Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Big Data Redux: New Issues and Challenges Moving Forward

As of the time of this writing, our HICSS-46 proceedings article has enjoyed over 520 Google Scholar citations. We have published several HICSS proceedings, articles and a book on this subject, but none of them have generated this level of interest. In an effort to update our findings six years later, and to understand what is driving this interest, we have downloaded the first 500 citations to our article and the corresponding citing article, when available. We conducted an in-depth literature review of the articles published in top journals and leading conference proceedings, along with articles with a high volume of citations. This paper provides a brief summary of the key concepts in our original paper and reports on the key aspects of interest we found in our review, and also updates our original paper with new directions for future practice and research in big data and analytics

Data Systems Fault Coping for Real-time Big Data Analytics Required Architectural Crucibles

This paper analyzes the properties and characteristics of unknown and unexpected faults introduced into information systems while processing Big Data in real-time. The authors hypothesize that there are new faults, and requirements for fault handling and propose an analytic model and architectural framework to assess and manage the faults and mitigate the risks of correlating or integrating otherwise uncorrelated Big Data, and to ensure the source pedigree, quality, set integrity, freshness, and validity of data being consumed. We argue that new architectures, methods, and tools for handling and analyzing Big Data systems functioning in real-time must design systems that address and mitigate concerns for faults resulting from real-time streaming processes while ensuring that variables such as synchronization, redundancy, and latency are addressed. This paper concludes that with improved designs, real-time Big Data systems may continuously deliver the value and benefits of streaming Big Data

A Methodology for Internet of Things Business Modeling and Analysis using Agent-Based Simulation

Internet of Things (IoT) is a new vision of an integrated network covering physical objects that are able to collect and exchange data. It enables previously unconnected devices and objects to become connected using equipping devices with communication technology such as sensors and radio frequency identification tags (RFID). As technology progresses towards new paradigm such as IoT, there is a need for an approach to identify the significance of these projects. Conventional simulation modeling and data analysis approaches are not able to capture the system complexity or suffer from a lack of data needed that can help to build a prediction. Agent-based Simulation (ABM) proposes an efficient simulation scheme to capture the structure of this dimension and offer a potential solution. Two case studies were proposed in this research. The first one introduces a conceptual case study addressing the use of agent-based simulations to verify the effectiveness of the business model of IoT. The objective of the study is to assess the feasibility of such application, of the market in the city of Orlando (Florida, United States). The second case study seeks to use ABM to simulate the operational behavior of refrigeration units (7,420) in one of largest retail organizations in Saudi Arabia and assess the economic feasibility of IoT implementation by estimating the return on investment (ROI)

Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Maritime data integration and analysis: Recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems

Security and blockchain convergence with internet of multimedia things : current trends, research challenges and future directions

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications. © 2020 Elsevier Lt

An architecture and protocol for smart continuous eHealth monitoring using 5G

[EN] Continuous monitoring of chronic patients improves their quality of life and reduces the economic costs of the sanitary system. However, in order to ensure a good monitoring, high bandwidth and low delay are needed. The 5G technology offers higher bandwidth, lower delays and packets loss than previous technologies. This paper presents an architecture for smart eHealth monitoring of chronic patients. The architecture elements include wearable devices, to collect measures from the body, and a smartphone at the patient side in order to process the data received from the wearable devices. We also need a DataBase with an intelligent system able to send an alarm when it detects that it is happening something anomalous. The intelligent system uses machine learning in BigData taken from different hospitals and the data received from the patient to diagnose and generate alarms. Experiment tests have been done to simulate the traffic from many users to the DataBase in order to evaluate the suitability of 5G in our architecture. When there are few users (less than 200 users), we do not find big differences of round trip time between 4G and 5G, but when there are more users, like 1000 users, it increases considerably reaching 4 times more in 4G The Packet Loss is almost null in 4G until 300 users, while in 5G it is possible to keep it null until 700 users. Our results point out that in order to have high number of patients continuously monitored, it is necessary to use the 5G network because it offers low delays and guarantees the availability of bandwidth for all users.This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)". Grant number FPU14/02953.Lloret, J.; Parra-Boronat, L.; Abdullah, MTA.; Tomás Gironés, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks. 129(2):340-351. https://doi.org/10.1016/j.comnet.2017.05.018S340351129