27,033 research outputs found
Operating systems for Internet of Things low-end devices: analysis and benchmarking
In the era of the Internet of Things (IoT), billions of wirelessly connected embedded devices rapidly became part of our daily lives. As a key tool for each Internet-enabled object, embedded operating systems (OSes) provide a set of services and abstractions which eases the development and speedups the deployment of IoT solutions at scale. This article starts by discussing the requirements of an IoT-enabled OS, taking into consideration the major concerns when developing solutions at the network edge, followed by a deep comparative analysis and benchmarking on Contiki-NG, RIOT, and Zephyr. Such OSes were considered as the best representative of their class considering the main key-points that best define an OS for resource-constrained IoT devices: low-power consumption, real-time capabilities, security awareness, interoperability, and connectivity. While evaluating each OS under different network conditions, the gathered results revealed distinct behaviors for each OS feature, mainly due to differences in kernel and network stack implementations.This work has been supported by national funds through FCT - Fundação para a Ciência e a Tecnologia within the Project Scope: UID/CEC/00319/2019
Connecting the World of Embedded Mobiles: The RIOT Approach to Ubiquitous Networking for the Internet of Things
The Internet of Things (IoT) is rapidly evolving based on low-power compliant
protocol standards that extend the Internet into the embedded world. Pioneering
implementations have proven it is feasible to inter-network very constrained
devices, but had to rely on peculiar cross-layered designs and offer a
minimalistic set of features. In the long run, however, professional use and
massive deployment of IoT devices require full-featured, cleanly composed, and
flexible network stacks.
This paper introduces the networking architecture that turns RIOT into a
powerful IoT system, to enable low-power wireless scenarios. RIOT networking
offers (i) a modular architecture with generic interfaces for plugging in
drivers, protocols, or entire stacks, (ii) support for multiple heterogeneous
interfaces and stacks that can concurrently operate, and (iii) GNRC, its
cleanly layered, recursively composed default network stack. We contribute an
in-depth analysis of the communication performance and resource efficiency of
RIOT, both on a micro-benchmarking level as well as by comparing IoT
communication across different platforms. Our findings show that, though it is
based on significantly different design trade-offs, the networking subsystem of
RIOT achieves a performance equivalent to that of Contiki and TinyOS, the two
operating systems which pioneered IoT software platforms
LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers
In today's computing environment, where Artificial Intelligence (AI) and data
processing are moving toward the Internet of Things (IoT) and the Edge
computing paradigm, benchmarking resource-constrained devices is a critical
task to evaluate their suitability and performance. The literature has
extensively explored the performance of IoT devices when running high-level
benchmarks specialized in particular application scenarios, such as AI or
medical applications. However, lower-level benchmarking applications and
datasets that analyze the hardware components of each device are needed. This
low-level device understanding enables new AI solutions for network, system and
service management based on device performance, such as individual device
identification, so it is an area worth exploring more in detail. In this paper,
we present LwHBench, a low-level hardware benchmarking application for
Single-Board Computers that measures the performance of CPU, GPU, Memory and
Storage taking into account the component constraints in these types of
devices. LwHBench has been implemented for Raspberry Pi devices and run for 100
days on a set of 45 devices to generate an extensive dataset that allows the
usage of AI techniques in different application scenarios. Finally, to
demonstrate the inter-scenario capability of the created dataset, a series of
AI-enabled use cases about device identification and context impact on
performance are presented as examples and exploration of the published data
Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services
Energy consumption and carbon emissions are expected to be crucial factors
for Internet of Things (IoT) applications. Both the scale and the
geo-distribution keep increasing, while Artificial Intelligence (AI) further
penetrates the "edge" in order to satisfy the need for highly-responsive and
intelligent services. To date, several edge/fog emulators are catering for IoT
testing by supporting the deployment and execution of AI-driven IoT services in
consolidated test environments. These tools enable the configuration of
infrastructures so that they closely resemble edge devices and IoT networks.
However, energy consumption and carbon emissions estimations during the testing
of AI services are still missing from the current state of IoT testing suites.
This study highlights important questions that developers of AI-driven IoT
services are in need of answers, along with a set of observations and
challenges, aiming to help researchers designing IoT testing and benchmarking
suites to cater to user needs.Comment: Presented at the 2nd International Workshop on Testing Distributed
Internet of Things Systems (TDIS 2022
Addressing the Challenges in Federating Edge Resources
This book chapter considers how Edge deployments can be brought to bear in a
global context by federating them across multiple geographic regions to create
a global Edge-based fabric that decentralizes data center computation. This is
currently impractical, not only because of technical challenges, but is also
shrouded by social, legal and geopolitical issues. In this chapter, we discuss
two key challenges - networking and management in federating Edge deployments.
Additionally, we consider resource and modeling challenges that will need to be
addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and
Paradigms; Editors Buyya, Sriram
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
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