Sensor Management Research Articles

Decomposed POMDP Optimization-Based Sensor Management for Multi-Target Tracking in Passive Multi-Sensor Systems

This paper presents an efficient information-theoretic sensor management method to maximize the performance of the passive multi-sensor system for multi-target tracking. We model the multi-target state as a generalized labeled multi-Bernoulli (GLMB) random finite set and formulate the dynamic sensor selection process as a partially observable Markov decision process (POMDP). The optimization objective is to maximum the information gain obtained from the observed data, which is measured by the Cauchy-Schwarz divergence. This is accomplished with two main technical innovations. The first is a tractable decomposed POMDP based sensor selection solution, in which the informative sensors are selected sequentially from the candidates based on the Cauchy-Schwarz divergence. The second is a novel dual-stage multi-sensor fusion strategy based on the iterated-corrector GLMB filter. Since the uncertainty of the passive multi-sensor system is generally large, the performance of the iterated-corrector scheme can be greatly influenced by the order of sensor updates. To fix this problem, the selected sensors are ranked in order of the Cauchy-Schwarz divergence obtained in sensor selection, followed by the iterated-corrector update. For the multi-sensor fusion, the effect of poor performance sensors is weakened and that of better performance sensors is enhanced. Simulation studies demonstrate the effectiveness and efficiency of the proposed method in challenging passive multi-target tracking scenarios.

Multisensor Management Method for Ground Moving Target Tracking Based on Doppler Blind Zone Information

Tracking ground moving target with sensors proves to be a challenge due to the uncertainty of target motion area, the existence of Doppler blind zone (DBZ), and the complex terrain. In this paper, a multisensor management method based on DBZ information is presented, in which the available sensors are selected to obtain the best operational revenues for ground moving target tracking. First, the ground target motion model is established considering the off-road/on-road state based on road topology information. Second, the sensor measurement model is given combined with the DBZ information, and a decorrelation method of measurement noise is proposed. Third, a target state estimation algorithm is derived using particle filter, in which the DBZ information is regarded as prior information. Then, combined with the variable structure interacting multiple model method, an estimation algorithm for tracking maneuvering target is proposed. Furthermore, an optimization model of nonmyopic sensor management is constructed to obtain the best sensor management scheme. Finally, the advancement and effectiveness of the proposed management method are verified in the simulations.

PMBM Filter With Partially Grid-Based Birth Model With Applications in Sensor Management

This paper introduces a Poisson multi-Bernoulli mixture (PMBM) filter in which the intensities of target birth and undetected targets are grid-based. A simplified version of the Rao-Blackwellized point mass filter is used to predict the intensity of undetected targets and to initialize tracks of targets detected for the first time. The grid approximation can efficiently represent intensities with abrupt changes with relatively few grid points compared to the number of Gaussian components needed in conventional PMBM implementations. This is beneficial in scenarios where the sensor's field of view is limited. The proposed method is illustrated in a sensor management setting where trajectories of sensors with limited fields of view are controlled to search for and track the targets in a region of interest.

Multi-Crop Irrigation in Precision Agriculture Using IOT

The agricultural sector, like the technologies used in it, is enormous. In many areas of India, multi-crop farming is practiced. The area occupied by each crop on any field where multi-crop agriculture is practiced changes throughout the year due to the changing seasons. The supply of water for irrigation in a place like India is also erratic due to the changing seasons. In a situation like this, where climatic conditions affect agricultural irrigation, a smart irrigation system that minimizes water consumption while also trying to eliminate irrigation labour, particularly in a multi-crop field with no fixed area for specific crops throughout the year, is currently required. Wireless sensor technology and management tools can help agriculture become more efficient and environmentally friendly. The goal of this research paper is to use KNN Algorithm and IOT to combat this issue.

