This dataset is designed for research and development in Wireless Sensor Networks (WSNs), focusing on decentralized power scheduling for reliable signal detection in unstructured environments. It simulates sensor nodes with attributes related to energy consumption, transmission power, signal strength, noise levels, and environmental factors.The target variable, Detection_Accuracy (%), is influenced by factors such as signal strength, noise level, energy efficiency, and optimization algorithms.This dataset is suitable for applications in machine learning, IoT network optimization, energy-efficient communication, and adaptive WSN deployments.

Dataset

This dataset was created by Halime Doğan

Contents

Dataset Description: Underwater Sensor Data from River

1. Data Source: The dataset comprises sensor readings obtained from underwater equipment deployed in a river environment. The sensors are designed to monitor various environmental parameters to provide insights into the river's conditions.

2. Data Fields:

   • Meta Data:
     • Source Address: The IP address of the device collecting the data (e.g., 192.168.0.1).
     • Destination Address: The IP address of the receiver or data storage system (e.g., 192.168.0.2).
     • Timestamp: The date and time when the data was recorded (e.g., 2023-08-17 18:28:00).
   • Environmental Parameters:
     • Temperature: The water temperature in degrees Celsius (e.g., 22.8°C).
     • Pressure: The pressure exerted by the water in millibars (e.g., 1011.0 mb).
     • Oxygen Level (O2): The percentage of dissolved oxygen in the water (e.g., 39.7%).
   • Obstacle Sensor Data: Indicates whether any obstacles are detected in the vicinity of the sensor equipment. This field provides binary information, signaling either the presence or absence of obstacles.

3. Data Collection Context: The sensors are deployed in a river environment to monitor and gather real-time data on crucial parameters. The collected data aids in understanding the river's ecosystem, assessing water quality, and detecting potential hazards such as obstacles or blockages.

4. Data Use Cases:

   • Environmental Monitoring: Researchers and environmentalists can utilize the dataset to study long-term trends in water temperature, oxygen levels, and pressure, facilitating assessments of the river's health and changes over time.
   • River Management: Authorities responsible for managing rivers can leverage the dataset to make informed decisions regarding resource allocation, pollution control measures, and infrastructure maintenance.
   • Research and Innovation: The dataset can serve as a valuable resource for academic research, enabling the development of advanced algorithms for obstacle detection, predictive modeling of water quality parameters, and more.

5. Data Integrity and Quality: Measures are taken to ensure the accuracy and reliability of the collected data. Calibration routines, quality control checks, and redundancy mechanisms may be implemented to minimize errors and maintain data integrity.

6. Ethical Considerations: Data collection adheres to ethical guidelines, ensuring minimal disturbance to the natural environment and compliance with relevant regulations governing data privacy and environmental protection.

7. Data Access and Availability: Access to the dataset may be restricted to authorized parties, such as researchers, governmental agencies, and environmental organizations. However, efforts may be made to promote data sharing and collaboration within the scientific community while respecting confidentiality and security protocols.

8. Maintenance and Updates: The dataset may be periodically updated with new observations as additional data is collected over time. Maintenance tasks, including sensor calibration, equipment servicing, and data validation, are conducted to sustain the dataset's reliability and relevance.

Wireless Sensor Network Deployments (2013-2017)

This is an open data repositiory.

It is concerned with systematically reviewing scientific publications containing actual Wireless Sensor Network deployments in five year span from 2013 to 2017.

Identification

Articles were first searched for in SCOPUS and Web of Science databases on 2018-06-12 using these queries/settings:

SCOPUS

Query: KEY({sensor network} OR {sensor networks}) AND TITLE-ABS-KEY(test* OR experiment* OR deploy*) AND NOT TITLE-ABS-KEY(review) AND NOT TITLE-ABS-KEY(simulat*) AND ( LIMIT-TO ( PUBYEAR,2017 ) OR LIMIT-TO ( PUBYEAR,2016 ) OR LIMIT-TO ( PUBYEAR,2015 ) OR LIMIT-TO ( PUBYEAR,2014 ) OR LIMIT-TO ( PUBYEAR,2013 ) )

Raw results: 11536 articles

De-duplicated results: 11374 articles

Contained 4814 articles not found in Web of Science

Web Of Science

Querry: TS = ("sensor network" OR "sensor networks") AND TS = (test* OR experiment* OR deploy*) NOT TI="review" NOT TS=simulat*

Additional query parameters: Indexes=SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC Timespan=2013-2017

Raw results: 10204 articles

De-duplicated results: 10196

Contained 3636 articles not found in SCOPUS

Final results

When article results were merged from both databases finally 15010 articles were identified as possible candiates. Of those 6560 were found in both databases.

