2,528 research outputs found
RL-IoT: Reinforcement Learning to Interact with IoT Devices
Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is the key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT, a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to recover the semantics of protocol messages and to take control of the device to reach a given goal, while minimizing the number of interactions. We assume to know only a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve both simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb in our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT paves the road for automatic interactions with poorly documented IoT protocols, thus enabling interoperable systems
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Event processing in web of things
The incoming digital revolution has the potential to drastically improve our productivity,
reduce operational costs and improve the quality of the products. However, the realization
of these promises requires the convergence of technologies — from edge computing
to cloud, artificial intelligence, and the Internet of Things — blurring the lines between
the physical and digital worlds. Although these technologies evolved independently over
time, they are increasingly becoming intertwined. Their convergence will create an unprecedented
level of automation, achieved via massive machine-to-machine interactions
whose cornerstone are event processing tasks.
This thesis explores the intersection of these technologies by making an in-depth analysis
of their role in the life-cycle of event processing tasks, including their creation, placement
and execution. First, it surveys currently existing Web standards, Internet drafts,
and design patterns that are used in the creation of cloud-based event processing. Then, it
investigates the reasons for event processing to start shifting towards the edge, alongside
with the standards that are necessary for a smooth transition to occur. Finally, this work
proposes the use of deep reinforcement learning methods for the placement and distribution
of event processing tasks at the edge. Obtained results show that the proposed
neural-based event placement method is capable of obtaining (near) optimal solutions in
several scenarios and provide hints about future research directions.A nova revolução digital promete melhorar drasticamente a nossa produtividade, reduzir
os custos operacionais e melhorar a qualidade dos produtos. A concretizac¸ ˜ao dessas promessas
requer a convergˆencia de tecnologias – desde edge computing à cloud, inteligência
artificial e Internet das coisas (IoT) – atenuando a linha que separa o mundo fĂsico do digital.
Embora as quatro tecnologias mencionadas tenham evoluĂdo de forma independente
ao longo do tempo, atualmente elas estĂŁo cada vez mais interligadas. A convergĂŞncia destas
tecnologias irá criar um nĂvel de automatização sem precedentes.The research published in this work was supported by the Portuguese Foundation for
Science and Technology (FCT) through CEOT (Center for Electronic, Optoelectronic and
Telecommunications) funding (UID/MULTI/00631/2020) and by FCT Ph.D grant to Andriy
Mazayev (SFRH/BD/138836/2018)
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