Large-scale Deployment Of Sensors Research Articles

Solar-powered shape-changing origami microfliers.

Using wind to disperse microfliers that fall like seeds and leaves can help automate large-scale sensor deployments. Here, we present battery-free microfliers that can change shape in mid-air to vary their dispersal distance. We designed origami microfliers using bistable leaf-out structures and uncovered an important property: A simple change in the shape of these origami structures causes two dramatically different falling behaviors. When unfolded and flat, the microfliers exhibit a tumbling behavior that increases lateral displacement in the wind. When folded inward, their orientation is stabilized, resulting in a downward descent that is less influenced by wind. To electronically transition between these two shapes, we designed a low-power electromagnetic actuator that produces peak forces of up to 200 millinewtons within 25 milliseconds while powered by solar cells. We fabricated a circuit directly on the folded origami structure that includes a programmable microcontroller, a Bluetooth radio, a solar power-harvesting circuit, a pressure sensor to estimate altitude, and a temperature sensor. Outdoor evaluations show that our 414-milligram origami microfliers were able to electronically change their shape mid-air, travel up to 98 meters in a light breeze, and wirelessly transmit data via Bluetooth up to 60 meters away, using only power collected from the sun.

Rivers Monitoring System Using Deep Learning Technique

Purpose: The aquatic environment, including rivers and lakes, is crucial for the breeding and survival of fish and other animals.
 Findings: However, these environments are vulnerable to both intentional and unintentional environmental pollution, which can have detrimental effects on the ecosystem and its inhabitants.
 Methodology: Therefore, there is a pressing need for continuous monitoring of the aquatic environment to detect and address pollution in a timely manner. The challenge lies in establishing a monitoring system that can provide a continuous flow of information regarding the quality and health of the aquatic environment. Traditional monitoring methods often involve costly and time-consuming procedures, limiting their effectiveness in providing real-time data.
 Recommendations: To address this, there is a demand for low-cost and timely sensor technologies that can be deployed extensively to monitor various parameters of the aquatic environment, such as water quality, pollution levels, and ecosystem health. The proposed system one step for developing and implementation a low-cost sensor technology that are capable of monitoring various parameters of the aquatic environment. To be established a robust and scalable monitoring system that can handle large-scale deployment of sensors in rivers and lakes.

Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning

With the large-scale deployment of sensors, both the smart grid and the power communication network should jointly deal with different kinds of big data. The fusion of both networks should bring unpredictable accidents, even leading a catastrophic destruction in our lives. However, data fusion (DF) and coordination treatment for two networks will greatly improve system performance, reduce system complexity, and improve the precision and control ability of both networks. Situation awareness (SA) is the key function for DF and accident avoidance for both networks with different network structures, data types, system mechanisms, and so on. This paper use tensor computing to provide a general data model for heterogeneous and multidimensional big data generated from smart grid and power communication network. A novel data fusion scheme is designed with multidimensional tensors. Deep reinforcement learning (DRL) algorithms are utilized to construct an optimal SA strategy based on tensor big data. A multi-agent actor-critic (MAAC) algorithm is used to achieve an optimal SA policy and improve system performance. The proposed DF and SA schemes based on tensor computing and DRL provide useful guidance for smart grid and power communication networks from theory and practice.

Environmental Monitoring System

Abstract: In recent times especially in India, people have become increasingly aware of the importance of the environment they reside in. This has led to a growing demand for a dependable environmental monitoring system. Apart from its industrial application, an environment friendly system to monitor the air quality is too essential for ensuring the safety of workers in chemical, mining, and food industries etc., where there are chances of air getting contaminated by baleful impurities. Largescale sensor deployment for such purposes raises concerns over gathering of the data, handling, connectivity, utilization of power and the potential of the system. Internet of Things (IoT) technology is ideally suited to address these challenges. This paper takes IOT as base and designed the whole system using sensors, micro controllers, and IoT-based technology to effectively monitor changes in the environment. The proposed module enables users to monitor the certain parameters of environment such as humidity, moisture, temperature and expose the present contaminants in the atmosphere. So, the paper also describes the development of a web application that provides vital information to users and allows them to set up notifications for the certain vital changes in data provided by sensor. Compared to other such parallel systems, this system which we have come up with is very cost effective, precise, friendly, and very simple to monitor and visualize the data provided. This proposed system has been checked at different stages The system has been evaluated in various stages and has demonstrated an intense perfection under different conditions.

The Study of Urban Residential’s Public Space Activeness Using Space-Centric Approach

With the advancement of the Internet of Things (IoT) and communication platform, large scale sensor deployment can be easily implemented in an urban city to collect various information. To date, there are only a handful of research studies about understanding the usage of urban public spaces. Leveraging IoT, various sensors have been deployed in an urban residential area to monitor and study public space utilization patterns. In this paper, we propose a data processing system to generate space-centric insights about the utilization of an urban residential region of multiple points of interest (PoIs) that consists of 190,000m$^2$ real estate. We identify the activeness of each PoI based on the spectral clustering, and then study their corresponding static features, which are composed of transportation, commercial facilities, population density, along with other characteristics. Through the heuristic features inferring, the residential density and commercial facilities are the most significant factors affecting public place utilization.

