The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT, hence, is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments, such as smart cities and societies. A timely fusion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable, and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications. The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments). The opportunities and challenges for each of the mathematical methods and environments are given. Future developments, including emerging areas that would intrinsically benefit from data fusion and IoT, autonomous vehicles, deep learning for data fusion, and smart cities, are discussed.

Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

Mobile devices are increasingly becoming an indispensable part of people’s daily life, facilitating to perform a variety of useful tasks. Mobile cloud computing integrates mobile and cloud computing to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of mobile cloud computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare. A cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and mobile cloud-computing technologies. An outlook on networked healthcare is given.

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Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications

Smart city advancements are driving massive transformations of healthcare, the largest global industry. The drivers include increasing demands for ubiquitous, preventive, and personalized healthcare, to be provided to the public at reduced risks and costs. Mobile cloud computing could potentially meet the future healthcare demands by enabling anytime, anywhere capture and analyses of patients’ data. However, network latency, bandwidth, and reliability are among the many challenges hindering the realization of next-generation healthcare. This paper proposes a ubiquitous healthcare framework, UbeHealth, that leverages edge computing, deep learning, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges. The framework enables an enhanced network quality of service using its three main components and four layers. Deep learning, big data, and HPC are used to predict network traffic, which in turn are used by the Cloudlet and network layers to optimize data rates, data caching, and routing decisions. Application protocols of the traffic flows are classified, enabling the network layer to meet applications’ communication requirements better and to detect malicious traffic and anomalous data. Clustering is used to identify the different kinds of data originating from the same application protocols. A proof of concept UbeHealth system has been developed based on the framework. A detailed literature review is used to capture the design requirements for the proposed system. The system is described in detail including the algorithmic implementation of the three components and four layers. Three widely used data sets are used to evaluate the UbeHealth system.

UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities

With the design of data communications in mind, 3GPP LTE-advanced (LTE-A) is probably the most promising technology for Internet of Things (IoT). For IoT applications, continuous low-rate streaming data may be reported from devices over a long period of time, imposing stringent requirements on power saving. To manage power consumption, 3GPP LTE-A has defined the discontinuous reception/transmission (DRX/DTX) mechanism to allow devices to turn off their radio interfaces and go to sleep in various patterns. Existing literature has paid much attention to evaluate the performance of DRX/DTX; however, how to tune DRX/DTX parameters to optimize energy cost is still left open. This paper addresses the DRX/DTX optimization, by asking how to maximize the sleep periods of devices while guarantee their quality-of-service (QoS), especially on the aspects of traffic bit-rate, packet delay, and packet loss rate in IoT applications. Efficient schemes to optimize DRX/DTX parameters and schedule devices' packets with the base station are proposed. The key idea of our schemes is to balance the impacts between QoS parameters and DRX/DTX configurations. Simulation results show that our schemes can guarantee traffic bit-rate, packet delay, and packet loss rate while save energy of user equipments .

An Energy-Efficient Sleep Scheduling With QoS Consideration in 3GPP LTE-Advanced Networks for Internet of Things

The education industry around the globe is undergoing major transformations. Organizations, such as Coursera are advancing new business models for education. A number of major industries have dropped degrees from the job requirements. While the economics of higher education institutions are under threat in a continuing gloomy global economy, digital and lifelong learners are increasingly demanding new teaching and learning paradigms from educational institutions. There is an urgent need to transform teaching and learning landscape in order to drive global economic growth. The use of distance eTeaching and eLearning (DTL) is on the rise among digital natives alongside our evolution toward smart societies. However, the DTL systems today lack the necessary sophistication due to several challenges including data analysis and management, learner-system interactivity, system cognition, resource planning, agility, and scalability. This paper proposes a personalised Ubiquitous eTeaching & eLearning (UTiLearn) framework that leverages Internet of Things, big data, supercomputing, and deep learning to provide enhanced development, management, and delivery of teaching and learning in smart society settings. A proof of concept UTiLearn system has been developed based on the framework. A detailed design, implementation, and evaluation of the UTiLearn system, including its five components, are provided using 11 widely used datasets.

UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies

Multimedia applications over metropolitan area networks (MANs)

Communication demands have grown from separate data and voice to integrated multimedia, paving the way to converging fixed, mobile and IP networks. Supporting Multimedia is a challenging task for wireless ad hoc network designers. Multimedia forms high data rate traffic with stringent Quality of Service (QoS) requirements. Wireless ad hoc networks are characterized by frequent topology changes, unreliable wireless channel, network congestion and resource contention. Providing scalable QoS is the most important challenge for multimedia delivery over ad hoc networks. We introduce here a provisioning and routing architecture for ad hoc networks which scales well while provisioning QoS. The proposed architecture is analysed using a mix of HTTP, voice and video streaming applications over 54 Mbps 802.11 g-based ad hoc networks. The architecture is simulated and compared to well-known routing protocols using the OPNET Modeller. The results show that our architecture scales well with increase in the network size, and outperforms well-known routing protocols.

A scalable multimedia QoS architecture for ad hoc networks

Multimedia applications have been the key driving force in converging fixed, mobile and IP networks. A major hurdle in the realisation of this convergence is obtaining Quality of Service from a heterogeneous, best-effort service network. Interactive voice requires strict bounds on delay, jitter and packet losses, video adds significant bandwidth requirements to the network, while internet only makes its best effort to deliver a packet. Hence, the end-to-end QoS management of heterogeneous networks supporting multimedia services is of paramount importance. This paper presents a performance study of multimedia applications over 802.11 networks within metropolitan area networking environment. Specifically, we study QoS performance of VoIP applications over 802.11, while sharing the network resources with HTTP and video applications. Using the OPNET simulator, we simulate a number of realistic application traffic scenarios and study the network performance by analysing QoS parameters including delay, jitter, MOS, and packet loss ratio.

End to End Wireless Multimedia Service Modelling over a Metropolitan Area Network

Multimedia applications have been the key driving force in converging fixed, mobile and IP networks. Supporting Multimedia is a challenging task for wireless ad hoc network designers. Multimedia forms high data rate traffic with stringent QoS requirements. Wireless ad hoc networks are characterized by frequent topology changes, unreliable wireless channel, network congestion and resource contention. Providing scalable QoS is believed to be the most important challenge for multimedia delivery over ad hoc networks. In this paper, we introduce a provisioning and routing scheme for ad hoc networks which scales well while provisioning QoS. The proposed scheme is analysed using a mix of HTTP, voice and video streaming applications over 54Mbps 802.11g-based ad hoc networks. The scheme is simulated and compared to well-known routing protocols using the OPNET Modeller. The results show that our scheme scales well with increase in the network size, and outperforms well-known routing protocols.

A Scalable Provisioning and Routing Scheme for Multimedia QoS over Ad Hoc Networks

The HCPR scheme is implemented as an extension to the OPNET simulation software and is analysed in detail for its QoS performance to deliver multimedia applications over ad hoc networks. It is compared with three well-known and widely used routing protocols: Ad Hoc On Demand Distance Vector (AODV), Optimised Link State Routing (OLSR), and Geographic Routing Protocol (GRP). Several networking scenarios have been carefully configured with variations in networks sizes, applications, codecs, and routing protocols to extensively analyse the proposed scheme. The HCPR enabled ad hoc network outperforms the well-known routing schemes, in particular for relatively large networks and high QoS network loads. These results are promising because many QoS schemes do work for small networks and low network loads but are unable to sustain performance for large networks and high QoS loads. Several directions to extend this research for future work are given.

Raad Alturki

Papers

Multimedia applications over metropolitan area networks (MANs)

A scalable multimedia QoS architecture for ad hoc networks

End to End Wireless Multimedia Service Modelling over a Metropolitan Area Network

A Scalable Provisioning and Routing Scheme for Multimedia QoS over Ad Hoc Networks

Cross-Layer Multimedia QoS Provisioning over Ad Hoc Networks