Sensor Information Processing Research Articles

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

Devising a method for the virtual clustering of the Internet of Things edge environment

The object of research is the process of load distribution in the edge environment of the Internet of Things. The task to improve the efficiency of the functioning of the network of computing devices in the Internet of Things edge environment has been solved. Free resources of heterogeneous single-board computers were used to this end. In the process of conducting research, an approach to the construction of an architecture for a virtual cluster of computers with limited resources was devised. The design took into account specific features of the edge environment on the Internet of Things. This has made it possible to propose a four-layer architecture instead of the standard seven-layer architecture of IoT sensor information processing device networks. Stages in the virtual cluster construction in the edge environment on the Internet of Things were also defined. A three-stage procedure to form a virtual cluster was justified. This procedure made it possible to devise a method for the virtual clustering in the Internet of Things edge environment based on the proposed virtual cluster architecture. The proposed method for building a virtual cluster in the Internet of Things edge environment was investigated. With a small network load, a virtual cluster has no advantage over a classic cluster. But with the growth of the network load, the virtual cluster prevails over the classic cluster in total performance; the advantage in total performance can exceed 10 %. It was also proven that for a heterogeneous environment, performance changes at full network load significantly depend on the number of virtual node groups. The research results on the method for building a virtual cluster in the Internet of Things edge environment can be explained by improving the balance of the network load at virtual clustering

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

IoT sensor data retrieval and analysis through cloud environment for effective power management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Other newly created techniques, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. In this study, the researchers look at the four systems mentioned above to identify their shared functions and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cloud service would have been used as the foundation technology for the proposed schema management platform, collecting the information generated by each sensor in the smart factory. It was easy to manage and manipulate remotely using IoT technology and a mobile device with a network connection. Here, to enhance safety of the entire building by installing multiple IoT devices in a smart building. The proposed systems were designed in a simulated Cooja simulator environment. The proposed approaches to the collection and manipulation of sensor information in smart cities can help us create a green, resource-efficient project.

Lightweight APIT with Bat Optimization with Simulated Annealing Localization for Resource-Constrained Sensor Networks

In a wireless sensor network, information processing, and information acquisition, localization technology is the key to making it practically possible application. Approximate Point-in-Triangulation (APIT) is the most widely used localization estimation which has high accuracy in localizing the nodes and ease of deployment of nodes in the real-time environment. Though it has numerous key advantages, some of the drawbacks which make it a little setback in preference are the unevenness in the distribution of nodes. Tracking is more appropriate for mobile sensor nodes than tracking is for static sensor nodes. The two main types of localization algorithms are range-based and range-free techniques. In an indoor setting, the projected range (distance) between an anchor and an unknown node is very inaccurate. By utilizing a large number of already existing access points (APs) in the range-free localization approach, this issue can be overcome to a great extent. The utilization of multisensor data, such as magnetic, inertial, compass, gyroscope, ultrasonic, infrared, visual, and/or odometer, is stressed in recent research to further increase localization accuracy. The tracking system also makes location predictions for the future based on historical location data. To overcome this issue, the proposed localization algorithm of APIT with Bat-SA proves its efficiency. Due to its low localization error, the traditional Bat method is more accurate than APIT. The proposed Bat using the SA algorithm is found to perform better than the traditional APIT algorithm in terms of convergence of computing rate and success rate. In order to mimic the suggested APIT method, it is paired with the Bat-SA localization technique. Simulation evaluation proves the performance efficiency of the proposed algorithm. The performance metrics parameters are latency, node distribution map, positioning error map, and neighbor relationship diagram which are used to evaluate the proposed method.

Integrated intelligent tactile system for a humanoid robot

Tactile perception is the basis of human motion. Achieving artificial tactility is one of the challenges in the fields of smart robotics and artificial intelligence (AI), because touch emulation relies on high-performance pressure sensor arrays, signal reading, information processing, and feedback control. In this paper, we report an integrated intelligent tactile system (IITS) that is integrated with a humanoid robot to achieve human-like artificial tactile perception. The IITS is a closed-loop system that includes a multi-channel tactile sensing e-skin, a data acquisition and information processing chip, and a feedback control. With customized preset values of threshold pressures, the IITS-integrated robot can flexibly grasp various objects. The IITS has potential applications in the design of prosthetic hands, space manipulators, deep-sea exploration robots, and human-robot interactions.

