Sensors In Network Research Articles

Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games.

Many machine learning problems can be formulated as non-convex multi-player games. Due to non-convexity, it is challenging to obtain the existence condition of the global Nash equilibrium (NE) and design theoretically guaranteed algorithms. This paper studies a class of non-convex multi-player games, where players' payoff functions consist of canonical functions and quadratic operators. We leverage conjugate properties to transform the complementary problem into a variational inequality (VI) problem using a continuous pseudo-gradient mapping. We prove the existence condition of the global NE as the solution to the VI problem satisfies a duality relation. We then design an ordinary differential equation to approach the global NE with an exponential convergence rate. For practical implementation, we derive a discretized algorithm and apply it to two scenarios: multi-player games with generalized monotonicity and multi-player potential games. In the two settings, step sizes are required to be O(1/k) and O(1/√k) to yield the convergence rates of O(1/ k) and O(1/√k), respectively. Extensive experiments on robust neural network training and sensor network localization validate our theory. Our code is available at https://github.com/GuanpuChen/Global-NE.

Matheamatical Modeling of Multi-Objective Adaptive Neuro Fuzzy Inference Based Optimization for IOT Based Wireless Sensor Network

The proliferation of the Internet of Things (IoT) and its integration with wireless sensor networks (WSNs) necessitates advanced optimization techniques to enhance performance and resource allocation while ensuring reliability and energy efficiency. This study introduces a mathematical modeling approach utilizing a Multi-Objective Adaptive Neuro-Fuzzy Inference System (MO-ANFIS) designed for optimizing IoT-based WSNs. The proposed model synergistically combines the adaptive learning capabilities of neural networks with the reasoning prowess of fuzzy inference systems, encapsulated within a multi-objective framework to concurrently address key operational objectives such as minimizing energy consumption, maximizing network lifetime, and enhancing data accuracy and throughput. The mathematical model is formulated to dynamically adapt to changing network conditions and sensor inputs, enabling real-time tuning of fuzzy rules and membership functions through backpropagation neural training. This adaptability ensures optimal performance despite the variable nature of IoT environments. Simulation results demonstrate that the MO-ANFIS model significantly outperforms traditional optimization methods, offering a robust, scalable solution for complex, dynamic WSNs in the IoT landscape. The findings suggest promising applications in various domains, including smart cities, environmental monitoring, and healthcare, where IoT integration is pivotal. This research not only bridges the gap between theoretical fuzzy-neural frameworks and practical IoT applications but also sets a foundation for future explorations into intelligent, adaptive network management systems.

Information-theoretic sensor placement for large sewer networks

Utility operators face a challenging task in managing wastewater networks to proactively enhance network monitoring. To address this issue, this paper develops a framework for optimized placing of sensors in sewer networks with the aim of maximizing the information obtained about the state of the network. To that end, mutual information is proposed as a measure of the evidence acquired about the state of the network by the placed sensors. The problem formulation leverages a stochastic description of the network states to analytically characterize the mutual information in the system and pose the sensor placement problem. To circumvent the combinatorial problem that arises in the placement configurations, we propose a new algorithm coined the one-step modified greedy algorithm, which employs the greedy heuristic for all possible initial sensor placements. This algorithm enables further exploration of solutions outside the initial greedy solution within a computationally tractable time. The algorithm is applied to two real sewer networks, the first is a sewer network in the South of England with 479 nodes and 567 links, and the second is the sewer network in Bellinge, a village in Denmark that contains 1020 nodes and 1015 links. Sensor placements from the modified greedy algorithm are validated by comparing their performance in estimating unmonitored locations against other heuristic placements using linear and neural network models. Results show the one-step modified greedy placements outperform others in most cases and tend to cluster sensors for efficiently monitoring parts of the network. The proposed framework and modified greedy algorithm provide wastewater utility operators with a sensor placement method that enables them to design the data acquisition and monitoring infrastructure for large networks.

Context-Aided Occupancy Detection and Tracking Using Networked SLEEPIR Sensors

Context-Aided Occupancy Detection and Tracking Using Networked SLEEPIR Sensors

Threat Modeling for Enhanced Security in the Healthcare Industry with a Focus on Mobile Health and IoT

The advancement of mobile health and the Internet of Things (IoT) promises to enhance healthcare quality while reducing costs, particularly with the transition from inpatient to home and ambulatory care. This shift, driven by an aging population, financial pressures, and a shortage of skilled healthcare professionals, presents significant opportunities and challenges. While mobile health improves access and encourages self-management, it also raises serious concerns regarding security and interoperability, especially with wearable devices equipped with sensors in a patient's Body Area Network (BAN). This paper critically analyzes the security and interoperability risks associated with these technologies, emphasizing the need for robust measures such as configuration and asset management. Utilizing recommendations from ENISA (2016) and conducting a risk and vulnerability assessment, this study develops a comprehensive security model tailored for healthcare architectures. Additionally, it applies the STRIDE threat modeling approach to identify and mitigate potential threats, providing valuable insights for securing healthcare systems and prioritizing critical assets vital to organizational operations.

