A survey on agent learning architecture that adopts internet of things and wireless sensor networks
18 Dec 2020-International Journal of Wavelets, Multiresolution and Information Processing (World Scientific Publishing Company)-pp 2030002
TL;DR: Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics as discussed by the authors. Agents introduced in various architectures represent its characteristics, and are used to represent its trustworthiness and reliability.
Abstract: Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its...
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TL;DR: In this paper , a Q-learning agent (QLA) system was proposed to obtain the optimal allocation approach for WBANs, where the effect of data transmission, relay selection, power consumption, and each sensor's energy constraints were considered.
Abstract: The eHealth service has been considered a potential resource issue for industry and academia and is remarkably similar to a promising technology for continuous monitoring of biomedical signals in the human body. Indeed, it deployed modern digital technologies to maintain patient health data in digital environments such as the Internet of Things (IoT). In this vein, Wireless Body Area Networks (WBANs) are essential components of eHealth systems for early detection and successful treatment. Because sensor batteries in WBANs are usually operated and inconvenient to recharge, an energy-efficient resource allocation scheme is critical to extending the length of networks while still meeting the stringent quality of service requirements inherent in WBANs. As a result, this paper investigates resource allocation issues for WBAN. Our objective is to maximize energy efficiency by considering the effect of data transmission, relay selection, power consumption, and each sensor's energy constraints. Due to the current problems' sophistication, we present a Q-learning Agent (QLA) system to obtain the optimal allocation approach. A Q-Sensor Network Management Unit (Q-SNMU) is implemented and designed to synchronize all body sensors appropriately. The results show that the proposed scheme works well and that the proposed Q-SNMU approach is very efficient at running.
8 citations
01 Jan 2017
TL;DR: In this paper, an intelligent API layer that employs an external service assembler, service auditor, service monitor and service router component to coordinate service publishing, subscription, decoupling and service combination within the architecture is proposed.
Abstract: The proliferation of the Internet of Things (IoT) has since seen a growing interest in architectural design and adaptive frameworks to promote the connection between heterogeneous IoT devices and IoT systems. The most widely favoured software architecture in IoT is the Service Oriented Architecture (SOA), which aims to provide a loosely coupled systems to leverage the use and reuse of IoT services at the middle-ware layer, to minimise system integration problems. However, despite the flexibility offered by SOA, the challenges of integrating, scaling and ensuring resilience in IoT systems persist. One of the key causes of poor integration in IoT systems is the lack of an intelligent, connection-aware framework to support interaction in IoT systems. This paper reviews existing architectural frameworks for integrating IoT devices and identifies the key areas that require further research improvements. The paper concludes by proposing a possible solution based on microservice. The proposed IoT integration framework benefits from an intelligent API layer that employs an external service assembler, service auditor, service monitor and service router component to coordinate service publishing, subscription, decoupling and service combination within the architecture.
2 citations
References
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TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >
20,028 citations
TL;DR: Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems, and its four areas of application are discussed by using real- world applications.
Abstract: Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.
3,969 citations
Book•
01 Apr 1996TL;DR: 1. architectural Styles, 2. Shared Information Systems, 3. Education of Software Architects, 4. Architectural Design Guidance.
Abstract: 1. Introduction. 2. Architectural Styles. 3. Case Studies. 4. Shared Information Systems. 5. Architectural Design Guidance. 6. Formal Models and Specifications. 7. Linguistic Issues. 8. Tools for Architectural Design. 9. Education of Software Architects. Bibliography. Index.
3,208 citations
TL;DR: This paper proposes a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent and utilizes variational autoencoders as the inference engine for generalizing optimal policies.
Abstract: Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users’ feedback for training purposes. In this paper, we propose a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes variational autoencoders as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends DRL to the semisupervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on Bluetooth low energy signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.
314 citations
TL;DR: This paper presents the basics of swarm robotics and introduces HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems and identifies the core concepts needed to design a human-swarm system.
Abstract: Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human–swarm interaction (HSI) and identifies the core concepts needed to design a human–swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human–swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.
312 citations