96,842 research outputs found
Design and Implementation of S-MARKS: A Secure Middleware for Pervasive Computing Applications
As portable devices have become a part of our everyday life, more people are unknowingly participating in a pervasive computing environment. People engage with not a single device for a specific purpose but many devices interacting with each other in the course of ordinary activity. With such prevalence of pervasive technology, the interaction between portable devices needs to be continuous and imperceptible to device users. Pervasive computing requires a small, scalable and robust network which relies heavily on the middleware to resolve communication and security issues. In this paper, we present the design and implementation of S-MARKS which incorporates device validation, resource discovery and a privacy module
Architecture and Implementation of a Trust Model for Pervasive Applications
Collaborative effort to share resources is a significant feature of pervasive computing environments. To achieve secure service discovery and sharing, and to distinguish between malevolent and benevolent entities, trust models must be defined. It is critical to estimate a device\u27s initial trust value because of the transient nature of pervasive smart space; however, most of the prior research work on trust models for pervasive applications used the notion of constant initial trust assignment. In this paper, we design and implement a trust model called DIRT. We categorize services in different security levels and depending on the service requester\u27s context information, we calculate the initial trust value. Our trust value is assigned for each device and for each service. Our overall trust estimation for a service depends on the recommendations of the neighbouring devices, inference from other service-trust values for that device, and direct trust experience. We provide an extensive survey of related work, and we demonstrate the distinguishing features of our proposed model with respect to the existing models. We implement a healthcare-monitoring application and a location-based service prototype over DIRT. We also provide a performance analysis of the model with respect to some of its important characteristics tested in various scenarios
Specification and Verification of Context-dependent Services
Current approaches for the discovery, specification, and provision of
services ignore the relationship between the service contract and the
conditions in which the service can guarantee its contract. Moreover, they do
not use formal methods for specifying services, contracts, and compositions.
Without a formal basis it is not possible to justify through formal
verification the correctness conditions for service compositions and the
satisfaction of contractual obligations in service provisions. We remedy this
situation in this paper. We present a formal definition of services with
context-dependent contracts. We define a composition theory of services with
context-dependent contracts taking into consideration functional,
nonfunctional, legal and contextual information. Finally, we present a formal
verification approach that transforms the formal specification of service
composition into extended timed automata that can be verified using the model
checking tool UPPAAL.Comment: In Proceedings WWV 2011, arXiv:1108.208
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
Domino: exploring mobile collaborative software adaptation
Social Proximity Applications (SPAs) are a promising new area for ubicomp software that exploits the everyday changes in the proximity of mobile users. While a number of applications facilitate simple file sharing between co–present users, this paper explores opportunities for recommending and sharing software between users. We describe an architecture that allows the recommendation of new system components from systems with similar histories of use. Software components and usage histories are exchanged between mobile users who are in proximity with each other. We apply this architecture in a mobile strategy game in which players adapt and upgrade their game using components from other players, progressing through the game through sharing tools and history. More broadly, we discuss the general application of this technique as well as the security and privacy challenges to such an approach
- …