Resilience Monitoring for Multi-Filter All-Source Navigation Framework With Assurance

<h3>Summary</h3> The Autonomous and Resilient Management of All-source Sensors (ARMAS) framework monitors residual-space test statistics across unique sensor-exclusion banks of filters (known as <i>subfilters</i>) to provide a resilient, fault-resistant all-source navigation architecture with assurance. A critical assumption of this architecture, demonstrated in this paper, is fully overlapping state observability across all subfilters. All-source sensors, particularly those that only provide partial state information (altimeters, TDoA, AOB, etc.), do not intrinsically meet this requirement. This paper presents a novel method to monitor real-time overlapping position state observability and introduces an observability bank within the ARMAS framework, known as stable observability monitoring (SOM). SOM uses a monitoring-epoch stability analysis to provide an intrinsic awareness to ARMAS of the capabilities of the fault detection and exclusion (FDE) functionality. We define the ability to maintain consistent all-source FDE to recover failed sensors as <i>navigation resilience</i>. A resilient FDE capability is one that is aware of when it requires more sensor information to protect the consistency of the FDE and integrity functions from corruption. SOM is the first demonstration of such a system for all-source sensors that the authors are aware of. A multi-agent 3D environment simulating both GNSS and position and velocity alternative navigation sensors was created and individual GNSS pseudorange sensor anomalies are utilized to demonstrate the capabilities of the novel algorithm. This paper demonstrates that SOM seamlessly integrates within the ARMAS framework, provides timely prompts to augment new sensor information from other agents, and indicates when framework stability and preservation of all-source navigation integrity are achieved.

Sensor network prediction based on spatial and temporal GNN

Multi-sensor prediction is a hotspot for research and development in sensor management technologies. Thanks to artificial intelligence, researchers have been able to effectively use neural networks and traditional artificial intelligence approaches to multi-sensor prediction in recent years. In this model, we try to present the sensors network as an unweighted graph, based on the GNN with spatial and temporal features, combine the characteristics of the Gated recurrent unit with temporal context, and use the Graph Neural Network to predict sensor feature. We tackle the issue of poor sensor network efficiency and sluggish speed without data fusion.

SensoMan: Social Management of Context Sensors and Actuators for IoT

Sensor networks that collect data from the environment can be utilized in the development of context-aware applications, bringing into sight the need for data collection, management, and distribution. Boards with microcontrollers, such as Arduino and Raspberry Pi, have gained wide acceptance and are used mainly for educational and research purposes. Utilizing the information available via sensors connected to these platforms requires extended technical knowledge. In this work, we present a sensor management framework, SensoMan, that manages a collection of sensors spread in the environment connected to microcontroller boards. We present the framework’s architecture, a method for sensor data management, and a prototype system. Sensor data can also trigger the execution of actions on actuators. Thus, we further propose a rule engine as well as social connectivity following a scheme where sensors and their data can be shared among users. Our work shows that the creation of such a system is feasible and can use simple equipment (e.g., sensors, controller plugs) that can be replicated in other environments. The use of SensoMan is demonstrated via two scenarios that show its potential in combining simple tools that do not require an extended learning curve. A small-scale user study was also performed.

Game Theory Based Sensor Management in Reducing Target Threat Level Assessment Risk

Game Theory Based Sensor Management in Reducing Target Threat Level Assessment Risk

Combining Blockchains, Smart Contracts, and Complex Sensors Management Platform for Hyper-Connected SmartCities: An IoT Data Marketplace Use Case

In this paper, we demonstrate the multiple points of innovation when combining blockchain technology with Internet of Things (IoT) and security frameworks. The deployment and use of IoT device networks in smart city environments has produced an enormous amount of data. The fact that those data are possessed by multiple sources that use independent systems for data collection, storage, and use impedes the exploitation of their value. Blockchains, as distributed ledgers, can be used for addressing the development of a universal system for data collection and distribution. Smart contracts can be used to automate all the processes of such a network, while at the same time, blockchain and the InterPlanetary File System (IPFS) protect sensitive data through anonymity and distributed storage. An innovative and open IoT blockchain market of applications, data, and services is proposed that: (i) provides the framework upon which objects and people can exchange value in form of virtual currencies, for assets (data and services) received; (ii) defines the motivation incentives according to social and business context for humans and smart objects to interact. The specific marketplace is piloted through a cross-border trial between Santander and Fujisawa, in the context of the M-Sec project, validating thus the interoperability, efficiency, and data protection principles.

