Sensor Networks Research Articles

Improving energy efficiency in wireless sensor networks (WSNs) using two-level fuzzy clustering and Artificial Bee Colony (ABC) optimization

ABSTRACT Improving network lifetime and energy efficiency in wireless sensor networks (WSNs) is a challenging but crucial issue. The ability to transport data over the network via a better route is a technology because of limitations on network lifetime and energy consumption. This paper presents a two-level fuzzy approach for optimising cluster formation and data routing in wireless networks. The approach focuses on determining cluster head nodes based on their unique properties, ensuring efficient cluster formation. The second level uses social traits to communicate among cluster nodes, calculating the cluster radius for better communication and data routing. The optimisation process also involves identifying optimal cluster head nodes and their corresponding radius for each cluster. Furthermore, we improved data routing by using the artificial bee colony technique, which included carefully selecting the best data transmission channels between the base station and the cluster head nodes. Thus, simulation results demonstrate that our technique outperforms other similar approaches in terms of energy usage, latency, packet transmissions, and average network lifespan. We tested the network performance from two perspectives: scalability and energy efficiency, in two stages. The experiments’ results demonstrated the superiority of the suggested approach over the methods that were compared.

An integrated localization and proactive disease forecasting system for cattle farming environments in wireless sensor networks

An integrated localization and proactive disease forecasting system for cattle farming environments in wireless sensor networks

Optimizing wireless sensor networks deployment with coverage and connectivity requirements

Optimizing wireless sensor networks deployment with coverage and connectivity requirements

High throughput cell stiffness measurement via multiplexed impedance sensors.

Since physiological and pathological events change the mechanical properties of cells, tools that rapidly quantify such changes at the single-cell level can advance the utility of cell mechanics as a label-free biomarker. We demonstrate the capability to probe the population-level elastic modulus and fluidity of MDA-MB-231cells at a throughput of up to 50cell/second within a portable microchip. Our sensing scheme adapts a code multiplexing scheme to implement a distributed network of sensors throughout the microchip, thereby compressing all sensing events into a single electrical output. To validate our approach, we prepared cell samples whose stiffnesses were manipulated with chemical agents. We confirmed the expected effect of the chemicals agreed with the stiffness measurements reported by our microchip. Such a low-cost electronic assay that rapidly measures mechanical properties enables previously infeasible studies to advance the science of mechanobiology.

A Bayesian-optimized 1D CNN-based outlier detection approach for wireless sensor networks

A Bayesian-optimized 1D CNN-based outlier detection approach for wireless sensor networks

Underwater Sensor Networks for Communication, Navigation, and Localization

Underwater Sensor Networks for Communication, Navigation, and Localization

Smart Energy Solutions for Wireless Medical Sensor Networks: Modulation Optimization in IoMT for Medical Applications

Smart Energy Solutions for Wireless Medical Sensor Networks: Modulation Optimization in IoMT for Medical Applications

Hierarchical Deployment and Square Tessellation for Connected k-Coverage in Heterogeneous Planar Wireless Sensor Networks

Hierarchical Deployment and Square Tessellation for Connected k-Coverage in Heterogeneous Planar Wireless Sensor Networks

Research on Node Fault Diagnosis in Wireless Sensor Networks Based on a Constrained Data Maximum Entropy BN Parameter Learning Algorithm

Currently, power communication wireless sensor networks (WSNs) exhibit the characteristics of uncertainty and complexity. Furthermore, the application environment is more complex, resulting in nodes damaging easily, power communication breakdown, and economic losses. Dynamic monitoring of power communication WSN nodes is necessary. To this end, a constraint data maximum entropy Bayesian network (BN) parameter learning algorithm is used to solve the problem of small data sample size in monitoring and improve the quality of power communication WSN node fault diagnosis. After the latest verification, it is found that the correct rate of WSN node fault diagnosis under small data set is as follows: in normal conditions, the fault rate is 96%, the sensor fault is 94%, the power supply fault is 100%, the wireless communication fault is 90%, and the processor fault is 91%. It can be seen that even under small sample data sets, better diagnostic accuracy can be brought.

