Design Of Sensor Networks Research Articles

Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node’s ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm.

Quantum-enhanced learning with a controllable bosonic variational sensor network

Abstract The emergence of quantum sensor networks has presented opportunities for enhancing complex sensing tasks, while simultaneously introducing significant challenges in designing and analyzing quantum sensing protocols due to the intricate nature of entanglement and physical processes. Supervised learning assisted by an entangled sensor network (SLAEN) (Zhuang and Zhang 2019 Phys. Rev. X 9 041023) represents a promising paradigm for automating sensor-network design through variational quantum machine learning. However, the original SLAEN, constrained by the Gaussian nature of quantum circuits, is limited to learning linearly separable data. Leveraging the universal quantum control available in cavity quantum electrodynamics experiments, we propose a generalized SLAEN capable of handling nonlinear data classification tasks. We establish a theoretical framework for physical-layer data classification to underpin our approach. Through training quantum probes and measurements, we uncover a threshold phenomenon in classification error across various tasks—when the energy of probes exceeds a certain threshold, the error drastically diminishes to zero, providing a significant improvement over the Gaussian SLAEN. Despite the non-Gaussian nature of the problem, we offer analytical insights into determining the threshold and residual error in the presence of noise. Our findings carry implications for radio-frequency photonic sensors and microwave dark matter haloscopes.

A novel differentiated coverage-based lifetime metric for wireless sensor networks

This paper delves into optimizing network lifetime (NL) subject to connected-coverage requirement, a pivotal issue for realistic wireless sensor network (WSN) design. A key challenge in designing WSNs consisting of energy-limited sensors is maximizing NL, the time a network remains functional by providing the desired service quality. To this end, we introduce a novel NL metric addressing target-specific coverage requirements that remedies the shortcomings imposed by conventional definitions like first node die (FND) and last node die (LND). In this context, while we want targets to be sensed by multiple sensors for a portion of the network lifetime, we let the periods, during which cells are monitored by at least one sensor, vary. We also allow the ratios of multiple and single tracking times to differ depending on the target and incorporate target-based prioritization in coverage. Moreover, we address role assignment to sensors and propose a selective target-sensor assignment strategy. As such, we aim to reduce redundant data transmissions and hence overall energy consumption in WSNs. We first propose a unique 0-1 mixed integer programming (MIP) model, to analyze the impact of our proposal on optimal WSN performance, precisely. Next, we present comprehensive comparative studies of WSN performance for alternative NL metrics regarding different coverage requirements and priorities across a wide range of parameters. Our test results reveal that by utilizing our novel NL metric total coverage time can be improved significantly, while facilitating more reliable sensing of the target region.

Stability Analysis through a Stability Factor Metric for IQRF Mesh Sensor Networks Utilizing Merged Data Collection.

This paper introduces a novel stability metric specifically developed for IQRF wireless mesh sensor networks, emphasizing flooding routing and data collection methodologies, particularly IQRF's Fast Response Command (FRC) technique. A key feature of this metric is its ability to ensure network resilience against disruptions by effectively utilizing redundant paths in the network. This makes the metric an indispensable tool for field engineers in both the design and deployment of wireless sensor networks. Our findings provide valuable insights, demonstrating the metric's efficacy in achieving robust and reliable network operations, especially in data collection tasks. The inclusion of redundant paths as a factor in the stability metric significantly enhances its practicality and relevance. Furthermore, this research offers practical ideas for enhancing the design and management of wireless mesh sensor networks. The stability metric uniquely assesses the resilience of data collection activities within these networks, with a focus on the benefits of redundant paths, underscoring the significance of stability in network evaluation.

Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring

Abstract In situ calibration of sensors delivering SI traceable measurement results still provides an open question to the design and operation of sensor networks. Particularly when addressing low-cost sensors, currently, the use of sensor networks for air quality monitoring is limited by the low or unknown accuracy of measurements that they can achieve, while the data quality of individual sensor networks is mainly derived by algorithms. Standardization bodies like DIN and CEN therefore announced the need for investigations of validation methods on gas phase species and particulate matter on the one hand side, and for the development of fully digitized quality assurance/quality control and calibration techniques for sensor networks on the other (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). This contribution concentrates on the metrological traceability of sensor networks for air quality monitoring to the international system of units (SI) based on FAIRified intra-network communications (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) and including delocalized Optical Gas Standards operated according to the digital TILSAM method (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Informed by related activities in EURAMET (Partnership project FunSNM, EMNs COO & POLMO, TC-IM 1551) (European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), EURAMET Project TC-IM 1551, Project Database) this contribution discusses the importance of measurement uncertainties in the context of sensor networks, comprising different sensor principles and promoting an efficient uptake of state-of-the-art methods. We discuss how the sensor network case can be addressed with sensors individually using the GUM principles (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)). For sensor network measurements becoming metrologically traceable to the SI, documented and unbroken chains of calibrations need to be implemented each contributing to the measurement uncertainty. This applies to each individual sensor of the network including the potential gold standard among them, but also to the network’s output viewed as a single entity. The contribution provides first approaches to be tested and validated that are underpinned by fundamental design strategies for sensor networks. It follows on practical applications in real world scenarios aside from model uncertainties discussed in artificial intelligence prospects.

Design of a low power consumption wireless sensor network for structural monitoring of bridges

Design of a low power consumption wireless sensor network for structural monitoring of bridges

A Review on Energy and Latency Efficient Routing Protocol Design for Wireless Sensor Networks

Wireless sensor networks (WSNs) have been utilized by various industries, such as disaster management, the chemical and heavy industries, marine research, and space exploration. One of the primary challenges faced by wireless sensor networks is their limited network lifetime. Several techniques have been devised to extend the duration of network operation. This proposed method introduces a threshold-based algorithm to minimize the amount of data that needs to be transferred. In this approach, the selection of cluster heads and their sizes is not fixed. Instead, they are dynamically determined at the beginning of each iteration. The design of routing algorithms for wireless sensor networks faces three main challenges: lowering the rate at which the average node energy declines, minimizing the number of dead nodes, and eventually prolonging the network's lifetime. This paper provides a thorough examination of efficient routing protocols for wireless sensor networks with the goal of improving network longevity. Keywords: Efficient Routing, WSN Clustering, Network Latency, Network Lifetime.

Design and Manufacture of Multifunctional 3-D Smart Skins with Embedded Sensor Networks for Robotic Applications.

An investigation was performed to develop a process to design and manufacture a 3-D smart skin with an embedded network of distributed sensors for non-developable (or doubly curved) surfaces. A smart skin is the sensing component of a smart structure, allowing such structures to gather data from their surrounding environments to make control and maintenance decisions. Such smart skins are desired across a wide variety of domains, particularly for those devices where their surfaces require high sensitivity to external loads or environmental changes such as human-assisting robots, medical devices, wearable health components, etc. However, the fabrication and deployment of a network of distributed sensors on non-developable surfaces faces steep challenges. These challenges include the conformal coverage of a target object without causing prohibitive stresses in the sensor interconnects and ensuring positional accuracy in the skin sensor deployment positions, as well as packaging challenges resulting from the thin, flexible form factor of the skin. In this study, novel and streamlined processes for making such 3-D smart skins were developed from the initial sensor network design to the final integrated skin assembly. Specifically, the process involved the design of the network itself (for which a physical simulation-based optimization was developed), the deployment of the network to a targeted 3D surface (for which a specialized tool was designed and implemented), and the assembly of the final skin (for which a novel process based on dip coating was developed and implemented.).

Intelligent design of sensor networks for data-driven sensor maintenance at railways

With rapid advances in digitization, many critical processes in transportation, industries, and our daily life rely on sensor measurements. With time, however, the measurements may get gradually biased and their precision deteriorates, leading to an enhanced risk of major disruptions caused by false sensor measurements. All single sensor measurements are uncertain and deviate from the true value. To detect malfunctioning sensors early on, a set of recent measurements of each sensor has to be constantly cross-checked against the measurements of a given number of other sensors, i.e., sensors should form a diagnosable network.In this article, we examine the intelligent positioning of safety-relevant sensors at railways such that the installed sensors can constantly cross-check each other and the number of the required sensors is minimized. The arising sensor positioning problem (SPP) belongs to the family of the coordinated set covering problems with two binary matrices: the choice of columns in one matrix implies the selection of specific columns and rows in the other matrix. We formulate an integer program, provide some formal analysis of the SPP and design a customized large neighborhood search metaheuristic RuM, which finds close-to-optimality solutions fast. In our computational experiments, we show that if we ignore the diagnosability requirement, the installed sensors cannot sufficiently cross-check each other in most cases. However, it costs only a few (or even no) additional sensors to ensure the diagnosability of the sensor network.

Integrated clustering and routing design and triangle path optimization for UAV-assisted wireless sensor networks

Integrated clustering and routing design and triangle path optimization for UAV-assisted wireless sensor networks

Some Characterizations of Total Outer-connected Domination in Graphs

This research related to total outer-connected dominance regarding graphs aids in revealing significant insights. Theoretical analysis establishes connections with tree-like structures, emphasizing the relationship with outer planarity. Effective algorithms, integrating the linear-time algorithm for trees and the genetic algorithm for general graphs, show effectiveness in locating minimal total outer-connected dominating sets. Practical implications aid in enlightening applications in wireless sensor networks, social networks, and network design, focusing on the benefits of defect tolerance and information dissemination. This research study aids in contributing towards the increased level of understanding, delivering the practical algorithms, suggesting real- world applications, and providing guidance to future research directions for total outer-connected dominance within the graph.

Integrated Sensing, Communication, and Computation Over the Air: Beampattern Design for Wireless Sensor Networks

Integrated Sensing, Communication, and Computation Over the Air: Beampattern Design for Wireless Sensor Networks

Joint Network Lifetime Maximization and Relay Selection Design in Underwater Acoustic Sensor Networks

Joint Network Lifetime Maximization and Relay Selection Design in Underwater Acoustic Sensor Networks

Improving the performance of low-frequency magnetic energy harvesters using an internal magnetic-coupled mechanism

In this study, we present a novel low-frequency magnetic field energy harvester (EH) employing beryllium bronze/Pb(Zr,Ti)O3 ceramic composited dual-beam structures with tip magnets attached to the inner and outer beams. This design incorporates the internal magnetic-coupled (IMC) effect, resulting in significantly enhanced coupling ability and a wide bandwidth. The validity of the IMC mechanism is confirmed through theoretical formulas and numerical simulations. By leveraging the IMC condition, the EH achieves an expanded bandwidth, which increases from 22 to 43 Hz. Moreover, the total output voltages at the inherent resonance and internal resonance are boosted by 15.4% and 32%, respectively. The performance of the IMC-EH can be further improved by increasing the number of the endmost magnets. Experimental investigations reveal that the IMC-EH generates a maximum RMS output power density of 56.25 μW Oe−2 cm−3, surpassing existing magnetically coupled piezoelectric energy harvesters. Remarkably, even under an ambient magnetic field as low as 1 Oe, the proposed IMC-EH still yields a total output power of 185 μW, sufficient to continuously power 26 LEDs in real time. This demonstrates its potential as a promising solution for low-power consumption small electronics. Furthermore, the implications of this work extend beyond its immediate benefits, as it inspires the design of future self-powered wireless sensor networks in the context of the Internet of Things.

Design of Resilient Sensor Networks Balancing Resilience and Efficiency

In recent years, the notion of resilience has been developed and applied in many technical areas, becoming exceptionally pertinent to disaster risk science. During a disaster situation, accurate sensing information is the key to efficient recovery efforts. In general, resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality, but resilient design may require redundancy and could increase costs. In this article, we describe a method based on binary linear programming for sensor network design balancing efficiency with resilience. The application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both two- and three-dimensional spaces. The method provides optimal sensor placement, taking into account critical functionality and a desired level of resilience and considering sensor type and availability. The problem formulation, resilience requirements, and application of the optimization algorithm are described in detail. Analysis of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent placement. Both tasks are successfully solved by the described method, which can be applied to strengthen the resilience of sensor networks by design. The proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.

Cross-Layer Energy Efficient (CLEE) Routing Algorithm for Mobile Ad-Hoc Networks

Ad-hoc routing algorithms in Wireless Sensor Networks (WSN) rely on nodes’ position awareness by regularly updating routing data of neighbouring nodes. Meanwhile, the transmission energy usage is not optimised as a result of this repeated updates and routing table deployment. Therefore, it is very critical to consider techniques of optimising or conserving energy in the design of wireless sensor networks (WSNs) in order to prolong the lifetime of the individual nodes. One of these techniques is the Cross-layer design which, is considered as an efficient method for addressing this challenge with WSNs. In this paper, we propose a Cross-Layer Energy Efficient (CLEE) routing algorithm to establish an optimal route from the source node to the destination node by selecting candidate nodes from neighbouring nodes based on the distance between these nodes and the rate of energy consumption by a possible candidate node. Then to select a designated node from the candidate nodes, the algorithm further computes the Signal Strength Quality (SQS) and the Link Lifetime (LL) as well as the Throughput (TH) rate of selected nodes. The proposed algorithm was simulated with a network size of 600 X 600 on Network Simulator 3 (NS3) in order to analyse its performance. An evaluation of the performance of the proposed CLEE protocol with existing similar protocols such as Ad-hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) reveals that CLEE outperformes AODV and DSR by conserving about 44% of available energy.

АРХІТЕКТУРА ТА МОДЕЛІ НАДІЙНОСТІ ГІБРИДНИХ СЕНСОРНИХ МЕРЕЖ СИСТЕМ ЕКОЛОГІЧНОГО ТА АВАРІЙНОГО МОНІТОРИНГУ

The authors study the aspects of developing and analyzing the hybrid sensor networks’ operability as subsystems of environmental and emergency monitoring systems for critical infrastructure. The proposed architecture of such a system is based on the technology of edge computing (EC) and combines stationary and mobile components, the first of which is implemented by a ground sensor network (GSN), and the second by a swarm of unmanned aerial vehicles that form a flying EC network. The data collection algorithms, scaling problems, and optimization of the operation of the GSN and monitoring systems in general are analyzed. The reliability models of the GSN in the conditions of failure of one and groups of sensors are developed and investigated. Analytical dependencies of reliability indicators on different sizes of sensor failure clusters and their intensity are obtained. Recommendations for the design and implementation of hybrid sensor networks are given. Keywords: hybrid sensor networks, edge computing, reliability models, multiple failures, environmental monitoring systems, emergency monitoring systems.

Molecular Beamforming for Actuation in Molecular Communication Networks.

The actuation accuracy of sensing tasks performed by molecular communication (MC) schemes is a very important metric. Reducing the effect of sensors fallibility can be achieved by improvements and advancements in the sensor and communication networks design. Inspired by the technique of beamforming used extensively in radio frequency communication systems, a novel molecular beamforming design is proposed in this paper. This design can find application in tasks related to actuation of nano machines in MC networks. The main idea behind the proposed scheme is that the utilization of more sensing nano machines in a network can increase the overall accuracy of that network. In other words, the probability of an actuation error reduces as the number of sensors that collectively take the actuation decision increases. In order to achieve this, several design procedures are proposed. Three different scenarios for the observation of the actuation error are investigated. For each case, the analytical background is provided and compared with the results obtained by computer simulations. The improvement in the actuation accuracy by means of molecular beamforming is verified for a uniform linear array as well as for a random topology.

Heterogeneous Wireless Sensor Network Design with Optimal Energy Conservation and Security through Efficient Routing Algorithm

A heterogeneous wireless sensor network (H-WSN) comprises multiple sensor nodes having varied abilities, like diverse processing power and sensing range. H-WSN deployment and topology control seem to be more difficult than homogeneous WSNs. Research on H-WSNs has increased in the last few years to improve real-time sensor networks' reliability and deliver better networking services than a homogenous WSN does. When it comes to H-WSN's energy consumption and security, the major problem remains the efficient routing process. To that end, this research aims at demonstrating how an efficient routing algorithm of hierarchical H-WSN can greatly enhance the network's performance. It is important to note that the nodes' capabilities mostly determine the suitability of a given routing algorithm. Hence, the H-WSN design issues for routing in a heterogeneous environment are discussed in this paper. This research designs an Optimal Energy Conservation and Security-aware Routing Algorithm (OECS-RA) for H-WSN using clustering and a secure-hop selection scheme. In this proposed model, the optimal cluster head selection and routing have been found through various computational stages based on the energy conservation of each sensor node. It further secures the transmission by selecting the secured node with credential factor computation and comparing each hop of the optimal route. The MATLAB simulation scenario finds the significant performance of the routing mechanism with security compared to existing models. The proposed OECS-RA gives highly recognizable throughput, lifetime, energy efficiency, and reliability. With these results, this proposed algorithm is suggested for real-time implementation in the medical industry, transportation, education, business, etc.

Design of a WiFi sensor network to identify transmission zones of respiratory diseases through air quality indicators

infants and postoperative patients. While air extraction and droplet filtration systems are effective in containing these viruses, interconnected ventilation ducts can still facilitate virus transmission in areas with lower viral concentration. This study introduces a Wireless Sensor Network based on WiFi technology, designed to monitor crucial environmental variables such as CO2, TVOC, temperature, and humidity, collectively influencing indoor air quality. Moreover, this network connects to an online server, collaborating with subscribers to store historical data. The stored information aids in identifying patterns and high-risk zones associated with the transmission of respiratory diseases. The outcomes demonstrate a comprehensive, adaptable, and scalable WiFi-based Indoor Air Quality sensor network. Employing the publish-subscribe pattern, the MQTT message transport protocol, and open-access platforms like Streamlit, Supabase, and HiveMQ, along with affordable boards and sensors, this solution offers an innovative, scalable, and cost-effective approach to identifying areas at risk of airborne disease transmission. The system's design prioritizes data precision and reliability, making it a valuable addition to indoor air quality monitoring systems.