Heterogeneous wireless access technology and its impact on forming and maintaining friendship through mobile social networks

Ou, Shumao; Hui, Pan; Li, Feng

doi:10.1002/dac.1314

Cited by 21 publications

(18 citation statements)

References 18 publications

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“…The algorithm relies on path monitoring, such as acknowledgement (ACK) information for periodic updates on throughput and delay. Reliability can be determined by measuring some PHY layer parameters of the subsystem, such as signal strength and continuous link uptime [17]. This is to adapt allocation ratios and transmission schedules to network changes.…”

Section: User Preference Validation -The Network Viewed By the Usermentioning

confidence: 99%

Multiple Interface Scheduling System for HeterogeneousWireless Vehicular Networks: Description and Evaluation

Román¹,

Ball²,

Ou³

2017

EAI Endorsed Transactions on Wireless Spectrum

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Reliable wireless communications between vehicles (V2V) and between vehicles and infrastructure (V2I) will play a key role in future transport networks. Where there is overlapping coverage of multiple Radio Access Technologies, with no cooperation between them, a vehicle can use the different technologies simultaneously. This paper proposes an uplink Multi Interface Scheduling System (MISS) located at an intermediate shim layer on the user side, to achieve efficient bandwidth aggregation, or lower end-to-end packet delay. MISS aims to find all the available networks that can meet multiple criteria based on user preference and required performance. Simulation results show that safety critical traffic can be prioritized where the resources are insufficient for all the services. Video delivery quality is also improved by prioritizing the most important frames. This algorithm is ideally suited to vehicular networks, where delivery of safety traffic and/or video is an essential requirement.

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Section: User Preference Validation -The Network Viewed By the Usermentioning

confidence: 99%

Multiple Interface Scheduling System for HeterogeneousWireless Vehicular Networks: Description and Evaluation

Román¹,

Ball²,

Ou³

2017

EAI Endorsed Transactions on Wireless Spectrum

Self Cite

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“…With the paradigm of SIoT, there are remarkable papers that proposed for access service evaluation and recommendation [11][12][13][14][15][16][18][19][20][21][22][23][24]. A community of interest-based system was described in [13], where objects are formed into communities of interest.…”

Section: Related Workmentioning

confidence: 99%

A scheme of access service recommendation for the Social Internet of Things

Chen

Ling

Huang

et al. 2015

Int J Communication

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The rapid increase in the complexity and the extent of personalization of services in the Internet of Things (IoT) has led to a greater demand for frequent collaboration among heterogeneous devices. Moreover, with the inseparable relations between human and devices, the paradigm of Social IoT (SIoT) is gaining popularity in recent years. How to effectively facilitate the access to quality services and credible devices in largescale networks via defining, establishing, and managing social architectures among things has become a critical issue. In this paper, a scheme of access service recommendation for the SIoT is presented with the understanding of inherent constraints and factors that influence the security and stability of IoT networks. In which, timeliness properties are considered in each transaction for dynamic performance enhancements. With the benefits of promoting service discovery and composition, social relationships among things are introduced in the proposed scheme. An energy-aware mechanism is also utilized as a restrictive factor in trustworthiness evaluation. Finally, the recommendation is based not only on the past performance but also on the social relationship and the energy status of nodes. Simulation experiments demonstrate the effectiveness and benefits of our scheme from three aspects including rating accuracy, dynamic behavior, and network stability.where the weight parameter γ ∈[0, 1]. According to (9), the judgment of a given node u's energy status is based on its remaining energy RE(v,t) and the energy consumption rate, which is reflected by 699 A SCHEME OF ACCESS SERVICE RECOMMENDATION FOR THE SIOT

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“…They are playing indispensable role for us to consume and share information in our daily lives [1]. Therefore, to optimize network resources and improve service qualities, mobile network operators, ISPs, and application developers are required to understand capabilities of cellular devices holding by their customers and related behavior patterns [3]. Specifically, attributes of a cellular device such as device model and features can convincingly depict a group of users as traditional demographic characteristics.…”

Section: Introductionmentioning

confidence: 99%

Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis

Liu

Nan

Zhang

et al. 2014

Int J Communication

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The user clients for accessing Internet are increasingly shifting from desktop computers to cellular devices. To be competitive in the rapidly changing market, operators, Internet service providers and application developers are required to have the capability of recognizing the models of cellular devices and understanding the traffic dynamics of cellular data network. In this paper, we propose a novel Jaccard measurement-based method to recognize cellular device models from network traffic data. This method is implemented as a scalable paralleled MapReduce program and achieves a high accuracy, 91.5%, in the evaluation with 2.9 billion traffic records collected from the real network. Based on the recognition results, we conduct a comprehensive study of three characteristics of network traffic from device model perspective, the network access time, the traffic volume, and the diurnal patterns. The analysis results show that the distribution of network access time can be modeled by a two-component Gaussian mixture model, and the distribution of traffic volumes is highly skewed and follows the power law. In addition, seven distinct diurnal patterns of cellular device usage are identified by applying unsupervised clustering algorithm on the collected massive traffic data. RECOGNIZING AND CHARACTERIZING DYNAMICS OF CELLULAR DEVICES 1885 a three-step method to identify an appropriate keyword from unformatted textual HTTP headers to represent a cellular device model. The first step is extracting all keywords that are possibly to be the description of a device model. The second step is selecting a small set of candidate keywords by evaluating the conditional probability of each keyword given a device model to decrease the computational workload. At the last step, a Jaccard coefficient [6] value of each candidate keyword is calculated, and the keyword with the largest value is selected to represent a device model. To meet the requirements of performance and scalability to handle billions of HTTP records, the algorithm is implemented as a paralleled program based on MapReduce programming model. Evaluation result in the real network shows that the proposed method can achieve pretty high recognition accuracy.With the capability of recognizing cellular device models, we study the traffic of cellular data network from a new perspective, the device model perspective. Our goal is to understand whether and how the traffic differs from cellular device models. In particular, we investigate the following three basic characteristics of different device models, the distribution of network access time, the distribution of traffic volume, and the diurnal usage patterns.The following key contributions of this paper are twofold:

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Heterogeneous wireless access technology and its impact on forming and maintaining friendship through mobile social networks

Cited by 21 publications

References 18 publications

Multiple Interface Scheduling System for HeterogeneousWireless Vehicular Networks: Description and Evaluation

Multiple Interface Scheduling System for HeterogeneousWireless Vehicular Networks: Description and Evaluation

A scheme of access service recommendation for the Social Internet of Things

Recognizing and characterizing dynamics of cellular devices in cellular data network through massive data analysis

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