Setup of a densely distributed and quality-controlled meteorological sensor network in Pune, India, for urban microclimate research and citizen participation in the context of climate change adaptation

Setup of a densely distributed and quality-controlled meteorological sensor network in Pune, India, for urban microclimate research and citizen participation in the context of climate change adaptation

Computer-Aided Supporting Models of Customized Crack Propagation Sensors for Analysis and Prototyping.

The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or periodically in frequent time stamps. This paper briefly introduces a new type of sensor, called a Customized Crack Propagation Sensor (CCPS), which is an alternative for crack gauges, but with enhanced functional features and customizability. Due to those characteristics, it is necessary to develop a family of computer-aided supporting models for rapid prototyping and analysis of the new designs of sensors of various shapes and configurations, which this paper presents by use of simulation tools. For a prototyping of the sensor lay out, an algorithm is elaborated, based on an application created in LabVIEW 2022 software, which generates two spreadsheets formatted by the requirements of Autodesk Inventor 2014 and COMSOL Multiphysics 5.6 software, based on data entered by the user. As a result, a tailored-in-shape CCPS layout is prepared. A parametric model of the sensor is prepared in Autodesk Inventor software, which automatically changes its geometric dimensions after changing data in an MS Excel spreadsheet. Then, the generated layout is analyzed to obtain electromechanical characteristics for defined CCPS geometry and materials used in the COMSOL Multiphysics software. Another application is devoted to purely mechanical analysis. The graphical user interface (GUI) add-on based on the Abaqus 2018 software engine is prepared for advanced mechanical analysis simulations of sensor materials in selected loading scenarios. The GUI is used for entering material libraries and the selection of loading conditions and a type of specimen, while the results of the numerical analysis are delivered through Abaqus. The main advantage of the developed GUI is the capacity for personnel inexperienced in using the Abaqus environment to perform analysis. Some results of simulation tests carried out in both COMSOL Multiphysics as well as Abaqus software are delivered in this paper, using a predefined parametric sensor model. For example, using a rigid epoxy resin for an insulating layer shows a negligible difference in the level of strain compared to the structure during a simulated tensile test, specifically in the tested layer thickness range of up to 0.3 mm. However, during bending tests, an approx. 17% change in principal strain level can be observed through the top to bottom edge of the epoxy resin layer. The adopted methodology for carrying out simulation studies assumes the parallel use of a set of various computer-aided tools. This approach allows for taking advantage of individual software environments, which allows for expanding the scope of analyses and using the developed models and applications in further research activities.

Performance and Energy Consumption Analysis for UWSNs with Priority Scheduling Based on Access Probability and Wakeup Threshold.

As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH). This approach aims to conserve energy while maintaining acceptable network latency levels. The wakeup threshold restricts the frequency of state switching for the CH, thereby reducing energy consumption. Meanwhile, the dynamic access probability regulates the influx of packets to mitigate system congestion based on current network conditions. Furthermore, to accommodate the network's varied transmission demands, packets generated by sensor nodes (SNs) are categorized into two types according to their sensitivity to latency. A discrete-time queueing model with preemptive priority is then established to evaluate the performance of different packets and the CH. Numerical results show how different parameters affect network performance and demonstrate that the proposed mixed packet forwarding mechanism can effectively manage the trade-off between latency and energy consumption, outperforming the traditional mechanism within a specific range of parameters.

An Experimental Network Analysis-based Approach for Detection of Jamming Attacks in Wireless Sensor Networks

An Experimental Network Analysis-based Approach for Detection of Jamming Attacks in Wireless Sensor Networks

Hybrid Archimedes and Arithmetic Optimization Algorithm for Cluster Head Selection and Multipath Routing in Wireless Sensor Network

Hybrid Archimedes and Arithmetic Optimization Algorithm for Cluster Head Selection and Multipath Routing in Wireless Sensor Network

A Hybrid Biologically-Inspired Optimization Algorithm for Data Gathering in IoT Sensor Networks

