Agent-Based M&S of Smart Sensors for Knowledge Acquisition Inside the Internet of Things and Sensor Networks

2015 ◽  
pp. 224-234 ◽  
Author(s):  
Michał Dyk ◽  
Andrzej Najgebauer ◽  
Dariusz Pierzchała
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Knowledge Acquisition ◽  
Smart Sensors ◽  
The Internet ◽  
Agent Based ◽  
The Internet Of Things

scholarly journals Problems of Powering End Devices in Wireless Networks of the Internet of Things

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2417
Author(s):  
Andrzej Michalski ◽  
Zbigniew Watral
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Wireless Sensor ◽  
Alternative Possibilities ◽  
The Internet ◽  
Power Sources ◽  
Wide Range ◽  
The Internet Of Things

This article presents the problems of powering wireless sensor networks operating in the structures of the Internet of Things (IoT). This issue was discussed on the example of a universal end node in IoT technology containing RFID (Radio Frequency Identification) tags. The basic methods of signal transmission in these types of networks are discussed and their impact on the basic requirements such as range, transmission speed, low energy consumption, and the maximum number of devices that can simultaneously operate in the network. The issue of low power consumption of devices used in IoT solutions is one of the main research objects. The analysis of possible communication protocols has shown that there is a possibility of effective optimization in this area. The wide range of power sources available on the market, used in nodes of wireless sensor networks, was compared. The alternative possibilities of powering the network nodes from Energy Harvesting (EH) generators are presented.

scholarly journals A Novel Bioinspired Multiobjective Optimization Algorithm for Designing Wireless Sensor Networks in the Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jun Huang ◽  
Liqian Xu ◽  
Cong-cong Xing ◽  
Qiang Duan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Chaotic Behavior ◽  
Ant Colony ◽  
Wireless Sensor ◽  
The Internet ◽  
The Internet Of Things

The design of wireless sensor networks (WSNs) in the Internet of Things (IoT) faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS). Unlike the ant colony optimization (ACO) algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant) to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.

scholarly journals Modules of Organizational and Technical Systems for Solving Problems of Adaptation in a Rapidly Changing Environment

2020 ◽  
Vol 24 (5) ◽  
pp. 82-90
Author(s):  
A. Mikryukov ◽  
V. M. Trembach ◽  
A. V. Danilov
Keyword(s):  
Internet Of Things ◽  
Knowledge Base ◽  
External Environment ◽  
The Internet ◽  
Cyberphysical Systems ◽  
Adaptation Algorithm ◽  
Cognitive Approach ◽  
Agent Based ◽  
Cyberphysical System ◽  
The Internet Of Things

