Distributed Wireless Sensor Networks Research Articles

Ubiquitous-cloud-inspired deterministic and stochastic service provider models with mixed-integer-programming

The ubiquitous computing system is a paradigm shift from personal computing to physical integration. This study focuses on the deterministic and stochastic service provider model to provide sub-services to computing nodes to minimize rejection values. This deterministic service provider model aims to reduce the cost of sending data from one place to another by considering the processing capacity at each node and the demand for each sub-service. At the same time, stochastic service provider aims to optimize service provision in a stochastic environment where parameters such as demand and capacity may change randomly. The novelties of this research are the deterministic and stochastic service provider models and algorithms with mixed integer programming (MIP). The test results show that the solution found meets all the constraints and the smallest objective function value. Stochastic modeling minimizes denial of service problems during wireless sensor network (WSN) distribution. The model resented is the ability of wireless sensors to establish connections between distributed computing nodes. Stochastic modeling minimizes denial of service problems during WSN distribution.

Enhanced radioactive waste drum monitoring: A sensorized LoRa-based network for identification and integrity assessment

A reliable monitoring and oversight system is essential to guarantee the safe operation and eventual closure of radioactive waste disposal facilities. With recent technological advancements, there is now the capability to deploy distributed wireless sensor networks that are cost-effective and low in power consumption. Such networks are versatile, finding applications in several civil, environmental, and industrial sectors. This work presents a novel system and method for identifying and continuously monitoring the integrity of stored radioactive waste packages. The proposed approach, developed by the University of Pisa in the context of the H2020 European Project PREDIS, employs a network of wireless node devices, provided with solid-state micro-power radiation detectors tasked with the measurement of gamma-rays and thermal neutrons coming from within cemented waste drums. Collected data are automatically transmitted across the storage site via Long Range radio technology and forwarded to cloud-based or offline platforms, allowing for further visualization, data processing, and safe storage. The system highlights the feasibility of autonomously and passively gathering data from radioactive waste drums over extended distances (more than 250 m in non-line-of-sight conditions), decreasing the reliance on portable or automated scanning mechanisms. In line with its capability for autonomous, long-range data collection, the system satisfies the requirement for structural integrity, containment, and confinement of packaging throughout both interim and long-term disposal of radioactive wastes, thereby playing a key role in reinforcing the safe management of radioactive waste.

Fully inkjet-printed Ag2Se flexible thermoelectric devices for sustainable power generation

Flexible thermoelectric devices show great promise as sustainable power units for the exponentially increasing self-powered wearable electronics and ultra-widely distributed wireless sensor networks. While exciting proof-of-concept demonstrations have been reported, their large-scale implementation is impeded by unsatisfactory device performance and costly device fabrication techniques. Here, we develop Ag2Se-based thermoelectric films and flexible devices via inkjet printing. Large-area patterned arrays with microscale resolution are obtained in a dimensionally controlled manner by manipulating ink formulations and tuning printing parameters. Printed Ag2Se-based films exhibit (00 l)-textured feature, and an exceptional power factor (1097 μWm−1K−2 at 377 K) is obtained by engineering the film composition and microstructure. Benefiting from high-resolution device integration, fully inkjet-printed Ag2Se-based flexible devices achieve a record-high normalized power (2 µWK−2cm−2) and superior flexibility. Diverse application scenarios are offered by inkjet-printed devices, such as continuous power generation by harvesting thermal energy from the environment or human bodies. Our strategy demonstrates the potential to revolutionize the design and manufacture of multi-scale and complex flexible thermoelectric devices while reducing costs, enabling them to be integrated into emerging electronic systems as sustainable power sources.