Integrating BIM into sensor-based facilities management operations

Integrating BIM into sensor-based facilities management operations

A Reliable Data Compression Scheme in Sensor-Cloud Systems Based on Edge Computing

The rapid development of the IoT and cloud computing has spawned a new network structure - sensor-cloud system (SCS) where sensors, sensor networks, and cloud computing are integrated to perform data sensing, collection, transmission, and decision making. The large-scale deployment of sensors creates a massive amount of data, posing new challenges in data transmission and storage. As an intermediate platform between IoT and cloud platforms, edge computing provides IoT with data collection, processing, and scheduling services. This paper proposes a hybrid data compression scheme that incorporates lossy and lossless compression in SCS based on edge computing to address the increasing challenges. Moreover, we propose a new reliable lossy compression algorithm DFan, based on the simplified Fan algorithm with a high compression ratio (CR). By introducing the data tolerable deviation, DFan transforms single-factor decision-making into multi-factor decision-making, reducing the error of lossy compression. Through experiments on IntelLab and MIT-BIH datasets, the proposed hybrid data compression scheme achieves an overall CR of 4.21× and 3.88×, respectively. The lossy CR of DFan is 6.42× and 5.1×, respectively, and the Percentage RMS Difference (PRD) caused by lossy compression is 0.27% and 0.56%, respectively. The hybrid compression scheme, high compression ratio, and reliable data restoration make this scheme attractive to the data processing of sensors in SCS.

Comparative Assessment of Process Mining for Supporting IoT Predictive Security

The growth of the Internet-of-Things (IoT) has been characterized by the large-scale deployment of sensors and connected objects. These ones are integrated with other Internet resources in order to elaborate more complex systems and applications. Security management is a major challenge for these systems due to their complexity, their heterogeneity and the limited resources of their devices. In this article we evaluate the exploitability and performance of a process mining approach for detecting misbehaviors in such systems. We describe the considered architecture and detail its operation, from the generation of behavioral models to the detection of potential attacks. We formalize several alternative commonly-used detection methods, including elliptic envelope, support-vector machine, local outlier factor, and isolation forest techniques. After presenting a proof-of-concept prototype, we quantify comparatively the benefits and limits of our process mining solution combined with data pre-processing, through extensive experiments based on different industrial datasets.

A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks.

Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector’s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.

Method for rapid deployment of low-cost sensors for a nationwide project in the Internet of things era: Air quality monitoring in Taiwan

When the air quality problem of PM2.5 first raised public attention and an emerging low-cost sensor technology appeared suitable as a monitoring measure for said problem, Taiwan’s Environmental Protection Administration devised a nationwide project involving large-scale sensor deployment for effective pollution monitoring and management. However, the conventional siting optimization methods were inadequate for deploying thousands of sensors. Therefore, this study develops a rapid deployment method. The current results may serve as a reference for the Taiwan government for use in the aforementioned nationwide project, which is an environmental Internet of things–based plan involving 10,200 sensors to be deployed throughout the country. The four monitoring targets are classified as types of industry, traffic areas, communities, and remoteness, and a three-phase implementation structure is devised in the method. The open-source geographic information system software named QGIS was used to implement the proposed method with relevant spatial data from local open-data resources, which generated new, necessary geographic features and estimated sensor deployment quantity in Taiwan. The deployment result of the 10,200 sensors is 4790 in the type of industry, 708 of the traffic area, 3935 of the communities, and 767 of remoteness. The proposed method could serve as a useful foundation for the sensor deployment of environmental Internet of things. Policymakers may apply this method to budget allocation or integrate this method alongside conventional siting methods for the modification of deployment results based on the local monitoring requirements.

A Stackelberg Game Pricing Through Balancing Trilateral Profits in Big Data Market

With the popularity of the Internet of Things (IoT) and large-scale deployment of sensors, data have exploded. Big data is utilized to extract useful knowledge and information and served as data services to consumers. While most of the current researches in the field of big data services focus on developing and improving algorithms for data mining and information extraction, and rarely studies “big data” from an economic perspective. This article thus studies the pricing and profit maximization problems in big data markets from an economic perspective. First, a method of quantifying the value of data is proposed to study the utility of raw data. Then, we build an economic model of the data market, which consists of three parties: 1) data vendor; 2) service provider; and 3) service users. The data vendor gathers various raw data and sells them to the service provider. The raw data are further processed into data services by a service provider, who provides service subscriptions to users to obtain profits. The interactions among them are formulated as a Stackelberg game to maximize the profits of all participators. The existence and uniqueness of equilibria pricing strategies are proved. Finally, numerical results show that the participators of the data market can achieve the maximum profit through the proposed pricing mechanism and economic model.

Indoor environmental quality and learning outcomes: protocol on large-scale sensor deployment in schools

IntroductionExposure to poor environmental conditions has been associated with deterioration of physical and mental health, and with reduction of cognitive performance. Environmental conditions may also influence cognitive development of children,...