Purpose of research. The aim of the research is to form modules of organizational and technical systems (OTS) using a cognitive approach to solve problems of adaptation of cyberphysical systems. Currently, there is a rapid development of elements of the Internet of things. New tasks related to self-organization and adaptation in a rapidly changing external environment are brought to the fore. These tasks occur when new elements appear in the telecommunications computer network, they fail, change the mode, new tasks occur, etc. To work out these tasks, the possibilities of approaches to support and decision-making such as situational, cognitive, and semiotic are considered. The authors consider the cognitive approach in more detail. Within the framework of the cognitive paradigm, the article describes the use of the cognitive approach for solving problems of adaptation of cyberphysical systems. To solve this problem on the basis of an agent-based approach, the structure of a cyberphysical system with the possibility of adaptation is presented and the functions of its agents are described. The main stages of solving problems of adaptation of cyberphysical systems are presented. An adaptation algorithm using the planning mechanism is presented. The demo example shows a knowledge base for solving the problem of adapting cyberphysical systems using a cognitive planning mechanism.Materials and methods of research. New approaches and methods are required to address adaptation issues in planning. The cognitive approach is one of the developing directions in solving many problems of the Internet of things. One of these tasks is the ability to adapt OTS modules in a rapidly changing external environment based on the planning mechanism. To solve the planning problem, we use the algorithm described by Aristotle more than 2,350 years ago and implemented in the GPS program. This algorithm can be considered the first description of the cognitive mechanism that a person uses. The knowledge base uses an integrated approach to knowledge representation. When developing OTS modules, an agent-based approach was used to solve the problem of adaptation.Results. The existing and developing approaches and methods for decision support and decision-making are considered for decisionmaking in newly emerging situations in OTS modules. The main provisions of such significant approaches as situational, cognitive and semiotic are presented. A cognitive approach to the adaptation of intelligent systems is proposed. The solution of the problem of adaptation of cyberphysical systems is considered within the framework of the cognitive paradigm. The structure of a cyberphysical system capable of solving adaptation problems is shown. The functions of OTS modules based on agent-oriented technology are described. A description of the adaptation algorithm using the cognitive planning mechanism is given. The main stages of solving problems of adaptation of cyberphysical systems are presented. A demo example of solving the problem of adaptation by a cyberphysical system-a cooking robot – is shown.Conclusion. Using the modular architecture of an intelligent system allows you to solve many problems. One of these tasks is to configure elements of the Internet of things when they deviate from their main function. The planning mechanisms proposed for parametric adaptation can be repeatedly applied in OTS modules as separate agents. This approach is relevant for elements of the Internet of things. In the case of expanding the functionality of the OTS modules of Internet of things, it is advisable to apply machine learning with fixing the results in the knowledge base of planning agents.

A probable effect of the development of internet technology on insurance & banking sectors, decision processes and service structures

2020 ◽  
Vol 1 (1) ◽  
pp. 36-41
Author(s):  
Ali Osman Serdar Citak
Keyword(s):  
Internet Of Things ◽  
Decision Processes ◽  
Internet Technology ◽  
Smart Sensors ◽  
The Internet ◽  
Future Expectations ◽  
History Of ◽  
Probable Effect ◽  
Near Future ◽  
The Internet Of Things

The history of the development of the Internet of Things (IoT) covers the last twenty years. Despite the short of time, the concept and implementation of the Internet of Things have widely spread all over the world. The impetus of the dissemination of the concept has exponential speed. In the near future, billions of smart sensors and devices will interact with one another without human intervention. The early impact of the Internet of Things has been observed and discussed in the areas of technology, transportation, production, and marketing. The prospective effect of the Internet of Things on the finance sector has been discussed recently. In this study, the development of the concept of the Internet of Things and it is effect on the finance sector and specifically the insurance and banking sectors and future expectations have been evaluated.

Multi-Layer Agent Based Architecture for Internet of Things Systems

2018 ◽  
Vol 11 (4) ◽  
pp. 32-52 ◽  
Author(s):  
Kouah Sofia ◽  
Kitouni Ilham
Keyword(s):  
Internet Of Things ◽  
Operative Management ◽  
Local System ◽  
Physical World ◽  
The Internet ◽  
Agent Based ◽  
Global Management ◽  
The Third ◽  
System Diagnosis ◽  
The Internet Of Things

Nowadays, the Internet of things (IoT) is becoming a promising technology which revolutionizes and simplifies our daily life style. It allows interaction and cooperation between a large variety of pervasive objects over wireless and wired connections, in order to achieve specific goals. Moreover, it provides a concise integration of physical world into computer systems through network infrastructure. This paper provides an agent-based architecture for developing IoT systems. The proposed architecture is multi-layer and generic. It encompasses four layers: Physical Component Management, Local Management -Coordination, Global Management-Coordination and Specialized Operative Management Layers. The first one can be seen as a smart layer that ensures connection and communication between things and the system. The second one constitutes the intelligent core of the system which acts locally to ensure coordination and further internal functioning. The third layer ensures coordination between the local system and the externals ones. The last layer supports additional behaviors which are domain dependent. The architecture is illustrated by an IoT system diagnosis.

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