Swaroop Kalasapur

Dynamic Service Composition in Pervasive Computing

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.

Jalangi: a selective record-replay and dynamic analysis framework for JavaScript

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.

Seamless service composition (SeSCo) in pervasive environments

It is a challenging task to develop applications and systems that cater to the needs of ever increasing multimedia applications. Additionally, in pervasive computing environments, multimedia data needs to be delivered to heterogeneous devices with varying capabilities over a variety of communication channels. The objective of this research is to dynamically compose services by effectively utilizing the collective capabilities of resources available to deliver multimedia. Existing schemes provide composite solutions to multimedia applications, work either on a centralized system or assume that the environment is ad-hoc in nature, resulting in additional overheads during composition. Further, some of the existing composition schemes are an extension to discovery, resulting in a discover + match + coordinate scheme. Such schemes would not be effective in dynamically changing environments, due to the uncertainties involved. In this paper, we present a novel composition scheme, called Seamless Service Composition (SeSCo), that operates on automatically configurable resource hierarchies for discovery and composition. SeSCo attempts to weave necessary services by utilizing available individual services seamlessly. Experimental results demonstrate the superiority of our scheme over existing broadcast based schemes.

Evaluating service oriented architectures (SOA) in pervasive computing

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

Service oriented pervasive computing for emergency response systems

Pervasive computing paradigm is characterized by the presence of a diverse variety of computing and communicating devices. The vision of pervasive computing is to enable effective support for user tasks by utilizing the devices carried by the users and those available within the infrastructure around the users. Emergency response is very critical to crisis situations. The process of emergency response can benefit greatly by the application of pervasive computing principles. In this paper, we describe a prototype designed to enhance the existing emergency response mechanism in attending to automobile accident victims. The prototype has been built using our event oriented middleware called pervasive information communities organization (PICO), and is built on top of a popular P2P framework called JXTA. By utilizing the middleware, we model the useful features of devices available within a car as services. Our proposed framework allows the creation, discovery and maintenance of continuous services such as crash detection. Further more, the framework allows the composition of complex services on occurrence of events by utilizing the available basic services with a goal to provide essential support in case of an accident. We present the design and implementation details of the prototype and snapshots of the working prototype

Personalized service composition for ubiquitous multimedia delivery

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.

Mobile Situation-Aware Task Recommendation Application

With more and more applications available on mobile devices, it has become increasingly difficult for users to find a desired application. Although research has been conducted for situation-awarere commendations on mobile devices, none addresses this problem; most research is for media content recommendations. Moreover, existing approaches assume predefined situations and/or user-specified profiles; some require users to intentionally train their devices before using them for recommendations. We believe that what defines a situation and what applications are preferred in the situation not only vary from user to user but also change over time, and therefore these assumptions and requirements are impractical for ordinary consumers. In this paper, we will describe our approach of using unsupervised learning, specifically co-clustering, to derive latent situation-based patterns from usage logs of user interactions with the device and environments and use the patterns for task and communication mode recommendations.

Jalangi: a tool framework for concolic testing, selective record-replay, and dynamic analysis of JavaScript

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.

Sensor fusion of physical and social data using Web SocialSense on smartphone mobile browsers

Modern smartphones offer a rich selection of onboard sensors, where sensor access is typically performed through API calls provided by the phones 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.

Nnon-collaborative interest mining for personal devices

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 peoples 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 users situation.