Framework for cost-effective analytical modelling for sensory data over cloud environment

In order to offer sensory data as a service over the cloud, it is necessary to execute a cost-effective and yet precise data analytical logic within the sensing units. However, it is quite questionable as such forms of analytical operation are quite resource dependent which cannot be offered by the resource constraint sensory units. Therefore, the proposed paper introduces a novel approach of performing cost-effective data analytical method in order to extract knowledge from big data over the cloud. The proposed study uses a novel concept of the frequent pattern along with a tree-based approach in order to develop an analytical model for carrying out the mining operation in the large-scale sensor deployment over the cloud environment. Using a simulation-based approach over the mathematical model, the proposed model exhibit reduced mining duration, controlled energy dissipation, and highly optimized memory demands for all the resource constraint nodes.

A method of electrical conductivity compensation in a low-cost soil moisture sensing measurement based on capacitance

The detection of soil moisture is one of the most important researches in the development of precision agriculture. At present, the detection of soil dielectric constant is the most effective and convenient method to measure soil moisture content. Most of the dielectric type soil moisture sensors reduce the influence of soil salinity, texture and other factors on the measurement by detecting at high frequency. However, this will increase the production cost of the measurement circuit and fail to meet the requirements for large-scale deployment of sensors in agricultural production. The method of low-frequency capacitance detection has a price advantage but a low measurement accuracy, thus the application is very limited. This paper intends to improve the measurement accuracy and application range of low-frequency capacitance moisture sensors by correcting the relationship between low-frequency capacitance and moisture through the detection of soil conductivity. Three modified models (logistic, exponential and polynomial) to estimate the moisture content was verified by comparing with the oven-drying method calibration value. The results indicated that the MAME and MAE of the logistic model was less than 3.55% and 2.50%, respectively, which can satisfy most agricultural production requirements for soil moisture detection. Therefore, the study provided an effective detection method for low-cost soil moisture detection in low salinity and low organic matter soils.

Machine Learning Quorum Decider (MLQD) for Large Scale IoT Deployments

The usage of sensors and actuators in industrial applications has increased widely. Cyber Physical Systems (CPS) has improved the usage of closed loop control systems using sensors and actuators. However the health of sensors & actuators and their impact on the efficiency of the industry is still a challenging area. The other important aspect is the network related issues that rise due to the large scale of sensors & actuators. There need an efficient mechanism that manages the network delay and packet drops associated with the large scale sensor deployments. There are machine learning techniques which are used to predict the sensor values when it is absent due to various reasons. Similarly, there are anomaly detection techniques that are used to detect faulty sensors. Both the prediction approaches fail to address the properties related to lifetime and service needs of sensors & actuators. This paper proposes a machine learning framework that combines the results of individual learning modules for achieving collective decisions. This proposed method provides the solution to address the delays and failures occurring in large scale deployments.

Bridging Cooperative Sensing and Route Planning of Autonomous Vehicles

Autonomous Vehicles (AV) are used to solve the problem of data gathering in large scale sensor deployments with disconnected clusters of sensors networks. Our take is that an efficient strategy for data collection with AVs should leverage i) cooperation amongst sensors in communication range of each other forming a sensor cluster, ii) advanced coding and data storage techniques for easing the cooperation process, and iii) AV route-planning that is both content and cooperation-aware. Our work formulates the problem of efficient data gathering as a cooperative route-optimization problem with communication constraints. We also analyze (network) coded data transmission and storage for simplifying cooperation amongst sensors as well as data collection by the AV. Given the complexity of the problem, we focus on heuristic techniques, such as particle swarm optimization, to calculate the AV's route and the times for communication with each sensor and/or cluster of sensors. We analyze two extreme cases, i.e., networks with and without intra- cluster cooperation, and provide numerical results to illustrate that the performance gap between them increases with the number of nodes. We show that cooperation in a 100 sensor deployment can increase the amount of data collected by up to a factor of 3 with respect to path planning without cooperation.

Integrating the Hardware and Software Computational Platform for the HiPerDNO (High Performance Distribution Network Operation) Project

The HiPerDNO project is a three-year EU-funded research projects which studies novel applications for Distribution Network Operations (DNOs). These applications (distributed state estimation, power restoration, monitoring of assets) emerge from the large-scale deployment of sensors and instrumentation devices, responsive loads and embedded generation. Their introduction requires HPC strategy. A HiPerDNO HPC platform, which takes into account DNOs' and applications requirements, has been built and several applications were developed and tested on it. In this paper we report on the results achieved.

A practical coverage algorithm for wireless sensor networks in real terrain surface

Large-scale sensor deployment and coverage of wireless sensor networks (WSNs) is one of the most important topics for real applications, especially on real terrain surface, such as forest monitoring. In our works, we exploit the Digital Elevation Model (DEM) in Geographic Information System to describe the real fields for sensors. An algorithm for sensor deployment is proposed to generate the location of each sensor on real terrain surface. However, the usage of grid DEM makes the algorithm more complicated in practice, especially the calculation of geodesic. To optimise the calculation, we present a genetic method to solve the new issue and make our method practical. The experiments with real data show that our algorithm is practical, considering time and complexity to get k-coverage of sensors in real terrain surface.