When the Internet and other interconnected networks are used in a health system, it is referred to as “e-Health.” In this paper, we examined research studies from 2017–2020 to explore the utilization of intelligent techniques in health and its evolution over time, particularly the integration of Internet of Things (IoT) devices and cloud computing. E-Health is defined as “the ability to seek, find, understand and appraise health information derived from electronic sources and acquired knowledge to properly solve or treat health problems. As a repository for health information as well as e-Health analysis, the Internet has the potential to protect consumers from harm and empower them to participate fully in informed health-related decision-making. Most importantly, high levels of e-Health integration mitigate the risk of encountering unreliable information on the Internet. Various research perspectives related to security and privacy within IoT-cloud-based e-Health systems are examined, with an emphasis on the opportunities, benefits and challenges of the implementation such systems. The combination of IoT-based e-Health systems integrated with intelligent systems such as cloud computing that provide smart objectives and applications is a promising future trend.

Security and Privacy in IoT-Cloud-Based e-Health Systems—A Comprehensive Review

Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.

A Recent Survey on Internet of Things (IoT) Communication Protocols

Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach

This paper deals with measuring the Air Quality using MQ135 sensor along with Carbon Monoxide CO using MQ7 sensor. Measuring Air Quality is an important element for bringing awareness to take care of the future generations and for a healthier life. Based on this, Government of India has already taken certain measures to ban Single Stroke and Two Stroke Engine based motorcycles which are emitting high pollution. We are trying to implement a system using IoT platforms like Thingspeak or Cayenne in order to bring awareness to every individual about the harm we are doing to our environment. Already, New Delhi is remarked as the most pollution city in the world recording Air Quality above 300 PPM. We have used easiest platform like Thingspeak and set the dashboard to public such that everyone can come to know the Air Quality at the location where the system is installed. Machine Learning analysis brings us a lot of depth in understanding the information that we obtained from the data. Moreover, we are proviing a reducement of the cost of components versus the state of the art.

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IOT based Air Quality Monitoring System Using MQ135 and MQ7 with Machine Learning Analysis

In recent years, the mobile devices become popular for communication and running advanced real time applications such as face reorganization and online games. Although, mobile devices advanced for providing significant benefits for mobile users. But still, these devices suffers with limited recourses such as computation power, battery and storage space due to the portable size. However, The Cloud Technology overcome the limitations of mobile computing with better performance and recourses. The cloud technology provides enough computing recourses to run mobile applications as storage computing power on cloud platform. Therefore, the novel technology called mobile cloud computing (MCC) is introduced by integrating two technologies (Mobile Computing, Cloud Computing) in order to overcome the limitations(such as Battery life, Storage capacity, Processing capacity) of Mobile Devices by offloading application to recourse rich Remote server. This paper presents an overview of MCC, the advantages of MCC, the related concepts and the technology beyond various offloading frameworks, the architecture of the MCC, Cloudlet technology, security and privacy issues and limitations of mobile cloud computing. Finally, we conclude with feature research directions in MCC.

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A Survey on Mobile Cloud Computing: Mobile Computing + Cloud Computing (MCC = MC + CC)

Nowadays, the uses of mobile devices have been increasing in many aspects of our life such as playing games, sending documents, transactions, and marketing, business, and conference meetings. But this mobile device has limited the resources in terms of battery lifetime, storage, and processing capacity. The new technology known as mobile cloud computing can help users to increase utilization of mobile cloud resources. The limitation of mobile device can be overcome by MCC. The technique called as offloading can transfer the resource-intensive task to remote cloud for processing, and the result will come back to the mobile device. The mobile device connecting to remote cloud with the help of 3G (or) LTE networks causes latency-related issues bandwidth and cost. In order to overcome these problems, MCC has introduced new technology which can reduce latency problems by providing secured and efficient model based on cloudlet. In this paper, we mainly focus on healthcare applications which can be processed by new cloudlet model for reducing processing time as well as providing enough security to user’s data. Initially, the user connects to the available cloudlet; if the cloudlet is not providing required resources or services, the user will redirect to remote cloud. In our model, the cloudlet is used to analyze patient medical records.

