Multi-agent approach to distributed processing big sensor data based on fog computing model for the monitoring of the urban infrastructure systems

2016 ◽  
Author(s):  
Danila Parygin ◽  
Nikita Nikitsky ◽  
Valery Kamaev ◽  
Anna Matokhina ◽  
Alexey Finogeev ◽  
...  
Keyword(s):  
Fog Computing ◽  
Distributed Processing ◽  
Urban Infrastructure ◽  
Sensor Data ◽  
Infrastructure Systems ◽  
Computing Model ◽  
Multi Agent

scholarly journals MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1853 ◽  
Author(s):  
Ammar Awad Mutlag ◽  
Mohd Khanapi Abd Ghani ◽  
Mazin Abed Mohammed ◽  
Mashael S. Maashi ◽  
Othman Mohd ◽  
...  
Keyword(s):  
Resource Availability ◽  
Contextual Information ◽  
Fog Computing ◽  
Main Role ◽  
Healthcare Applications ◽  
Network Resource ◽  
Difficult Decision ◽  
Computing Model ◽  
Multi Agent ◽  
Decision Tables

In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.

Fault-Tolerant Strategies in the Tree-Based Fog Computing Model

2020 ◽  
Vol 11 (4) ◽  
pp. 72-91
Author(s):  
Ryuji Oma ◽  
Shigenari Nakamura ◽  
Tomoya Enokido ◽  
Makoto Takizawa
Keyword(s):  
Fault Tolerant ◽  
Fog Computing ◽  
Minimum Energy ◽  
Sensor Data ◽  
The Internet ◽  
Process Data ◽  
General Process ◽  
Computing Model ◽  
The Internet Of Things ◽  
Parent Node

In the Fog Comput$ing (FC) model of the Internet of Things (IoT), application processes to handle sensor data are distributed to fog nodes and servers. In the Tree-based FC (TBFC) model proposed by the authors, fog nodes are hierarchically structured. In this article, the authors propose a TBFC for a General Process (TBFCG) model to recover from the faults of fog nodes. If a node gets faulty, the child nodes are disconnected. The authors propose Minimum Energy in the TBFCG tree (MET) and selecting Multiple Parents for recovery in the TBFCG tree (MPT) algorithms to select a new parent node for the disconnected nodes. A new parent node has to process data from not only the disconnected nodes, but also its own child nodes. In the evaluation, the energy consumption and execution time of a new parent node can be reduced by the proposed algorithms.

scholarly journals Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1928 ◽  
Author(s):  
Alfonso González-Briones ◽  
Fernando De La Prieta ◽  
Mohd Mohamad ◽  
Sigeru Omatu ◽  
Juan Corchado
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Energy Optimization ◽  
Sensor Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Research Perspectives ◽  
Wide Range ◽  
Multi Agent

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

scholarly journals A case study on availability of sensor data in agent cooperation

2010 ◽  
Vol 7 (3) ◽  
pp. 597-615
Author(s):  
Christian Johansson ◽  
Fredrik Wernstedt ◽  
Paul Davidsson
Keyword(s):  
Production Costs ◽  
Sensor Data ◽  
Energy Market ◽  
Physical Processes ◽  
Agent Based ◽  
Production And Distribution ◽  
Multi Agent ◽  
Problem Characteristic ◽  
Agent Cooperation

Multi-agent cooperation can in several cases be used in order to mitigate problems relating to task sharing within physical processes. In this paper we apply agent based solutions to a class of problems defined by their property of being predictable from a macroscopic perspective while being highly stochastic when viewed at a microscopic level. These characteristic properties can be found in several industrial processes and applications, e.g. within the energy market where the production and distribution of electricity follow this pattern. Another defining problem characteristic is that the supply is usually limited as well as consisting of several layers of differentiating production costs. We evaluate and compare the performance of the agent system in three different scenarios, and for each such scenario it is shown to what degree the optimization system is dependent on the level of availability of sensor data.

Multi-Agent DDPG-Based Joint Task Partitioning and Power Control in Fog Computing Networks

2021 ◽  
pp. 1-1
Author(s):  
Zhipeng Cheng ◽  
Minghui Min ◽  
Minghui Liwang ◽  
Lianfen Huang ◽  
Zhibin Gao
Keyword(s):  
Power Control ◽  
Fog Computing ◽  
Task Partitioning ◽  
Joint Task ◽  
Multi Agent

Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System

2018 ◽  
pp. 141-154 ◽  
Author(s):  
Chandu Thota ◽  
Revathi Sundarasekar ◽  
Gunasekaran Manogaran ◽  
Varatharajan R ◽  
Priyan M. K.
Keyword(s):  
Healthcare System ◽  
Data Transfer ◽  
Fog Computing ◽  
Asynchronous Communication ◽  
Security And Privacy ◽  
Sensor Data ◽  
Cloud Environment ◽  
Authentication And Authorization ◽  
Remote Healthcare ◽  
Secure Authentication

This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients' medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.

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