scholarly journals IoT Cloud Computing Middleware for Crowd Monitoring and Evacuation

2021 ◽  
Vol 15 ◽  
pp. 1790-1802
Author(s):  
Alexandros Gazis ◽  
Eleftheria Katsiri
Keyword(s):  
Cloud Computing ◽  
Low Power ◽  
Programming Model ◽  
Fault Tolerant ◽  
Low Cost ◽  
Single Point ◽  
Network Connectivity ◽  
Large Data ◽  
Map Reduce ◽  
Raspberry Pi

Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the contrary, wireless sensor networks (WSNs) are distributed systems that scale and feature large numbers of small, computationally limited, low-power, unreliable nodes. In this article, we provide a top-down approach explaining the architecture, implementation and rationale of a distributed fault-tolerant IoT middleware. Specifically, this middleware consists of multiple mini-computing devices (Raspberry Pi) connected in a WSN which implement the Map-Reduce algorithm. First, we explain the tools used to develop this system. Second, we focus on the Map-Reduce algorithm implemented to overcome common network connectivity issues, as well as to enhance operation availability and reliability. Lastly, we provide benchmarks for our middleware as a crowd tracking application for a preserved building in Greece (i.e., M. Hatzidakis’ residence). The results of this study show that IoT middleware with low-power and low-cost components are viable solutions for medium-sized cloud computing distributed and parallel computing centres. Potential uses of this middleware apply for monitoring buildings and indoor structures, in addition to crowd tracking to prevent the spread of COVID-19.

scholarly journals Design and Implementation of Data-at-Rest Encryption for Hadoop

2018 ◽  
Vol 7 (2.15) ◽  
pp. 54 ◽  
Author(s):  
Siti Hanisah Kamaruzaman ◽  
Wan Nor Shuhadah Wan Nik ◽  
Mohamad Afendee Mohamed ◽  
Zarina Mohamad
Keyword(s):  
Cloud Computing ◽  
File System ◽  
Programming Model ◽  
Service Level Agreement ◽  
Large Data ◽  
Service Level ◽  
Encryption Algorithm ◽  
Map Reduce ◽  
Computing Environment ◽  
Aes Encryption Algorithm

The manuscript should contain an abstract. The security aspects in Cloud computing is paramount in order to ensure high quality of Service Level Agreement (SLA) to the cloud computing customers. This issue is more apparent when very large amount of data is involved in this emerging computing environment. Hadoop is an open source software framework that supports large data sets storage and processing in a distributed computing environment and well-known implementation of Map Reduce. Map Reduce is one common programming model to process and handle a large amount of data, specifically in big data analysis. Further, Hadoop Distributed File System (HDFS) is a distributed, scalable and portable file system that is written in java for Hadoop framework. However, the main problem is that the data at rest is not secure where intruders can steal or converts the data stored in this computing environment. Therefore, the AES encryption algorithm has been implemented in HDFS to ensure the security of data stored in HDFS. It is shown that the implementation of AES encryption algorithm is capable to secure data stored in HDFS to some extent.     

Handling Imbalance Data in Reduce task of MapReduce in Cloud Environment

2017 ◽  
Vol 7 (11) ◽  
pp. 168
Author(s):  
Chetana Tukkoji ◽  
Seetharam K
Keyword(s):  
Ad Hoc ◽  
Programming Model ◽  
Low Cost ◽  
Large Data ◽  
Map Reduce ◽  
Data Sets ◽  
Parallel Database ◽  
Data Skew ◽  
Intermediate Data ◽  
The People

There is a growing need for an ad-hoc analysis of extremely large data sets, especially at web based companies where innovation critically depends on being able to analyze terabytes of data collected every day. Parallel database products, over a solution, but are usually prohibitively ex-pensive at this scale. But, most of the people who analyze data are called procedural programmers. The success of the more procedural map-reduce programming model and its associated scalable implementations on commodity hardware (low cost), is evidence of the above. However, the map-reduce paradigm is too low-level and rigid, and leads to a great deal of custom user code that is hard to maintain, and reuse. The map reduce is an effective tool for parallel data processing. One significant issue in practical map reduce application is the data skew. The imbalance of the amount of the data assigned to each tasks to take much longer to finish than the others. Now we need to propose a framework, to solve the data skew problem to reduce side application in the map reduce. It usage a innovative sampling of the data input accurate approximation to the distribution of the intermediate data by sampling only small fraction of the intermediate data. It does not contain the any type of the data to prevent the overlap between the maps and reduce stages.

scholarly journals An Internet of Things (IoT) Application on Volcano Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4651 ◽  
Author(s):  
Shadia Awadallah ◽  
David Moure ◽  
Pedro Torres-González
Keyword(s):  
Internet Of Things ◽  
Low Power ◽  
Low Cost ◽  
Thermal Anomaly ◽  
Raspberry Pi ◽  
Area Network ◽  
Wide Area Network ◽  
Volcanic Surveillance ◽  
First Results ◽  
Network Operation

In the last few years, there has been a huge interest in the Internet of Things (hereinafter IoT) field. Among the large number of IoT technologies, the low-power wide-area network (hereinafter LPWAN) has emerged providing low power, low data-rate communication over long distances, enabling battery-operated devices to operate for long time periods. This paper introduces an application of long-range (hereinafter LoRa) technology, one of the most popular LPWANs, to volcanic surveillance. The first low-power and low-cost wireless network based on LoRa to monitor the soil temperature in thermal anomaly zones in volcanic areas has been developed. A total of eight thermometers (end devices) have been deployed on a Teide volcano in Tenerife (Canary Islands). In addition, a repeater device was developed to extend the network range when the gateway did not have a line of sight connection with the thermometers. Combining LoRa communication capabilities with microchip microcontrollers (end devices and repeater) and a Raspberry Pi board (gateway), three main milestones have been achieved: (i) extreme low-power consumption, (ii) real-time and proper temperature acquisition, and (iii) a reliable network operation. The first results are shown. These results provide enough quality for a proper volcanic surveillance.

Performance Analysis of Mail Clients on Low Cost Computer With ELGamal and RSA Using SNORT

2018 ◽  
pp. 332-353
Author(s):  
Sreerama Murthy Kattamuri ◽  
Vijayalakshmi Kakulapati ◽  
Pallam Setty S.
Keyword(s):  
Intrusion Detection ◽  
Low Power ◽  
Intrusion Detection System ◽  
Detection System ◽  
Low Cost ◽  
Raspberry Pi ◽  
Ip Address ◽  
Intrusion Prevention ◽  
Plain Text ◽  
Intrusion Prevention System

An intrusion detection system (IDS) focuses on determining malicious tasks by verifying network traffic and informing the network administrator for restricting the user or source or source IP address from accessing the network. SNORT is an open source intrusion detection system (IDS) and SNORT also acts as an intrusion prevention system (IPS) for monitoring and prevention of security attacks on networks. The authors applied encryption for text files by using cryptographic algorithms like Elgamal and RSA. This chapter tested the performance of mail clients in low cost, low power computer Raspberry Pi, and verified that SNORT is efficient for both algorithms. Within low cost, low power computer, they observed that as the size of the file increases, the run time is constant for compressed data; whereas in plain text, it changed significantly.

scholarly journals Low-Cost Conversion of Single-Zone HVAC Systems to Multi-Zone Control Systems Using Low-Power Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3611
Author(s):  
Julio Antonio Jornet-Monteverde ◽  
Juan José Galiana-Merino
Keyword(s):  
Low Power ◽  
Energy Savings ◽  
Low Cost ◽  
Transport Layer ◽  
Raspberry Pi ◽  
Air Conditioner ◽  
Efficient System ◽  
Ultra Low Power ◽  
Zone Control ◽  
Novel Approach

This paper presents a novel approach to convert a conventional house air conditioning installation into a more efficient system that individually controls the temperature of each zone of the house through Wi-Fi technology. Each zone regulates the air flow depending on the detected temperature, providing energy savings and increasing the machine performance. Therefore, the first step was to examine the communication bus of the air conditioner and obtain the different signal codes. Thus, an alternative Controller module has been designed and developed to control and manage the requests on the communication bus (Bus–Wi-Fi gateway). A specific circuit has been designed to adapt the signal of the serial port of the Controller with the communication bus. For the acquisition of the temperature and humidity data in each zone, a Node module has been developed, which communicates with the Controller through the Wi-Fi interface using the Message Queuing Telemetry Transport (MQTT) protocol with Secure Sockets Layer / Transport Layer Security (SSL/TLS) certificates. It has been equipped with an LCD touch screen as a human-machine interface. The Controller and the Node modules have been developed with the ultra-low power consumption CC3200 microController of Texas Instruments and the code has been implemented under the TI-RTOS real-time operating system. An additional module based on the Raspberry Pi computer has been designed to create the Wi-Fi network and implement the required network functionalities. The developed system not only ensures that the temperature in each zone is the desired one, but also controls the fan velocity of the indoor unit and the opening area of the vent registers, which considerably improves the efficiency of the system. Compared with the single-zone system, the experiments carried out show energy savings between 75% and 94% when only one of the zones is selected, and 44% when the whole house is air-conditioned, in addition to considerably improving user comfort.

scholarly journals Cloud Computing and Its Application in Big Data Processing of Distance Higher Education

2015 ◽  
Vol 10 (8) ◽  
pp. 55 ◽  
Author(s):  
Guolei Zhang ◽  
Jia Li ◽  
Li Hao
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Distance Education ◽  
Big Data ◽  
Data Processing ◽  
Clustering Algorithm ◽  
Evaluation Method ◽  
Low Cost ◽  
Science And Technology ◽  
Large Data