Machine Learning based Suggestion Method for Land Suitability Assessment and Production Sustainability

The global population is projected to increase by an additional two billion by 2050, as per the assessment conducted by Food and Agriculture Management. However, the arable land is anticipated to expand by just 5%. Consequently, intelligent and effective agricultural practices are essential to enhancing farming production. Evaluating rural Land Suitability (LS) is a crucial instrument for agricultural growth. Numerous novel methods and concepts are being adopted in agriculture as alternatives for gathering and processing farm data. The swift advancement of wireless Sensor Networks (WSN) has prompted the creation of economical and compact sensor gadgets, with the Internet of Things (IoT) serving as a viable instrument for automation and decision-making in farmers. To evaluate agricultural LS, this study offers an expert system integrating networked sensors with Machine Learning (ML) technologies, including neural networks. The suggested approach would assist farmers in evaluating agricultural land for cultivating across four decision categories: very appropriate, suitable, somewhat suitable, and inappropriate. This evaluation is based on the data gathered from various sensor devices for system training. The findings achieved with the MLP with four concealed layers demonstrate efficacy for the multiclass categorization method compared to other current models. This trained system will assess future evaluations and categorize the land post-cultivation.

Micro water energy harvesting system based on tubular triboelectric nanogenerator

Abstract Constructing a micro-water energy-harvesting system for the power supply of micro-nano sensors in pipeline liquid transmission networks is of great significance. In this study, a micro water energy harvesting system based on a tubular triboelectric nanogenerator was designed, which achieves efficient energy harvesting and conversion by directly collecting the charge of the friction layer without the electrostatic induction effect. First, the working principle of the liquid-solid triboelectric nanogenerator is analyzed based on multi-physic coupling, and the potential distribution in the charge transfer process is analyzed. Second, the effects of liquid flow, pipe length, inner diameter, and material on the output characteristics of the triboelectric nanogenerator were investigated. Finally, the load power supply characteristics of the triboelectric nanogenerator were presented. The output characteristics of the liquid-solid triboelectric nanogenerator were proportional to the flow rate and inversely proportional to the inner diameter of the pipe. The output power of the liquid-solid triboelectric nanogenerator can be charged and stored by the capacitor through the rectifier bridge, and at least 30 LED lamp beads can be driven at a flow rate of 650 ml·min-1. This research has the potential to be applied to the construction of self-powered liquid-state monitoring intelligent sensors in pipeline networks.

Enhancing Network Security in Distributed Systems Using Middle Roman Dominating Functions

A Middle Roman dominating function (MRDN) on a graph G = (V,E) is a function f:v→{0,1,2,3} satisfying the condition that every vertex u with f(u)=0 is adjacent to at most one vertex v with f(v)=2 or 3. Further if a vertex is assigned 2, then at most two of its vertices can be assigned 0 and if a vertex is assigned 3, then all its neighbours can be assigned 0. The weight of a MRDF is the value f(V(G))=∑_uϵV▒〖f(u〗). The Middle Roman domination number γ_MR (G) is the minimum weight of a MRDF on G. In this paper, we introduce Middle Roman Domination number denoted as γ_MR (G), study the properties of the function, present some characterization and determine γ_MR (G)-value for some graphs. Middle Roman Dominating Functions (MRDFs) present a unique approach within graph theory, with significant implications for various fields in computer science. By assigning values to vertices under specific constraints, MRDFs enable the optimization of resources, enhancing network security, load balancing, and energy efficiency in distributed systems. This paper explores the application of MRDFs in scenarios such as intrusion detection, resilient network design, task scheduling, and sensor activation. By minimizing the overall weight while maintaining functional requirements, MRDFs provide an effective strategy for addressing challenges in network topology, resource allocation, and fault tolerance. The versatility of MRDFs makes them a valuable tool in the development of robust and efficient computing systems.

An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field.

