USING EMBEDDED TECHNOLOGY IN END-USER PROGRAMMING OF SMART SPACES WITH MOBILE DEVICES

A recent shift in computing paradigm from stand-alone microcomputers and mainframes towards entirely pervasivecomputing where billions of miniature, ubiquitous inter-connected computing elements weave themselves into thefabric of everyday life. Embedded systems run the computing devices hidden inside every object and appliance suchas cell phones, toys, handheld PDAs, cameras, microwave ovens, cars, airplanes, etc. These numerous, easilyaccessible devices connected to each other and to network infrastructure exhibit context-awareness of anenvironment in order to optimize their operation in that environment. In this paper, we examined embedded systemsin end-user programming of smart spaces with mobile devices. We designed and implemented a microcontrollerbasedsystem capable of monitoring and controlling the electronic appliances in a home from any location. Weadopted a task-driven computing approach of the composition of the semantic web. The end user uses thefunctionality of the networked devices in the home as semantic web services to arbitrarily form his request whichinvolves the typing of SMS through the user-friendly interface of a Java enabled mobile phone. An Arduinomicrocontroller for generating the timing and control signals programmed using Wiring language was used. TheGSM wireless technology was used for transmission and reception of the data. Our work addresses the problem ofenergy wastage and domestic accidents by enabling end-users to easily use their mobile devices to monitor andinstruct their home devices from any location over a wireless network.Keywords: Embedded Technology, Smart Spaces, End-User Programming, Mobile Devices, Pervasive Networkin

Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes

Industrial networks are introducing Internet of Things (IoT) technologies in their manufacturing processes in order to enhance existing methods and obtain smarter, greener and more effective processes. Global predictions forecast a massive widespread of IoT technology in industrial sectors in the near future. However, these innovations face several challenges, such as achieving short response times in case of time-critical applications. Concepts like in-network computing or edge computing can provide adequate communication quality for these industrial environments, and data plane programming has been proved as a useful mechanism for their implementation. Specifically, P4 language is used for the definition of the behavior of programmable switches and network elements. This paper presents a solution for industrial IoT (IIoT) network communications to reduce response times using in-network computing through data plane programming and P4. Our solution processes Message Queuing Telemetry Transport (MQTT) packets sent by a sensor in the data plane and generates an alarm in case of exceeding a threshold in the measured value. The implementation has been tested in an experimental facility, using a Netronome SmartNIC as a P4 programmable network device. Response times are reduced by 74% while processing, and delay introduced by the P4 network processing is insignificant.This work was supported in part by the Spanish Ministry of Science and Innovation through the national project (PID2019-108713RB-C54) titled “Towards zeRo toUch nEtwork and services for beyond 5G” (TRUE-5G), and in part by the “Smart Factories of the Future” (5G-Factories) (COLAB19/06) project

Reasoning About a Service-oriented Programming Paradigm

This paper is about a new way for programming distributed applications: the service-oriented one. It is a concept paper based upon our experience in developing a theory and a language for programming services. Both the theoretical formalization and the language interpreter showed us the evidence that a new programming paradigm exists. In this paper we illustrate the basic features it is characterized by

Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution

While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel computing systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive results in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoiding common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach

City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201