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.

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Deep Learning in Mobile and Wireless Networking: A Survey

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Information processing about a user's situation or user's context is one of the most significant paradigms in nowadays end user services development, supporting ambient intelligence, or applications that are able to react smartly to the condition of users and their surroundings. In the Internet of Things, where a plethora of connected devices produces and consumes information, there are many actors who will play the roles of context providers and context consumers. However, in a global environment, acquisition, processing and consumption of user's context information would be much easier if performed in a controlled and ordered fashion within an information exchange framework. In this paper, a proposal for a Convergent Context Enabler CCE is described and specific details are provided about the user's context processing and the interfaces designed to support the architecture. Finally, a use case of context-aware services in the Internet of Things is presented and analysed in detail.

Intelligent context retrieval and management for services in the Internet of Things

All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modelling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on five public datasets, also achieving competitive results when compared to boosted-tree solutions.

Graph Neural Network contextual embedding for Deep Learning on Tabular Data

The information processing of user situation (or user's context) is one of the most significant paradigms in end user telecommunication services development. There are many actors that will play a role, from context providers, acquiring context, to context consumers, context-aware services. Telco operators are the most adequate player to take the role of context aggregators and processors. That is based on the convergence and interoperability of the Next Generation Networks (NGN). Based on NGN capabilities, different domains can interoperate in order to acquire and consume user's context information of different nature. In this paper a global analysis of the technological situation is provided, followed by the architecture of the whole user's context management system proposed. Such system is based on the deployment of a hosted entity, the Convergent Context Enabler (CCE). Specific details are provided about the user's context processing and the interfaces designed to support the elements interworking. Finally, some examples of context-aware services are presented and its signaling flows are analyzed in detail.

Convergence2.0 on end user services based on telco hosted context processing

Mobility is probably the most important ongoing revolution in the information society of today. The outstanding achievements made in computing and networking during the last decades (which have been available using personal computers at homes and workplaces) are progressively permeating all aspects of life thanks to the plethora of mobile devices at the disposal of the users, including netbooks, PDAs and smartphones. These are also increasingly intelligent and powerful, allowing for rich user interfaces and complex operations and software. On the other hand, the advance of the Internet has given birth to a converged Web which offers a myriad of different services just with a couple of clicks. However, in order to lower the barriers for accessing these services, a platform for ubiquitous service delivery and management is still required, and a viable alternative is presented along this work.

Platform for ubiquitous mobile service composition, management and delivery

Low-thrust trajectory design and in-ﬂight control remain two of the most challenging topics for new-generation satellite operations. Most of the solutions currently implemented are based on reference trajectories and lead to sub-optimal fuel usage. Other solutions are based on simple guidance laws that need to be updated periodically, increasing the cost of operations. Whereas some optimization strategies leverage Artiﬁcial Intelligence methods, all of the approaches studied so far need either previously generated data or a strong a priori knowledge of the satellite dynamics. This study uses model-free Reinforcement Learning to train an agent on a constrained pericenter raising scenario for a low-thrust medium-Earth-orbit satellite. The agent does not have any prior knowledge of the environment dynamics, which makes it unbiased from classical trajectory optimization patterns. The trained agent is then used to design a trajectory and to autonomously control the satellite during the cruise. Simulations show that a dynamics-agnostic agent is able to learn a quasi-optimal guidance law and responds well to uncertainties in the environment dynamics. The results obtained open the door to the usage of Reinforcement Learning on more complex scenarios, multi-satellite problems, or to explore trajectories in environments where a reference solution is not known.

Antonio Sánchez-Esguevillas

Papers

Intelligent context retrieval and management for services in the Internet of Things

Graph Neural Network contextual embedding for Deep Learning on Tabular Data

Convergence2.0 on end user services based on telco hosted context processing

Platform for ubiquitous mobile service composition, management and delivery

Low-Thrust Orbital Transfer using Dynamics-Agnostic Reinforcement Learning