A piezoelectric oscillator for low-speed wind energy collection

ABSTRACT The exploration of low-speed wind harvesters is key to harnessing wind energy and enabling self-powered Internet of Things (IoT) sensor networks. In this paper, a piezoelectric oscillator (APO) for low-speed wind energy collection (B-WEC) is proposed and applied to low-speed wind energy harvesting. The magnetic model and power generation characteristics of the harvester are formulated, the magnetic torque is simulated, and the performance under different parameter combinations is tested experimentally. The results show that the APO rotation reduces the magnetic resistive torque by 26.00%. The highest peak-to-peak voltage is 92.35 V for an APO magnet diameter is 8 mm and the angle of the stopper (γ) is 60°, which is 1258.09% higher than fixed (γ is non-existent). Moreover, it has a maximum power of 0.56 mW at 4 m/s, and the optimally matched resistance is 200 kΩ. The harvester can light up about 150 LEDs. The B-WEC designed in this paper can effectively reduce the driving wind speed of piezoelectric wind energy harvesters and can be used as a power source for self-powered networked sensor networks. This study provides a new solution idea for the exploitation of wind energy.

A fault tolerant node placement algorithm for WSNs and IoT networks

The operation of Internet of Things (IoT) Network and Wireless Sensor Networks (WSNs) can be often disrupted by a number of factors, such as node faults, security attacks, as well as path disconnections. Despite any disruptions or any failures of network components the functionality and performance of the network should remain unaltered. An approach that can assist in resolving the above issues is the use of mobile nodes. In this work, we present a Fault Tolerant Node Placement Algorithm (FTNPA) that utilizes mobile nodes for addressing failures in the network. We initially propose a Mobile Fault Tolerant (MobileFT) Framework, that supplies the two main functionalities of the Fault Tolerant Node Placement Algorithm (FTNPA), which are the detection and the recovery. Then, we present two variations of the FTNPA algorithm, the Decentralized FTNPA and the Centralized FTNPA. The first variation uses a decentralized detection method, where the detection is performed by the neighboring nodes in the network, as well as a local recovery method, where a mobile node is placed in a certain position to assist the affected area. Whereas, the second variation employs a centralized detection method, where the sink is responsible for the detection, and a recovery method that creates alternatives paths of mobile nodes towards a destination node. Simulation results show that the proposed algorithms can significantly contribute to the detection and recovery of faults in IoT Networks and WSNs.

Machine Learning-Based Nanozyme Sensor Array as an Electronic Tongue for the Discrimination of Endogenous Phenolic Compounds in Food.

The detection of endogenous phenolic compounds (EPs) in food is of great significance in elucidating their bioactivity and health effects. Here, a novel bifunctional vanillic acid-Cu (VA-Cu) nanozyme with peroxidase-like and laccase-like activities was successfully prepared. The peroxidase mimic behavior of VA-Cu nanozyme can catalyze 3,3',5,5'-tetramethylbenzidine (TMB) to generate oxidized TMB (oxTMB). Owing to the high reducing power of EPs, this process can be inhibited, and the degree of inhibition increases with the increase of reaction time. Additionally, owing to the outstanding laccase mimic behavior of the VA-Cu, it can facilitate the oxidation of various EPs, resulting in the formation of colored quinone imines, and the degree of catalysis increases with the increase of reaction time. Based on the interesting experimental phenomena mentioned above, a six-channel nanozyme sensor array (2 enzyme-mimic activities × 3 time points = 6 sensing channels) was constructed, successfully achieving discriminant analysis of nine EPs. In addition, the combination of artificial neural network (ANN) algorithms and sensor arrays has successfully achieved accurate identification and prediction of nine EPs in black tea, honey, and grape juice. Finally, a portable method for identifying EPs in food has been proposed by combining it with a smartphone.

A High-Gain Microstrip RF Energy Harvester for IoT Sensors in 5G Network

A High-Gain Microstrip RF Energy Harvester for IoT Sensors in 5G Network

Critical infrastructure monitoring in CBRNe scenarios: a reliable and robust communication network for distributed multimodal sensors

The malicious misuse of CBRNe agents can inflict extensive damage to critical infrastructure and terrorize public society. The effects of such attacks can range from financial or structural drawbacks to significant casualties. Recent terror attacks in Cucuta (2021) or Kabul (2021) demonstrate the need to protect infrastructures such as airports, power plants, and transportation infrastructure. In the face of these modern-day threats, the need for a reliable and effective monitoring system for critical infrastructure has become increasingly important. In a first contribution to the long-term development, we present an initial version of a concept that requires the implementation paradigm of diverse redundancy and secure communication using MQTTS relying on the included cipher suite. An initial set of node types is defined and assigned requirements for implementation. The software tools architecture we propose based on these requirements is designed to support network operators and developers by providing standard features for network management and sensor node implementation. This includes the definition of a secure semi-automatic onboarding process for new sensor nodes, which is presented in detail. We strive for independency from specific hardware platforms, software frameworks, and network technologies to enable an open standard for communication within a critical infrastructure sensor network and also between such networks in the future.