WIRELESS SENSOR SYNCHRONIZATION METHOD FOR MONITORING SHORT-TERM EVENTS

Topicality. The key part of experimental research is obtaining the most accurate data about the studied object or event. Often, it is necessary to record parameters of processes that are c hallenging to precisely localize in space and time. The process or event under consideration can occur very rapidly, within fractions of a second, making it difficult for the researcher to deploy and configure recording equipment. This necessitates the creation of a network consisting of numerous recording devices to not miss critical events related to the studied process and obtain a sufficient volume of experimental data. Another issue is the synchronization of data obtained from different measurement devices. Spatially distributed recording devices must operate with a high degree of autonomy, leading to discrepancies in timekeeping and the need for synchronization. In processes with sub-millisecond durations, imperfections in timekeeping at each node have a significant impact: undetected and unaccounted discrepancies can lead to distortion or a misunderstanding of the overall picture of the studied process or event, even after acquiring all the necessary data. This is why the development and improvement of methods for synchronizing data recording nodes in distributed wireless sensor networks is important and urgent task. Task statement. One practical application of the proposed solution is the study of injuries caused by the penetration of foreign objects with high kinetic energy into the human body. These studies are conducted using artificial simulators of the human body made from composite materials and ballistic gelatin, with implanted electronic devices for recording changes in physical parameters. Results and conclusions. The article presents a hardware and software method, along with the technical implementation of the process for synchronizing the local clocks of wireless nodes, integrated into a unified information-measurement system located on the simulator. The proposed method allows achieving synchronization accuracy of no more than 12 μs/second using low-cost commercial off-the-shelf components. The practical part of the research discusses microprocessors from the ESP family, which, in general, provide sufficient time synchronization accuracy when using the proposed method, allowing for cost-effective node development within the system. The proposed method can also be applied in other fields, such as measuring vibrations in electrical machines and engines, as well as structural health monitoring.

AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with >43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations.

Application of Jaya algorithm for solving localization problem in a distributed Wireless Sensor Network

Application of Jaya algorithm for solving localization problem in a distributed Wireless Sensor Network

Energy Optimization Using Reptile Search in Wireless Sensor Networks

Energy saving in wireless sensor networks (WSNs) is a critical problem for diversity of applications. Data aggregation between sensor nodes is huge unless a suitable sensor data flow management is adopted. Clustering the sensor nodes is considered an effective solution to this problem. Each cluster should have a controller denoted as a cluster head (CH) and a number of nodes located within its supervision area. Clustering demonstrated an effective result in forming the network into a linked hierarchy. Thus, balancing the load distribution in WSNs to make efficient use of the available energy sources and reducing the traffic transmission can be achieved. In solving this problem we need to find the optimal distribution of sensors and CHs; thus, we can increase the network lifetime while minimizing the energy consumption. In this paper, a Reptile Search Algorithm (RSA) for preserving location privacy and congestion avoidance with less delay guaranteed is proposed. With this routing technique, the complete sensor field is divided into different subdivisions and each subdivision elects a target area by computing its transmission distance. The backbone of the dynamic routing protocol consists of a virtual ring called bell nodes and a radial line called tentacle nodes employs more nodes to construct the network. The amount of radial line and radius of the virtual ring in a network are conjointly determined to ease the communication path from the node to sink. The radial line paths are routed directionally and bell nodes are routed with angular directions probabilistically. From the routing path, the tentacle nodes collect the data to dynamic sink which will assure that the information is going to be collected with less delay and attacker cannot guess their positions. The experimental results show that the proposed RSA method accomplishes enhanced performance in terms of energy consumption, packet delivery delay and lifetime.

A free-floating structure triboelectric nanogenerator based on natural wool ball for offshore wind turbine environmental monitoring

The marine distributed wireless sensor network system can promote the development of offshore wind turbine environmental monitoring. However, traditional ocean sensors and large power generation equipment are hard to meet the requirements of distributed sensor networks. Hence, we proposed a free-floating structure triboelectric nanogenerator (FF-TENG) based on the all-in-one ocean energy harvesting and sensing device concept for offshore wind turbine environmental monitoring. The FF-TENG device follows the integrated structure design of the sphere and floating body, which brings high wave energy capture efficiency and high wave sensing sensitivity. In this design, the wool ball plays the role of the triboelectric material owning to excellent positive triboelectricity. And the lightweight characteristic of the wool ball endows the FF-TENG device with fine wave-following properties in the ocean environment. In rolling working mode, when the motion amplitude and frequency are 5 cm and 4.5 Hz, the instantaneous open-circuit voltage (Voc) and short-circuit current (Isc) of a single FF-TENG can arrive at 462 V and 15.5 μA, respectively. The transferred charge (Qsc) of a single FF-TENG can reach 107 nC. According to the results, the FF-TENG device can achieve a 4.26 times energy harvesting effect compared with nine-independent spherical TENG. Moreover, the Isc of FF-TENG represents a nicely linear relationship with the swing angle (minimum frequency: 0.2 Hz), which indicates that the FF-TENG can monitor the change of wave frequency and amplitude. This research provides a new path for all-in-one ocean energy harvesting and sensing device in offshore wind turbine environmental monitoring.

