An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

/pdf/deep-learning-in-mobile-and-wireless-networking-a-survey-ct8cy2kuqi.pdf

Deep Learning in Mobile and Wireless Networking: A Survey

Since its formal introduction to IP networks in 1993 as a viable complementary approach for congestion control, there has been a steady stream of research output with respect to Active Queue Management (AQM). This survey attempts to travel the trajectory of AQM research from 1993 with the first algorithm, Random Early Detection (RED), to current work in 2011. In this survey we discuss the general attributes of AQM schemes, and the design approaches taken such as heuristic, control-theoretic and deterministic optimization. Of interest is the role of AQM in QoS provisioning particularly in the DiffServ context, as well as the role of AQM in the wireless domain. For each section, example algorithms from the research literature are presented.

Active Queue Management: A Survey

Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions

An identity management tool provides identity management across multiple domains, providing a single access point not only for information for runtime, network, and service layer information, but also for systems such as business support systems (BSS), operational support systems (OSS), and third party systems. The identity management tool creates a unified user profile that maps customer identities across the various domains, and presents the information in a virtual directory wherein the information appears to come from a single source, creating a central data hub for the various systems. This aggregation provides for the same experience and service across various access networks and using various channels. Such an architecture allows billing and service information to be shared with subscription and customer information, even though the data is substantially unrelated data from disparate data sources, without the need to combine schemas or aggregate the data into a single data store.

Shared view of customers across business support systems (BSS) and a service delivery platform (SDP)

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model.

https://www.mdpi.com/1424-8220/17/3/489/pdf

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications

In order to reduce the increasing packet loss rates caused by an exponential increase in network traffic, the IETF (Internet Engineering Task Force) is considering the deployment of active queue management techniques such as RED (random early detection). While active queue management in routers and gateways can potentially reduce packet loss rates in the Internet, this paper has demonstrated the inherent weakness of current techniques and shows that they are ineffective in preventing high loss rates. The inherent problem with these queue management algorithms is that they all use queue lengths as the indicator of the severity of congestion. In order to solve this problem, a new active queue management algorithm called MRED is proposed. MRED computes the packet drop probability based on our heuristic method rather than the simple method used in RED. Using simulation, MRED is shown to perform better than existing queue management schemes.

MRED: a new approach to random early detection

Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX

Path loss modeling of millimeter waves is regarded as one of the most challenging problems in the design of the fifth-generation (5G) mobile communication networks due to the high susceptibility to various environmental influences. We present a novel convolutional neural network (CNN)-based path loss modeling method based on a set of 28 GHz mmWave field measurements in suburban scenarios. Enhanced local area multi-scanning (E-LAMS) algorithm that provides a CNN with path loss environmental information is proposed. A new CNN structure with four subnetworks and feature-sharing layers added between convolutional layers is also proposed. The proposed methods demonstrate their superior performance over empirical models and deterministic models in terms of accuracy and complexity. The root-mean-square error of 8.59 dB has been achieved in path loss prediction in the test scenarios.

CNN-Based mmWave Path Loss Modeling for Fixed Wireless Access in Suburban Scenarios

The communication network is in a transition toward the NGN (Next Generation Networks), which can support both of voice and data at the same time, according to the explosive demand of new services. The NGN allows the third-party application provisioning by defining the networks as layers of Services, Distributed Processing Environment and Transport. The Service layer can further be divided into Application and Service Component layer. In order to realize the third-party application provisioning, the Parlay Group has adopted an open Parlay API approach as an interface between the Application and the Service Component layer. Using Parlay API, the third parties may build and deploy the new applications at the Application layer exploiting the service components within network operators’ domain. In this paper, we presents the implementation details about the two-party call setup and release using the third party service logic based on Parlay API Rel. 3.0., where SIP is used as a signaling protocol in Transport layer.

Hyukjoon Lee

Papers

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications

MRED: a new approach to random early detection

Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX

CNN-Based mmWave Path Loss Modeling for Fixed Wireless Access in Suburban Scenarios

Implementation of Third Party Based Call Control Using Parlay Network API in SIP Environment