scholarly journals Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System

2020 ◽  
Vol 12 (5) ◽  
pp. 86
Author(s):  
Georgios Tsoumanis ◽  
Asterios Papamichail ◽  
Vasileios Dragonas ◽  
George Koufoudakis ◽  
Constantinos T. Angelis ◽  
...  
Keyword(s):  
Low Cost ◽  
Fog Computing ◽  
Limited Resources ◽  
Computing Technology ◽  
New Paradigm ◽  
Medium Access ◽  
High Expectations ◽  
Iot Applications ◽  
Fifth Generation ◽  
Communications Technologies

The emerging new paradigm under the fifth generation of wireless communications technologies (5G) and high expectations for massively expanding today’s Internet of Things (IoT) under 5G, are expected to support a large plurality of low-cost devices for an all-increasing number of new IoT applications. Many emerging IoT applications are going to take advantage of techniques and technologies that have high demands from low-cost devices in terms of processing large amounts of data and communication. For example, in systems based on fog computing technology, low-cost devices have to assign some of their limited resources for processing purposes. Considering the drawbacks emerging from using low-cost devices and the fact that many applications are in need for time-constrained approaches, TDMA-based Medium Access Control (MAC) policies need to be revisited and implemented in low-cost devices of today. In this sense, a policy independent of the underlying topology, TiMAC policy, is considered here and is implemented in low-cost devices using 433 MHz RF modules. Even though the implementation is limited by synchronization issues and a small number of nodes, the obtained experimental results demonstrate the potential for employing TDMA-based MAC policies on IoT systems consisting of low-cost devices.

scholarly journals Implementation of Fog computing in IoT-based healthcare system

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Mirjana Maksimović
Keyword(s):  
Healthcare System ◽  
Low Cost ◽  
Fog Computing ◽  
Healthcare Systems ◽  
Healthcare Sector ◽  
Smart Devices ◽  
New Paradigm ◽  
Healthcare Applications ◽  
Privacy And Security ◽  
Main Challenge

Nowhere do the technology advancements bring improvements than in the healthcare sector, constantly creating new healthcare applications and systems which completely revolutionize the healthcare domain. The appearance of Internet of Things (IoT) based healthcare systems has immensely improved quality and delivery of care, and significantly reduced the costs. At the same time, these systems generate the enormous amount of health-associated data which has to be properly gathered, analyzed and shared. The smart devices, as the components of IoT-driven healthcare systems, are not able to deal with IoT-produced data, neither data posting to the Cloud is the appropriate solution. To overcome smart devices’ and Cloud’s limitations the new paradigm, known as Fog computing, has appeared, where an additional layer processes the data and sends the results to the Cloud. Despite numerous benefits Fog computing brings into IoT-based environments, the privacy and security issues remain the main challenge for its implementation. The reasons for integrating the IoT-based healthcare system and Fog computing, benefits and challenges, as well as the proposition of simple low-cost system are presented in this paper.

Cognitive Radio Techniques for M2M Environments

2015 ◽  
pp. 909-927
Author(s):  
Daniele Tarchi ◽  
Romano Fantacci ◽  
Dania Marabissi
Keyword(s):  
Cognitive Radio ◽  
Access Control ◽  
Medium Access Control ◽  
Low Cost ◽  
Mac Protocols ◽  
Primary System ◽  
New Paradigm ◽  
Medium Access ◽  
Radio Communications ◽  
Cognitive Techniques

Machine to Machine (M2M) communications have been recently introduced as a viable paradigm for allowing low cost and efficient communications among devices mainly in an autonomous manner. Even if M2M protocols need dedicated resources, a new paradigm, called Cognitive M2M (CM2M) communications, has been recently considered exploiting cognitive/opportunistic radio communications. After having introduced the problem of applying cognitive techniques in M2M scenarios, the authors focus their attention on the Medium Access Control (MAC) protocols for CM2M scenarios, with a particular attention on the OFDMA-based primary systems. Among other approaches, the authors focus on a data-aided approach for the access of the secondary devices aiming to reduce interference toward the primary system.

scholarly journals An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Giovanny Mondragón-Ruiz ◽  
Alonso Tenorio-Trigoso ◽  
Manuel Castillo-Cara ◽  
Blanca Caminero ◽  
Carmen Carrión
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Low Cost ◽  
Fog Computing ◽  
Iot Applications ◽  
Local Resources ◽  
Latency Analysis ◽  
Real Time Applications ◽  
Time Required ◽  
Processing Architecture

AbstractInternet of Things (IoT) has posed new requirements to the underlying processing architecture, specially for real-time applications, such as event-detection services. Complex Event Processing (CEP) engines provide a powerful tool to implement these services. Fog computing has raised as a solution to support IoT real-time applications, in contrast to the Cloud-based approach. This work is aimed at analysing a CEP-based Fog architecture for real-time IoT applications that uses a publish-subscribe protocol. A testbed has been developed with low-cost and local resources to verify the suitability of CEP-engines to low-cost computing resources. To assess performance we have analysed the effectiveness and cost of the proposal in terms of latency and resource usage, respectively. Results show that the fog computing architecture reduces event-detection latencies up to 35%, while the available computing resources are being used more efficiently, when compared to a Cloud deployment. Performance evaluation also identifies the communication between the CEP-engine and the final users as the most time consuming component of latency. Moreover, the latency analysis concludes that the time required by CEP-engine is related to the compute resources, but is nonlinear dependent of the number of things connected.

Programmable Integrated Photonics

2020 ◽  
Author(s):  
José Capmany ◽  
Daniel Pérez
Keyword(s):  
Integrated Circuit ◽  
Performance Monitoring ◽  
Low Cost ◽  
Limiting Factors ◽  
External Control ◽  
Integrated Photonics ◽  
New Paradigm ◽  
Photonic Integrated Circuit ◽  
And Performance ◽  
Application Fields

Programmable Integrated Photonics (PIP) is a new paradigm that aims at designing common integrated optical hardware configurations, which by suitable programming can implement a variety of functionalities that, in turn, can be exploited as basic operations in many application fields. Programmability enables by means of external control signals both chip reconfiguration for multifunction operation as well as chip stabilization against non-ideal operation due to fluctuations in environmental conditions and fabrication errors. Programming also allows activating parts of the chip, which are not essential for the implementation of a given functionality but can be of help in reducing noise levels through the diversion of undesired reflections. After some years where the Application Specific Photonic Integrated Circuit (ASPIC) paradigm has completely dominated the field of integrated optics, there is an increasing interest in PIP justified by the surge of a number of emerging applications that are and will be calling for true flexibility, reconfigurability as well as low-cost, compact and low-power consuming devices. This book aims to provide a comprehensive introduction to this emergent field covering aspects that range from the basic aspects of technologies and building photonic component blocks to the design alternatives and principles of complex programmable photonics circuits, their limiting factors, techniques for characterization and performance monitoring/control and their salient applications both in the classical as well as in the quantum information fields. The book concentrates and focuses mainly on the distinctive features of programmable photonics as compared to more traditional ASPIC approaches.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

Sign in / Sign up

Export Citation Format

Share Document