Adaptive Data Replication for Load Sharing in a Sensor Data Center

Clouds provide a platform for efficiently and flexibly aggregating, storing, and processing large amounts of data. Eventually, sensor networks will automatically collect such data. A particular challenge regarding sensor data in Clouds is the inherent sensitive nature of sensed information. For current Cloud platforms, the data owner loses control over her sensor data once it enters the Cloud. This imposes a major adoption barrier for bridging Cloud computing and sensor networks, which we address henceforth. After analyzing threats to sensor data in Clouds, the authors propose a Cloud architecture that enables end-to-end control over sensitive sensor data by the data owner. The authors introduce a well-defined entry point from the sensor network into the Cloud, which enforces end-to-end data protection, applies encryption and integrity protection, and grants data access. Additionally, the authors enforce strict isolation of services. The authors show the feasibility and scalability of their Cloud architecture using a prototype and measurements.

In developing countries, traffic in a road network is a major issue. In this paper we investigate the tradeoff between speed versus accuracy of predicting the severity of road traffic congestion. The timely prediction of traffic congestion using semantic web technologies that will be helpful in various applications like better road guidance, vehicle navigation system. In the proposed work, ontology is created based on sensor and video data. By using rule inference of ontology on parallel processing of sensor and video data, our system gives the timely prediction of traffic congestion.

The Web becomes a large knowledge database in the world. Most of the data stored in the Web are text based. Recently, many kinds of sensors, including obstacle sensors, video cameras, thermometers, and so on, have been installed in various places in our daily living environments: stations, streets, malls, etc. It becomes important to open the data obtained by the sensors existing in our daily living environment for various purposes. Since the data obtained by observing the real world directly with sensors include real-world information different from the Web, a new worldwide social information infrastructure, which we call Sensing Web, may be realized. In the workshop, we would like to discuss technical research issues related to pattern recognition for realizing the Sensing Web. The fields of interest are as follows, but not limited: • Sensor networks, sensor data transmission • Sensor data processing, including visual data, audio data, access logs, and other sensor data • Privacy handling for sensor data • Sensor data description for data exchange and data retrieval • Sensor data presentation • Other applications for sensor data

The basics Internet of things based on Wireless Sensor Network (WSN) has grown rapidly in Industrial Revolution 4.0. Many researchers use the Wireless Sensor Network technology for obtaining the sensor data and stored in the Data Center. The collected data through WSN from an environment was sent to a Data Center. In the Data Center, all the sensor data can be accessed by everyone using their devices. In this case, the user can get the data with a smartphone, laptop, and personal computer through the HTTP Protocol. Data Center without protection from illegal access is extremely dangerous. All the sensor data stored in the data center can be intercepted, tracked, and even changed by a user without access. There is required a security mechanism for protecting all of the data stored in the Data Center. Ciphertext Policy Attributed-Based Encryption can be deployed as a security mechanism for protecting all the sensor data in the Environmental Monitoring. The security mechanism protected Data Center with encryption, decryption, verification, and revocation access for all the users. The only user with the access right can get the sensor data from environmental monitoring. Our security mechanism offers revocation and verification with a dynamic attribute for each user with a lower computational time. We used the CP-ABE with Dynamical attribute and timestamp digital signature based on Elliptic Curve Digital Signature Algorithm (ECDSA) 384 bits.

We present an approach to a general decision support system. The aim is to cover the complete process for automatic target recognition, from sensor data to the user interface. The approach is based on a query-based information system, and include tasks like feature extraction from sensor data, data association, data fusion and situation analysis. Currently, we are working with data from laser radar, infrared cameras, and visual cameras, studying target recognition from cooperating sensors on one or several platforms. The sensors are typically airborne and at low altitude. The processing of sensor data is performed in two steps. First, several attributes are estimated from the (unknown but detected) target. The attributes include orientation, size, speed, temperature etc. These estimates are used to select the models of interest in the matching step, where the target is matched with a number of target models, returning a likelihood value for each model. Several methods and sensor data types are used in both steps. The user communicates with the system via a visual user interface, where, for instance, the user can mark an area on a map and ask for hostile vehicles in the chosen area. The user input is converted to a query in ΣQL, a query language developed for this type of applications, and an ontological system decides which algorithms should be invoked and which sensor data should be used. The output from the sensors is fused by a fusion module and answers are given back to the user. The user does not need to have any detailed technical knowledge about the sensors (or which sensors that are available), and new sensors and algorithms can easily be plugged into the system.

An improved multi-channel graph convolutional network and its applications for rotating machinery diagnosis

MapReduce has been widely used in Hadoop for parallel processing larger-scale data for the last decade. However, remote-sensing (RS) algorithms based on the programming model are trapped in dense disk I/O operations and unconstrained network communication, and thus inappropriate for timely processing and analyzing massive, heterogeneous RS data. In this paper, a novel in-memory computing framework called Apache Spark (Spark) is introduced. Through its merits of transferring transformation to in-memory datasets of Spark, the shortages are eliminated. To facilitate implementation and assure high performance of Spark-based algorithms in a complex cloud computing environment, a strip-oriented parallel programming model is proposed. By incorporating strips of RS data with resilient distributed datasets (RDDs) of Spark, all-level parallel RS algorithms can be easily expressed with coarse-grained transformation primitives and BitTorrent-enabled broadcast variables. Additionally, a generic image partition method for Spark-based RS algorithms to efficiently generate differentiable key/value strips from a Hadoop distributed file system (HDFS) is implemented for concealing the heterogeneousness of RS data. Data-intensive multitasking algorithms and iteration-intensive algorithms were evaluated on a Hadoop yet another resource negotiator (YARN) platform. Experiments indicated that our Spark-based parallel algorithms are of great efficiency, a multitasking algorithm took less than 4 h to process more than half a terabyte of RS data on a small YARN cluster, and 9*9 convolution operations against a 909-MB image took less than 260 s. Further, the efficiency of iteration-intensive algorithms is insensitive to image size.

