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The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results. [ABSTRACT FROM AUTHOR]

Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency. [ABSTRACT FROM AUTHOR]

Mobile Agent (MA) technology brings many benefits into Wireless Sensor Networks (WSNs), such as saving network bandwidth and enabling energy efficient mechanisms for collecting sensor data. Nowadays, itinerary planning for MAs is one of the most important features of the WSN. However, the way in which all dispatched MAs are routed inside the sensor networks must be intelligently planned to reduce energy consumption and improve information accuracy. There have been many research efforts designing itinerary planning algorithms to deploy multiple MAs in a given sensor network, where routes are generated so that MAs can follow different routes to collect data from sensor nodes efficiently and effectively. This paper proposes a new energy efficient Graph-based Static Mutli-Mobile Agent Itinerary Planning approach (GSMIP). GSMIP applies Directed Acyclic Graph (DAG) related techniques and divide sensor nodes into different groups based on the routes defined by MAs itineraries. MAs follow the predefined routes and only collect data from the groups they are responsible for. The experimental findings demonstrate the effectiveness and superiority of the proposed approach compared to the existing approaches in terms of energy consumption and task delay (time). [ABSTRACT FROM AUTHOR]

It is estimated that there will be approximately 125 billion Internet of Things (IoT) devices connected to the Internet by 2030, which are expected to generate large amounts of data. This will challenge data processing capability, infrastructure scalability, and privacy. Several studies have demonstrated the benefits of using distributed intelligence (DI) to overcome these challenges. We propose a Mobile-Agent Distributed Intelligence Tangle-Based approach (MADIT) as a potential solution based on IOTA (Tangle), where Tangle is a distributed ledger platform that enables scalable, transaction-based data exchange in large P2P networks. MADIT enables distributed intelligence at two levels. First, multiple mobile agents are employed to cater for node level communications and collect transactions data at a low level. Second, high level intelligence uses a Tangle based architecture to handle transactions. The Proof-of-Work offloading computation mechanism improves efficiency and speed of processing, while reducing energy consumption. Extensive experiments show that transaction processing speed is improved by using mobile agents, thereby providing better scalability. [ABSTRACT FROM AUTHOR]

Since today's real-world graphs, such as social network graphs, are evolving all the time, it is of great importance to perform graph computations and analysis in these dynamic graphs. Due to the fact that many applications such as social network link analysis with the existence of inactive users need to handle failed links or nodes, decremental computation and maintenance for graphs is considered a challenging problem. Shortest path computation is one of the most fundamental operations for managing and analyzing large graphs. A number of indexing methods have been proposed to answer distance queries in static graphs. Unfortunately, there is little work on answering such queries for dynamic graphs. In this paper, we focus on the problem of computing the shortest path distance in dynamic graphs, particularly on decremental updates (i.e., edge deletions). We propose maintenance algorithms based on distance labeling, which can handle decremental updates efficiently. By exploiting properties of distance labeling in original graphs, we are able to efficiently maintain distance labeling for new graphs. We experimentally evaluate our algorithms using eleven real-world large graphs and confirm the effectiveness and efficiency of our approach. More specifically, our method can speed up index re-computation by up to an order of magnitude compared with the state-of-the-art method, Pruned Landmark Labeling (PLL). [ABSTRACT FROM AUTHOR]

XML data broadcast is an efficient way to disseminate semistructured information in wireless mobile environments. In this paper, we propose a novel two-tier index structure to facilitate the access of XML document in an on-demand broadcast system. It provides the clients with an overall image of all the XML documents available at the server side and hence enables the clients to locate complete result sets accordingly. A pruning strategy is developed to cut down the index size and a two-tier structure is proposed to further remove any redundant information. In addition, two index distribution strategies, namely naive distribution and partial distribution, have been designed to interleave the index information with the XML documents in the wireless channels. Theoretical analysis and simulation experiments are also put forward to show the benefits of our indexing methods. [ABSTRACT FROM AUTHOR]

One of the challenges of managing an RDF database is predicting performance of SPARQL queries before they are executed. Performance characteristics, such as the execution time and memory usage, can help data consumers identify unexpected long-running queries before they start and estimate the system workload for query scheduling. Extensive works address such performance prediction problem in traditional SQL queries but they are not directly applicable to SPARQL queries. In this paper, we adopt machine learning techniques to predict the performance of SPARQL queries. Our work focuses on modeling features of a SPARQL query to a vector representation. Our feature modeling method does not depend on the knowledge of underlying systems and the structure of the underlying data, but only on the nature of SPARQL queries. Then we use these features to train prediction models. We propose a two-step prediction process and consider performances in both cold and warm stages. Evaluations are performed on real world SPRAQL queries, whose execution time ranges from milliseconds to hours. The results demonstrate that the proposed approach can effectively predict SPARQL query performance and outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), in general, is a compelling paradigm that aims to connect everyday objects to the Internet. Nowadays, IoT is considered as one of the main technologies which contribute towards reshaping our daily lives in the next decade. IoT unlocks many exciting new opportunities in a variety of applications in research and industry domains. However, many have complained about the absence of the real-world IoT data. Unsurprisingly, a common question that arises regularly nowadays is ' Does the IoT already exist?'. So far, little has been known about the real-world situation on IoT, its attributes, the presentation of data, and user interests. To answer this question, in this work, we conduct an in-depth analytical investigation on real IoT data. More specifically, we identify IoT data sources over the Web and develop a crawler engine to collect large-scale real-world IoT data for the first time. We make the results of our work available to the public in order to assist the community in the future research. In particular, we collect the data of nearly two million Internet connected objects and study trends in IoT using a real-world query set from an IoT search engine. Based on the collected data and our analysis, we identify the typical characteristics of IoT data. The most intriguing finding of our study is that IoT data is mainly disseminated using Web Mapping while the emerging IoT solutions such as the Web of Things are currently not well adopted. On top of our findings, we further discuss future challenges and open research problems in the IoT area. [ABSTRACT FROM AUTHOR]

Graph matching is important for a wide variety of applications in different domains such as social network analysis and knowledge discovery. Despite extensive research over the last few decades, graph matching is still challenging particularly when it comes with new conditions and constraints. In this paper, we focus on a new class of graph matching, in which each node can accept multiple labels instead of one. In particular, we address the problem of finding the top- k nodes of a data graph which best match a labeled query node from a given pattern graph. We firstly prove this to be an NP-Complete problem. Then, to address this issue and improve the scalability of our approach, we introduce a more flexible graph simulation, namely surjective simulation . This new graph simulation reduces the unnecessary complexity that is due to the unnecessary constraints imposed by the existing definitions while achieving high-quality matching results. In addition, our approach is associated with an early stop strategy to further boost the performance. To approximate the maximum size of a simulation, our approach utilizes Metropolis Hastings algorithm and ranks the top-k matches after computing the set of surjective simulations. The experimental results over social network graphs demonstrate the efficiency of the proposed approach and superiority over existing approaches. [ABSTRACT FROM AUTHOR]

The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remain many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness. [ABSTRACT FROM AUTHOR]