arXiv subjects

Dinh Thai Hoang

Publications and source records attributed to Dinh Thai Hoang.

140 records · Page 8Linked to original sources

arXiv2018

Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks

This letter introduces a novel wireless-powered backscatter communication system which allows sensors to utilize RF signals transmitted from a dedicated RF energy source to transmit data. In the proposed system, when the RF energy source transmits RF signals, the sensors are able to backscatter the RF signals to transmit date to the gateway and/or harvest energy from the RF signals for their operations. By integrating backscattering and energy harvesting techniques, we can optimize the network throughput of the system. In particular, we first formulate the time scheduling problem for the system, and then propose an optimal solution using convex optimization to maximize the overall network throughput. Numerical results show a significant throughput gain achieved by our proposed design over two other baseline schemes.

cs.NI↗

arXiv2017

Optimal Cross Slice Orchestration for 5G Mobile Services

5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed framework and solution are efficient not only in providing slice-as-a-service based on the service requirements, but also in maximizing the provider's revenue.

cs.NI↗

arXiv2017

A Stochastic Programming Approach for Risk Management in Mobile Cloud Computing

The development of mobile cloud computing has brought many benefits to mobile users as well as cloud service providers. However, mobile cloud computing is facing some challenges, especially security-related problems due to the growing number of cyberattacks which can cause serious losses. In this paper, we propose a dynamic framework together with advanced risk management strategies to minimize losses caused by cyberattacks to a cloud service provider. In particular, this framework allows the cloud service provider to select appropriate security solutions, e.g., security software/hardware implementation and insurance policies, to deal with different types of attacks. Furthermore, the stochastic programming approach is adopted to minimize the expected total loss for the cloud service provider under its financial capability and uncertainty of attacks and their potential losses. Through numerical evaluation, we show that our approach is an effective tool in not only dealing with cyberattacks under uncertainty, but also minimizing the total loss for the cloud service provider given its available budget.

cs.CR↗

arXiv2017

Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach

With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution.

cs.CR↗

arXiv2017

Ambient Backscatter Communications: A Contemporary Survey

Recently, ambient backscatter communications has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks. It is expected to realize numerous Internet-of-Things (IoT) applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.

cs.NI↗

arXiv2017

Performance Analysis and Application of Mobile Blockchain

Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. However, the blockchain technology has not been adopted and implemented widely in m-commerce because its mining processes usually require to be performed on standard computing units, e.g., computers. Therefore, in this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.

cs.CR↗

arXiv2017

Applications of Economic and Pricing Models for Wireless Network Security: A Survey

This paper provides a comprehensive literature review on applications of economic and pricing theory to security issues in wireless networks. Unlike wireline networks, the broadcast nature and the highly dynamic change of network environments pose a number of nontrivial challenges to security design in wireless networks. While the security issues have not been completely solved by traditional or system-based solutions, economic and pricing models recently were employed as one efficient solution to discourage attackers and prevent attacks to be performed. In this paper, we review economic and pricing approaches proposed to address major security issues in wireless networks including eavesdropping attack, Denial-of-Service (DoS) attack such as jamming and Distributed DoS (DDoS), and illegitimate behaviors of malicious users. Additionally, we discuss integrating economic and pricing models with cryptography methods to reduce information privacy leakage as well as to guarantee the confidentiality and integrity of information in wireless networks. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to wireless security issues.

cs.GT↗

arXiv2017

Charging and Discharging of Plug-In Electric Vehicles (PEVs) in Vehicle-to-Grid (V2G) Systems: A Cyber Insurance-Based Model

