Socially Aware Heterogeneous Wireless Networks (original) (raw)

Intelligence-driven mobile networks for smart cities

Computing, 2021

For the last twenty years, artificial intelligence (AI) has become a significant technology to influence our daily life with its ability of promoting the quality of living. AI can find the patterns and create actions from the proper amount of data, then make proper decisions for applications. It is promising to involve AI in the smart cities to enable the better intelligent services. Among all services, mobile networking can be also improved by applying AI, in which such enhanced networking will allow broad coverage, high capacity, high resource availability, and extensive connectivity. It will meet the key metrics including bandwidth, jitter, throughput, transmission delay, and availability. Therefore, this special issue is vital to foster the application and innovation of AI and ML technologies for mobile networking within 5G, MANETs, VANETs, wireless sensor networks and as well as other forms of networks. This is to explore a way

Mobile social networks and dynamic spectrum demand : models and applications

2011

Models of mobile social networks and wireless spectrum demand are useful in optimizing the performance of hybrid wireless networks, as well as for applications such as spectrum market trading and understanding cascading failures in inter-dependent infrastructures. These are commonly modeled by simple stochastic processes, which do not adequately capture key aspects of real systems. Here, we discuss a first-principles based approach for developing synthetic mobile social networks and spectrum demand models, and two applications of it on urban-scale networks. Some of the key challenges for our approach include: (i) the computational issues in processing very large complex networks of urban populations, and (ii) statistical and data mining issues in modeling and validation.

Towards a Novel Engine to Underlie the Data Transmission of Social Urban Sensing Applications

Proceedings of the 17th International Conference on Enterprise Information Systems, 2015

Social urban sensing is a new paradigm which exploits human-carried or vehicle-mounted sensors to ubiquitously collect data for large-scale urban sensing. A challenge of such scenario is how to transmit sensed data in situations where the networking infrastructure is intermittent or unavailable. In this context, this paper outlines the early stages of our research which is concerned with a novel engine that uses Opportunistic Networks paradigm to underlie the data transmission of social urban sensing applications. It applies Situation awareness, Neural Networks and Fuzzy Logic for routing and decision-making process. As we know, this is the first paper to use such approaches in Smart Cities area with focus on social sensing application. As well as being original, the preliminary results from our simulations signals the way that further research can be carried out in this area.

Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

In this paper, a novel approach for optimizing and managing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows to jointly exploit both the wireless and social context of wireless users for optimizing the overall allocation of resources and improving traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel, selforganizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome ar...

Trade-offs in Social and Behavioral Modeling in Mobile Networks

Mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today's smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and behavior prediction. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones. In particular, we look at the dynamic learning process over time with various sizes of sampling groups and examine the interplay between these two parameters. We validate our model using extensive simulations carried out using the "Friends and Family" dataset which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community for over a year and is one of the most comprehensive mobile phone datasets gathered in academia to date.

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Smart Cities, 2021

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the ...

Modeling Mobile Cellular Networks Based on Social Characteristics

International Journal of Computers Communications & Control, 2016

Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consis...

Social-Aware Load Balancing System for Crowds in Cellular Networks

IEEE Access, 2021

Over time, load balancing systems in cellular networks have been key to avoiding overload problems in the network and to maintaining a correct resource allocation and performance. However, the classical approaches were not designed for the dynamism generated by user behavior. The big crowds of users at certain social venues are one of the main concerns of mobile operators due to the load imbalance generated. Additionally, the mobility of users who attend social events (e.g., sports events, concerts, etc.) greatly impacts network performance due to its high correlation with network traffic. The availability to inform about events, particularly regarding venue location (e.g., stadiums, concert halls, convention centers) is exponentially growing thanks to its proliferation in social networks through geolocation databases and other functionalities. Therefore, the present work proposes a novel load balancing system integrating a fuzzy logic controller algorithm with social-awareness, which considers the relative position between cell sites and the social event venue in order to configure the network parameters. This approach is evaluated for different configurations of load balancing methods simulated on an urban macro scenario, mitigating the impact of the number of users per cell without degrading the signal quality. In this way, results show that social event data information plus soft or aggressive transmission power changes in cells can help to maintain the balance in the number of users per cell during mass events. INDEX TERMS Social events, mobile communication, fuzzy control, load balancing, social-awareness, communication system operations and management.

When Machine Learning Meets Big Data: A Wireless Communication Perspective

arXiv (Cornell University), 2019

We have witnessed an exponential growth in commercial data services, which has lead to the 'big data era'. Machine learning, as one of the most promising artificial intelligence tools of analyzing the deluge of data, has been invoked in many research areas both in academia and industry. The aim of this article is twin-fold. Firstly, we briefly review big data analysis and machine learning, along with their potential applications in next-generation wireless networks. The second goal is to invoke big data analysis to predict the requirements of mobile users and to exploit it for improving the performance of "social network-aware wireless". More particularly, a unified big data aided machine learning framework is proposed, which consists of feature extraction, data modeling and prediction/online refinement. The main benefits of the proposed framework are that by relying on big data which reflects both the spectral and other challenging requirements of the users, we can refine the motivation, problem formulations and methodology of powerful machine learning algorithms in the context of wireless networks. In order to characterize the efficiency of the proposed framework, a pair of intelligent practical applications are provided as case studies: 1) To predict the positioning of drone-mounted areal base stations (BSs) according to the specific tele-traffic requirements by gleaning valuable data from social networks. 2) To predict the content caching requirements of BSs according to the users' preferences by mining data from social networks. Finally, open research opportunities are identified for motivating future investigations. Index Terms-Next-generation networks, big data, machine learning, UAV movement and wireless caching placement.

Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

Data science or " data-driven research " is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.