Context awareness

About: Context awareness is a research topic. Over the lifetime, 5790 publications have been published within this topic receiving 119944 citations.

Enhancing Situation-Aware Systems through Imprecise Reasoning

Abstract: Context awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. We focus our work on situation awareness; a more holistic variant of context awareness where situations are regarded as logically aggregated contexts. One important problem that arises in such systems is the imperfect observations (e.g., sensor readings) that lead to the estimation of the current context of the user. Hence, the knowledge upon which the context / situation aware paradigm is built is rather vague. To deal with this shortcoming, we propose the use of Fuzzy Logic theory with the purpose of determining (inferring) and reasoning about the current situation of the involved user. We elaborate on the architectural model that enables the system to assume actions autonomously according to previous user reactions and current situation. The captured, imperfect contextual information is matched against pre-developed ontologies in order to approximately infer the current situation of the user. Finally, we present a series of experimental results that provide evidence of the flexible, efficient nature of the proposed situation awareness architecture.

Context and resource awareness in opportunistic network data dissemination

Abstract: Opportunistic networks are challenging mobile ad hoc networks characterised by frequent disconnections and partitioning. In this paper we focus on data dissemination services, i.e. cases in which data should be disseminated in the network without a priori knowledge about the set of intended destinations. We propose a general autonomic data dissemination framework that exploits information about the userspsila context and social behaviour, to decide how to replicate and replace data on nodespsila buffers. Furthermore, our data dissemination scheme explicitly takes into account resource constraints, by jointly considering the expected utility of data replication and the associated costs. The results we present show that our solution is able to improve data availability, provide fairness among nodes, and reduce the network load, with respect to reference proposals available in the literature.

A Framework of Adaptive Interaction Support in Cloud-Based Internet of Things (IoT) Environment

Abstract: This paper discusses the Internet of Things (IoT) within the cloud computing concepts and architectures. We review different frameworks of combined IoT architecture with cloud being in the center. Then we investigate adaptive interaction support concept. Finally, we propose a novel framework that incorporates and supports adaptive interaction of the user with the IoT cloud architecture based on the quality of context information and quality of services. The propsed framework increases user satisfaction and reduces user annoyance towards the IoT cloud environment.

FogStore: A Geo-Distributed Key-Value Store Guaranteeing Low Latency for Strongly Consistent Access

Abstract: We design Fogstore, a key-value store for event-based systems, that exploits the concept of relevance to guarantee low-latency access to relevant data with strong consistency guarantees, while providing tolerance from geographically correlated failures. Distributed event-based processing pipelines are envisioned to utilize the resources of densely geo-distributed infrastructures for low-latency responses - enabling real-time applications. Increasing complexity of such applications results in higher dependence on state, which has driven the incorporation of state-management as a core functionality of contemporary stream processing engines a la Apache Flink and Samza. Processing components executing under the same context (like location) often produce information that may be relevant to others, thereby necessitating shared state and an out-of-band globally-accessible data-store. Efficient access to application state is critical for overall performance, thus centralized data-stores are not a viable option due to the high-latency of network traversals. On the other hand, a highly geo-distributed datastore with low-latency implemented with current key-value stores would necessitate degrading client expectation of consistency as per the PACELC theorem. In this paper we exploit the notion of contextual relevance of events (data) in situation-awareness applications - and offer differential consistency guarantees for clients based on their context. We highlight important systems concerns that may arise with a highly geo-distributed system and show how Fogstore's design tackles them. We present, in detail, a prototype implementation of Fogstore's mechanisms on Apache Cassandra and a performance evaluation. Our evaluations show that Fogstore is able to achieve the throughput of eventually consistent configurations while serving data with strong consistency to the contextually relevant clients.