Thesis (Ph. D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 94-101).

Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Ingram Mary Ann, Committee Chair ; Durgin, Gregory David, Committee Member ; Williams, Douglas B, Committee Member.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2009. / Includes bibliographical references (p. 53-56).

Context-aware computing enables mobile applications to discover and benefit from valuable context information, such as user location, time of day and current activity. However, determining the users’ context throughout their daily activities is one of the main challenges of context-aware computing. With the increasing number of built-in mobile sensors and other input sources, existing context models do not effectively handle context information related to personal user context. The objective of this research was to develop an improved context-aware model to support the context awareness needs of mobile applications. An existing context-aware model was selected as the most complete model to use as a basis for the proposed model to support context awareness in mobile applications. The existing context-aware model was modified to address the shortcomings of existing models in dealing with context information related to personal user context. The proposed model supports four different context dimensions, namely Physical, User Activity, Health and User Preferences. A prototype, called CoPro was developed, based on the proposed model, to demonstrate the effectiveness of the model. Several experiments were designed and conducted to determine if CoPro was effective, reliable and capable. CoPro was considered effective as it produced low-level context as well as inferred context. The reliability of the model was confirmed by evaluating CoPro using Quality of Context (QoC) metrics such as Accuracy, Freshness, Certainty and Completeness. CoPro was also found to be capable of dealing with the limitations of the mobile computing platform such as limited processing power. The research determined that the proposed context-aware model can be used to successfully support context awareness in mobile applications. Design recommendations were proposed and future work will involve converting the CoPro prototype into middleware in the form of an API to provide easier access to context awareness support in mobile applications.

No description available.

No description available.

Encryption of confidential data with a secret key has become a widespread technique for securing wireless transmissions. However, existing key distribution methods that either deliver the secret key with a key distribution center or exchange the secret key using public-key cryptosystems are unable to establish perfect secret keys necessary for symmetric encryption techniques. This research considers secret key establishment, under the broad research area of information theoretic security, using the reciprocal wireless channel as common randomness for the extraction of perfect secret keys in multiple-input multiple-output (MIMO)communication systems. The presentation discusses the fundamental characteristics of the time-variant MIMO wireless channel and establishes a realistic channel simulation model useful for assessing key establishment algorithms. Computational examples show the accuracy and applicability of the model. The discussion then turns to an investigation of the influence of the spatial and temporal correlation of the channel coefficients on the bound of the key size generated from the common channel, and it is found that a sampling approach exists that can generate a key using the minimum sampling time. A practical key generation protocol is then developed based on an enhancement of a published channel coefficient quantization method that incorporates flexible quantization levels, public transmission of the correlation eigenvector matrix and low-density parity-check (LDPC) coding to improve key agreement. This investigation leads to the development of improved channel quantization techniques that dynamically shift the quantization boundaries at one node based on the information provided by the other node. Analysis based on a closed-form bound for the key error rate (KER) and simulations based on the channel model and measurement data show that the enhanced algorithms are able to dramatically reduce key mismatch and asymptotically approach the KER bound. Finally, other secret key generation algorithms based on channel-encryption rather than quantization are discussed, leading to a new concept for secret key generation using the common wireless channel.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / Spatial multiplexing (SM) systems have received significant attention because the architecture offers high spectral efficiency. However, relatively little research exists on optimization of SM systems in the presence of jamming. In a spatially uncoded SM system, such as V-BLAST, the channel state information is assumed to be unavailable a priori at both transmitter and receiver. Here, Kalman filtering is used to estimate the Rayleigh fading channel at the receiver. The spatial correlation of the jammer plus noise is also estimated, and spatial whitening to reject the jammers is employed in both the Kalman channel estimator and detector. To avoid the exponential complexity of maximum-likelihood (ML) detection, the QRD-M algorithm is employed. In contrast to sphere decoding, QRD-M has fixed decoding complexity of order O(M), and is thus attractive for hardware implementation. The performance of the joint Kalman filter channel estimator, spatial whitener and QRD-M detector is verfied by simulations.

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy