Security Implications of Fog Computing on the Internet of Things

Recently, the use of IoT devices and sensors has been rapidly increased which also caused data generation (information and logs), bandwidth usage, and related phenomena to be increased. To our best knowledge, a standard definition for the integration of fog computing with IoT is emerging now. This integration will bring many opportunities for the researchers, especially while building cyber-security related solutions. In this study, we surveyed about the integration of fog computing with IoT and its implications. Our goal was to find out and emphasize problems, specifically security related problems that arise with the employment of fog computing by IoT. According to our findings, although this integration seems to be non-trivial and complicated, it has more benefits than the implications.Comment: 5 pages, conference paper, to appear in Proceedings of the ICCE 2019, IEEE 37th International Conference on Consumer Electronics (ICCE), Jan 11- 13, 2019, Las Vegas, NV, US

TOWARD A SMART ECOSYSTEM WITH AUTOMATED SERVICES

New ICT architectures enable a better response to constant pressure on the industry and services to improve their business performance and productivity, especially in data processing. At the same time, due to the growing number of sensor modules, the amount of data that needs to be processed, in real time, is growing. Delays in communication with the cloud environment can lead to poor management decisions or user dissatisfaction. In automation and services, one of the new ICT architectures is Edge computing in the data processing. Edge computing is a networking architecture that brings computing close to the source of data in order to reduce latency and bandwidth use. Edge computing brings new power to data processing and the ability to process large amounts of data in real time. This is essential for predicting the behavior of machines, systems, or customers in order to detect errors or provide personalized service as in the case of smart vending machines. In that way, Edge computing enables taking steps toward establishing a smart ecosystem in automation and services

Internet of Things Based Smart Vending Machine using Digital Payment System

The advent of the Internet envisions a cashless society by enabling financial transactions through digital payments. Significantly, the emergence of coronavirus (COVID-19) disrupted our traditional cash handling means and triggered an inflection point for switching towards contactless digital payments from physical cash payments. Furthermore, Internet of Things (IoT) technology escalates digital payments to the next level by enabling devices to render goods and services without requiring any human interaction. This research proposed an IoT-enabled cashless vending machine that incorporates both cloud computing and payment gateway for ordering and purchasing items through digital payment systems by using a mobile application. The system enables a pre-installed mobile application to scan the Quick Response (QR) code attached to the body of a vending machine, opens the portal of a web-based virtual machine through the code, allows user to choose and order items from the virtual vending, initiates and authorizes a digital payment through an IoT gateway installed inside the physical vending machine by establishing a connection between user's and vendor's financial entities, and finally, dispenses the ordered items by unlocking the shelves of the vending machine after the successful payment transaction. It operates in the Arduino platform with an ATmega 2560 Microcontroller and Esp8266 Wi-fi module as hardware components, mobile application software, and payment gateway API. The system performed an average response time of 14500 milliseconds to pick a product after running 150 consecutive API test calls. This result shows a satisfying time for enhancing customers' buying experiences with digital payment systems and a customizable and cost-effective IoT-based intelligent vending machine to introduce for mass production

Making vending machines smarter with the use of Machine Learning and Artificial Intelligence: Set-up and Architecture

Machine Learning and Robust Optimization techniques can significantly improve logistics operations and improve stock quantity and maintenance intervals. Machine Learning will be used to forecast item demands for each of the vending machines, taking into account past demands and calendar effects. By performing such predictions which are forwarded to a Robust Optimization model, and whose outputs will be the cash transport that each vending machine should require. These transports guarantee that demand is fulfilled up to the desired confidence level, preventing downtime of vending machines due to unplanned maintenance and out-of-stock situations, while also satisfying additional constraints arising in this particular domain. As a result of such operations, we expect productivity improvements of vending machines from 20-40%

Automatic Coffee Maker Machine Based on Internet of Things (IoT)