Comparison of H2S Gas Sensors: A Sensor Management Procedure for Sewer Monitoring

Hydrogen sulphide (H2S) emissions are one of the major problems associated with sewer networks. This gas, with its characteristic smell of rotten eggs is highly toxic and leads to the corrosion of sewer infrastructures. To protect cities and ensure the safety of sewer workers, sewers are commonly monitored using H2S gas sensors. In this work, three commercial H2S gas sensors for air quality monitoring were compared at two different sites in Berlin, Germany. Two of the sensors provide online access to data, while the other one is a data logger. Moreover, based on statistical measures (RMSE, MAE, MB, and a graphical analysis), we evaluated whether a rotation/exchange between data logger (reference) and online sensors is possible without significant differences in the gas measurements. Experimental evaluation revealed that measurement differences are dependent on the H2S concentration range. The deviation between sensors increases as the H2S concentration rises. Therefore, the interchange between reference and online sensors depends on the application site and the H2S levels. At lower ranges (0–10 ppm) there were no observed problems. Finally, to support practitioners on-site, a management procedure in the form of a decision-making tool is proposed for assessing whether gas sensors should be exchanged/rotated.

Experiment Control and Monitoring System for LOG-a-TEC Testbed.

The LOG-a-TEC testbed is a combined outdoor and indoor heterogeneous wireless testbed for experimentation with sensor networks and machine-type communications, which is included within the Fed4FIRE+ federation. It supports continuous deployment principles; however, it is missing an option to monitor and control the experiment in real-time, which is required for experiment execution under comparable conditions. The paper describes the implementation of the experiment control and monitoring system (EC and MS) as the upgrade of the LOG-a-TEC testbed. EC and MS is implemented within existing infrastructure management and built systems as a new service. The EC and MS is accessible as a new tab in sensor management system portal. It supports several commands, including start, stop and restart application, exit the experiment, flash or reset the target device, and displays the real-time status of the experiment application. When nodes apply Contiki-NG as their operating system, the Contiki-NG shell tool is accessible with the help of the newly developed tool, giving further experiment execution control capabilities to the user. By using the ZeroMQ concurrency framework as a message exchange system, information can be asynchronously sent to one or many devices at the same time, providing a real-time data exchange mechanism. The proposed upgrade does not disrupt any continuous deployment functionality and enables remote control and monitoring of the experiment. To evaluate the EC and MS functionality, two experiments were conducted: the first demonstrated the Bluetooth Low Energy (BLE) localization, while the second analysed interference avoidance in the 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) wireless technology for the industrial Internet of Things (IIoT).

Feasibility of Molten Salt Reactor Heat Exchanger Online Monitoring

Abstract Online structural health corrosion monitoring in advanced molten salt reactor heat exchangers is desirable for detecting tube degradation prior to leaks that would either cause mixing of heat exchanger fluids or release of radiologically contaminated fluids beyond the design containment boundary. This program seeks to demonstrate the feasibility for a torsional wave mode sensor to attach to the outside of a long (30-m) heat exchanger tube in the stagnant flow area where the tube joins the heat exchanger plenum and where it is possible to protect a sensor cable from high-force flow connecting through a heat exchanger shell to a monitoring instrument. The envisioned sensor and cable management approach will be impractical to implement on existing heat exchangers; rather, sensors must be installed in conjunction with the heat exchanger fabrication. Initially, flaw surrogates of interest (50% notch and 50% flat-bottom hole) have been detected in a 3-m tube using low-temperature PZT piezoelectric crystals. The transducer consisted of multiple shear elements placed circumferentially around a tube. The program will continue to investigate higher temperature piezoelectric ceramics, long-term performance of high-temperature adhesives, and flaw sensitivity on long (30-m +) tubes.

