Browsing by Subject "Mobile"

Now showing 1 - 4 of 4
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    Collaborative data processing in wireless sensor networks.
    (2008-12) Zhang, Qingquan
    Wireless Sensor Networks (WSNs) have been used in many application domains, such as target tracking or environmental monitoring. Due to limitations of power supplies, power management and power efficient target tracking techniques have become more and more critical. In this dissertation, systematic approaches are proposed to address the above problems. In particular, efficient energy-aware architectural design aspects of a sensor network are developed, with the goal to reduce the control scheduling algorithm complexity and the power consumption of various components while maintaining the data quality and performance requirements. Research results on an efficient error-bounded sensing scheduling algorithm, a novel collaborative global error implied assisted scheduling algorithm(CIES) and fast target localization for mobile wireless sensor network are presented. Dynamic scheduling management in wireless sensor networks is one of the most challenging problems in long-lifetime monitoring applications. In this thesis, we propose and evaluate a novel data correlation-based stochastic scheduling algorithm, called Cscan. Our system architecture integrates an empirical data prediction model with a stochastic scheduler to adjust a sensor node’s operational mode. We demonstrate that substantial energy savings can be achieved while assuring that the data quality meets specified system requirements. We have evaluated our model using a light intensity measurement experiment on a Micaz testbed, which indicates that our approach works well in an actual wireless sensor network environment. We have also investigated the system performance using Wisconsin- Minnesota historical soil temperature data. The simulation results demonstrate that the system error meets specified error tolerance limits and up to a 70 percent savings in energy can be achieved in comparison to fixed probability sensing schemes. Building on the results obtained from CScan, we further propose and evaluate a collaborative error implication assisted scheduling algorithm, called CIES. This computationdistributive system integrates an implied-error based prediction model together with a stochastic scheduler to adjust neighboring sensors’ operational modes during the occurrence of rare or unusual sensing events. We demonstrate that substantial energy savings can be achieved while also satisfying a global error constraint. We have conducted extensive simulations to investigate the system performance by using realistic Wisconsin-Minnesota historical soil temperature data. The simulation results demonstrate that the system error meets the specified error tolerance and produces up to a 60 percent energy savings compared several fixed probability sensing references. In order to manage data link quality, a distributed sensor network with mobility provides an ideal system platform for surveillance as well as search and rescue applications. We consider a system design consisting of a set of autonomous robots communicating with each other and with a base station to provide image and other sensor data. A robot-mounted sensor which detects interesting information will coordinate with other mobile robots in its vicinity to stream its data back to the base station in a robust and energy-efficient fashion. The system is partitioned into twin sub-networks in such a way that any transmitting sensor will pair itself with another nearby robot to cooperatively transmit its data in a multiple-input, multiple-output (MIMO) fashion. At the same time, other robots in the system will cooperatively position themselves in such a way that the overall link quality is maximized and the total transmission energy in minimized. We efficiently simulate the system’s behavior using the Transaction Level Modeling (TLM) capability of SystemC. Our results demonstrate the efficiency of our simulation approach and provide insights into operation of the network. Finally, a fast target acquisition algorithm without the assistance of a map, call GraDrive, is introduced for search and rescue applications. We evaluate a novel gradientdriven method, which integrates per-node prediction with global collaborative prediction to estimate the position of a stationary target and to direct mobile nodes towards the target along the shortest path. We demonstrate that a high accuracy in localization can be achieved much faster than with random walk models, without any assistance from stationary sensor networks. We evaluate our model through a light-intensity matching experiment using MicaZ motes, which indicates that our model works well in a wireless sensor network environment. Through simulation, we demonstrate almost a 40% reduction in the target acquisition time, compared to a random walk model, while obtaining a small error in the estimate of the target position.