Cloudlet Services for Healthcare Applications in Mobile Cloud Computing

Mobile devices have become essential in our daily lives but it has limited resources such as battery life, storage, and processing capacity. Offloading resource intensive task into the cloud is an efficient approach to improve battery utilization, storage capacity and processing capabilities. Efficiently computing using cloud resources to process offloaded task in order to improve response time and reduce both tasks waiting time and latency problems is one of the main goals in mobile cloud computing (MCC). In order to improve user satisfaction and performance of the mobile application, a cloudlet framework concept has been developed to reduce latency problems which improve response time. The cloudlet brings the cloud closer to the user to perform a computational task. This article proposes a new balancing model among cloudlets in mobile cloud computing environment to find the required resources and create an impact on performance. The efficient load balancing model makes mobile cloud computing more attractive and improves user satisfaction. This paper introduces a Round Robin with Load degree algorithm for public cloudlets in mobile cloud computing using a switch mechanism to choose different approaches for different situations. This algorithm uses game theory based load balancing approach to improve application response time in public mobile cloud environments.

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Round Robin with Load Degree: An Algorithm for Optimal Cloudlet Discovery in Mobile Cloud Computing

The mobile cloud computing has been introduced to address limitations of mobile devices. These limitations include battery lifetime, storage capacity, and processing power. User can offload resource intensive mobile application into cloud for processing can return the result to device. This increases transmission delay and power consumption. The new cloudlet concept has been proposed to address problems such as long delay and huge power consumption. But selection of cloudlet in mobile cloud computing is an important issue for offloading task into cloudlet data center. In this paper, we proposed honey bee inspired cloudlet selection for resource allocation for resource intensive applications from users. The proposed algorithm not only selects low loaded cloudlet but also perform load balancing among cloudlets in such way that the waiting time of user offloading task in queue will be reduced. The experimental result shows that performance of the proposed algorithm optimal compare with existing algorithm. The proposed algorithm represents significant improvement in reduction of user’s waiting time in queue.

A Honey Bee Inspired Cloudlet Selection for Resource Allocation

Twitter is among the most popular social networking Web sites today [1], with approximately 317 million monthly active users (Quarter 3 2016). Of these, 67 million users are from the USA. Twitter being a micro-blogging platform is widely used by people to express their opinions. Approximately, 500 million tweets are posted in a day, which is around 6000 tweets per second. Assuming, even one-tenth of these tweets reflect an emotion that results in a lot of people-generated data, which can prove to be a treasure trove of information if studied carefully. We intend to perform sentimental analysis on Twitter data of the US Presidential Election 2016 and then overlay our findings with respect to the two main candidates: Hillary Clinton and Donald Trump with the actual election result, to be able to categorically state whether Twitter can be used as a proper indication of any election.

Twitter Sentiment Analysis Based on US Presidential Election 2016

The mobile cloud computing (MCC) is an emerging technology, and its popularity is increasing drastically day by day. The mobile terminals’ (MTs) utilization can be improved by using remote cloud. In MCC technology, the MT energy consumption can be reduced, and this technology improves processing power, but still mobile terminals are suffering with dead zone issues. The previous works had not focused on this problem in mobile cloud platforms. In this paper, we address this problem by proposing energy-saving algorithm and also provide mathematical analysis. This proposed algorithm works based on user-provided resources called as properly offloading using user-provided recourses (POUPR). The mobile user shares processing power based on available devices in cluster when they are not close to MCC. The performance of the proposed algorithm POUPR is validated mathematically.

Ramasubbareddy Somula

Papers

Cloudlet Services for Healthcare Applications in Mobile Cloud Computing

Round Robin with Load Degree: An Algorithm for Optimal Cloudlet Discovery in Mobile Cloud Computing

A Honey Bee Inspired Cloudlet Selection for Resource Allocation

Twitter Sentiment Analysis Based on US Presidential Election 2016

POUPR: Properly Utilizing User-Provided Recourses for Energy Saving in Mobile Cloud Computing