Policy Rollout Action Selection in Continuous Domains for Sensor Path Planning

Policy rollout is a method for the online computation of future costs in approximate dynamic programming and has been utilized for various problems, including sensor management. In previous work, it has predominately been applied to the selection of actions from discrete sets. In this article, we present methods for action selection from continuous sets and analyze their tradeoffs. The methods are evaluated on the problem of sensor path planning, with the intent of minimizing the time to localize an emitter using bearing measurements.

Distributed Intelligence Model for IoT Applications Based on Neural Networks

Increasing the implication of IoT data puts a focus on extracting the knowledge from sensors’ raw data. The management of sensors’ data is inefficient with current solutions, as studies have generally focused on either providing cloud-based IoT solutions or inefficient predefined rules. Cloud-based IoT solutions have problems with latency, availability, security and privacy, and power consumption. Therefore, Providing IoT gateways with relevant intelligence is essential for gaining knowledge from raw data to make the decision of whether to actuate or offload tasks to the cloud. This work proposes a model that provides an IoT gateway with the intelligence needed to extract the knowledge from sensors’ data in order to make the decision locally without needing to send all raw data to the cloud over the Internet. This speeds up decisions and actions for real-time data and overcomes the limitations of cloud-based IoT solutions. When the gateway is unable to process a task locally, the data and task are offloaded to the cloud.

Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study.

Smartphone use is widely spreading in society. Their embedded functions and sensors may play an important role in therapy monitoring and planning. However, the use of smartphones for intrapersonal behavioral and physical monitoring is not yet fully supported by adequate studies addressing technical reliability and acceptance. The objective of this paper is to identify and discuss technical issues that may impact on the wide use of smartphones as clinical monitoring tools. The focus is on the quality of the data and transparency of the acquisition process. QuantifyMyPerson is a platform for continuous monitoring of smartphone use and embedded sensors data. The platform consists of an app for data acquisition, a backend cloud server for data storage and processing, and a web-based dashboard for data management and visualization. The data processing aims to extract meaningful features for the description of daily life such as phone status, calls, app use, GPS, and accelerometer data. A total of health subjects installed the app on their smartphones, running it for 7 months. The acquired data were analyzed to assess impact on smartphone performance (ie, battery consumption and anomalies in functioning) and data integrity. Relevance of the selected features in describing changes in daily life was assessed through the computation of a k-nearest neighbors global anomaly score to detect days that differ from others. The effectiveness of smartphone-based monitoring depends on the acceptability and interoperability of the system as user retention and data integrity are key aspects. Acceptability was confirmed by the full transparency of the app and the absence of any conflicts with daily smartphone use. The only perceived issue was the battery consumption even though the trend of battery drain with and without the app running was comparable. Regarding interoperability, the app was successfully installed and run on several Android brands. The study shows that some smartphone manufacturers implement power-saving policies not allowing continuous sensor data acquisition and impacting integrity. Data integrity was 96% on smartphones whose power-saving policies do not impact the embedded sensor management and 84% overall. The main technological barriers to continuous behavioral and physical monitoring (ie, battery consumption and power-saving policies of manufacturers) may be overcome. Battery consumption increase is mainly due to GPS triangulation and may be limited, while data missing because of power-saving policies are related only to periods of nonuse of the phone since the embedded sensors are reactivated by any smartphone event. Overall, smartphone-based passive sensing is fully feasible and scalable despite the Android market fragmentation.