Poyen’s Fuzzy Logic Controlled Automatic Irrigation (FCAI): Precision Irrigation Scheduling Scheme

Water wastage is a grave problem in irrigation due to outdated methods. This work illustrates how a well-structured fuzzy rule base can eliminate the issue and utilize water at an optimized level. Poyen’s fuzzy logic controlled automatic irrigation (Poyen’s FCAI) efficiently manages the water requirement needs in irrigation processes considering the evapotranspiration (ET) loss and cultivating conditions. Poyen’s FCAI essentially comprises two modules, the wireless sensor network (WSN) and information processing module to perform the assigned task effectively and efficiently. The wireless sensor unit (WSU) accumulates the necessary data, determines the ET loss, and runs the decision logic before conveying it to the execution unit, i.e., the wireless information processing unit (WIPU). The WIPU executes the decision by manipulating the water delivery system, ensuring that the field receives the optimum volume of water. This article illustrates the fuzzy model with simulation output. The model is field-tested with and without the control action over two seasons and in adjacent plots. The results show promising improvements in both water management and crop yield due to more accurate conditions. Hence, we obtain a superior automatic irrigation prototype model for all soil types, across different growth stages, and for all crops with an exhaustive lookup table in place.

Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition

Hand gesture recognition has attracted the attention of many scientists, because of its high applicability in fields such as sign language expression and human machine interaction. Many approaches have been deployed to detect and recognize hand gestures, like wearable devices, image information, and/or a combination of sensors and computer vision. However, the method of using wearable sensors brings much higher accuracy and is less affected by occlusion, lighting conditions, and complex background. Existing solutions separately utilize sensor information and/or only use sensor information processing and decision-making algorithms over conventional threshold comparison algorithms and do not analyze data or utilize machine learning algorithms. In this paper, a multi-modal solution is proposed that combines information for measuring the curvature of the fingers and sensors for measuring angular velocity and acceleration. The provided information from the sensors is normalized and analyzed and various fusion strategies are used. Then, the most suitable algorithm for these sensor-based multiple modalities is proposed. The proposed system also analyzes the differences between gestures and actions that are almost similar but in fact, they are just normal moving gestures.

Load Balancing Optimization of In-Memory Database for Massive Information Processing of Internet of Things (IoTs)

Based on the analysis of the key technologies of the Internet of Things service platform architecture, a load balancing optimization scheme of in-memory database based on the massive information processing of the Internet of Things service platform is proposed. This scheme firstly proposes a system model that can satisfy the mass sensor information processing under the open platform environment and designs several functional unit modules of the system. By combining these functional units, the service can be configured for thousands of services and tenants. This paper presents an adaptive strategy selection method, which can automatically select the optimization strategy by dividing the position and querying the selection rate to improve the efficiency of the adaptive index algorithm. The index structure is initialized by parallel sorting algorithm, and the query statement is executed and the index structure is optimized by thread level parallel and radix sort methods. An elastic pipeline technique is proposed, which includes an elastic iterator model and a dynamic scheduler. The elastic iterator model is an upgrade of the traditional iterator model, adding the characteristics of dynamic multicore execution. In the process of query processing, dynamic scheduler monitors the load of each node in real time and dynamically adjusts the parallelism, so as to realize the load balance of in-memory database and maximize the utilization of hardware resources. The elastic pipeline realizes the isolation of parallelism from query compilation to avoid inappropriate parallelism allocation caused by missing and insufficient information during query compilation.

Development of a Control System for Multilink Manipulators on Unmanned Underwater Vehicles Dynamically Positioned over Seafloor Objects

This article considers an approach to synthesizing a multilevel system to control movements of a multilink manipulator (MM) mounted on an unmanned underwater vehicle (UUV) for performing autonomous manipulative operations in the mode of dynamic positioning over various objects on the seafloor. The system is based on an upgraded method that, using point clouds received from the machine vision systems (MVS), provides high-accuracy determination of the shape and location of the work object relative to UUV. The preset trajectories of the MM working tool are overlaid on the identified surface of the object, with possible silting, fouling or deformation of the latter taken into account. To execute the programmed trajectories with the MM working tool, the following methods have been implemented in software: stabilization of UUV in hovering mode near the object, high-precision control of working tool’s movements, and also corrections of its trajectory taking into account UUV’s displacements relative to the object. The synthesized system has been developed in the C++ programming language. The operation of the system has been numerically simulated using a model of UUV with MM, as well as models of the environment and the target object, in the Matlab/Simulink and V-REP software packages. The results of the study show a high efficiency of the system both in processing sensor information and in providing the dynamic control of movements of an UUV with a MM.

