Optimal Sensor Network Research Articles

Accurate Range-Free Localization Using Cuckoo Search Optimization in IoT and Wireless Sensor Networks

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended for its simplicity and robustness. However, despite its popularity, DV-Hop suffers from significant accuracy issues, primarily due to its reliance on average hop size for distance estimation. This limitation often results in substantial localization errors, compromising the overall network effectiveness. To address this gap, we developed an enhanced DV-Hop approach that integrates the cuckoo search algorithm (CS). Our solution improves the accuracy of node localization by introducing a normalized average hop size calculation and leveraging the optimization capabilities of CS. This hybrid approach refines the distance estimation process, significantly reducing the errors inherent in traditional DV-Hop. Findings from simulations reveal that the developed approach surpasses the accuracy of both the original DV-Hop and multiple other current localization methods, providing a more precise and reliable localization method for IoT and WSN applications.

Revisiting the traffic flow observability problem: A matrix-based model for traffic networks with or without centroid nodes

This study introduces a graph theory-based model that addresses the link flow observability problem in traffic networks by optimizing passive sensor deployment. The model aims to determine the minimal number of sensors and their optimal placement. It constructs a virtual network and uses isomorphic graph theory to map between the original and virtual networks, ensuring consistency in nodes, links, and link directions. Two formulas are proposed to calculate the minimum number of observable links required across different networks, factoring in links, ordinary nodes, centroid nodes, and added links. Key concepts such as chords, cut sets, and loops, along with their matrices, are analyzed. A matrix-based framework is developed to consider flow conservation conditions. Results show that solving the full link flow observability problem using node flow conservation equations yields a fixed number of sensors with non-unique deployment schemes, Additionally, a resource-constrained sensor network optimization (RSNO) model is presented, employing null space projection (NSP) as an objective function to quantify the impact of budget constraints particularly under the condition if all the link flows cannot be observed. Numerical examples demonstrate the RSNO model's applications.

An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks

This manuscript presents ABSAS-CS-GSA, an improved iteration of the beetle antennae search (BAS) algorithm, tailored to address the shortcoming of the BAS with low convergence accuracy and easily falling into local optima. The improvements are structured into three principal components: first, dynamic step-size adjustment predicated on initial population to bolster solution accuracy and expedite convergence; second, position update mechanism integrating golden sine algorithm to diversify search patterns and expedite convergence; and third, population disturbance based on vertical and horizontal cross strategy to avoid falling into local optima. Experimental results on 12 benchmark functions demonstrate ABSAS-CS-GSA's superiority over the standard BAS and its four variants, as well as over two classic swarm intelligence algorithms. The simulation results of the algorithm applied to coverage optimization of wireless sensor networks with 30 nodes and 50 nodes reveal a marked improvement in both optimal and average coverage metrics relative to seven alternative algorithms. The refined algorithm exhibits excellent performance and is well-suited for tackling the coverage issue within wireless sensor networks.

Dynamic clustering optimization for energy efficient IoT Network: A simple constrastive graph approach

The growing demand for IoT spans sectors includes industrial automation, home control, healthcare, military, and surveillance. Optimal Wireless Sensor Networks (WSNs), consisting of thousands of sensor nodes (SN) play an essential role in randomly distributing and transmitting environmental data like, temperature, pressure, humidity, light, sound. Energy efficiency is significant for these sensor nodes to prompt research and the development of various design techniques and protocols, especially for IoT applications. Thus, achieving energy-optimized WSNs with dynamic functionality and automatic self-configuration of sensor nodes is an essential research objective. In this work, Dynamic Clustering Optimization for Energy Efficient IoT Network: A Simple Constrastive Graph Approach (DCO-EEN-SCGA) is proposed. The Mean Standard Deviation Shortest Path Problem (MSDSPP) is used in multiple parameters for detecting the shortest valid path. Simple Constrastive Graph Clustering (SCGC) is used to decrease the transmission overhead and energy consumption. Finally, the performance of proposed DCO-EEN-SCGA method provides 15.17%, 17.42%, and 19.25% higher energy consumption, 16.35%, 18.62% and 20.11% higher throughput and 17.45%, 18.72%, and 19.19% higher end to end delay. When comparing with existing techniques like An Opportunistic Energy‐effective Dynamic Self‐Configuration Clustering Algorithm in WSN‐based IoT Networks (OEDSCC-WSNN), Clustering at the Edge: Load Balancing with Energy Efficiency for IoT (CE-LBEE-IOT) and An Energy-effective Hybrid Clustering Technique for IoT-based Multilevel Heterogeneous WSN (EEHT-IOT-MHWN) methods respectively.