Analysis of the opportunities and costs of energy saving in lightning system of library buildings with the aid of building information modelling and Internet of things

The electrical lighting technique is one of the primary culprits behind a library's high energy usage and operating expenses. Unfortunately, lighting systems are often unregulated, resulting in suboptimal lighting conditions, while older buildings consume significant amounts of electricity. To reduce energy waste and usage, it is crucial to evaluate the entire library's performance, including indoor lighting, solar radiation, and interior temperature, from the initial design phase. The paucity of high-quality real-world data and procedural knowledge has hindered the accuracy of artificial intelligence (AI) models used in previous research. Consequently, the purpose of this study is to integrate distributed wireless sensor networks (WSN) and sensor techniques employing Internet of Things (IoT) and Building Information Modelling (BIM) approaches in order to reduce energy consumption by implementing a smart illumination technique. The purpose of this study is to examine the strategies and options involved in prototyping an IoT- and BIM-based intelligent illumination system and its effect on library maintenance. This paper presents several strategies for transforming the inefficient lighting system of a typical library into a sustainable one with a quick repayment period. This technology can substantially improve lighting control and energy efficiency by employing motion sensors to detect human movement and adjusting lighting in particular areas. As a result, this solution provides libraries with the opportunity to establish a more sustainable environment while simultaneously increasing productivity. This study concludes with strategies and solutions for instituting energy-efficient measures in libraries, particularly lighting systems. By integrating distributed wireless sensor networks and sensor techniques with IoT and BIM approaches, this research seeks to address the challenges posed by conventional lighting systems and enhance the energy efficiency of libraries as a whole.

Asynchronous broadcast‐based event‐triggered control for discrete‐time clock synchronization

AbstractPrecise timing plays a key role in the time‐sensitive industrial Internet of Things (IIoT). However, precise time synchronization requires more frequent packet exchange, which consumes more communication bandwidth and energy. This is a particular challenge in battery‐powered wireless nodes, and low communication costs have become an important factor in clock synchronization. To address the challenge of achieving low communication cost clock synchronization in distributed wireless sensor networks, this paper proposes an improved event‐triggered control and synchronization scheme with a novel asynchronous broadcast packet exchange protocol. Unlike the traditional event‐triggered control scheme which is based on synchronous polling packet exchange, this proposed asynchronous broadcast packet exchange is more communication efficient and requires fewer number of packet exchanges. And it is worth noting that the proposed algorithm in this paper is a distributed algorithm and does not require real‐time acquisition of information from neighbouring nodes. Finally, a numerical example is given to illustrate the effectiveness of the proposed event‐triggered control strategy. The efficiency and precision of the proposed clock synchronization method is evaluated by intensive simulations, which show that the number of packet exchange is reduced by 60% for a moderate IIoT network and is particularly useful for large scale network.

English

The ubiquitous nature of wireless sensor networks (WSNs) means that their underlying network architecture may be used to serve a broad range of use cases, from "smart cities" to "smart homes" and beyond. Other serious problems, such as energy use and security, have persisted. When it comes to WSN security, the blockchain might be the answer. Traditional blockchains have limitations, such as inefficient transaction processing and limited scalability. When Wireless Sensor Networks (WSNs) are deployed, most of the time, the nodes are left unmanaged and vulnerable to various security threats. Reliable data distribution in WSNs is nontrivial because of the limited resources and dynamic behavior of sensor nodes. Traditional cryptography and authentication-based methods have been shown to be unsuitable for countering node misbehavior attacks due to their high cost and incapability. The lightweight authentication encryption system was developed to solve the problems of sensor resource scarcity and data security (data integrity and secrecy). Current trust-based solutions, on the other hand, increase traffic congestion and decrease the network's lifespan due to the high costs associated with trust estimates and network-wide dissemination. As a result, the objective of this study is to present an intrusion prevention architecture for mobile Internet of Things devices, along with its integration into WSN, to ensure data security while simultaneously improving the network delivery ratio. The architecture that has been suggested is made up of two different discrete components. To begin, the ambiguity principle is used to construct non-overlapping and independently structured clusters, which are then used to preserve the clusters' stability. Second, based on the architecture of the blockchain, encrypted multi-hop routing pathways with end-to-end coverage are being established. By reducing end-to-end latency, time consumption, packet loss rate, and energy usage while increasing throughput, our recommended model's simulation results reveal good results and security gains.