In the development of information technology the development of scientific theory has brought the progress of science and technology. The progress of science and technology has an impact on the educational field, which changes the way of education. The arrival of the era of big data for the promotion and dissemination of educational resources has played an important role, it makes more and more people benefit. Modern distance education relies on the background of big data and cloud computing, which is composed of a series of tools to support a variety of teaching mode. Clustering algorithm can provide an effective evaluation method for students' personality characteristics and learning status in distance education. However, the traditional K-means clustering algorithm has the characteristics of randomness, uncertainty, high time complexity, and it does not meet the requirements of large data processing. In this paper, we study the parallel K-means clustering algorithm based on cloud computing platform Hadoop, and give the design and strategy of the algorithm. Then, we carry out experiments on several different sizes of data sets, and compare the performance of the proposed method with the general clustering method. Experimental results show that the proposed algorithm which is accelerated has good speed up and low cost. It is suitable for the analysis and mining of large data in the distance higher education.

scholarly journals A Comprehensive Overview of Privacy and Data Security for Cloud Storage

2021 ◽  
pp. 113-152
Author(s):  
Dr. Nikhat Akhtar ◽  
Dr. Bedine Kerim ◽  
Dr. Yusuf Perwej ◽  
Dr. Anurag Tiwari ◽  
Dr. Sheeba Praveen
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Cloud Storage ◽  
Low Cost ◽  
Network Connectivity ◽  
Data Encryption ◽  
Security And Privacy ◽  
Comprehensive Overview ◽  
Resource Pooling

People used to carry their documents about on CDs only a few years ago. Many people have recently turned to memory sticks. Cloud computing, in this case, refers to the capacity to access and edit data stored on remote servers from any Internet-connected platform. Cloud computing is a self-service Internet infrastructure that allows people to access computing resources at any location worldwide. The world has altered as a result of cloud computing. Cloud computing can be thought of as a new computing typology that can provide on-demand services at a low cost. By increasing the capacity and flexibility of data storage and providing scalable compute and processing power that fits the dynamic data requirements, cloud computing has aided the advancement of IT to higher heights. In the field of information technology, privacy and data security have long been a serious concern. It becomes more severe in the cloud computing environment because data is stored in multiple locations, often across the globe. Users' primary challenges regarding the cloud technology revolve around data security and privacy. We conduct a thorough assessment of the literature on data security and privacy issues, data encryption technologies, and related countermeasures in cloud storage systems in this study. Ubiquitous network connectivity, location-independent resource pooling, quick resource flexibility, usage-based pricing, and risk transference are all features of cloud computing.

scholarly journals Personalized Overseas Chinese Education Model Based on Map-Reduce Model of Cloud Computing

2016 ◽  
Vol 11 (04) ◽  
pp. 16 ◽  
Author(s):  
Zhehuang Huang ◽  
Jianxin Huang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Scale ◽  
Programming Model ◽  
Map Reduce ◽  
Data Sets ◽  
Overseas Chinese ◽  
Chinese Education ◽  
Model Based ◽  
Education Model

The rapid updates of the resources and media in the big data age provide new opportunities for oversea Chinese education. It is an urgent task to effectively use the big data to boost the development of oversea Chinese education. However, very few studies are conducted in this area. Map-Reduce is a programming model of cloud computing used for the parallel computing of the large-scale data sets and this model enables programmers to run their own programs in the distributed system. In this paper we proposed a personalized overseas Chinese education model based on Map-Reduce mechanism , which can analyze the behavioral habits and personal preferences of users from a large pool of Chinese educational resources. In this way, the customer needs can be accurately grasped and their favorite resources are recommended from huge amounts of resources. The proposed model has a good application prospect for overseas Chinese education .

Study of Map-Reduce over Hadoop Based Cloud Computing Environment

2014 ◽  
Vol 509 ◽  
pp. 175-181
Author(s):  
Wu Min Pan ◽  
Li Bai Ha
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Large Scale ◽  
Programming Model ◽  
Sql Server ◽  
Map Reduce ◽  
Data Set ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Scale Data

Popularity for the term Cloud-Computing has been increasing in recent years. In addition to the SQL technique, Map-Reduce, a programming model that realizes implementing large-scale data processing, has been a hot topic that is widely discussed through many studies. Many real-world tasks such as data processing for search engines can be parallel-implemented through a simple interface with two functions called Map and Reduce. We focus on comparing the performance of the Hadoop implementation of Map-Reduce with SQL Server through simulations. Hadoop can complete the same query faster than SQL Server. On the other hand, some concerned factors are also tested to see whether they would affect the performance for Hadoop or not. In fact more machines included for data processing can make Hadoop achieve a better performance, especially for a large-scale data set.

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