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Routing protocols, as a crucial component of the internet of things (IoT), play a significant role in data collection and environmental monitoring tasks. However, existing clustering routing protocols suffer from issues such as uneven network energy consumption, high communication delays, and inadequate adaptation to topology changes. To address these issues, this study proposes an adaptive routing algorithm to balance energy consumption and delay using game theory and deep Q-network (DQN) algorithms (EDRP-GTDQN). Specifically, EDRP-GTDQN evaluates the importance of node positions using node centrality and integrates a game-theoretic-based approach to select optimal cluster heads in terms of node centrality and residual energy. Moreover, graph convolutional networks (GCN) and DQN are incorporated to construct transmission paths for cluster heads, adapt to network topology changes, and balance energy consumption and performance. Furthermore, a cluster rotation mechanism is employed to optimize overall network energy consumption and prevent the formation of hotspots. Experimental results demonstrate that EDRP-GTDQN achieves average performance improvements of 19.76%, 30.04%, 44.2%, and 61.42% in average energy consumption, network lifetime, and average end-to-end delay compared to conventional routing protocols such as EECRAIFA, MRP-GTCO, DEEC, and MH-LEACH. Therefore, EDRP-GTDQN is undoubtedly an effective solution to reduce energy consumption and enhance service quality in wireless sensor networks.

Optimisation of underwater acoustic sensor networks

The oceans are the scene of intense economical and industrial activities. It is becoming even more competitive with recent military activities and the upcoming development of marine renewable energies. All of these activities have a significant impact on marine ecosystems and must be carefully evaluated for any maritime planning. The marine environment is also a very effective medium for the propagation of acoustic waves over long distances, depending on the frequency, the source level, the static (bathymetry) and the dynamic (hydrology) oceanographic conditions. Such conditions make acoustic sensors primary candidates for underwater investigations and the distribution of mixed sensors with various sensitivities, frequency bandwidths or positioning can be optimized for detection, localization or monitoring purposes. This contribution aims to present a methodology based on evolutionary algorithms which considers the overall physical state of the marine environment and determines the optimal configurations of mixed underwater acoustic networks. The relevance of various objectives, or metrics, can be discussed, as well as specific search operators for crossover and mutation based on preliminary results.

Sparse regularized graph pooling network optimal sensor placement method for diesel engine vibration fault perception system

Vibration sensor network optimization increases monitoring effectiveness and reduces sensor quantity and transmission burden. However, the traditional model-driven methods depend on precise finite element models, challenging for complex machinery. This paper presents a data-driven approach using the Sparse Regularized Graph Pooling Network (SRGPN), which conceptualizes sensor networks as graphs and uses graph pooling to identify optimal sensor combinations. A sparsity regularization term related to the number of sensors is included in the loss function, aiming for the minimal yet effective sensor combination. Additionally, a monitoring capability metric suited for diesel engines with multi-source impulse signals is proposed, reflecting the sensors’ monitoring capacity. Validated through simulations and tests on a diesel engine test bench, the results show that SRGPN optimally places sensors near excitation sources, balancing sensor count and monitoring needs. This approach shows potential for optimizing sensor placements in condition monitoring.

Deployment optimization in wireless sensor networks using advanced artificial bee colony algorithm

Deployment optimization in wireless sensor networks using advanced artificial bee colony algorithm

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

A Coverage Model of FMCW Radar for Optimizing Sensor Network Deployment

A Coverage Model of FMCW Radar for Optimizing Sensor Network Deployment

Study on Sensor Network Optimization for MS/AE Monitoring System Using Fisher Information and Improved Encoding Framework

Study on Sensor Network Optimization for MS/AE Monitoring System Using Fisher Information and Improved Encoding Framework

Enhancing Reptile search algorithm with shifted distribution estimation strategy for coverage optimization in wireless sensor networks

The rapid development of the Internet of Things (IoT) has extensively promoted the development of Wireless Sensor Networks (WSNs), an essential technology for series displaying perception and data collected from the physical world. In densely distributed areas, sensor nodes are unevenly distributed, which leads to the network coverage build-up and the consequent efficiency and effectiveness of WSNs. To address this issue, this paper proposes a new method for WSN coverage optimization based on the Reptile Search Algorithm (RSA). In the past, the Reptile Search algorithm has been used to solve optimization problems, which means it can improve different processes. However, the RSA needs to track the trajectory of optimal individuals in each iteration, which will ignore non-optimal individuals' bioeconomic characteristics. Therefore, the paper introduces a distribution estimation strategy into the RSA framework, which can fully mine all the positional information hidden in the entire population. We selected several functions as optimization test benchmark functions to evaluate the feasibility of the proposed method. This paper compares the proposed improved RSA with the standard RSA and some traditional optimization algorithms. The result has been calculated through a series of experiments on network coverage optimization, and the change of parameters also determines the effect of the RSA in the optimization of network coverage. The simulated results of the three similar network coverage optimization experiments show that the improved RSA can be used efficiently within different scenarios.

