Modern smartphones offer a rich selection of onboard sensors, where sensor access is typically performed through API calls provided by the phone's operating system. In this paper we evaluate the viability of implementing sensor processing entirely in the Web browser layer with Web SocialSense, a JavaScript framework for Tizen smartphones that uses a graph topology-based paradigm. This framework enables programmers to write personalized, context-aware applications that can dynamically fuse time-series signals from physical sensors (such as the accelerometer and geolocation services) and social software sensors (such as social network services and personal information management applications). To demonstrate the framework we implemented components for physical sensing and social software sensing to drive two context-aware applications, ActVertisements and Social Map.

JavaScript is widely used for writing client-side web applications and is getting increasingly popular for writing mobile applications. However, unlike C, C++, and Java, there are not that many tools available for analysis and testing of JavaScript applications. In this paper, we present a simple yet powerful framework, called Jalangi, for writing heavy-weight dynamic analyses. Our framework incorporates two key techniques: 1) selective record-replay, a technique which enables to record and to faithfully replay a user-selected part of the program, and 2) shadow values and shadow execution, which enables easy implementation of heavy-weight dynamic analyses. Our implementation makes no special assumption about JavaScript, which makes it applicable to real-world JavaScript programs running on multiple platforms. We have implemented concolic testing, an analysis to track origins of nulls and undefined, a simple form of taint analysis, an analysis to detect likely type inconsistencies, and an object allocation profiler in Jalangi. Our evaluation of Jalangi on the SunSpider benchmark suite and on five web applications shows that Jalangi has an average slowdown of 26X during recording and 30X slowdown during replay and analysis. The slowdowns are comparable with slowdowns reported for similar tools, such as PIN and Valgrind for x86 binaries. We believe that the techniques proposed in this paper are applicable to other dynamic languages.

We describe a tool framework, called Jalangi, for dynamic analysis and concolic testing of JavaScript programs. The framework is written in JavaScript and allows implementation of various heavy-weight dynamic analyses for JavaScript. Jalangi incorporates two key techniques: 1) selective record-replay, a technique which enables to record and to faithfully replay a user-selected part of the program, and 2) shadow values and shadow execution, which enables easy implementation of heavy-weight dynamic analyses such as concolic testing and taint tracking. Jalangi works through source-code instrumentation which makes it portable across platforms. Jalangi is available at https://github.com/SRA-SiliconValley/jalangi under Apache 2.0 license. Our evaluation of Jalangi on the SunSpider benchmark suite and on five web applications shows that Jalangi has an average slowdown of 26X during recording and 30X slowdown during replay and analysis. The slowdowns are comparable with slowdowns reported for similar tools, such as PIN and Valgrind for x86 binaries.

To provide consumers with the right information at the time of need, we developed a SmartSearch application that is able to extract a user's situational interests from usage data. It automatically constructs search queries based on situational interests. We extract the situational interests automatically without prior training and user involvement. In addition, SmartSearch is a client-side solution running completely on a mobile device to protect user's privacy.

In our daily life we frequently use mobile devices to interact with the people and things on the Internet. However, finding the right things when needed is getting difficult and frustrating. In this paper, we introduce a relatively new problem of non-collaborative personal interest mining using contexts and ratings available for items of interest. We present multi-step algorithms to extract personal situational interests from mobile phone usage logs without depending on other people's data. The algorithms are based on clustering or a direct analogy from collaborative filtering. We provide extensive experimental results with our accuracy measure for synthetic data sets. The main advantages of our algorithms are: 1) no need for the user to train the phone actively, 2) no need for prior knowledge of the situations contained in a data set, 3) light-weight and running completely on a personal mobile phone and 4) good performance over low data densities. We also present a SmartSearch application. Upon user request, it automatically constructs search queries based on learned user interests and obtains information and advertisements for the user that suit the user's situation.