Machine Learning Techniques for Increasing Efficiency of the Robot's Sensor and Control Information Processing.

Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.

Methods and Algorithms for Correcting Kinematic Equations in the Problem of Determining the Orientation of an Object

Algorithms of a strapdown inertial orientation system with an inertial measurement module consisting of a three-component gyroscope, accelerometer, and magnetometer are considered. The aim of the work is to improve the algorithms for processing sensor information to ensure the asymptotic stability of the system, tuning for the Schuler period and the low-pass filter with a given bandwidth. The kinematic Poisson equations with positional and integral-positional correction based on the information of accelerometers and magnetometers are considered. The stability and frequency characteristics of the system in relation to the sensor output signals are analyzed. It is shown that the positional correction in each channel does not allow you to adjust the system for the Schuler period. The integral-positional correction allows this adjustment, but in relation to the gyroscope signals, the system is a bandpass filter and does not suppress noise in the bandwidth. The advantages of using positional correction with cross-links in the sense of tuning the frequency characteristics of the system to the Sharper frequency and the third-order low-pass filter are shown. The analysis of the influence of angular velocities confirmed the asymptotic stability of the system with their changes in a given range. The results of mathematical modeling confirmed the compensation of errors in the initial orientation system display, the reduction of noise power in the estimates of the orientation angles in relation to the noise in the sensor signals, and the ability to configure the system for the Schuler period.

State-of-the-Art in Perception Technologies for Collaborative Robots

The developments in sensor technology, information processing, computer science, and artificial intelligence significantly improved robots’ autonomy. Robots’ external perception relies on sensing technology. Thus, capturing accurate sensor information is vital for ensuring robotic security and improving human-machine interaction performance. This paper classifies the main robotic sensors, describes multi-sensor information fusion and processing and contrasts the state-of-the-art sensor technologies for collaborative robots with other state-of-the-art technologies in related fields. In addition, this paper also introduces collaborative robots’ perception applications of the state-of-the-art representative products, the new designs for collaborative robots, the interactive applications of the intelligent Kinect sensor with collaborative robots, and the important applications of collaborative robots in the medical fields. Through a deep analysis of relevant information, this paper aims to introduce the integration of the state-of-the-art sensor technologies and collaborative robots, with hoping of guiding significance for the applications of robot sensors. This paper finally emphasizes the sensors’ impact on robot performance and discusses future research on sensor technologies in robotics.

Smart Sensing Based Functional Control for Reducing Uncertainties in Agricultural Farm Data Analysis

Smart farming is a promising application area that relies on diverse intelligent and communication technologies to ease the outcome of the farming process. The multi-level processes in a farming scenario are automated through smart sensors and intelligent decision-making system. This article presents a smart sensor information processing method for controlling the farming devices' functions. This method is named smart sensing-based functional control (SSFC) that is devised to identify and mitigate the uncertainties in handling information. Uncertainties in information processing are addressed using Boltzmann machines (BM) with limited and effective layered processing. Based on the recommendations of the BM output, the dissemination of device controls is preceded. In particular, both analysis and control dissemination are filtered based on BM constraints and training sets, improving the devices' accuracy. With the help of experimental analysis and real-time data set, the performance of the proposed SSFC is investigated using different metrics such as analysis time, analyzed rate, dissemination delay, uncertain controls, and accuracy. From the investigation, it is seen that the proposed SSFC achieves 13.2% analysis time, 6.27% high analyzed rate, 18.15% less dissemination delay, 22.04% uncertain control, and 7.17% accuracy.

Centrifuge testing of soil–structure interaction effects on cyclic failure potential of fine-grained soil

We describe two experiments performed on a 9-m-radius geotechnical centrifuge to evaluate dynamic soil–structure interaction effects on the cyclic failure potential of fine-grained soil. Each experiment incorporated three different structures with a range of mass and stiffness properties. Structures were founded on strip footings embedded in a thin layer of sand overlying lightly overconsolidated low-plasticity fine-grained soil. Shaking was applied to the base of the model container, consisting of scaled versions of recorded earthquake ground motions, sweep motions, and step waves. Data recorded during testing were processed and published on the platform DesignSafe. We describe the model configuration, sensor information, shaking events, and data processing procedures and present selected processed data to illustrate key model responses and to provide a benchmark for data use.