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    Enhancing the Performance of Mobile Video Streaming Ecosystems
    (2022-12) Shehata, Eman
    Recent years have witnessed a rapid increase in video streaming services (e.g., Netflix, YouTube, Amazon Video, ... etc) to meet users' interests as a result of the massive content published by content providers, high-speed Internet, the wide use of social networks, along with the growth in smart mobile devices. Additionally, the recent deployment of commercial 5G in 2019 and its potential for ultra-high bandwidth has enabled a new era for bandwidth-intensive networked applications such as volumetric video streaming. This growth in available content and demand places a significant burden on the Internet infrastructure. In addition to the complex structure of videos as each video is encoded in multiple resolutions, and different bitrate quality levels to support diverse end-user devices and network conditions. Thus, large-scale content providers have resorted to employing one or more content distribution networks (CDNs) to cache video content and handle user requests, as well as resorting to edge computing and machine learning to improve the performance perceived by their end users. Poor performance impacts user engagement, which leads to significant revenue loss for content providers. In this thesis, we discuss crucial research problems to improve the performance of mobile video streaming ecosystems to meet the scalability and user QoE performance requirements. First, we study the performance of intermediate caches in a hierarchical cache network. We show that when cache servers at different layers act independently this leads to caching objects which are evicted before their next request arrives leading to cache under-utilization.To overcome this issue, we proposed "BIG" cache abstraction which deals with distributed cache pieces as if they are "glued" together to form one "virtual" "BIG" cache. Thus, allowing any existing caching strategy to be applied as a single consistent policy for this "BIG" Cache. Consequently, "BIG" cache improves object hit probability, thereby minimizing the origin server load, and network bandwidth. Second, object access patterns are frequently changing due to the frequent changes in object popularity due to its diurnal access pattern, and during its life span. Due to these frequent changes, caching algorithms cannot rely on the locally observed object access patterns for making caching decisions. On the other hand, manually tuning the caching algorithm for each cache server according to the changes in the request access patterns is very expensive and is not scalable. To address this issue, we developed a machine-learning LSTM Encoder-Decoder model for content popularity prediction. Our DEEPCACHE is a self-adaptive caching framework for making end-to-end caching decisions based on the predicted popularity. We show that it manages to increase the number of cache hits for existing caching policies. Third, routing is a central problem to ensure the resiliency of CDNs. Purely distributed routing algorithms such as Bellman-Ford suffer from the "count-to-infinity" problem, whereas Dijkstra's algorithm requires global topology dissemination and route recomputation. Much of the recent literature on resilient routing is resilient to k link/node failures for a constant k (and often placing topological constraints on the graphs), and none of them work under arbitrary link failures. To address this issue, we developed a proactive routing algorithm that ensures the connectivity between any pair of nodes under arbitrary failures without the need for global topology dissemination and route recomputation as in purely distributed routing algorithms. Our algorithm limits the number of nodes involved in the recovery process as well as the number of link reversals, and convergence time. An additional advantage is the ability to utilize multiple paths to send traffic between nodes due to utilizing directed edges between nodes even upon failures. Finally, with the recent deployment of commercial 5G in 2019 and its potential for ultra-high bandwidth, we studied the characteristics of 5G throughput and its impact on video streaming applications. Our findings show that the wild fluctuations in 5G throughput and its dead zones lead to a large stall time while streaming videos. We redesigned video streaming applications to be 5G-Aware taking full advantage of the ultra-high bandwidth and overcoming its varying throughput. Our experiments show that our proposed strategies consistently deliver high video quality close to the theoretical optimal results reducing (if not eliminating) the stall time.
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    Examining young consumers' adoption intention of SMS-based and APP-based mobile coupon services: a perceived value perspective
    (2014-10) Park, MinJung
    The objectives of this study were (1) to examine the adoption of young consumers' mobile coupon services in an apparel retailing context from the perspective of perceived value and (2) to assess whether differences existed between the two types of mobile coupon services (SMS-based vs. app-based) in consumers' perceptions and adoption intention of mobile coupon services. The author tested three specific benefits (i.e., economic benefits, psychological benefits, and customization) as well as costs as antecedents of perceived value. The results revealed that the identified antecedents, with the exception of customization, had a strong impact on mobile coupon value perceptions, which in turn influenced adoption intention. The author also found that consumers' responses to mobile coupon services were more favorable to the app-based mobile coupon services than the SMS-based mobile coupon services.
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    Phloem functions revealed by the nakr1-1 mutant.
    (2011-01) Tian, Hui
    Na+ is a non- essential element for plant growth. Na+ accumulation within plants, especially the shoot tissue causes osmotic stress and Na+-specific toxicity that threatens plant survival and reduces crop yield. The control of Na+ accumulation in the shoot is mainly at the root level, by regulating net Na+ uptake into roots and Na+ transport from root to the shoot. My thesis work is mainly on the characterization of a fast neutron mutagenized Arabidopsis mutant, nakr1-1, that accumulates Na+ and K+ in the shoot tissue and has pleiotrophic developmental phenotypes (including short roots, late flowering and loss of apical dominance). Using traditional mapping together with DNA- chip based mapping, a 7-bp deletion was identified that caused loss- of- function mutation of a gene encoding a putative heavy-metal-binding protein. The metalbinding feature of the protein was confirmed by elemental analysis of maltose binding protein (MBP)- tagged NaKR1 expressed and purified from Escherichia coli. AtNaKR1 was specifically expressed in the phloem companion cells. NaKR1 protein was phloem mobile and unloaded at the phloem terminal into the proximal root meristem region. nakr1-1 mutation caused severe phloem function defects as demonstrated by less efficient 14C- sucrose loading and starch accumulation in rosette leaves. Phloem function defects were also responsible for the Na+/K+ accumulation in the shoot tissue based on the reciprocal grafting results together with ICP- MS analyses. Moreover elemental analysis of xylem sap indicated Na+ and K+ accumulation phenotypes were not caused by increased root- to- shoot transport of Na+ and K+. nakr1-1 mutation affects root meristem maintenance after germination as revealed by study of root meristem size, cell pattern and starch accumulation in root columella cells and quiescent center activity. My work provided evidence that phloem recirculation plays more important roles than had been suggested by previous literature in controlling shoot Na+ accumulation. Understanding how Na+ and K+ redistribution is regulated might have potential application in improving salinity tolerance of crop plants and the improvement of seed quality.