Time-fluid field-based coordination

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in “space and time” of distributed data structures, called fields. More specifically, regarding time, in field-based coordination it is assumed that local activities in each device, called computational rounds, are regulated by a fixed clock, typically, a fair and unsynchronized distributed scheduler. In this work, we challenge this assumption, and propose an alternative approach where the round execution scheduling is naturally programmed along with the usual coordination specification, namely, in terms of a field of causal relations dictating what is the notion of causality (why and when a round has to be locally scheduled) and how it should change across time and space. This abstraction over the traditional view on global time allows us to express what we call “time-fluid” coordination, where causality can be finely tuned to select the event triggers to react to, up to to achieve improved balance between performance (system reactivity) and cost (usage of computational resources). We propose an implementation in the aggregate computing framework, and evaluate via simulation on a case study

Time-fluid field-based coordination

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in \u201cspace and time\u201d of distributed data structures, called fields. More specifically, regarding time, in field-based coordination it is assumed that local activities in each device, called computational rounds, are regulated by a fixed clock, typically, a fair and unsynchronized distributed scheduler. In this work, we challenge this assumption, and propose an alternative approach where the round execution scheduling is naturally programmed along with the usual coordination specification, namely, in terms of a field of causal relations dictating what is the notion of causality (why and when a round has to be locally scheduled) and how it should change across time and space. This abstraction over the traditional view on global time allows us to express what we call \u201ctime-fluid\u201d coordination, where causality can be finely tuned to select the event triggers to react to, up to to achieve improved balance between performance (system reactivity) and cost (usage of computational resources). We propose an implementation in the aggregate computing framework, and evaluate via simulation on a case study

A methodology for the design of application-specific cyber-physical social sensing co-simulators

Cyber-Physical Social Sensing (CPSS) is a new trend in the context of pervasive sensing. In these new systems, various domains coexist in time, evolve together and influence each other. Thus, application-specific tools are necessary for specifying and validating designs and simulating systems. However, nowadays, different tools are employed to simulate each domain independently. Mainly, the cause of the lack of co-simulation instruments to simulate all domains together is the extreme difficulty of combining and synchronizing various tools. In order to reduce that difficulty, an adequate architecture for the final co-simulator must be selected. Therefore, in this paper the authors investigate and propose a methodology for the design of CPSS co-simulation tools. The paper describes the four steps that software architects should follow in order to design the most adequate co-simulator for a certain application, considering the final users’ needs and requirements and various additional factors such as the development team’s experience. Moreover, the first practical use case of the proposed methodology is provided. An experimental validation is also included in order to evaluate the performing of the proposed co-simulator and to determine the correctness of the proposal

Variable link performance due to weather effects in a long-range, low-power LoRa sensor network

When aiming for the wider deployment of low-power sensor networks, the use of sub-GHz frequency bands shows a lot of promise in terms of robustness and minimal power consumption. Yet, when deploying such sensor networks over larger areas, the link quality can be impacted by a host of factors. Therefore, this contribution demonstrates the performance of several links in a real-world, research-oriented sensor network deployed in a (sub)urban environment. Several link characteristics are presented and analysed, exposing frequent signal deterioration and, more rarely, signal strength enhancement along certain long-distance wireless links. A connection is made between received power levels and seasonal weather changes and events. The irregular link performance presented in this paper is found to be genuinely disruptive when pushing sensor-networks to their limits in terms of range and power use. This work aims to give an indication of the severity of these effects in order to enable the design of truly reliable sensor networks

Securing Digital Images through Simple Permutation-Substitution Mechanism in Cloud-Based Smart City Environment

Data security plays a significant role in data transfer in cloud-based smart cities. Chaotic maps are commonly used in designing modern cryptographic applications, in which one-dimensional (1D) chaotic systems are widely used due to their simple design and low computational complexity. However, 1D chaotic maps suffer from different kinds of attacks because of their chaotic discontinuous ranges and small key-space. To own the benefits of 1D chaotic maps and avoid their drawbacks, the cascading of two integrated 1D chaotic systems has been utilized. In this paper, we report an image cryptosystem for data transfer in cloud-based smart cities using the cascading of Logistic-Chebyshev and Logistic-Sine maps. Logistic-Sine map has been utilized to permute the plain image, and Logistic-Chebyshev map has been used to substitute the permuted image, while the cascading of both integrated maps has been utilized in performing XOR procedure on the substituted image. The security analyses of the suggested approach prove that the encryption mechanism has good efficiency as well as lower encryption time compared with other related algorithms

FIN-DM: finantsteenuste andmekaeve protsessi mudel

Andmekaeve hõlmab reeglite kogumit, protsesse ja algoritme, mis võimaldavad ettevõtetel iga päev kogutud andmetest rakendatavaid teadmisi ammutades suurendada tulusid, vähendada kulusid, optimeerida tooteid ja kliendisuhteid ning saavutada teisi eesmärke. Andmekaeves ja -analüütikas on vaja hästi määratletud metoodikat ja protsesse. Saadaval on mitu andmekaeve ja -analüütika standardset protsessimudelit. Kõige märkimisväärsem ja laialdaselt kasutusele võetud standardmudel on CRISP-DM. Tegu on tegevusalast sõltumatu protsessimudeliga, mida kohandatakse sageli sektorite erinõuetega. CRISP-DMi tegevusalast lähtuvaid kohandusi on pakutud mitmes valdkonnas, kaasa arvatud meditsiini-, haridus-, tööstus-, tarkvaraarendus- ja logistikavaldkonnas. Seni pole aga mudelit kohandatud finantsteenuste sektoris, millel on omad valdkonnapõhised erinõuded. Doktoritöös käsitletakse seda lünka finantsteenuste sektoripõhise andmekaeveprotsessi (FIN-DM) kavandamise, arendamise ja hindamise kaudu. Samuti uuritakse, kuidas kasutatakse andmekaeve standardprotsesse eri tegevussektorites ja finantsteenustes. Uurimise käigus tuvastati mitu tavapärase raamistiku kohandamise stsenaariumit. Lisaks ilmnes, et need meetodid ei keskendu piisavalt sellele, kuidas muuta andmekaevemudelid tarkvaratoodeteks, mida saab integreerida organisatsioonide IT-arhitektuuri ja äriprotsessi. Peamised finantsteenuste valdkonnas tuvastatud kohandamisstsenaariumid olid seotud andmekaeve tehnoloogiakesksete (skaleeritavus), ärikesksete (tegutsemisvõime) ja inimkesksete (diskrimineeriva mõju leevendus) aspektidega. Seejärel korraldati tegelikus finantsteenuste organisatsioonis juhtumiuuring, mis paljastas 18 tajutavat puudujääki CRISP- DMi protsessis. Uuringu andmete ja tulemuste abil esitatakse doktoritöös finantsvaldkonnale kohandatud CRISP-DM nimega FIN-DM ehk finantssektori andmekaeve protsess (Financial Industry Process for Data Mining). FIN-DM laiendab CRISP-DMi nii, et see toetab privaatsust säilitavat andmekaevet, ohjab tehisintellekti eetilisi ohte, täidab riskijuhtimisnõudeid ja hõlmab kvaliteedi tagamist kui osa andmekaeve elutsüklisData mining is a set of rules, processes, and algorithms that allow companies to increase revenues, reduce costs, optimize products and customer relationships, and achieve other business goals, by extracting actionable insights from the data they collect on a day-to-day basis. Data mining and analytics projects require well-defined methodology and processes. Several standard process models for conducting data mining and analytics projects are available. Among them, the most notable and widely adopted standard model is CRISP-DM. It is industry-agnostic and often is adapted to meet sector-specific requirements. Industry- specific adaptations of CRISP-DM have been proposed across several domains, including healthcare, education, industrial and software engineering, logistics, etc. However, until now, there is no existing adaptation of CRISP-DM for the financial services industry, which has its own set of domain-specific requirements. This PhD Thesis addresses this gap by designing, developing, and evaluating a sector-specific data mining process for financial services (FIN-DM). The PhD thesis investigates how standard data mining processes are used across various industry sectors and in financial services. The examination identified number of adaptations scenarios of traditional frameworks. It also suggested that these approaches do not pay sufficient attention to turning data mining models into software products integrated into the organizations' IT architectures and business processes. In the financial services domain, the main discovered adaptation scenarios concerned technology-centric aspects (scalability), business-centric aspects (actionability), and human-centric aspects (mitigating discriminatory effects) of data mining. Next, an examination by means of a case study in the actual financial services organization revealed 18 perceived gaps in the CRISP-DM process. Using the data and results from these studies, the PhD thesis outlines an adaptation of CRISP-DM for the financial sector, named the Financial Industry Process for Data Mining (FIN-DM). FIN-DM extends CRISP-DM to support privacy-compliant data mining, to tackle AI ethics risks, to fulfill risk management requirements, and to embed quality assurance as part of the data mining life-cyclehttps://www.ester.ee/record=b547227

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included