Data Acquisition for Estimating Energy-Efficient Solar-Powered Sensor Node Performance for Usage in Industrial IoT

In the era of rapid technological growth, we are facing increased energy consumption. The question of using renewable energy sources is also essential for the sustainability of wireless sensor networks and the Industrial Internet of Things, especially in scenarios where there is a need to deploy an extensive number of sensor nodes and smart devices in industrial environments. Because of that, this paper targets the problem of monitoring the operations of solar-powered wireless sensor nodes applicable for a variety of Industrial IoT environments, considering their required locations in outdoor scenarios and the efficient solar power harvesting effects. This paper proposes a distributed wireless sensor network system architecture based on open-source hardware and open-source software technologies to achieve that. The proposed architecture is designed for acquiring solar radiation data and other ambient parameters (solar panel and ambient temperature, light intensity, etc.). These data are collected primarily to define estimation techniques using nonlinear regression for predicting solar panel voltage outputs that can be used to achieve energy-efficient operations of solar-powered sensor nodes in outdoor Industrial IoT systems. Additionally, data can be used to analyze and monitor the influence of multiple ambient data on the efficiency of solar panels and, thus, powering sensor nodes. The architecture proposal considers the variety of required data and the transmission and storage of harvested data for further processing. The proposed architecture is implemented in the small-scale variants for evaluation and testing. The platform is further evaluated with the prototype sensor node for collecting solar panel voltage generation data with open-source hardware and low-cost components for designing such data acquisition nodes. The sensor node is evaluated in different scenarios with solar and artificial light conditions for the feasibility of the proposed architecture and justification of its usage. As a result of this research, the platform and the method for implementing estimation techniques for sensor nodes in various sensor and IoT networks, which helps to achieve edge intelligence, is established.

Timestamp-Free Synchronization Under Delays With Arbitrary Distribution in Wireless Sensor Networks

Clock synchronization is a key technology in wireless sensor networks (WSNs). It is significant and challenging to track clock parameters under arbitrary delay distributions. This letter proposes an Adaptive Gaussian mixture extended Kalman particle filter (AGMEKPF), which is robust to arbitrarily distributed delays in practical WSNs and can jointly track clock skew and offset. It is combined with the timestamp-free synchronization, which is a clock synchronization protocol that can effectively reduce energy consumption without exchanging timestamps. Simulation results show that the proposed method outperforms similar methods in terms of accuracy and complexity.

Key Encryption in Wireless Sensor Network

The information needed by smart environments is provided by Distributed Wireless Sensor Networks, which are responsible for detecting the relevant quantities, monitoring and collecting the data, assessing and evaluating the information, formulating meaningful user displays, and performing decision-making and alarm functions are enormous. The importance of sensor networks is highlighted by the number of recent funding initiatives, including the DARPA SENSIT program, military programs, and NSF Program Announcements. We described the architecture of sensor networks and present the Important features of security in sensor networks , some Types of Attacks on sensor networks and the defense against them finally we propose a public key encryption system by using an algorithm of ElGamal and present the algorithms of encryption , decryption and show the efficiency and security of the proposed system.