The Development of an Automated Vehicle Simulator for On-Road Study

&#x0D; &#x0D; &#x0D; In the current development of Automated Vehicle (AV) technology, one of the challenges yet to be resolved is when the AV is driving in urban environments. There will be mixed traffic scenarios where the AV and Vulnerable Road Users (VRU) such as pedestrians, cyclists, and motorcyclists, will share the road infrastructures. In this study, an AV simulator is developed and will be used to study the interaction between an AV and motorcyclist when they encounter a specific situation on the real roads. The study begins with developing a test vehicle based on the three main systems: the vehicle's interior, outer appearance, and behavior. The vehicle's interior comprises four subsystems: data acquisition, sensor, monitoring, and power management system. The outer appearance system has a look-alike LiDAR system, while the driving style sub-system monitors the behavior system. Future improvement will include developing ghost drivers to be implemented together with this system to enhance this on- road simulator's saliency.&#x0D; &#x0D; &#x0D;

Impact of Innovative Pulse Oximeter Sensor Management Strategy

Following a merger of two children's hospitals, leadership discovered a considerable utilization volume of single-use sensors that was associated with declining hospital reimbursements. This discovery resulted in the establishment of a new sensor management strategy, the goal of which was to decrease costs and waste associated with disposable pulse oximetry sensors. The sensor management strategy involved using replacement tapes with single-patient-use pulse oximeter sensors instead of the current practice of reprobing with a new sensor. A 60% utilization goal was set, with the focus shifted from sensors used per patient to replacement tapes per sensor. The implementation of a new sensor management strategy between the years 2006 and 2019 in a hospital system decreased sensor volume by more than 780,000 sensors and realized a cost avoidance of more than 7 million dollars. A sensor management strategy can substantially reduce the cost and medical waste commonly associated with the use of disposable, single-patient pulse oximetry sensors.

Impact of Innovative Pulse Oximeter Sensor Management Strategy

Following a merger of two children's hospitals, leadership discovered a considerable utilization volume of single-use sensors that was associated with declining hospital reimbursements. This discovery resulted in the establishment of a new sensor management strategy, the goal of which was to decrease costs and waste associated with disposable pulse oximetry sensors. The sensor management strategy involved using replacement tapes with single-patient-use pulse oximeter sensors instead of the current practice of reprobing with a new sensor. A 60% utilization goal was set, with the focus shifted from sensors used per patient to replacement tapes per sensor. The implementation of a new sensor management strategy between the years 2006 and 2019 in a hospital system decreased sensor volume by more than 780,000 sensors and realized a cost avoidance of more than 7 million dollars. A sensor management strategy can substantially reduce the cost and medical waste commonly associated with the use of disposable, single-patient pulse oximetry sensors.

Impact of Innovative Pulse Oximeter Sensor Management Strategy.

Following a merger of two children's hospitals, leadership discovered a considerable utilization volume of single-use sensors that was associated with declining hospital reimbursements. This discovery resulted in the establishment of a new sensor management strategy, the goal of which was to decrease costs and waste associated with disposable pulse oximetry sensors. The sensor management strategy involved using replacement tapes with single-patient-use pulse oximeter sensors instead of the current practice of reprobing with a new sensor. A 60% utilization goal was set, with the focus shifted from sensors used per patient to replacement tapes per sensor. The implementation of a new sensor management strategy between the years 2006 and 2019 in a hospital system decreased sensor volume by more than 780,000 sensors and realized a cost avoidance of more than 7 million dollars. A sensor management strategy can substantially reduce the cost and medical waste commonly associated with the use of disposable, single-patient pulse oximetry sensors.

Sensor Management for Search and Track Using the Poisson Multi-Bernoulli Mixture Filter

A sensor management method for joint multitarget search and track problems is proposed, where a single user-defined parameter allows for a tradeoff between the two objectives. The multitarget density is propagated using the Poisson multi-Bernoulli mixture filter, which eliminates the need for a separate handling of undiscovered targets and provides the theoretical foundation for a unified search and track method. Monte Carlo simulations of two scenarios are used to evaluate the performance of the proposed method.