Enabling Smarter Societies through Mobile Big Data Fogs and Clouds

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge

Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

An Analytical Survey of WSNs Integration with Cloud and Fog Computing

Wireless sensor networks (WSNs) are spatially scattered networks equipped with an extensive number of nodes to check and record different ecological states such as humidity, temperature, pressure, and lightning states. WSN network provides different services to a client such as monitoring, detection, and runtime decision-making against events occurrence. However, the WSN network still has some limitations in computing power, storage resources, and battery life, which make the network is restricted for data transformation. It is due to less supportive battery power, and limited memory of nodes. The integration of WSN and cloud offers an open, adaptable, and more reconfigurable stage for different security checks and regulating requirements. In this paper, we discovered how WSN and cloud computing (CC) are integrated and help to accomplish different goals. Additionally, a comprehensive study about procedures and issues for an effective combination of WSN-CC is presented. This work also presents the work proposed by the research community for WSN-CC. Besides, we explored the integration of WSN/IoT with Fog computing (FC). Based on investigations, WSN integration with Fog computing (FC) has many benefits with respect to latency, energy consumption, data processing, and real-time data streaming. FC is not a substitute for distributed computing, so far it is utilized to improve the productivity of the sensor.

UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

5G networks and Internet of Things (IoT) offer a powerful platform for ubiquitous environments with their ubiquitous sensing, high speeds and other benefits. The data, analytics, and other computations need to be optimally moved and placed in these environments, dynamically, such that energy-efficiency and QoS demands are best satisfied. A particular challenge in this context is to preserve privacy and security while delivering quality of service (QoS) and energy-efficiency. Many works have tried to address these challenges but without a focus on optimizing all of them and assuming fixed models of environments and security threats. This paper proposes the UbiPriSEQ framework that uses Deep Reinforcement Learning (DRL) to adaptively, dynamically, and holistically optimize QoS, energy-efficiency, security, and privacy. UbiPriSEQ is built on a three-layered model and comprises two modules. UbiPriSEQ devises policies and makes decisions related to important parameters including local processing and offloading rates for data and computations, radio channel states, transmit power, task priority, and selection of fog nodes for offloading, data migration, and so forth. UbiPriSEQ is implemented in Python over the TensorFlow platform and is evaluated using a real-life application in terms of SINR, privacy metric, latency, and utility function, manifesting great promise.

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics.

Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems.

Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs

Road transportation is the backbone of modern economies, albeit it annually costs 1.25 million deaths and trillions of dollars to the global economy, and damages public health and the environment. Deep learning is among the leading-edge methods used for transportation-related predictions, however, the existing works are in their infancy, and fall short in multiple respects, including the use of datasets with limited sizes and scopes, and insufficient depth of the deep learning studies. This paper provides a novel and comprehensive approach toward large-scale, faster, and real-time traffic prediction by bringing four complementary cutting-edge technologies together: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). We trained deep networks using over 11 years of data provided by the California Department of Transportation (Caltrans), the largest dataset that has been used in deep learning studies. Several combinations of the input attributes of the data along with various network configurations of the deep learning models were investigated for training and prediction purposes. The use of the pre-trained model for real-time prediction was explored. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for smart cities, big data, high performance computing, and their convergence.