The wireless sensor networks are autonomous Ad hoc Networks that form the backbone of IoT technology. Their vital role is to collect data from their physical environments and transfer these data to the Base Station (BS). This task implies high energy consumption when a suitable data routing policy is not applied. Then, the data collection method determines the efficiency of WSNs. Moreover, the IoT-based applications that require the mobility of the IoT sensor nodes introduce a new challenge regarding network connectivity. The IoT sensor node’s mobility leads to regular changes in the network topology, resulting in high packet loss during communication. The management of network energy use in order to prolong the battery life of IoT sensor nodes is a crucial issue, as IoT sensor nodes possess limited battery capacity. This paper addresses these issues by proposing an efficient Hybrid Biologically-Inspired Optimization Algorithm, named HBIP, for Data Gathering in IoT Sensor Networks. The flagship idea of the HBIP algorithm is to incorporate the swarming stage of the Bacterial Foraging Optimization (BFO) into the Artificial Bee Colony (ABC) Optimization’s exploitation phase. Simulation results show that the HBIP protocol outperforms the LEACH protocol and ABC and BFO metaheuristics regarding network energy consumption, lifetime, and data packets received at the BS. In practice, we demonstrated that the HBIP protocol increases the data collection by 84.40% over LEACH, 19.43% over BFO, and 7.26% over ABC in the network scenario considered. Further on, we extend the study of the HBIP algorithm on Mobile IoT Sensor Networks by proposing a Multi-Objective Optimization-based Data Gathering Protocol (MOO-DGP). The MOO-DGP protocol employs the HBIP algorithm to determine the best solution that balances Velocity, Cluster Stability, Link Quality, and Energy Consumption. An extensive evaluation of the MOO-DGP protocol shows it outperforms the existing Mobility-Based Clustering (MBC) protocol regarding effective data packet delivery rate, average control overhead, network lifespan, and energy consumption.

IoT-enabled wireless sensor networks optimization based on federated reinforcement learning for enhanced performance

IoT-enabled wireless sensor networks optimization based on federated reinforcement learning for enhanced performance

Novel joint data collection and wireless charging algorithm for rechargeable wireless sensor networks

Novel joint data collection and wireless charging algorithm for rechargeable wireless sensor networks

LoRa Resource Allocation Algorithm for Higher Data Rates.

LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (Ttx) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa's Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting Ttx to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces Ttx by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above -5 dB in line-of-sight communication scenarios.

Database energy saving strategy using blockchain and Internet of Things

This paper aims to construct a green environmental protection system by advancing database energy-saving techniques and optimizing the energy-saving mechanism against the backdrop of blockchain integration. The protocol classification of wireless sensor networks is examined within the context of the rapid growth of information technology. The analysis draws upon the database storage and sharing model and recent research examples that connect blockchain and database technology. Additionally, the paper investigates the blockchain data structure and storage path by analyzing the spatial–temporal correlation properties of blockchain data. The findings demonstrate that increasing the number of network nodes in the database system within a reasonable range can reduce the overall execution time. Additionally, as time progresses, the query traffic of the system database continues to increase. The research has practical implications in the domains of databases and the Internet of Things.

Design and Implementation of the Marsh Climate Monitoring System Using the Internet of Things

The Internet of Things (IoT) has become a major enabler for sustainable development and has begun to have an impact on society as a whole. Marshes are significant ecosystems for the environment that are essential to biodiversity support and ecological equilibrium. However, environmental changes and human activity are posing an increasing threat to these fragile ecosystems. An Internet of Things (IoT)-based marsh monitoring system was created and put into operation to gather data in real-time on a variety of environmental factors, such as wind speed, CO2 and hydrogen levels, temperature, humidity, voltage, and current. The system makes use of a network of sensors spread out throughout the marsh, which may promote sustainable development, send data to a central node for processing before sending it to a platform hosted in the cloud. After that, an interactive online application is used to visualize the data, giving stakeholders important information about the condition and health of the marsh. Because the suggested system makes it possible to monitor and manage marsh ecosystems effectively, it may promote sustainable development.