Conference Paper

Detection of Behavioral Contextual Properties in Asynchronous Pervasive Computing Environments.

DOI: 10.1109/ICPADS.2010.24 Conference: IEEE 16th International Conference on Parallel and Distributed Systems, ICPADS 2010, 8-10 Dec. 2010, Shanghai, China
Source: DBLP

ABSTRACT Detection of contextual properties is one of the primary approaches to enabling context-awareness. In order to adapt to temporal evolution of the pervasive computing environment, context-aware applications often need to detect behavioral properties specified over the contexts. This problem is challenging mainly due to the intrinsic asynchrony of pervasive computing environments. However, existing schemes implicitly assume the availability of a global clock or synchronous coordination, thus not working in asynchronous environments. We argue that in pervasive computing environments, the concept of time needs to be reexamined. Toward this objective, we propose the Ordering Global Activity (OGA) algorithm, which detects behavioral contextual properties in asynchronous environments. The essence of our approach is to utilize the message causality and its on-the-fly coding as logical vector clocks. The OGA algorithm is implemented and evaluated based on the open-source context-aware middleware MIPA. The evaluation results show the impact of asynchrony on the detection of contextual properties, which justifies the primary motivation of our work. They also show that OGA can achieve accurate detection of contextual properties in dynamic pervasive computing environments.

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    ABSTRACT: The proliferation of sensing and monitoring applications motivates adoption of the event stream model of computation. Though sliding windows are widely used to facilitate effective event stream processing, it is greatly challenged when the event sources are distributed and asynchronous. To address this challenge, we first show that the snapshots of the asynchronous event streams within the sliding window form a convex distributive lattice (denoted by Lat-Win). Then we propose an algorithm to maintain Lat-Win at runtime. The Lat-Win maintenance algorithm is implemented and evaluated on the open-source context-aware middleware we developed. The evaluation results first show the necessity of adopting sliding windows over asynchronous event streams. Then they show the performance of detecting specified predicates within Lat-Win, even when faced with dynamic changes in the computing environment.
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    ABSTRACT: Formal specification and runtime detection of contextual properties is one of the primary approaches to enabling context awareness in pervasive computing environments. Due to the intrinsic dynamism of the pervasive computing environment, dynamic properties, which delineate concerns of context-aware applications on the temporal evolution of the environment state, are of great importance. However, detection of dynamic properties is challenging, mainly due to the intrinsic asynchrony among computing entities in the pervasive computing environment. Moreover, the detection must be conducted at runtime in pervasive computing scenarios, which makes existing schemes do not work. To address these challenges, we propose the property detection for asynchronous context (PDAC) framework, which consists of three essential parts: 1) Logical time is employed to model the temporal evolution of environment state as a lattice. The active surface of the lattice is introduced as the key notion to model the runtime evolution of the environment state; 2) Specification of dynamic properties is viewed as a formal language defined over the trace of environment state evolution; and 3) The SurfMaint algorithm is proposed to achieve runtime maintenance of the active surface of the lattice, which further enables runtime detection of dynamic properties. A case study is conducted to demonstrate how the PDAC framework enables context awareness in asynchronous pervasive computing scenarios. The SurfMaint algorithm is implemented and evaluated over MIPA - the open-source context-aware middleware we developed. Performance measurements show the accuracy and cost-effectiveness of SurfMaint, even when faced with dynamic changes in the asynchronous pervasive computing environment.
    IEEE Transactions on Parallel and Distributed Systems 01/2013; 24(8):1546-1555. · 1.80 Impact Factor

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