In this paper, we target the problem of the situation-aware application (task) recommendation on mobile devices. To tackle this problem, we develop both supervised and unsupervised approaches. We use Naive Bayesian as a supervised approach, and co-clustering and vector quantization (VQ) as unsupervised approaches. We evaluate the performance of the proposed approaches with both synthetic and actual user log data that we have collected for six months. Our initial experiment shows that the co-clustering-based approach results in comparable purity performance with much less computation time than VQ. Therefore, the co-clustering approach can be practical for high dimensional data. Furthermore, we characterize the recommendation performance of the proposed approaches in terms of the receiver-operating-characteristics (ROC). One interesting observation is that the unsupervised approaches perform well with a single identical threshold over all applications, while the supervised approach does better with a different threshold for each application.

Having reliable context sources is very important for context-aware applications and the devices around a user can be a useful context for many applications. While the importance of ‘devices around’ as a context has been highlighted many times, to the best of our knowledge, there is no systematic me

WSDL provides the potential for Web services to enrich consumers' lives. However, it has had only limited success in enterprise environments and even less in the mass market. Apart from the difficulties and high costs involved in current approaches, another reason is the low precision for Web Services discovery using the most widely known tool: search engines. Seeking for effective ways to change the situation, we conducted an experiment to examine better approaches of using general-purpose search engines to discover Web Services. We used nine different approaches for publishing Web Services and two groups of total 18 queries for retrieving them using Yahoo and Google search engines. The queries were fired to each search engine daily over a week and the top 100 search results returned from every search are collected and analyzed. The results show that for both search engines, embedding a WSDL specification in a Web page that provides semantic description of the service yield the best results.

Service-oriented architectures (SOAs) promise to provide transparency to resource access by exposing the resources available as services. SOAs have been employed within pervasive computing systems to provide essential support to user tasks by creating services representing the available resources. The mechanism of combining two or more basic services into a possibly complex service is known as service composition. Existing solutions to service composition employ a template-matching approach, where the user needs are expressed as a request template, and through composition, a system would identify services to populate the entities within the request template. However, with the dynamism involved in pervasive environments, the user needs have to be met by exploiting available resources, even when an exact match does not exist. In this paper, we present a novel service composition mechanism for pervasive computing. We employ the service-oriented middleware platform called Pervasive Information Communities Organization (PICO) to model and represent resources as services. The proposed service composition mechanism models services as directed attributed graphs, maintains a repository of service graphs, and dynamically combines multiple basic services into complex services. Further, we present a hierarchical overlay structure created among the devices to exploit the resource unevenness, resulting in the capability of providing essential service-related support to resource-poor devices. Results of extensive simulation studies are presented to illustrate the suitability of the proposed mechanism in meeting the challenges of pervasive computing-user mobility, heterogeneity, and the uncertain nature of involved resources.

Increasing popularity of the pervasive computing paradigm on one hand and the technological developments on the other, have paved the way for development and deployment of pervasive services in the everyday habitat. The service providers, who build, operate and manage services would want to maximize their revenues, and at the same time, the users would seek guarantees to support their applications. While there are a number of mechanisms proposed to build and operate service oriented architectures (SOAs) in pervasive computing arena, very little work has been done in terms of evaluating such schemes. The work presented in this paper proposes mechanisms to evaluate SOAs in pervasive computing. We also present a mechanism to evaluate service composition techniques, which are an effective way of delivering services to the end user. In earlier work, we proposed a service composition mechanism in pervasive computing environments called seamless service composition (SeSCo). We evaluate and compare SeSCo with a discover+match mechanism for the deployment of pervasive services within an automobile. We also present performance results of the two approaches through simulation.

Digital multimedia has gained popularity due to the huge success of the Internet. Users acquire and disseminate multimedia information using different types of devices and communication channels. On the other hand, personal devices, such as laptops, PDAs and smart phones, are not only capable of producing and rendering multimedia, but also support associated computing and communication tasks. However, there is a lack of support for ensuring automated, continuous access to multimedia in the presence of dynamicity and heterogeneity. Pervasive computing concepts can be effectively employed to provide anytime anywhere multimedia support to users over heterogeneous wireless environments. We present a novel approach to support adaptive services for multimedia delivery in heterogeneous wireless networks. The service adaptation and composition mechanism exploits middleware tools developed for pervasive information communities of software agents. We also demonstrate the feasibility of the proposed scheme through an intuitive scenario, and present our prototype results.