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

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

Easy Innovation and the Iron Cage: Best Practice, Benchmarking, Ranking, and the Management of Organizational Creativity

The use of what came to be known as best practices, benchmarking, and ranking, which took corporate America by storm in the 1980s as a method for managing innovation, has seeped into government and nonprofit organizations in the intervening years. In fact, as H. George Frederickson demonstrates in this Kettering Foundation occasional paper, these practices have proven to be counterproductive both in the business and the public sector. Frederickson suggests, instead, a more flexible, less directive, model he calls "sustained innovation." He offers abundant evidence that this model is more effective in producing organizational effectiveness

Towards evaluation design for smart city development

Smart city developments integrate digital, human, and physical systems in the built environment. With growing urbanization and widespread developments, identifying suitable evaluation methodologies is important. Case-study research across five UK cities - Birmingham, Bristol, Manchester, Milton Keynes and Peterborough - revealed that city evaluation approaches were principally project-focused with city-level evaluation plans at early stages. Key challenges centred on selecting suitable evaluation methodologies to evidence urban value and outcomes, addressing city authority requirements. Recommendations for evaluation design draw on urban studies and measurement frameworks, capitalizing on big data opportunities and developing appropriate, valid, credible integrative approaches across projects, programmes and city-level developments

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

Information Outlook, September 2005

Volume 9, Issue 9https://scholarworks.sjsu.edu/sla_io_2005/1008/thumbnail.jp