Network Connectivity and Sensor Response of an OpenThread WSN Platform for Crop Monitoring

Network Connectivity and Sensor Response of an OpenThread WSN Platform for Crop Monitoring

Energy-efficient routing protocol for WSN based on relay node selection and A-star algorithm

Due to miniaturization constraints and manufacturing costs, wireless sensor networks (WSN) sensor nodes are generally outfitted with restricted resources concerning computing capability, energy, data storage capacity, and data delivery rate. These constraints encourage a significant portion of the research issues in WSNs, particularly the energy constraint, which is a fundamental issue. This paper suggests an innovative routing protocol that optimizes energy use called RNS_A-star, which combines a routing algorithm using a relay node and an improved heuristic for the A-star algorithm. The RNS_A-star protocol first applies a data routing algorithm using a relay node, which allows either using a relay node or simply sending the intra-cluster data to the CH. Then, the A-star algorithm is used to construct a multi-hop routing route between the CHs and the base station (BS) in order to find the best path for reducing energy consumption during data transmission. Simulation results show that the suggested RNS_A-star protocol performs better than ECO-BAT, LEACH-IACA, and LEACH protocols regarding energy conservation and extending network lifetime.

Artificial Intelligence System Based on Wireless Network and Optical Sensor Recognition Application in Museum Interactive VR Design

Artificial Intelligence System Based on Wireless Network and Optical Sensor Recognition Application in Museum Interactive VR Design

Research on Intelligent Fitness Personalized Training Scheme Based on Wireless Network Sensors and Optical Measurement

Research on Intelligent Fitness Personalized Training Scheme Based on Wireless Network Sensors and Optical Measurement

Dempster Combination Rule-Aided Multi-Sensor Decision-Level-Based Data Fusion in Industrial Internet of Things (IIoT) over Zero-Trust Security

Data fusion is a process of accommodating data from discrete sources to generate more relevant information with enhanced accuracy and compatibility rather than the data gathered and information produced by the identical or particular source. To achieve better accuracy in the information inferred by the Industrial Internet of Things (IIoT) system, data fusion is an emerging technology that takes data from different involved devices as input and generates more consistent and accurate information. To safeguard the IIoT data involved in communication foremost is to ensure the authenticity of the devices to block unauthorized access and thus enhance the data confidentiality and data integrity as well. Zero-trust security is thus employed in the IIoT infrastructure to meet the security requirements in question. The proposed approach, Dempster combination rule-aided multi-sensor decision-level-based data fusion (MS-DLDF) in Industrial Internet of Things (IIoT) over zero-trust security, breaks down the whole concept into phases – initially, the device authenticity is ensured before entering and accessing the network, next, the device data is then encoded to avoid data theft or breach from any unauthorized device, and lastly, the data fusion algorithm is executed to gather data from multiple sources as input and reach to more appropriate and relevant information as compared to the information inferred from a single source. The proposed MS-DLDF concludes with the outstanding results of the Dempster combination rule with the increased number of sensors in complex networks as compared to the single-source data fusion and Bayesian estimation data fusion with enhanced accuracy by 6%, minimal computation time by 15%, increased precision rate 6%, reduced false positive rate by 31%, and the amplified data confidentiality rate by 2%, respectively.

A comprehensive survey on an IoT-based smart public street lighting system application for smart cities

The swift advancement and updating of urban lighting systems, along with the incorporation of smart and Internet of Things (IoT) infrastructure, have opened up numerous opportunities for technological progress across various facets of life. This paper offers a comprehensive overview on the development of smart public street lighting infrastructure tailored for IoT applications in smart cities. Initially, the focus lies on transitioning from conventional lighting to Light-Emitting Diodes (LEDs) technology in street lighting. Complementing this transition, the incorporation of the wireless networked sensors and controllers ensures dynamic brightness control in operational zones, envisioning substantial energy savings. Furthermore, the notion characterizing smart cities denotes incorporating modern digital infrastructures to develop innovative functionalities and connect various application, following the IoT paradigm. The key findings from the proposed study have enhanced knowledge regarding smart public street lighting application. This system integrates smart poles equipped with LEDs lamps technology, smart sensors, communication network and monitoring unit, leveraging current technological advancements in IoT applications. The implementation of IoT-based smart public street lighting systems presents several challenges, including integrating diverse sensors and actuators ensuring robust device communication, secure data management, and effective system scaling and maintenance. Despite these challenges, this system significantly advances smart city infrastructure by enhancing energy efficiency, safety, and sustainability. However, addressing their high initial costs, data privacy and security concerns, and ongoing maintenance are crucial in future studies to realize their full potential in smart cities.