Screening

Data was exported as bibtex files and imported in Mendeley software for screening.

4910 articles were left after the screening phase

Eligibility check

Then all screened included articles were checked for eligibility and 3017 eligible articles were identified.

Data extraction

In these articles 3059 wireless sensor network deployments were identified and codified data extracted from them.

Timeline

This data analysis took total time (including validation and error checking) from 2018-06-12 till 2020-05-29, after which the data was prepared for publiching till 2020-07-02.

Smart lighting systems in low energy commercial buildings can be expensive to implement and commission. Studies have also shown that only 50% of these systems are used after installation, and those used are not operated at full capacity due to inadequate commissioning and lack of personalization. Wireless sensor networks (WSN) have great potential to enable personalized smart lighting systems for real-time model predictive control of integrated smart building systems. In this paper we present a framework for using a WSN to develop a real-time indoor lighting inverse model as a piecewise linear function of window and artificial light levels, discretized by sub-hourly sun angles. Applied on two days of daylight and ten days of artificial light data, this model was able to predict the light level at seven monitored workstations with accuracy sufficient for daylight harvesting and lighting control around fixed work surfaces. The reduced order model was also designed to be used for long term evaluation of energy and comfort performance of the predictive control algorithms. This paper describes a WSN experiment from an implementation at the Sustainability Base at NASA Ames, a living laboratory that offers opportunities to test and validate information-centric smart building control systems.

The WSN-LOC: Wireless Sensor Network Node Localization Dataset is designed to support research and development in node localization for Wireless Sensor Networks (WSNs). It provides a structured dataset containing essential parameters such as node positions, anchor nodes, received signal strength (RSSI), obstacle presence, and localization errors.

The dataset enables researchers to: ✅ Test localization algorithms in different network topologies. ✅ Simulate dynamic conditions by modifying node positions and obstacle settings. ✅ Evaluate model accuracy by comparing predicted and ground-truth positions.

This dataset comprises 1,092 rows of integrated multi-modal data collected from a smart urban sensor network. It includes vehicle traffic, power consumption, crowd behavior, motion detection, heat signatures, and blockchain-based transmission metadata from various smart city zones.

Key attributes span:

Traffic Monitoring: Vehicle count, speed, signal status, congestion.

Smart Grid Data: Energy usage (kWh & Joules), voltage, frequency.

Surveillance Data: Sound levels, IR heat, motion level, and camera feeds.

Blockchain & IoT Metadata: Node & transaction IDs, data size, consensus method.

Anomalies: Flags for congestion, power failures, crowd suspicion.

Automated in situ soil sensor network - the data set includes hourly and daily measurements of volumetric water content, soil temperature, and bulk electrical conductivity, collected at 42 monitoring locations and 5 depths (30, 60, 90, 120, and 150 cm) across Cook Agronomy Farm. Data collection was initiated in April 2007 and is ongoing. Description of data Tabular data CAF_sensors: folder with Daily and Hourly subfolders, each containing 42 '.txt' files of water content and temperature sensor readings. Each file represents readings from a single location, indicated in the file name (i.e. CAF003.txt) and in the 'Location' field of the table. Readings are organized by 'Date' (4/20/2007 - 6/16/2016), ‘Time’ (24 hr clock, only in hourly files), and with property (VW or T) and sensor 'Depth' as follows: VW_30cm: volumetric water readings at 30 cm depth (m^3/m^3) VW_60cm: volumetric water readings at 60 cm depth (m^3/m^3) VW_90cm: volumetric water readings at 90 cm depth (m^3/m^3) VW_120cm: volumetric water readings at 120 cm depth (m^3/m^3) VW_150cm: volumetric water readings at 150 cm depth (m^3/m^3) T_30cm: temperature readings at 30 cm depth (C) T_60cm: temperature readings at 60 cm depth (C) T_90cm: temperature readings at 90 cm depth (C) T_120cm: temperature readings at 120 cm depth (C) T_150cm: temperature readings at 150 cm depth (C) Volumetric water content readings are calibrated according to: Gasch, CK, DJ Brown, ES Brooks, M Yourek, M Poggio, DR Cobos, CS Campbell. 2017. A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil specific correction. Computers and Electronics in Agriculture, 137: 29-40. Temperature readings are factory calibrated. CAF_BulkDensity.txt: file containing bulk density values ('BulkDensity' in g/cm^3) for sensor depths at each of the 42 instrumented locations at Cook Farm. Location is indicated in 'Location' field, and sample depths are defined (in cm) by the ’Depth’ field. CAF_CropID.txt: file containing crop codes for each sub-field (A, B and C) and strip (1-6 for A and B, 1-8 for C) at Cook Farm for 2007-2016. This is also part of the attribute table for 'CAF_strips.shp' CAF_CropCodes.txt: file containing crop code names and crop identities, used in 'CAF_CropID.txt' and 'CAF_strips.shp' CAF_ParticleSize.txt: file containing particle size fractions ('Sand', 'Silt', and 'Clay' as percent) for each 'Location' at sensor depths ('Depth', in cm). Spatial data All spatial data have spatial reference NAD83, UTM11N CAF_sensors.shp: file containing locations of each of the 42 monitoring locations, the 'Location' field contains the location name, which coincides with locations in tabular files. CAF_strips.shp: file containing areal extents of each sub-field, stip, and crop identities for 2007-2016. Crop identity codes are listed in 'CAF_CropCodes.txt' CAF_DEM.tif: file containing a 10 x 10 m elevation (in m) grid for Cook Farm. CAF_Spring_ECa.tif, CAF_Fall_ECa.tif: files containing 10 x 10 m apparent electrical conductivity (dS/m) grids to 1.5 m depth for spring and fall at Cook Farm. CAF_Bt_30cm.tif, CAF_Bt_60cm.tif, CAF_Bt_90cm.tif, CAF_Bt_120cm.tif, CAF_Bt_150cm.tif: files containing 10 x 10 m predictive surfaces for probability (0-1) of Bt horizon at the five sensor depths. Quality Control The Flags folder consists of the files containing the quality control flags for the Cook Farm Sensor Dataset. The nomenclature for the files indicates flags for either temperature (T) or water content (VW) and sensor depths. For example: T_30 is for the temperature data at 30cm. depth VW_120 is for the Volumetric water content at 120 cm. depth Files starting with “missing” contain flags (“M”) for locations and dates (mm/dd/yyyy) with missing data (NA in original dataset). Files starting with “range” contain flags for locations and dates (mm/dd/yyyy) with values outside acceptable ranges: Soil moisture (0-0.6 m^3/m^3) flagged as “C” Soil temperature (<0 deg. C) flagged as “D” Files starting with the name “flats” contain flags (“D”) for locations, dates (mm/dd/yyyy), and times (hh:mm) with constant values (within 1%) for a 24 hour period, as in Dorigo et al. 2013. Files starting with the name “spikes” contain flags (“D”) for locations, dates (mm/dd/yyy), and times (hh:mm) with sudden spikes in VWC readings. Files starting with the name “breaks” contain flags (“D”) for locations, dates (mm/dd/yyy), and times (hh:mm) with sudden breaks (jumps or drops) in VWC readings. Code (implemented in R) for the screening and flagging is included in “Code Snippet.txt” A list of the sensor versions as of 06/16/16 at each location and depth. Resources in this dataset:Resource Title: Data package for automated in situ soil sensor network. File Name: CAF_Sensor_Dataset.zipResource Description: Data file descriptions for Cook Farm sensor network data set (CAF_Sensor_Dataset). Data set compiled by Caley Gasch, under supervision of David Brown, Department of Crop and Soil Sciences, Washington State University, Pullman, WA. Updated: 04/01/2017 Tabular data: CAF_sensors: folder with Daily and Hourly subfolders, each containing 42 '.txt' files of water content and temperature sensor readings. Each file represents readings from a single location, indicated in the file name (i.e. CAF003.txt) and in the 'Location' field of the table. Readings are organized by 'Date' (4/20/2007 - 6/16/2016), ‘Time’ (24 hr clock, only in hourly files), and with property (VW or T) and sensor 'Depth' as follows: VW_30cm: volumetric water readings at 30 cm depth (m^3/m^3) VW_60cm: volumetric water readings at 60 cm depth (m^3/m^3) VW_90cm: volumetric water readings at 90 cm depth (m^3/m^3) VW_120cm: volumetric water readings at 120 cm depth (m^3/m^3) VW_150cm: volumetric water readings at 150 cm depth (m^3/m^3) T_30cm: temperature readings at 30 cm depth (C) T_60cm: temperature readings at 60 cm depth (C) T_90cm: temperature readings at 90 cm depth (C) T_120cm: temperature readings at 120 cm depth (C) T_150cm: temperature readings at 150 cm depth (C) Volumetric water content readings are calibrated according to: Gasch, CK, DJ Brown, ES Brooks, M Yourek, M Poggio, DR Cobos, CS Campbell. 2017. A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil specific correction. Computers and Electronics in Agriculture, 137: 29-40. Temperature readings are factory calibrated. CAF_BulkDensity.txt: file containing bulk density values ('BulkDensity' in g/cm^3) for sensor depths at each of the 42 instrumented locations at Cook Farm. Location is indicated in 'Location' field, and sample depths are defined (in cm) by the ’Depth’ field. CAF_CropID.txt: file containing crop codes for each sub-field (A, B and C) and strip (1-6 for A and B, 1-8 for C) at Cook Farm for 2007-2016. This is also part of the attribute table for 'CAF_strips.shp' CAF_CropCodes.txt: file containing crop code names and crop identities, used in 'CAF_CropID.txt' and 'CAF_strips.shp' CAF_ParticleSize.txt: file containing particle size fractions ('Sand', 'Silt', and 'Clay' as percent) for each 'Location' at sensor depths ('Depth', in cm). Spatial data: all spatial data have spatial reference NAD83, UTM11N CAF_sensors.shp: file containing locations of each of the 42 monitoring locations, the 'Location' field contains the location name, which coincides with locations in tabular files. CAF_strips.shp: file containing areal extents of each sub-field, stip, and crop identities for 2007-2016. Crop identity codes are listed in 'CAF_CropCodes.txt' CAF_DEM.tif: file containing a 10 x 10 m elevation (in m) grid for Cook Farm. CAF_Spring_ECa.tif, CAF_Fall_ECa.tif: files containing 10 x 10 m apparent electrical conductivity (dS/m) grids to 1.5 m depth for spring and fall at Cook Farm. CAF_Bt_30cm.tif, CAF_Bt_60cm.tif, CAF_Bt_90cm.tif, CAF_Bt_120cm.tif, CAF_Bt_150cm.tif: files containing 10 x 10 m predictive surfaces for probability (0-1) of Bt horizon at the five sensor depths. (Dataset updated on 10/23/2017 to include QC information.)

We present an open-source database for evaluation of time synchronization algorithms for wireless acoustic sensor networks . More Information and examples on how to use the database can be found on our GitHub page: https://github.com/fgnt/paderwasn

We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.

The WSN-DS contains information on nodes in Wireless Sensor Networks (WSN), encompassing various types of Denial of Service (DoS) attacks such as blackhole attacks, scheduling attacks, grayhole attacks, flooding attacks, as well as the normal behavior of nodes. It consists of the following key details: Dataset Name: WSN-DS Number of Rows: 374,661 Number of Columns: 19 The first 18 columns correspond to the attributes of nodes, while the last column represents the node labels.

The aim is to automate the detection of DoS attacks in WSN. The collected data is based on node attributes and labels representing different types of attacks and normal behavior. The goal is to build a reliable machine learning model that can accurately classify these attacks to assist in network security monitoring.

The dataset used in this paper is a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming.

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods.

The Wireless Sensor Network (WSN) market is booming, projected to reach [estimated market size in 2033] by 2033, with a CAGR of 26.59%. Driven by IoT growth and advancements in sensor technology, this market spans diverse sectors like healthcare, industrial automation, and oil & gas. Discover key trends, regional insights, and leading companies shaping this dynamic landscape. Recent developments include: March 2024: Brown researchers developed a brain-inspired wireless system to gather data from salt-sized sensors. These sensor networks are designed so the chips can be implanted into the body or integrated into wearable devices. Each submillimeter-sized silicon sensor mimics how neurons in the brain communicate through spikes of electrical activity. The sensors detect specific events as spikes and then transmit that data wirelessly in real time using radio waves, saving both energy and bandwidth., March 2024: IIT-Mandi introduced a groundbreaking power management unit designed explicitly for directly cloud-enabled indoor wireless sensor network (WSN) nodes. These nodes, offering distinct advantages over low-power wireless communication technologies, often face challenges related to the consumption of higher peak current during data transmission, leading to battery capacity degradation and reduced lifespan.. Key drivers for this market are: Increasing Adoption of Wireless Technologies, Reducing Cost of Wireless Sensors. Potential restraints include: Associated Complexities Challenge the Market Growth. Notable trends are: Medical Segment is Expected to Witness Significant Growth.

The Wireless Sensor Network (WSN) market is experiencing robust growth, driven by the increasing adoption of IoT technologies across diverse sectors. The market size in 2025 is estimated at $178.98 billion (based on the provided value of 178980 million). While the CAGR is not specified, considering the rapid advancements in sensor technology, low-power wide-area networks (LPWANs), and the expanding applications in smart cities, industrial automation, and healthcare, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 12%. This implies substantial market expansion, reaching an estimated value exceeding $500 billion by 2033. Key growth drivers include the decreasing cost of sensors, enhanced data analytics capabilities, and the rising need for real-time monitoring and control in various industries. Emerging trends like AI-powered sensor data processing and the integration of 5G networks are further accelerating market growth. However, challenges such as data security concerns, interoperability issues, and the need for robust power management solutions continue to pose restraints. The competitive landscape is characterized by the presence of both established players like Intel, Texas Instruments, and Cisco, and emerging technology providers focusing on specialized sensor solutions and network management. The market is fragmented, with companies vying for market share through innovation in sensor technology, network architecture, and data analytics platforms. The North American and European markets currently dominate, but significant growth opportunities are expected in Asia-Pacific driven by rapid industrialization and urbanization. The market is segmented based on technology (e.g., Zigbee, Z-Wave, Bluetooth), application (e.g., environmental monitoring, industrial automation, healthcare), and deployment (e.g., indoor, outdoor). Future growth will hinge on the successful development of more energy-efficient sensors, improved data security protocols, and the standardization of network protocols to foster interoperability.

The Wireless Sensor Network (WSN) market is experiencing robust growth, projected to reach $11.71 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 26.59% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing demand for real-time monitoring and data acquisition across diverse sectors, such as industrial automation, smart cities, and healthcare, is a primary catalyst. The rising adoption of Internet of Things (IoT) technologies and the decreasing cost of sensor hardware and communication modules further propel market growth. Furthermore, advancements in low-power wide-area networks (LPWAN) technologies, enabling long-range and energy-efficient data transmission, are significantly contributing to market expansion. The integration of artificial intelligence (AI) and machine learning (ML) for data analysis and predictive maintenance enhances the value proposition of WSNs, attracting wider adoption. However, challenges such as security concerns related to data privacy and network vulnerabilities, along with the complexity of deploying and managing large-scale WSNs, pose potential restraints. The market segmentation reveals significant opportunities. The military and security segment is a major revenue contributor, driven by the need for enhanced surveillance and situational awareness. The medical sector showcases substantial growth potential with the increasing use of WSNs in remote patient monitoring and in-body sensor applications. Similarly, the transportation and logistics sector leverages WSNs for efficient fleet management and asset tracking. Other significant sectors include oil and gas, water and wastewater management, and consumer packaged goods, where WSNs enable optimized processes and enhanced efficiency. Geographically, North America and Europe currently hold substantial market shares, but the Asia-Pacific region is projected to witness the fastest growth due to rapid industrialization and infrastructure development. Key players such as ABB Ltd, Siemens AG, and Honeywell International Inc are driving innovation and market competition through continuous product development and strategic partnerships. The forecast period of 2025-2033 promises substantial growth, driven by ongoing technological advancements and increased demand across diverse application domains. Recent developments include: March 2024: Brown researchers developed a brain-inspired wireless system to gather data from salt-sized sensors. These sensor networks are designed so the chips can be implanted into the body or integrated into wearable devices. Each submillimeter-sized silicon sensor mimics how neurons in the brain communicate through spikes of electrical activity. The sensors detect specific events as spikes and then transmit that data wirelessly in real time using radio waves, saving both energy and bandwidth., March 2024: IIT-Mandi introduced a groundbreaking power management unit designed explicitly for directly cloud-enabled indoor wireless sensor network (WSN) nodes. These nodes, offering distinct advantages over low-power wireless communication technologies, often face challenges related to the consumption of higher peak current during data transmission, leading to battery capacity degradation and reduced lifespan.. Key drivers for this market are: Increasing Adoption of Wireless Technologies, Reducing Cost of Wireless Sensors. Potential restraints include: Increasing Adoption of Wireless Technologies, Reducing Cost of Wireless Sensors. Notable trends are: Medical Segment is Expected to Witness Significant Growth.

In this data set, we present data collected for the purpose of carrying out a systematic review of the available Wireless Sensor Network and Internet of Things testbed facilities. The data was collected through multiple stages and in each stage the pre-defined criteria were applied. We provide a dataset describing the hardware and software aspects of Wireless Sensor Network and Internet of Things testbed facilities available in the market and scientific community. The data were gathered through an extensive systematic review process of scientific articles published between the years 2011 and 2021. The review aims to obtain good quality data for people who are actively researching the Internet of Things facilities or anyone who is interested in that field.

Clustering algorithms.

The datasets are uploaded for the publication "Optimization of Sensor and Sensor Node Positions for Shape Sensing with a Wireless Sensor Network – A Case Study using the Modal Method and a Physics-informed Neural Network" in MDPI Journal of Sensors. These datasets result from strain measurements for shape sensing (displacement field reconstruction) using a wireless sensor network. As specimen, an aluminium rectangular tube with 2000 mm length, 120 mm x 60 mm cross section and 4 mm thickness is used, which is clamped at one end for a length of 120 mm. On the other end, a load in form of a displacement is applied with a servo-hydraulic test cylinder.

The load case is simulated in Abaqus CAE Standard/Explicit 2024 to generate data for optimal sensor placement. The picture "Setup_of_Test_and_FE-Model.jpg" depicts the setup on the SCALE multi-axial, dynamic test bench and the resulting FE model of it. The data from this simulation is available in the two *.zip files. The file "Modal_Method_Simulation_Data.zip" contains data for the load case with additional simulation data on the eigenmodes, which is required to apply the modal method for shape sensing for this part. In the file "iPINN_Training_Data.zip" additional simulation data is available with a variation of the applied force to generate data for training a physics-informed neural network for solving inverse problems in shape sensing (iPINN).

From the optimal sensor placement with this simulation data, a sensor configuration is derived. For this configuration, a sensor node position optimization is carried out. The resulting WSN configuration is available in the sheet "WSN Configuration" in the file "2025-07-25_Test-Data_WSN_Strain-Measurements.xlsx". It is realized with ten HBM 1-LY11-6/120 strain gauges, self-soldered quarter bridge completions, HX711 A/D converters and amplifiers and Arduino Nano 33 IoT microprocessors. Installed on the specimen, the test is carried out by applying the displacement-over-time from the sheet "Test Setup - Displacement" in the file "2025-07-25_Test-Data_WSN_Strain-Measurements.xlsx". This is repeated three times and the strain over measurement time results logged by the sensor nodes is available in the other three datasheets "Test Run 1", "Test Run 2" and "Test Run 3" in the file "2025-07-25_Test-Data_WSN_Strain-Measurements.xlsx".

The global Wireless Sensor Network (WSN) market is experiencing robust growth, projected to reach an estimated $47,160 million in value. This expansion is driven by a significant Compound Annual Growth Rate (CAGR) of 10.5%, indicating a dynamic and rapidly evolving industry. The increasing adoption of IoT devices, coupled with the growing demand for real-time data collection and analysis across diverse sectors, forms the bedrock of this growth. Key applications fueling this surge include the Military and Security sector, where WSNs enhance surveillance and operational efficiency, and the Medical field, enabling remote patient monitoring and improved healthcare delivery. Furthermore, the Transportation and Logistics industry benefits from WSNs for fleet management and supply chain optimization, while Environmental Monitoring leverages these networks for critical data collection on pollution, climate change, and natural resources. The WSN market is characterized by technological advancements and evolving market dynamics. MEMS and CMOS-based sensors are leading the charge in terms of innovation, offering smaller, more power-efficient, and cost-effective solutions. While the broad adoption across industrial, building automation, and other monitoring applications presents significant opportunities, certain factors may influence the pace of growth. These include the inherent complexities in WSN deployment and maintenance, potential cybersecurity concerns, and the initial investment costs associated with integrating these systems. However, the overwhelming benefits in terms of enhanced efficiency, reduced operational costs, and improved decision-making are expected to outweigh these restraints, propelling the WSN market towards sustained expansion throughout the forecast period. Leading companies such as ABB, Siemens, General Electric, and Honeywell International are at the forefront, driving innovation and catering to the increasing global demand. This report delves into the dynamic landscape of Wireless Sensor Networks (WSNs), offering a granular analysis of market trends, technological advancements, and strategic imperatives. The study encompasses a Study Period from 2019 to 2033, with the Base Year and Estimated Year set at 2025. The Forecast Period for this analysis is 2025-2033, building upon Historical Period data from 2019-2024. The report provides actionable insights for stakeholders navigating this rapidly evolving sector, projecting market value potentially reaching billions of millions (this phrasing is intentionally used to represent a very large, unspecified number in the millions, as per the instruction for "million unit" without a specific value).