utilizing the protocols that were designed for the constrained IoT environment. Lightweight protocols provide an energy efficient way of communication. Finally, this thesis highlights the energy conservation of the mobile Web service provisioning, the developed framework, extensively. Several case studies with the use of the proposed framework were implemented on real devices and has been thoroughly tested as a proof-of-concept.https://www.ester.ee/record=b522498

Adaptive Process Distribution at the Edge of IoT using the Integration of BPMS and Containerization

Täna levivad pilvepõhised värkvõrgu (asjade interneti) süsteemid tuginevad protsesside halduseks kaugel asuvatel andmekeskustel, mis toob endaga kaasa latentsusprobleeme. Vastusena sellele probleemile on varem välja pakutud servaarvutuse lähenemine, kus arvutused viiakse läbi asjade interneti süsteemi võrgule füüsiliselt lähemal. Mitmete servaarvutuse metoodikate seas on uduarvutus lähenemine, kus rõhk on arvutuste liigutamisel värkvõrgu seadmetele endile. Ehkki uduarvutusel põhinev arhitektuur on paljutõotav, tõstatab see küsimuse – kuidas värkvõrgu protsessihaldussüsteemid (BPMS4IoT-süsteemid) äriprotsesse heterogeensetele värkvõrgu seadmetele jaotama peaksid? Levinud on lähenemine, kus protsesside töövooülesannete käituseks tuginetakse ühisele platvormile. Näiteks, kui haldusserver defineerib teatud töövoo ülesandena Pythoni skripti ja määrab selle seadmele, siis peab seadme töövookäitusmootor toetama vastavat mehhanismi skriptide jooksutamiseks. Selline nõue ei ole paindlik, arvestades värkvõrgu seadmete heterogeensust. Käesolevas magistritöös pakub autor välja raamistiku, mis eraldab töövoo ülesannete käitusmeetodi käitusmootorist kasutades selleks konteinertehnoloogiat. Töö käigus arendati välja raamistiku prototüüp ning viidi läbi katseid mikroarvutitel põhinevail seadmetel. Lisaks võrreldi väljapakutud uduarvutuse raamistiku jõudlust pilvearvutusel põhineva süsteemiga.Emerging cloud-centric Internet of Things (IoT) system relies on distant data centers to manage the entire processes, which raises the issue of latency. To address the issue, researchers have introduced the Edge computing methodologies that carry out computation closer to the edge network of IoT system. Among the numerous Edge computing approaches, Mist computing paradigm emphasises the mechanism that moves the computation further to the front-end IoT devices. Although the architecture of Mist computing is promising, it raises a new challenge in how the Business Process Management System for IoT (BPMS4IoT) distributes the business process workflow to the heterogeneous IoT devices? In general, executing business process workflows relies on the common platform for executing customized tasks. For example, if the management server defines a Python script task in a workflow, which has been allocated to an IoT device, the workflow engine of the IoT device must have the compatible execution method. Such a requirement is less flexible when one considers the heterogeneity of the IoT devices. Therefore, in this thesis, the author proposes a framework to decouple the workflow task execution method from the workflow engines using the containerization technology. A proof-of-concept prototype has been developed and has been tested on several single-board computers-based IoT devices. Further, a case study has been performed to demonstrate the performance of the proposed framework comparing to the cloud-centric system

Performance Evaluation Metrics for Cloud, Fog and Edge Computing: A Review, Taxonomy, Benchmarks and Standards for Future Research

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments

Performance and efficiency optimization of multi-layer IoT edge architecture

Abstract. Internet of Things (IoT) has become a backbone technology that connects together various devices with diverse capabilities. It is a technology, which enables ubiquitously available digital services for end-users. IoT applications for mission-critical scenarios need strict performance indicators such as of latency, scalability, security and privacy. To fulfil these requirements, IoT also requires support from relevant enabling technologies, such as cloud, edge, virtualization and fifth generation mobile communication (5G) technologies. For Latency-critical applications and services, long routes between the traditional cloud server and end-devices (sensors /actuators) is not a feasible approach for computing at these data centres, although these traditional clouds provide very high computational and storage for current IoT system. MEC model can be used to overcome this challenge, which brings the CC computational capacity within or next on the access network base stations. However, the capacity to perform the most critical processes at the local network layer is often necessary to cope with the access network issues. Therefore, this thesis compares the two existing IoT models such as traditional cloud-IoT model, a MEC-based edge-cloud-IoT model, with proposed local edge-cloud-IoT model with respect to their performance and efficiency, using iFogSim simulator. The results consolidate our research team’s previous findings that utilizing the three-tier edge-IoT architecture, capable of optimally utilizing the computational capacity of each of the three tiers, is an effective measure to reduce energy consumption, improve end-to-end latency and minimize operational costs in latency-critical It applications

Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System

While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.Comment: 32 page