Matching Items (3)
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- All Subjects: Mobile computing
- Creators: Xue, Guoliang
Description
The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.
ContributorsZhang, Rui (Author) / Zhang, Yanchao (Thesis advisor) / Duman, Tolga Mete (Committee member) / Xue, Guoliang (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Mobile Cloud computing has shown its capability to support mobile devices for
provisioning computing, storage and communication resources. A distributed mobile
cloud service system called "POEM" is presented to manage the mobile cloud resource
and compose mobile cloud applications. POEM considers resource management not
only between mobile devices and clouds, but also among mobile devices. It implements
both computation offloading and service composition features. The proposed POEM
solution is demonstrated by using OSGi and XMPP techniques.
Offloading is one major type of collaborations between mobile device and cloud
to achieve less execution time and less energy consumption. Offloading decisions for
mobile cloud collaboration involve many decision factors. One of important decision
factors is the network unavailability. This report presents an offloading decision model
that takes network unavailability into consideration. The application execution time
and energy consumption in both ideal network and network with some unavailability
are analyzed. Based on the presented theoretical model, an application partition
algorithm and a decision module are presented to produce an offloading decision that
is resistant to network unavailability.
Existing offloading models mainly focus on the one-to-one offloading relation. To
address the multi-factor and multi-site offloading mobile cloud application scenarios,
a multi-factor multi-site risk-based offloading model is presented, which abstracts the
offloading impact factors as for offloading benefit and offloading risk. The offloading
decision is made based on a comprehensive offloading risk evaluation. This presented
model is generic and expendable. Four offloading impact factors are presented to show
the construction and operation of the presented offloading model, which can be easily
extended to incorporate more factors to make offloading decision more comprehensive.
The overall offloading benefits and risks are aggregated based on the mobile cloud
users' preference.
The offloading topology may change during the whole application life. A set of
algorithms are presented to address the service topology reconfiguration problem in
several mobile cloud representative application scenarios, i.e., they are modeled as
finite horizon scenarios, infinite horizon scenarios, and large state space scenarios to
represent ad hoc, long-term, and large-scale mobile cloud service composition scenarios,
respectively.
provisioning computing, storage and communication resources. A distributed mobile
cloud service system called "POEM" is presented to manage the mobile cloud resource
and compose mobile cloud applications. POEM considers resource management not
only between mobile devices and clouds, but also among mobile devices. It implements
both computation offloading and service composition features. The proposed POEM
solution is demonstrated by using OSGi and XMPP techniques.
Offloading is one major type of collaborations between mobile device and cloud
to achieve less execution time and less energy consumption. Offloading decisions for
mobile cloud collaboration involve many decision factors. One of important decision
factors is the network unavailability. This report presents an offloading decision model
that takes network unavailability into consideration. The application execution time
and energy consumption in both ideal network and network with some unavailability
are analyzed. Based on the presented theoretical model, an application partition
algorithm and a decision module are presented to produce an offloading decision that
is resistant to network unavailability.
Existing offloading models mainly focus on the one-to-one offloading relation. To
address the multi-factor and multi-site offloading mobile cloud application scenarios,
a multi-factor multi-site risk-based offloading model is presented, which abstracts the
offloading impact factors as for offloading benefit and offloading risk. The offloading
decision is made based on a comprehensive offloading risk evaluation. This presented
model is generic and expendable. Four offloading impact factors are presented to show
the construction and operation of the presented offloading model, which can be easily
extended to incorporate more factors to make offloading decision more comprehensive.
The overall offloading benefits and risks are aggregated based on the mobile cloud
users' preference.
The offloading topology may change during the whole application life. A set of
algorithms are presented to address the service topology reconfiguration problem in
several mobile cloud representative application scenarios, i.e., they are modeled as
finite horizon scenarios, infinite horizon scenarios, and large state space scenarios to
represent ad hoc, long-term, and large-scale mobile cloud service composition scenarios,
respectively.
ContributorsWu, Huijun (Author) / Huang, Dijiang (Thesis advisor) / Xue, Guoliang (Committee member) / Dasgupta, Partha (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2016
Description
The presence of a rich set of embedded sensors on mobile devices has been fuelling various sensing applications regarding the activities of individuals and their surrounding environment, and these ubiquitous sensing-capable mobile devices are pushing the new paradigm of Mobile Crowd Sensing (MCS) from concept to reality. MCS aims to outsource sensing data collection to mobile users and it could revolutionize the traditional ways of sensing data collection and processing. In the meantime, cloud computing provides cloud-backed infrastructures for mobile devices to provision their capabilities with network access. With enormous computational and storage resources along with sufficient bandwidth, it functions as the hub to handle the sensing service requests from sensing service consumers and coordinate sensing task assignment among eligible mobile users to reach a desired quality of sensing service. This paper studies the problem of sensing task assignment to mobile device owners with specific spatio-temporal traits to minimize the cost and maximize the utility in MCS while adhering to QoS constraints. Greedy approaches and hybrid solutions combined with bee algorithms are explored to address the problem.
Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible.
Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible.
ContributorsWang, Zhijie (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2016