Delay Analysis of Network Architectures for Machine-to-Machine Communications in LTE System

Machine-to-machine communications has emerged to provide autonomic communications for a wide variety of intelligentservices and applications. Among different communication technologies available for connecting machines, cellular-basedsystems have gained more attention as backhaul networks due to ubiquitous coverage and mobility support. The diverse ranges of service requirements as well as machine constraints require adopting different network architectures. This paper reviews three M2M network architectures to integrate machines into the LTE system and analyzes their associated communication delays. It also presents how the appropriate networks can be selected for some machine-to-machine applications, fulfilling their latency constraints.Peer reviewe

Research challenges in 5G networks: a HetNets perspective

This paper highlights use cases, emerging machine type communication (MTC) technologies, ongoing research activities, and existing research challenges in 5G networks. 5G networks are faced with the following challenges: (i) handling large amounts of data, (ii) coping with different types of data traffic, i.e., human-type, machine-type, and combined-type (iii) connecting billions of machines, and (iv) severe resource limitations of devices. The ubiquitous nature of cellular networks make them the preferred choice for access networks, but a lack of communication resources is a problem. To address the resource scarcity issue, different wireless access networks may combine to form a heterogeneous network (HetNet) and hence become a single 5G network. For long-term success of 5G networks, we envision the following as important research outputs: (i) a scalable 5G network architecture that can handle a large number of human users and machines considering different constraints, (ii) a comprehensive quality of service (QoS) framework to satisfy heterogeneous users and machines requirements, (iii) a procedure for intelligent access network selection, and (iv) comprehensive inter-network handover mechanisms

Machine-Learning-as-a-Service for Optical Networks: Use Cases and Benefits

Machine Learning (ML) models have been a valuable tool to assist on the design and operation of optical networks. Several use cases have benefited from ML models, such as Quality-of-Transmission (QoT) estimation, device modeling, constellation shaping, and attack/anomaly prediction/detection. ML models are expected to be ubiquitous in optical network management and operations thereof. However, the amount of human intervention and empirical decisions needed to select the exact ML model, train and evaluate its performance, and ultimately deploy and use the model, may become a bottleneck for widespread ML use in optical networks. Machine-Learning-as-a-Service (MLaaS) has the potential to greatly reduce human intervention and empirical decisions during the creation, evaluation, and deployment of ML models. In this talk, we will firstly discuss optical network use cases that can benefit from MLaaS. Then, we detail our proposed architecture for MLaaS. Finally, performance results for two use cases will be presented

Performance Analysis of C/U Split Hybrid Satellite Terrestrial Network for 5G Systems

Over the last decade, the explosive increase in demand of high-data-rate video services and massive access machine type communication (MTC) requests have become the main challenges for the future 5G wireless network. The hybrid satellite terrestrial network based on the control and user plane (C/U) separation concept is expected to support flexible and customized resource scheduling and management toward global ubiquitous networking and unified service architecture. In this paper, centralized and distributed resource management strategies (CRMS and DRMS) are proposed and compared com- prehensively in terms of throughput, power consumption, spectral and energy efficiency (SE and EE) and coverage probability, utilizing the mature stochastic geometry. Numerical results show that, compared with DRMS strategy, the U-plane cooperation between satellite and terrestrial network under CRMS strategy could improve the throughput and EE by nearly 136% and 60% respectively in ultra-sparse networks and greatly enhance the U-plane coverage probability (approximately 77%). Efficient resource management mechanism is suggested for the hybrid network according to the network deployment for the future 5G wireless network

Quality assessment technique for ubiquitous software and middleware

The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

A Study to Optimize Heterogeneous Resources for Open IoT

Recently, IoT technologies have been progressed, and many sensors and actuators are connected to networks. Previously, IoT services were developed by vertical integration style. But now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. We also implemented elemental technologies of Tacit Computing. In this paper, we propose three layers optimizations to reduce operation cost and improve performance of Tacit computing service, in order to make as a continuous service of discovered devices by Tacit Computing. In optimization process, appropriate function allocation or offloading specific functions are calculated on device, network and cloud layer before full-scale operation.Comment: 3 pages, 1 figure, 2017 Fifth International Symposium on Computing and Networking (CANDAR2017), Nov. 201