From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

Improving the Efficacy of Context-Aware Applications

In this dissertation, we explore methods for enhancing the context-awareness capabilities of modern computers, including mobile devices, tablets, wearables, and traditional computers. Advancements include proposed methods for fusing information from multiple logical sensors, localizing nearby objects using depth sensors, and building models to better understand the content of 2D images. First, we propose a system called Unagi, designed to incorporate multiple logical sensors into a single framework that allows context-aware application developers to easily test new ideas and create novel experiences. Unagi is responsible for collecting data, extracting features, and building personalized models for each individual user. We demonstrate the utility of the system with two applications: adaptive notification filtering and a network content prefetcher. We also thoroughly evaluate the system with respect to predictive accuracy, temporal delay, and power consumption. Next, we discuss a set of techniques that can be used to accurately determine the location of objects near a user in 3D space using a mobile device equipped with both depth and inertial sensors. Using a novel chaining approach, we are able to locate objects farther away than the standard range of the depth sensor without compromising localization accuracy. Empirical testing shows our method is capable of localizing objects 30m from the user with an error of less than 10cm. Finally, we demonstrate a set of techniques that allow a multi-layer perceptron (MLP) to learn resolution-invariant representations of 2D images, including the proposal of an MCMC-based technique to improve the selection of pixels for mini-batches used for training. We also show that a deep convolutional encoder could be trained to output a resolution-independent representation in constant time, and we discuss several potential applications of this research, including image resampling, image compression, and security

MobiQuery: A Spatiotemporal Query Service for Mobile Users in Sensor Networks

This paper presents MobiQuery, a spatiotemporal query service that allows mobile users to periodically collect sensor data from the physical environment through wireless sensor networks. A salient feature of \MQ is that it can meet stringent spatiotemporal performance constraints, including query latency, data freshness, and changing areas of interest due to user mobility. We present three just-in-time prefetching protocols that enable MobiQuery to achieve desired spatiotemporal performance despite low node duty cycles, while significantly reducing communication overhead. We validate our approach through both theoretical analysis and extensive simulations under realistic settings including varying user movement patterns and location errors

The Smart Mobile Application Framework (SMAF) - Exploratory Evaluation in the Smart City Contex

What makes mobile apps "smart"? This paper challenges this question by seeking to identify the inherent characteristics of smartness. Starting with the etymological foundations of the term, elements of smart behavior in software applications are extracted from the literature, elaborated and contrasted. Based on these findings we propose a Smart Mobile Application Framework incorporating a set of activities and qualities associated with smart mobile software. The framework is applied to analyze a specific mobile application in the context of Smart Cities and proves its applicability for uncovering the implementation of smart concepts in real-world settings. Hence, this work contributes to research by conceptualizing a new type of application and provides useful insights to practitioners who want to design, implement or evaluate smart mobile applications

Improved Designs for Application Virtualization

We propose solutions for application virtualization to mitigate the performance loss in streaming and browser-based applications. For the application streaming, we propose a solution which keeps operating system components and application software at the server and streams them to the client side for execution. This architecture minimizes the components managed at the clients and improves the platform-level incompatibility. The runtime performance of application streaming is significantly reduced when the required code is not properly available on the client side. To mitigate this issue and boost the runtime performance, we propose prefetching, i.e., speculatively delivering code blocks to the clients in advance. The probability model on which our prefetch method is based may be very large. To manage such a probability model and the associated hardware resources, we perform an information gain analysis. We draw two lower bounds of the information gain brought by an attribute set required to achieve a prefetch hit rate. We organize the probability model as a look-up table: LUT). Similar to the memory hierarchy which is widely used in the computing field, we separate the single LUT into two-level, hierarchical LUTs. To separate the entries without sorting all entries, we propose an entropy-based fast LUT separation algorithm which utilizes the entropy as an indicator. Since the domain of the attribute can be much larger than the addressable space of a virtual memory system, we need an efficient way to allocate each LUT\u27s entry in a limited memory address space. Instead of using expensive CAM, we use a hash function to convert the attribute values into addresses. We propose an improved version of the Pearson hashing to reduce the collision rate with little extra complexity. Long interactive delays due to network delays are a significant drawback for the browser-based application virtualization. To address this, we propose a distributed infrastructure arrangement for browser-based application virtualization which reduces the average communication distance among servers and clients. We investigate a hand-off protocol to deal with the user mobility in the browser-based application virtualization. Analyses and simulations for information-based prefetching and for mobile applications are provided to quantify the benefits of the proposed solutions

Using context-awareness for storage services in edge computing

Modern mobile networks face a dynamic environment with massive devices and heterogeneous service expectations that will need to significantly scale for 5G. Edge computing approaches aim at enhancing scalability through strategies like computation offloading and local storage services, which will be fundamental to deploying large-scale distributed applications. Unlike the cloud, edge resources are limited, which call for novel techniques to handle large volumes of up- and downstream data under a changing environment. Being closer to data consumers and producers, a compelling view is to adopt context-aware techniques for enabling the edge to work with patterns from mobile traffic at different spatiotemporal scales. In this article, we overview the challenges and opportunities of edge storage from the perspective of context-awareness. We introduce a conceptual architecture to learn and exploit context information for enhancing uplink and downlink scenarios. Finally, we outline future directions for edge applications