Probabilistic reconstruction for spatiotemporal sensor data integrated with Gaussian process regression

ObjectiveAssess utility of wearable impact sensors to monitor and reduce cumulative brain injury risks in athletic and military environments.DesignAnalysis of results generated in multiple laboratory and on-field cohort studies.SettingYouth, college,...

Neuroalgorithms and neuronetwork structures were analyzed, basic components of neuronetworks were defined and the principles of their development were chosen. It was shown that using the method of parallel vertical group data processing for the implementation of the neuronetworks basic components provides speed increase, reduce of hardware costs and increasing of the equipment use efficiency. Parallel vertical group codes converter, which provides time alignment of data receipt processes and bit sections formation, was developed. The methods and the structures of the components with parallel vertical group data processing for definition of maximum and minimum numbers in the arrays, calculation of the sum of differences squares and scalar product, which due to parallel processing of bit sections groups, provide speed increase, were developed. It was shown that use of the developed basic components for neuronetworks synthesis will provide reduction of time and development cost.

Data intensive applications are now ubiquitous in many fields, e.g. high-energy physics, astronomy, climate modeling, and the geosciences. Especially for spatial data processing in the distributed platforms, the parallel tasks are generally required to handle massive spatial datasets and usually treated as data-intensive applications. In order to address the challenge of massive spatial data processing, we propose a hypergraph based tasks scheduling strategy on a master-slave platform. Our task scheduling strategy involves two consecutive steps: mapping and scheduling. In the mapping process, we formulate a hypergraph partition model to decide which tasks will be executed by each slave processor. At the same time, the scheduling process determines the execution order of selected tasks and the order in which the master transfers the files to the slaves. An experiment based on the GridSim toolkit was conducted to evaluate and compare our scheduling algorithm with Min-min heuristic. Our experimental results show that our scheduling strategy outperforms the Min-min heuristic.

This paper presents a scalable and collaborative mobile crowdsensing framework for efficient collective understanding of users, contexts, and their environments. Collaborative mobile crowdsensing enables information to be gathered and shared by users who are directly involved (participatory sensing) or integrated seamlessly as needed (opportunistic sensing) through user mobile platforms. To address the scalability needs of the mobile ecosystem, we additionally employ compressive sensing techniques for approximate gathering and processing of sensor data - this requires new mechanisms for sensor data collection, tunable approximate processing, and mobile networking architecture, to create a compressive collaborative mobile crowdsensing platform called SenseDroid. The proposed framework is build using a multi-tired hierarchical architecture to sense spatial variations of a parameter of interest, perceive spatio-temporal fields, and enable energy efficient local mobile sensing with a small number of measurements. This approximate, yet tunable approach combines different sensing approaches opportunistically while trading scalability (and coverage) for data accuracy (and energy efficiency). In this paper we propose and discuss the framework and the challenges associated with compressive and collaborative mobile sensing for multi-tired hierarchical mobile network architecture for emerging mobile collaborative applications.

This paper presents a smart mobile system for a body sensor network (BSN) that collects, displays, analyzes and streams multiple sensor data to a centralized computing server. Multiple wireless protocols including the Bluetooth, cellular data network, Wi-Fi and Near Field Communication (NFC) are used to transmit sensor data. An Intelligent Personal Communication Node (iPCN) using an Android smartphone is introduced consisting of sensor data collection, processing, analysis and transmission by the smartphone. Various sensor data are tested including acceleration, temperature and electrocardiography (ECG) data to demonstrate system extensibility. Particularly for ECG sensor data, a QRS detection algorithm for heart beat rate (HBR) calculation is implemented to demonstrate Android system computation feasibility for real-time signal processing. A major advantage of the Android system is the ability to communicate with sensor nodes on-demand and to acquire realtime multiple sensor data simultaneously. The proposed smart sensing system is not restricted to BSN, but also can have applications for any critical environment that requires instantaneous and remotely accessible monitoring system.

Human fall detection is a significant healthcare concern, particularly among the elderly, due to its links to muscle weakness, cardiovascular issues, and locomotive syndrome. Accurate fall detection is crucial for timely intervention and injury prevention, which has led many researchers to work on developing effective detection systems. However, existing unimodal systems that rely solely on skeleton or sensor data face challenges such as poor robustness, computational inefficiency, and sensitivity to environmental conditions. While some multimodal approaches have been proposed, they often struggle to capture long-range dependencies effectively. In order to address these challenges, we propose a multimodal fall detection framework that integrates skeleton and sensor data. The system uses a Graph-based Spatial-Temporal Convolutional and Attention Neural Network (GSTCAN) to capture spatial and temporal relationships from skeleton and motion data information in stream-1, while a Bi-LSTM with Channel Attention (CA) processes sensor data in stream-2, extracting both spatial and temporal features. The GSTCAN model uses AlphaPose for skeleton extraction, calculates motion between consecutive frames, and applies a graph convolutional network (GCN) with a CA mechanism to focus on relevant features while suppressing noise. In parallel, the Bi-LSTM with CA processes inertial signals, with Bi-LSTM capturing long-range temporal dependencies and CA refining feature representations. The features from both branches are fused and passed through a fully connected layer for classification, providing a comprehensive understanding of human motion. The proposed system was evaluated on the Fall Up and UR Fall datasets, achieving a classification accuracy of 99.09% and 99.32%, respectively, surpassing existing methods. This robust and efficient system demonstrates strong potential for accurate fall detection and continuous healthcare monitoring.

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.