A Hadoop Processing Method for Massive Sensor Network Data Based on Internet of Things

Based on the analysis of the architecture of the Internet of Things service platform and the key technologies of cloud computing, a massive sensing information processing scheme based on the Internet of Things service platform is proposed. The scheme first proposes a system architecture model that can satisfy the massive sensor information processing in an open platform environment, and designs multiple functional unit modules of the system. By combining these functional units, service configurability can be realized, facing thousands of services and Tenant. Then, Hadoop open source framework is used to realize the distributed computing of the system, which makes full use of the processing advantages of MapReduce computing model, HBase distributed database and HDFS distributed file system in Hadoop framework, and uses Oracle database as a supplement to realize the system high. Finally, the mass sensor information was deployed and tested. The effectiveness of the Hadoop processing method was verified by analyzing the results of MapReduce parallel computing experiments. The average cache hit rate is 93.1%, which has a high cache hit rate, greatly reduces MySQL database I/O, and improves system performance.

Implementation of the MIALinx User Interface for Future Manufacturing Environments

The flexible and easy-to-use integration of production equipment and IT systems on the shop floor becomes more and more a success factor for manufacturing to adapt rapidly to changing situations. The approach of the Manufacturing Integration Assistant (MIALinx) is to simplify this challenge. The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. This paper presents the implementation of MIALinx to retrofit legacy machines for Industry 4.0 in a manufacturing environment and focus on the concept and implementation of the easy-to-use user interface as a key element.

Implementation of the MIALinx integration concept for future manufacturing environments to enable retrofitting of machines

Manufacturing has to adapt to changing situations in order to stay competitive. It demands a flexible and easy-to-use integration of production equipment and ICT systems. The contribution of this paper is the presentation of the implementation of the Manufacturing Integration Assistant (MIALinx). The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. Furthermore, we describe the implementation of MIALinx by commissioning it in a manufacturing environment to retrofit legacy machines for Industrie 4.0. Finally, we validate the suitability of our approach by applying our solution in a medium-size company.

A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain

Abstract The type, model, quantity, and location of sensors installed on the intelligent vehicle test platform are different, resulting in different sensor information processing modules. The driving map used in intelligent vehicle test platform has no uniform standard, which leads to different granularity of driving map information. The sensor information processing module is directly associated with the driving map information and decision-making module, which leads to the interface of intelligent driving system software module has no uniform standard. Based on the software and hardware architecture of intelligent vehicle, the sensor information and driving map information are processed by using the formal language of driving cognition to form a driving situation graph cluster and output to a decision-making module, and the output result of the decision-making module is shown as a cognitive arrow cluster, so that the whole process of intelligent driving from perception to decision-making is completed. The formalization of driving cognition reduces the influence of sensor type, model, quantity, and location on the whole software architecture, which makes the software architecture portable on different intelligent driving hardware platforms.

Burst communication by using self-adaptive buffer allocation with energy-efficient in-body sensor networks

The revolution of wireless sensory devices in healthcare services is monitoring of human movements. Such type of motion analysis uses human's inertial information. This study is achieved by using Body Sensor Network (BSN) in which many wireless sensor nodes are placed on different parts of the body. Disease is recognised by shared processing of sensor information from multiple locations of the body. Even though this platform has high potential in healthcare, the commercialisation of these systems is difficult because of power requirements and wearability problems. In this paper, a self-adaptive greedy buffer allocation and scheduling algorithm (SGBAS) is presented for optimisation of communication model for BSN applications which uses self-adaptive buffers for limitation of communication to short bursts, power usage and transmitting sensed data to the backend servers periodically. The optimisation of QoS is achieved by introducing buffers. This project provides a self-adaptive heuristic scheme which is formulated to reduce transmissions among sensor nodes. For early diagnosis, it sends the data at right time for the analysis of healthcare professionals. Several experimental evaluation and simulations is to be done to assess the proposed SGBAS technique for allocation of buffers in real time under various simulation setups. Experiments demonstrate the effectiveness of SGBAS power reduction techniques.