Context Awareness by Noise-Pattern Analysis of a Smart Factory

Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.

Age-Constrained Dynamic Content Replacing and Delivering for UAV-Assisted Context Awareness

In this work,we employ the cache-enabled UAV to provide context information delivery to end devices that make timely and intelligent decisions.Different from the traditional network traffic,context information varies with time and brings in the ageconstrained requirement.The cached content items should be refreshed timely based on the age status to guarantee the freshness of user-received contents,which however consumes additional transmission resources.The traditional cache methods separate the caching and the transmitting,which are not suitable for the dynamic context information.We jointly design the cache replacing and content delivery based on both the user requests and the content dynamics to maximize the offloaded traffic from the ground network.The problem is formulated based on the Markov Decision Process(MDP).A sufficient condition of cache replacing is found in closed form,whereby a dynamic cache replacing and content delivery scheme is proposed based on the Deep Q-Network(DQN).Extensive simulations have been conducted.Compared with the conventional popularity-based and the modified Least Frequently Used(i.e.,LFU-dynamic)schemes,the UAV can offload around 30%traffic from the ground network by utilizing the proposed scheme in the urban scenario,according to the simulation results.

Context-Aware信息检索技术与模型

Context-aware信息检索技术是一种具有自适应性和自组织性的智能信息检索技术,而且是富有挑战性的研究方向。文章首先叙述了与Context-aware相关的基本概念及实现的基本原理,接着介绍了基于不同Context信息的检索模型,并且讨论了各种模型的实现技术及适用范围。

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.

Context Aware Wireless Sensor Network Suitable for Precision Agriculture

Using the Wireless Sensor Networks WSNs in a wide variety of applications is currently considered one of the most challenging solutions. For instance, this technology has evolved the agriculture field, with the precision agriculture challenge. In fact, the cost of sensors and communication infrastructure continuously trend down as long as the technological advances. So, more growers dare to implement WSN for their crops. This technology has drawn substantial interests by improving agriculture productivity. The idea consists of deploying a number of sensors in a given agricultural parcel in order to monitor the land and crop conditions. These readings help the farmer to make the right inputs at the right moment. In this paper, we propose a complete solution for gathering different type of data from variable fields of a large agricultural parcel. In fact, with the in-field variability, adopting a unique data gathering solution for all kinds of fields reveals an inconvenient approach. Besides, as a fault-tolerant application, precision agriculture does not require a high precision value of sensed data. So, our approach deals with a context aware data gathering strategy. In other words, depending on a defined context for the monitored field, the data collector will decide the data gathering strategy to follow. We prove that this approach improves considerably the lifetime of the application.

CAND-IDS: A Novel Context Aware Intrusion Detection System in Cooperative Wireless Sensor Networks by Nodal Node Deployment

Cooperative wireless sensor networks have drastically grown due to node co-opera- tive in unaltered environment. Various real time applications are developed and deployed under cooperative network, which controls and coordinates the flow to and from the nodes to the base station. Though nodes are interlinked to give expected state behavior, it is vital to monitor the malicious activities in the network. There is a high end probability to compromise the node behavior that leads to catastrophes. To overcome this issue a Novel Context Aware-IDS approach named Context Aware Nodal Deployment-IDS (CAND-IDS) is framed. During data transmission based on node properties and behavior CAND-IDS detects and eliminates the malicious nodes in the explored path. Also during network deployment and enhancement, node has to follow Context Aware Cooperative Routing Protocol (CCRP), to ensure the reliability of the network. CAND-IDS are programmed and simulated using Network Simulator software and the performance is verified and evaluated. The simulation result shows significant improvements in the throughput, energy consumption and delay made when compared with the existing system.

Interactive data styling and multifocal visualization for a multigrid web-based Digital Earth

Globe-based Digital Earth(DE)is a promising system that uses 3D models of the Earth for integration,organization,processing,and visualization of vast multiscale geospatial datasets.The growing size and scale of geospatial datasets present significant obstacles to interactive viewing and meaningful visualizations of these DE systems.To address these challenges,we present a novel web-based multiresolution DE system using a hierarchical discretization of the globe on both server and client sides.The presented web-based system makes use of a novel data encoding technique for rendering large multiscale geospatial datasets,with the additional capability of displaying multiple simultaneous viewpoints.Only the data needed for the current views and scales are encoded and processed.We leverage the power of GPU acceleration on the client-side to perform real-time data rendering and dynamic styling.Efficient rendering of multiple views allows us to support multilevel focus+context visualization,an effective approach to navigate through large multiscale global datasets.The client–server interaction as well as the data encoding,rendering,styling,and visualization techniques utilized by our presented system contribute toward making DE more accessible and informative.

Voice-driven fleet management system for agricultural operations

Food consumption is constantly increasing at global scale.In this light,agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products.However,due to by environmental and biological factors(e.g.soil compaction)the weight and size of the machinery cannot be further physically optimized.Thus,only marginal improvements are possible to increase equipment effectiveness.On the contrary,late technological advances in ICT provide the ground for significant improvements in agriproduction efficiency.In this work,the V-Agrifleet tool is presented and demonstrated.VAgrifleet is developed to provide a “hands-free”interface for information exchange and an “Olympic view”to all coordinated users,giving them the ability for decentralized decision-making.The proposed tool can be used by the end-users(e.g.farmers,contractors,farm associations,agri-products storage and processing facilities,etc.)order to optimize task and time management.The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations.Its vendorindependent architecture,voice-driven interaction,context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system.