Towards Sustainable Distributed Sensor Networks: An Approach for Addressing Power Limitation Issues in WSNs

Distributed wireless sensor networks (WSNs) have been implemented in multiple applications. Those networks are intended to support the quality of operations and enhance applications' productivity and safety. WSNs are constructed of a large amount of sensor nodes that are battery powered. Typically, wireless sensors are deployed in complex terrain which makes battery replacement extremely difficult. Therefore, it is critical to adopt an energy sustainability approach to enhance the lifetime of each sensor node since each node contributes to the lifetime of the entire WSN. In this work, we propose an approach to reduce power consumption in wireless sensors. The approach addresses power reduction in a sensor node at the sensing level, as well as the communication level. First, we propose configuring the microcontroller of the sensor to conserve energy based on the performed tasks. Then, we implement an interface to reduce consumed power by the radio module. Based on the approach, we carried out field experiments and we measure the improvement of power-consumption reduction. The results show that the approach contributes to saving up to 50% of the wasted energy at the sensor node and it improves communication reliability especially when the number of sensors in a network scales.

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.

A Graph Correlated Anomaly Detection with Fuzzy Model for Distributed Wireless Sensor Networks

Wireless sensor networks have limited power for processing data, storage, and communication. Due to power shortages and anonymous attacks, sensor nodes may produce faulty or anomaly data which affects the accuracy of the entire system. Effective anomaly detection is essential to make an accurate prediction of the result. Moreover, clustering-based anomaly detection reduces energy consumption by avoiding individual sensory data reporting to the base station. The proposed methodology consists of two phases: Correlated graph clustering, and anomaly detection using a Fuzzy model. In the first phase, the spatial correlation of the sensor readings is used to generate a graph, partitioned into clusters. The intra-cluster and inter-cluster temporal correlations are analyzed to refine the optimized cluster structure. Finally, a fuzzy Mamdani model is used to classify the clusters as either normal or anomalous based on their membership values. The proposed approach leverages both spatial and temporal correlation between sensor measurements to form optimized clusters that are more effective for anomaly detection. The Experiments performed on a real-world dataset of WSNs indicate the efficacy of the proposed methodology, which shows significant improvement over traditional anomaly detection methods the electronic file of your paper will be formatted further for final publication.

Application of matrix multiplication in signal sensor image perception

Abstract LOT wireless sensor nodes are limited by physical factors, usually have weak computing power and endurance, and wireless communication methods are very vulnerable to information theft. Therefore, it is of great significance to ensure the safe and efficient transmission of images in new application scenarios. In view of the need for an efficient image transmission, this paper combines compressed sensing technology with p-tensor product theory, applies the above theory to distributed wireless sensor networks, and uses the correlation of adjacent sensor nodes in wireless sensor networks to propose an improved a joint sparse model for measurement matrices and reduction algorithms. The feasibility is verified by simulation experiments, and the comparison between joint reconstruction and single reconstruction, and the application of various algorithms in other algorithms is carried out, and the actual completion time and storage capacity are analysed. The minimum completion time for wavelet transform is 1.29, the sparse estimated time for the selection of preliminary P waves is 0.07 and the compressed sensing time is 0.20. The maximum completion time for wavelet transform was 1.32, for sparse estimation, it is 0.62, for preliminary P-wave selection, it is 0.17, and for compressed sensing, it is 0.88. The processing time is no >3 s and the runtime is only 0.22–0.88 s. The results show that compared with the compressed sensing of a single node, the joint sparse model based on distributed compressed sensing has a smaller reconstruction error, and can achieve high-precision signal reconstruction when the measurement value is small.

Delay Compensated One-Way Time Synchronization in Distributed Wireless Sensor Networks

Wireless sensor networks require accurate time synchronization to perform collaborative tasks on multiple distributed sensor nodes. However, the performance is greatly affected by the unpredictability of message delays in the synchronization process, which causes cumulative error and challenges to clock parameter estimations. We propose a delay compensated consensus Kalman-based time synchronization (DCCKTS) protocol to estimate the fluctuated delay effectively. We propose an algorithm to update all parameters in a one-way message exchange while maintaining time synchronization accuracy and energy consumption. The performance of the proposed algorithm is compared with traditional algorithms. Simulation results demonstrate that the algorithm performs better than conventional algorithms under an unknown Gaussian delay model.

An Enhanced Anomaly Forecasting in Distributed Wireless Sensor Network Using Fuzzy Model

An Enhanced Anomaly Forecasting in Distributed Wireless Sensor Network Using Fuzzy Model