Edge Computing and IoT Based Research for Building Safe Smart Cities Resistant to Disasters

2017 ◽  
Author(s):  
Teruo Higashino ◽  
Hirozumi Yamaguchi ◽  
Akihito Hiromori ◽  
Akira Uchiyama ◽  
Keiichi Yasumoto
Keyword(s):  
Smart Cities ◽  
Edge Computing

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

Solving Critical Events through Mobile Edge Computing: An Approach for Smart Cities

2016 ◽  
Author(s):  
Marco Sapienza ◽  
Ermanno Guardo ◽  
Marco Cavallo ◽  
Giuseppe La Torre ◽  
Guerrino Leombruno ◽  
...  
Keyword(s):  
Smart Cities ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Critical Events

scholarly journals Public safety in smart cities under the edge computing concept

2021 ◽  
Author(s):  
Evangelos Maltezos ◽  
Lazaros Karagiannidis ◽  
Aris Dadoukis ◽  
Konstantinos Petousakis ◽  
Fay Misichroni ◽  
...  
Keyword(s):  
Public Safety ◽  
Smart Cities ◽  
Edge Computing

Cloud/edge computing for smart cities

2021 ◽  
pp. 153-169
Author(s):  
J. Nulyn Punitha Markavathi ◽  
D. Kesavaraja
Keyword(s):  
Smart Cities ◽  
Edge Computing

scholarly journals Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

2018 ◽  
Vol 8 (11) ◽  
pp. 2220 ◽  
Author(s):  
Hind Bangui ◽  
Said Rakrak ◽  
Said Raghay ◽  
Barbora Buhnova
Keyword(s):  
Selection Process ◽  
Smart Cities ◽  
Cloud Model ◽  
Research Question ◽  
Edge Computing ◽  
Cloud Services ◽  
Fuzzy Similarity ◽  
Customer Services ◽  
Topsis Technique ◽  
Selection Of

Smart cities aim at integrating various IoT (Internet of Things) technologies by providing many opportunities for the development, governance, and management of user services. One of the ways to support this idea is to use cloud and edge computing techniques to reduce costs, manage resource consumption, enhance performance, and connect the IoT devices more effectively. However, the selection of services remains a significant research question since there are currently different strategies towards cloud computing, including services for central remote computing (traditional cloud model) as well as distributed local computing (edge computing). In this paper, we offer an integrated view of these two directions and the selection among the edge technologies based on MCDA (Multiple Criteria Decision Analysis) algorithms. To this end, we propose a foglet as a middleware that aims at achieving satisfactory levels of customer services by using fuzzy similarity and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to facilitate the rating and selection of services in the fog-to-cloud environment. Then, we describe the selection process with a numerical example, and conclude our work with an outline of future perspectives.

scholarly journals An Edge Computing Based Smart Healthcare Framework for Resource Management

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4307 ◽  
Author(s):  
Soraia Oueida ◽  
Yehia Kotb ◽  
Moayad Aloqaily ◽  
Yaser Jararweh ◽  
Thar Baker
Keyword(s):  
Resource Utilization ◽  
Waiting Time ◽  
Smart Cities ◽  
Real Life ◽  
Edge Computing ◽  
Utilization Rate ◽  
Resource Assignment ◽  
Smart Healthcare ◽  
Patient Waiting Time ◽  
Patient Waiting

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.

scholarly journals Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6309
Author(s):  
Mohammad Peyman ◽  
Pedro J. Copado ◽  
Rafael D. Tordecilla ◽  
Leandro do C. Martins ◽  
Fatos Xhafa ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Edge Computing ◽  
Analytical Models ◽  
Current Status ◽  
Ride Sharing ◽  
The City

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

scholarly journals SAP: An IoT Application Module Placement Strategy Based on Simulated Annealing Algorithm in Edge-Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Juan Fang ◽  
Kai Li ◽  
Juntao Hu ◽  
Xiaobin Xu ◽  
Ziyi Teng ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Energy Consumption ◽  
Simulated Annealing Algorithm ◽  
Smart Cities ◽  
Structural Characteristics ◽  
Edge Computing ◽  
The Internet ◽  
Sensors And Actuators ◽  
Application Module

The Internet of Things (IoT) is rapidly growing and provides the foundation for the development of smart cities, smart home, and health care. With more and more devices connecting to the Internet, huge amounts of data are produced, creating a great challenge for data processing. Traditional cloud computing has the problems of long delays. Edge computing is an extension of cloud computing, processing data at the edge of the network can reduce the long processing delay of cloud computing. Due to the limited computing resources of edge servers, resource management of edge servers has become a critical research problem. However, the structural characteristics of the subtask chain between each pair of sensors and actuators are not considered to address the task scheduling problem in most existing research. To reduce processing latency and energy consumption of the edge-cloud system, we propose a multilayer edge computing system. The application deployed in the system is based on directed digraph. To fully use the edge servers, we proposed an application module placement strategy using Simulated Annealing module Placement (SAP) algorithm. The modules in an application are bounded to each sensor. The SAP algorithm is designed to find a module placement scheme for each sensor and to generate a module chain including the mapping of the module and servers for each sensor. Thus, the edge servers can transmit the tuples in the network with the module chain. To evaluate the efficacy of our algorithm, we simulate the strategy in iFogSim. Results show the scheme is able to achieve significant reductions in latency and energy consumption.

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