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With the increased population in urban areas, it has become a challenge to a service provider to manage big data coming from various users and devices. One of the fastest growing devices used by the citizens of urban areas is the smartphone. Today we can find almost nobody without at least one smartphone in their possession. While talking through a smartphone, surrounding environmental noises are added to the speech of the talker. Sometimes these noises are very annoying to the listeners. In this article, we propose a communication framework for urban mobile big data integrating an urban environment classification system. The proposed system utilizes a deep learning approach to classify environmental noises so that an appropriate noise cancellation algorithm can be used to reduce the effect of noise while having a conversation through smartphones. The proposed framework uses mobile edge computing technology to provide low-latency and efficient transmission. Experimental results demonstrate that the proposed system is very efficient in classifying environmental noises. [ABSTRACT FROM AUTHOR]

The advancement of next-generation network technologies provides a huge improvement in healthcare facilities. Technologies such as 5G, edge computing, cloud computing, and the Internet of Things realize smart healthcare that a client can have anytime, anywhere, and in real time. Edge computing offers useful computing resources at the edge of the network to maintain low-latency and real-time computing. In this article, we propose a smart healthcare framework using edge computing. In the framework, we develop a voice disorder assessment and treatment system using a deep learning approach. A client provides his or her voice sample captured by smart sensors, and the sample goes to the edge computing for initial processing. Then the edge computing sends data to a core cloud for further processing. The assessment and management are controlled by a service provider through a cloud manager. Once the automatic assessment is done, the decision is sent to specialists, who prescribe appropriate treatment to the clients. The proposed system achieves 98.5 percent accuracy and 99.3 percent sensitivity using the Saarbrucken Voice Disorder database. [ABSTRACT FROM AUTHOR]

With the invention of new communication technologies, new features and facilities are provided in a smart healthcare framework. The features and facilities aim to provide a seamless, easy-to-use, accurate, and real-time healthcare service to clients. As health is a sensitive issue, it should be taken care of with utmost security and caution. This article proposes a new medical image forgery detection system for the healthcare framework to verify that images related to healthcare are not changed or altered. The system works on a noise map of an image, applies a multi-resolution regression filter on the noise map, and feeds the output to support-vector-machine-based and extreme-learning-based classifiers. The noise map is created in an edge computing resource, while the filtering and classification are done in a core cloud computing resource. In this way, the system works seamlessly and in real time. The bandwidth requirement of the proposed system is also reasonable. [ABSTRACT FROM AUTHOR]

The integration of the IoT and cloud technology is very important to have a better solution for an uninterrupted, secured, seamless, and ubiquitous framework. The complementary nature of the IoT and the could in terms of storage, processing, accessibility, security, service sharing, and components makes the convergence suitable for many applications. The advancement of mobile technologies adds a degree of flexibility to this solution. The health industry is one of the venues that can benefit from IoT?Cloud technology, because of the scarcity of specialized doctors and the physical movement restrictions of patients, among other factors. In this article, as a case study, we discuss the feasibility of and propose a solution for voice pathology monitoring of people using IoT?cloud. More specifically, a voice pathology detection system is proposed inside the monitoring framework using a local binary pattern on a Mel-spectrum representation of the voice signal, and an extreme learning machine classifier to detect the pathology. The proposed monitoring framework can achieve high accuracy of detection, and it is easy to use. [ABSTRACT FROM PUBLISHER]

The ever accelerating process of globalization enables more than half the population to live in cities. Thus, the air quality in cities exerts critical influence on the health status of more and more urban residents. In this article, based on urban air quality data collected through meteorological sites, mobile crowdsourcing, and IoT sensing, along with users' body signals, we propose an urban healthcare big data system named UH-BigDataSys. In this article, we first introduce a method of integrating multi-source air quality data for the data preparation of artificial-intelligence- based smart urban services. Then a testbed of UH-BigDataSys is set up with the deployment of air-quality-aware healthcare applications. Finally, we provide health guidance for urban residents in aspects of respiratory diseases, outdoor travel, sleep quality, and so on. The ultimate goal of UH-BigDataSys is for urban residents to lead healthier lives. [ABSTRACT FROM AUTHOR]

We propose a fog-cloud hybrid architecture that can support a massive ad hoc crowd composed of a massive social network and distributed IoT nodes around a smart city environment. The fog computing framework is introduced to support energy efficiency by incorporating IoT nodes that act as an interface to the ad hoc crowd, is aware of user contexts within its vicinity, can do real-time processing of user requests, supports a constrained amount of data offloading, and pass the geo-tagged multimedia data available from the subset of the ad hoc crowd to the big data repository in the cloud. The framework leverages a sustainable crowdsourcing incentive model for both the ad hoc crowd and the IoT infrastructure provider. The proposed sustainable framework can potentially support context-aware smart city services such as finding a lost person within the crowd, showing green and health risk prone zones, semantic and location-aware notifications of events of interest, semantic IoT-based routing, and dealing with emergency situations. We present a communication architecture between mobile users and fog nodes, and between fog nodes and the cloud, a sustainability and energy efficiency model, and massive geo-tagged, multimedia big data architecture. [ABSTRACT FROM PUBLISHER]