58,769 research outputs found
IETF standardization in the field of the Internet of Things (IoT): a survey
Smart embedded objects will become an important part of what is called the Internet of Things. However, the integration of embedded devices into the Internet introduces several challenges, since many of the existing Internet technologies and protocols were not designed for this class of devices. In the past few years, there have been many efforts to enable the extension of Internet technologies to constrained devices. Initially, this resulted in proprietary protocols and architectures. Later, the integration of constrained devices into the Internet was embraced by IETF, moving towards standardized IP-based protocols. In this paper, we will briefly review the history of integrating constrained devices into the Internet, followed by an extensive overview of IETF standardization work in the 6LoWPAN, ROLL and CoRE working groups. This is complemented with a broad overview of related research results that illustrate how this work can be extended or used to tackle other problems and with a discussion on open issues and challenges. As such the aim of this paper is twofold: apart from giving readers solid insights in IETF standardization work on the Internet of Things, it also aims to encourage readers to further explore the world of Internet-connected objects, pointing to future research opportunities
HALLS: An Energy-Efficient Highly Adaptable Last Level STT-RAM Cache for Multicore Systems
Spin-Transfer Torque RAM (STT-RAM) is widely considered a promising
alternative to SRAM in the memory hierarchy due to STT-RAM's non-volatility,
low leakage power, high density, and fast read speed. The STT-RAM's small
feature size is particularly desirable for the last-level cache (LLC), which
typically consumes a large area of silicon die. However, long write latency and
high write energy still remain challenges of implementing STT-RAMs in the CPU
cache. An increasingly popular method for addressing this challenge involves
trading off the non-volatility for reduced write speed and write energy by
relaxing the STT-RAM's data retention time. However, in order to maximize
energy saving potential, the cache configurations, including STT-RAM's
retention time, must be dynamically adapted to executing applications' variable
memory needs. In this paper, we propose a highly adaptable last level STT-RAM
cache (HALLS) that allows the LLC configurations and retention time to be
adapted to applications' runtime execution requirements. We also propose
low-overhead runtime tuning algorithms to dynamically determine the best
(lowest energy) cache configurations and retention times for executing
applications. Compared to prior work, HALLS reduced the average energy
consumption by 60.57% in a quad-core system, while introducing marginal latency
overhead.Comment: To Appear on IEEE Transactions on Computers (TC
Sensor function virtualization to support distributed intelligence in the internet of things
It is estimated that-by 2020-billion devices will be connected to the Internet. This number not only includes TVs, PCs, tablets and smartphones, but also billions of embedded sensors that will make up the "Internet of Things" and enable a whole new range of intelligent services in domains such as manufacturing, health, smart homes, logistics, etc. To some extent, intelligence such as data processing or access control can be placed on the devices themselves. Alternatively, functionalities can be outsourced to the cloud. In reality, there is no single solution that fits all needs. Cooperation between devices, intermediate infrastructures (local networks, access networks, global networks) and/or cloud systems is needed in order to optimally support IoT communication and IoT applications. Through distributed intelligence the right communication and processing functionality will be available at the right place. The first part of this paper motivates the need for such distributed intelligence based on shortcomings in typical IoT systems. The second part focuses on the concept of sensor function virtualization, a potential enabler for distributed intelligence, and presents solutions on how to realize it
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