43,606 research outputs found
A Data Annotation Architecture for Semantic Applications in Virtualized Wireless Sensor Networks
Wireless Sensor Networks (WSNs) have become very popular and are being used
in many application domains (e.g. smart cities, security, gaming and
agriculture). Virtualized WSNs allow the same WSN to be shared by multiple
applications. Semantic applications are situation-aware and can potentially
play a critical role in virtualized WSNs. However, provisioning them in such
settings remains a challenge. The key reason is that semantic applications
provisioning mandates data annotation. Unfortunately it is no easy task to
annotate data collected in virtualized WSNs. This paper proposes a data
annotation architecture for semantic applications in virtualized heterogeneous
WSNs. The architecture uses overlays as the cornerstone, and we have built a
prototype in the cloud environment using Google App Engine. The early
performance measurements are also presented.Comment: This paper has been accepted for presentation in main technical
session of 14th IFIP/IEEE Symposium on Integrated Network and Service
Management (IM 2015) to be held on 11-15 May, 2015, Ottawa, Canad
Tools for distributed application management
Distributed application management consists of monitoring and controlling an application as it executes in a distributed environment. It encompasses such activities as configuration, initialization, performance monitoring, resource scheduling, and failure response. The Meta system (a collection of tools for constructing distributed application management software) is described. Meta provides the mechanism, while the programmer specifies the policy for application management. The policy is manifested as a control program which is a soft real-time reactive program. The underlying application is instrumented with a variety of built-in and user-defined sensors and actuators. These define the interface between the control program and the application. The control program also has access to a database describing the structure of the application and the characteristics of its environment. Some of the more difficult problems for application management occur when preexisting, nondistributed programs are integrated into a distributed application for which they may not have been intended. Meta allows management functions to be retrofitted to such programs with a minimum of effort
Security in Wireless Sensor Networks: Issues and Challenges
Wireless Sensor Network (WSN) is an emerging technology that shows great
promise for various futuristic applications both for mass public and military.
The sensing technology combined with processing power and wireless
communication makes it lucrative for being exploited in abundance in future.
The inclusion of wireless communication technology also incurs various types of
security threats. The intent of this paper is to investigate the security
related issues and challenges in wireless sensor networks. We identify the
security threats, review proposed security mechanisms for wireless sensor
networks. We also discuss the holistic view of security for ensuring layered
and robust security in wireless sensor networks.Comment: 6 page
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
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