In addition to being environment-friendly, vehicle-to-grid (V2G) systems can help the plug-in electric vehicle (PEV) users in reducing their energy costs and can also help stabilizing energy demand in the power grid. In V2G systems, since the PEV users need to obtain system information (e.g., locations of charging/discharging stations, current load and supply of the power grid) to achieve the best charging and discharging performance, data communication plays a crucial role. However, since the PEV users are highly mobile, information from V2G systems is not always available for many reasons, e.g., wireless link failures and cyber attacks. Therefore, in this paper, we introduce a novel concept using cyber insurance to "transfer" cyber risks, e.g., unavailable information, of a PEV user to a third party, e.g., a cyber insurance company. Under the insurance coverage, even without information about V2G systems, a PEV user is always guaranteed the best price for charging/discharging. In particular, we formulate the optimal energy cost problem for the PEV user by adopting a Markov decision process framework. We then propose a learning algorithm to help the PEV user make optimal decisions, e.g., to charge or discharge and to buy or not to buy insurance, in an online fashion. Through simulations, we show that cyber insurance is an efficient solution not only in dealing with cyber risks, but also in maximizing revenue of the PEV user.

cs.GT↗

arXiv2016

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the main component of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in Machine-to-Machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.

cs.GT↗

arXiv2016

The Tradeoff Analysis in RF-Powered Backscatter Cognitive Radio Networks

In this paper, we introduce a new model for RF-powered cognitive radio networks with the aim to improve the performance for secondary systems. In our proposed model, when the primary channel is busy, the secondary transmitter is able either to backscatter the primary signals to transmit data to the secondary receiver or to harvest RF energy from the channel. The harvested energy then will be used to transmit data to the receiver when the channel becomes idle. We first analyze the tradeoff between backscatter communication and harvest-then-transmit protocol in the network. To maximize the overall transmission rate of the secondary network, we formulate an optimization problem to find time ratio between taking backscatter and harvest-then-transmit modes. Through numerical results, we show that under the proposed model can achieve the overall transmission rate higher than using either the backscatter communication or the harvest-then-transmit protocol.

cs.NI↗

arXiv2015

Smart Data Pricing Models for Internet-of-Things (IoT): A Bundling Strategy Approach

Internet of things (IoT) has emerged as a new paradigm for the future Internet. In IoT, enormous devices are connected to the Internet and thereby being a huge data source for numerous applications. In this article, we focus on addressing data management in IoT through using a smart data pricing (SDP) approach. With SDP, data can be managed flexibly and efficiently through intelligent and adaptive incentive mechanisms. Moreover, it is a major source of revenue for providers and partners. We propose a new pricing scheme for IoT service providers to determine the sensing data buying price and IoT service subscription fee offered to sensor owners and service users, respectively. Additionally, we adopt the bundling strategy that allows multiple providers to form a coalition and bid their services as a bundle, attracting more users and achieving higher revenue. Finally, we outline some important open research issues for SDP and IoT.

cs.GT↗

arXiv2015

Optimal Energy Allocation Policy for Wireless Networks in the Sky

Google's Project Loon was launched in 2013 with the aim of providing Internet access to rural and remote areas. In the Loon network, balloons travel around the Earth and bring access points to the users who cannot connect directly to the global wired Internet. The signals from the users will be transmitted through the balloon network to the base stations connected to the Internet service provider (ISP) on Earth. The process of transmitting and receiving data consume a certain amount of energy from the balloon, while the energy on balloons cannot be supplied by stable power source or by replacing batteries frequently. Instead, the balloons can harvest energy from natural energy sources, e.g., solar energy, or from radio frequency energy by equipping with appropriate circuits. However, such kinds of energy sources are often dynamic and thus how to use this energy efficiently is the main goal of this paper. In this paper, we study the optimal energy allocation problem for the balloons such that network performance is optimized and the revenue for service providers is maximized. We first formulate the stochastic optimization problem as a Markov decision process and then apply a learning algorithm based on simulation-based method to obtain optimal policies for the balloons. Numerical results obtained by extensive simulations clearly show the efficiency and convergence of the proposed learning algorithm.

cs.NI↗

arXiv2015

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.

cs.GT↗

arXiv2015

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.

cs.NI↗