131 research outputs found
MODELING AND RESOURCE ALLOCATION IN MOBILE WIRELESS NETWORKS
We envision that in the near future, just as Infrastructure-as-a-Service (IaaS), radios and radio resources in a wireless network can also be provisioned as a service to Mobile Virtual Network Operators (MVNOs), which we refer to as Radio-as-a-Service (RaaS). In this thesis, we present a novel auction-based model to enable fair pricing and fair resource allocation according to real-time needs of MVNOs for RaaS. Based on the proposed model, we study the auction mechanism design with the objective of maximizing social welfare. We present an Integer Linear Programming (ILP) and Vickrey-Clarke-Groves (VCG) based auction mechanism for obtaining optimal social welfare. To reduce time complexity, we present a polynomial-time greedy mechanism for the RaaS auction. Both methods have been formally shown to be truthful and individually rational.
Meanwhile, wireless networks have become more and more advanced and complicated, which are generating a large amount of runtime system statistics. In this thesis, we also propose to leverage the emerging deep learning techniques for spatiotemporal modeling and prediction in cellular networks, based on big system data. We present a hybrid deep learning model for spatiotemporal prediction, which includes a novel autoencoder-based deep model for spatial modeling and Long Short-Term Memory units (LSTMs) for temporal modeling. The autoencoder-based model consists of a Global Stacked AutoEncoder (GSAE) and multiple Local SAEs (LSAEs), which can offer good representations for input data, reduced model size, and support for parallel and application-aware training.
Mobile wireless networks have become an essential part in wireless networking with the prevalence of mobile device usage. Most mobile devices have powerful sensing capabilities. We consider a general-purpose Mobile CrowdSensing(MCS) system, which is a multi-application multi-task system that supports a large variety of sensing applications.
In this thesis, we also study the quality of the recruited crowd for MCS, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. More specifically, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine- grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency.
In a MCS system, a sensing task is dispatched to many smartphones for data collections; in the meanwhile, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this thesis, we also consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to re- port their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as two effective polynomial-time heuristic algorithms, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem.
Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions, compared to several baseline methods
Task Allocation among Connected Devices: Requirements, Approaches and Challenges
Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning
Empowering Non-Terrestrial Networks with Artificial Intelligence: A Survey
6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs
New Approaches in Cognitive Radios using Evolutionary Algorithms
Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. Due many techniques have become a topic of discussion on cognitive radios, the aim of this paper was developed a contemporary survey of evolutionary algorithms in Cognitive Radio. According to the art state, this work had been collected the essential contributions of cognitive radios with the particularity of base they research in evolutionary algorithms. The main idea was classified the evolutionary algorithms and showed their fundamental approaches. Moreover, this research will be exposed some of the current issues in cognitive radios and how the evolutionary algorithms will have been contributed. Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. With a more comprehensive and systematic understanding of evolutionary algorithms in cognitive radios, more research in this direction may be motivated and refined
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
Artificial Intelligence (AI) is expected to play an instrumental role in the
next generation of wireless systems, such as sixth-generation (6G) mobile
network. However, massive data, energy consumption, training complexity, and
sensitive data protection in wireless systems are all crucial challenges that
must be addressed for training AI models and gathering intelligence and
knowledge from distributed devices. Federated Learning (FL) is a recent
framework that has emerged as a promising approach for multiple learning agents
to build an accurate and robust machine learning models without sharing raw
data. By allowing mobile handsets and devices to collaboratively learn a global
model without explicit sharing of training data, FL exhibits high privacy and
efficient spectrum utilization. While there are a lot of survey papers
exploring FL paradigms and usability in 6G privacy, none of them has clearly
addressed how FL can be used to improve the protocol stack and wireless
operations. The main goal of this survey is to provide a comprehensive overview
on FL usability to enhance mobile services and enable smart ecosystems to
support novel use-cases. This paper examines the added-value of implementing FL
throughout all levels of the protocol stack. Furthermore, it presents important
FL applications, addresses hot topics, provides valuable insights and explicits
guidance for future research and developments. Our concluding remarks aim to
leverage the synergy between FL and future 6G, while highlighting FL's
potential to revolutionize wireless industry and sustain the development of
cutting-edge mobile services.Comment: 32 pages, 7 figures; 9 Table
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect
behavioral and health-related information from individuals and provide timely
intervention to promote health and well-beings, such as mental health and
chronic cares. As the objectives of mobile sensing could be either \emph{(a)
personalized medicine for individuals} or \emph{(b) public health for
populations}, in this work we review the design of these mobile sensing apps,
and propose to categorize the design of these apps/systems in two paradigms --
\emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both
sensing paradigms might incorporate with common ubiquitous sensing
technologies, such as wearable sensors, mobility monitoring, mobile data
offloading, and/or cloud-based data analytics to collect and process sensing
data from individuals, we present a novel taxonomy system with two major
components that can specify and classify apps/systems from aspects of the
life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \&
Participation}, \emph{(2) Health Surveillance \& Data Collection}, and
\emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different
goals of the two paradigms, this work systematically reviews this field, and
summarizes the design of typical apps/systems in the view of the configurations
and interactions between these two components. In addition to summarization,
the proposed taxonomy system also helps figure out the potential directions of
mobile sensing for health from both personalized medicines and population
health perspectives.Comment: Submitted to a journal for revie
Deep Learning with Partially Labeled Data for Radio Map Reconstruction
In this paper, we address the problem of Received Signal Strength map
reconstruction based on location-dependent radio measurements and utilizing
side knowledge about the local region; for example, city plan, terrain height,
gateway position. Depending on the quantity of such prior side information, we
employ Neural Architecture Search to find an optimized Neural Network model
with the best architecture for each of the supposed settings. We demonstrate
that using additional side information enhances the final accuracy of the
Received Signal Strength map reconstruction on three datasets that correspond
to three major cities, particularly in sub-areas near the gateways where larger
variations of the average received signal power are typically observed.Comment: 42 pages, 39 figure
From Things to Services: A Social IoT Approach for Tourist Service Management
In the context of Internet of Things (IoT), the cooperation and synergy between varied and disparate communicating objects is strained by trustworthiness, confidentiality and interoperability concerns. These restrictions can limit the development of IoT-based applications especially considering the emergent boost in the number of communicating objects and their growing itinerant nature in a collective service context. A new perspective arises with the paradigm of Social Internet of Things (SIoT), that relies on the implementation of semi-independent communicating objects with cooperation assessed by social relations and social feed-back. In this article, we present the development and expansion of the IoT concept towards SIoT in the context of the interactions between tourist services as communicating objects. As a proof-of-concept we propose a composition of services as virtualized social objects and the interaction between them, by taking into consideration the balance, trustworthiness, cooperation and synergy of services. Furthermore we present a solution to integrate also accessibility in SIoT services. The presented concept is presented using a demonstrator build for tourist services
- …