3,161 research outputs found
CSM-H-R: An Automatic Context Reasoning Framework for Interoperable Intelligent Systems and Privacy Protection
Automation of High-Level Context (HLC) reasoning for intelligent systems at
scale is imperative due to the unceasing accumulation of contextual data in the
IoT era, the trend of the fusion of data from multi-sources, and the intrinsic
complexity and dynamism of the context-based decision-making process. To
mitigate this issue, we propose an automatic context reasoning framework
CSM-H-R, which programmatically combines ontologies and states at runtime and
the model-storage phase for attaining the ability to recognize meaningful HLC,
and the resulting data representation can be applied to different reasoning
techniques. Case studies are developed based on an intelligent elevator system
in a smart campus setting. An implementation of the framework - a CSM Engine,
and the experiments of translating the HLC reasoning into vector and matrix
computing especially take care of the dynamic aspects of context and present
the potentiality of using advanced mathematical and probabilistic models to
achieve the next level of automation in integrating intelligent systems;
meanwhile, privacy protection support is achieved by anonymization through
label embedding and reducing information correlation. The code of this study is
available at: https://github.com/songhui01/CSM-H-R.Comment: 11 pages, 8 figures, Keywords: Context Reasoning, Automation,
Intelligent Systems, Context Modeling, Context Dynamism, Privacy Protection,
Context Sharing, Interoperability, System Integratio
Cybersecurity Hygiene in the Era of Internet of Things (IoT): Best Practices and Challenges
The rapid growth of the Internet of Things (IoT) has resulted in an increasing number of interconnected devices, creating new opportunities for data collection and automation. However, this expansion also brings with it unique cybersecurity challenges. This research paper aims to investigate the best practices for maintaining cybersecurity hygiene in the IoT environment and explore the challenges that need to be addressed to ensure robust security for these connected devices. This study will delve into the vulnerabilities associated with IoT devices, their impact on overall system security, and the potential solutions that can be implemented to enhance cybersecurity hygiene in the IoT environment
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Resource Management in a Peer to Peer Cloud Network for IoT
Software-Defined Internet of Things (SDIoT) is defined as merging heterogeneous objects in a form of interaction among physical and virtual entities. Large scale of data centers, heterogeneity issues and their interconnections have made the resource management a hard problem specially when there are different actors in cloud system with different needs. Resource management is a vital requirement to achieve robust networks specially with facing continuously increasing amount of heterogeneous resources and devices to the network. The goal of this paper is reviews to address IoT resource management issues in cloud computing services. We discuss the bottlenecks of cloud networks for IoT services such as mobility. We review Fog computing in IoT services to solve some of these issues. It provides a comprehensive literature review of around one hundred studies on resource management in Peer to Peer Cloud Networks and IoT. It is very important to find a robust design to efficiently manage and provision requests and available resources. We also reviewed different search methodologies to help clients find proper resources to answer their needs
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
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