In this era of globalization, technological developments are very rapid penetrated in all fields in particular. in the food and beverage sector..During a pandemic like this, as entrepreneurs, food and drink must adapt. Adaptation in question is hygiene in the process of making drinks and food. One example. it is an automatic drink making machine. that can help in the process. make drinks more practical and hygienic to keep more sterile. when making drinks in terms of blending drink ingredients. the process of stirring and pouring hot water. In an automatic drink making machine, in a supermarket there are still many weaknesses. ie the user is still making physical contact, such as approaching and selecting the menu and waiting for the process to complete. Based on the problems that exist among the general public, an idea was formed to design and manufacture an Internet of Things (IOT) based automatic drinking machine. which can help users in terms of making a drink. Users only need to select the drink menu that they want to make on the Android smartphone application that is connected to the access point, then the machine will carry out the process of making drinks. according to the menu selected in the application. The user only takes drinks to the places that are available. Based on the results of testing and analysis carried out on a machine this helps users to place orders and can enjoy drinks that are processed automatically

Living lab approach for developing massmarket IoT products and services

Internet of Things (IoT) has emerged as a central concept in both the industrial as in the academic world. In this context, Living Lab research has been shown as an effective means for the design, implementation, development, testing and validation of Internet of Things system’s pervasiveness. However, IoT products are not yet designed based on the needs of a larger, non-technical group of end-users. Therefore, in this paper we describe the AllThingsTalk Living Lab research track in which tangible end-user products are defined to be implemented on an online IoT platform. More specifically, by using both qualitative and quantitative methodologies (i.e., desk research, online survey, probe research and co-creation) and by selecting different types of users (i.e., based on Rogers’ adoption profiles) for these interaction moments, we were able to combine the input of these users to define tangible products that meet the needs of a heterogeneous group of end-users

Smart Contracts in Traditional Contract Law, Or: The Law of the Vending Machine

Smart contracts are the new norm, yet state legislatures and courts have not developed set rules and answers to legal disputes that these contracts create. Is traditional contract law sufficient? Or should we create an entirely new legislative or common law scheme to deal with these disputes? The common law has proven to be successful in dealing with new technologies and contracts, particularly because of its flexibility. Although a major overhaul may be in the future, there are still solutions that we can find today with the current legal landscape given the state of contract law and its evolution over time. One particularly analogous body of case law is instructive: the law of the vending machine. In the end, thinking about smart contracts as vending machines may be fruitful for the future of this evolving area of the law

The Next Wave of CRM Innovation: Implications for Research, Teaching, and Practice

Globalization and customers’ ever-changing needs have created a hyper-competitive market. As a result, customer relationship management (CRM) has become a core topic of interest among both practitioners and academics. Further, over the years, with the advancements in the technology landscape, such as digital technologies, CRM has improved in myriad ways. This paper summarizes a panel discussion on CRM innovations held at the 2016 Pacific Asia Conference on Information Systems (PACIS 2016) in Chiyai, Taiwan. The panel discussed CRM fundamentals and how traditional CRM systems work in organizations. Then, the panel focused on the advancement in technology landscape such as big data, analytics, Internet of things, and artificial intelligence and how such technologies have transformed innovations in the CRM landscape. Finally, the panel highlighted the limitations in the current CRM curricula in the universities and how the curriculum today needs to reflect such advancements to enhance the union between the CRM curricula and the industry needs. Further, this paper provides future research ideas for academia and contributes to research interests on CRM in general

Wrapped and Stacked: ‘Smart Contracts’ and the Interaction of Natural and Formal Language

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This article explores ‘smart contracts’ from first principles: What they are, whether they are properly called ‘contracts’, and what issues they raise for national contract law. A ‘smart’ contract purports to record contractual promises in language which is both intelligible to human beings and (ultimately) executable by machines. The formalisation of contracting language that this entails is, I argue, the most important aspect for lawyers—just as important as the automation of contractual performance. Rather than taking a doctrinal approach focused on the presence of traditional indicia of contract formation, I examine the nature of contracts as legal entities created by words and documents. In most cases, smart contracts will be ‘wrapped in paper’ and nested in a national legal system. Borrowing from the idiom of computer science, I introduce the term ‘contract stack’ to highlight the complex nature of contracts as legal entities incorporating different ‘layers’, including speech acts by the parties in both natural and formal languages as well as mandatory legal rules. It is the interactions within this contract stack that will be most important to the development of contract law doctrines appropriate to smart contracts. To illustrate my points, I explore a few issues that smart contracts might raise for English contract law. I touch on the questions of illegality, jurisdiction, and evidence, but my focus in this paper is on exploring issues in contract law proper. This contribution should be helpful not only to lawyers attempting to understand smart contracts, but to those involved in coding smart contracts—and writing the languages used to code them.Peer Reviewe