Sorensen Trust Based and Invasive Weed Algorithm Based Wireless Sensor Network Optimization

Sorensen Trust Based and Invasive Weed Algorithm Based Wireless Sensor Network Optimization

Validation of an Abaqus impact wave propagation model

Structural Health Monitoring (SHM) is an important topic in aircraft industry to reduce the maintenance costs and increase the availability of an aircraft fleet through automating the determination of the health of the aircraft structure and systems using on-site sensors, like piezoelectric (PZT) and optic fibre Bragg grating (FBG) sensors that are often applied in SHM sensor networks. A finite element model for wave propagation due to an impact is very desirable to enable the design of optimal sensor network configurations for SHM applications, to obtain a better understanding of wave propagation (especially in composite structures) and also to generate simulated (virtual) test data for SHM algorithm development. Therefore, an Abaqus explicit finite element (FE) model was developed for wave propagation due to impacts. To validate such a model, a series of impact tests were performed with a drop tower on an aluminium clamped square panel, at different impact energy levels and different impact locations. On the panel, eight FBG and six PZT sensors were installed. These experimental results were used to validate the FE model, in which the impacts were simulated and the computed PZT and FBG sensor responses were compared against the experimental results. The paper will present the impact test setup, the finite element model and the validation results. The main focus of the paper will be the sensitivity analysis, identifying the model parameters that significantly affect the impact sensor responses and their optimal settings. A very good correlation between the numerical and experimental results was obtained. In near future work, the model will be extended to a (thermoplastic) composite panel, for which experimental results on a flat composite panel already have been collected, and a stiffened composite panel representative of an actual horizontal tail plane are planned.

Wireless optimization for sensor networks using IoT-based clustering and routing algorithms.

Wireless sensor networks (WSN) are among the most prominent current technologies. Its popularity has skyrocketed because of its capacity to operate in difficult situations. The WSN market encompasses various industries, including building automation, security networks, healthcare systems, logistics, and military operations. Therefore, increasing the energy efficiency of these networks is of utmost importance. Hierarchical topology, which typically uses a clustering methodology, is one of the most well-known methods for WSN energy optimization. To achieve energy efficiency in WSN, hierarchical topology low-energy adaptive clustering hierarchy (LEACH) was first introduced, and this served as the foundation. However, conventional LEACH has several limitations, which have led to extensive research into improving LEACH's efficacy in its current form. The use of particular algorithms and strategies to enhance the functionality of the conventional LEACH protocol forms the basis of ongoing efforts. Utilizing this enhanced LEACH, performance in terms of throughput and network life may be enhanced by concentrating on elements such as cluster head formation and transmission energy consumption. The enhanced LEACH algorithm demonstrates significant improvements in both throughput and network lifetime compared with conventional LEACH. Through rigorous experimentation, it was found that the enhanced algorithm increases the throughput by 25% on average, which is attributed to its dynamic clustering and optimized routing strategies. Furthermore, the network lifetime is extended by approximately 30%, primarily because of enhanced energy efficiency through adaptive clustering and transmission power control.

Enhancing IoT Applications with Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) in Heterogeneous Wireless Sensor Networks (HWSN)

Enhancing IoT Applications with Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) in Heterogeneous Wireless Sensor Networks (HWSN)

An innovative approach for cluster head selection and Energy Optimization in wireless sensor networks using Zebra Fish and Sea Horse Optimization techniques

In recent times, Wireless Sensor Networks (WSNs) have become an indispensable technology across various industries, offering diverse applications and services. Among the crucial performance metrics for WSNs, optimal cluster head (CH) selection and energy efficiency are paramount for cost-effective network operations. This paper proposes a novel approach for WSNs that tackles both challenges using Zebra Fish Optimization (ZFO) and Sea Horse Optimization (SHO) algorithms. The proposed approach focuses on dynamic cluster formation and CH selection. The ZFO algorithm, enhanced with a new multi-level threading technique, dynamically selects the most suitable CH based on a fitness function. Subsequently, the SHO algorithm, equipped with an innovative adaptive parameter tuning mechanism, optimizes energy consumption within the network. This two-phased approach ensures balanced performance. Performance evaluation is validated using key metrics like packet delivery ratio (PDR), throughput, network lifetime, and residual energy. Experimental results and statistical analysis demonstrate that the proposed hybrid scheme outperforms existing popular algorithms in all these metrics. The improvements range from 1.8 % to 6.9 % for PDR, 6.7 % to 24 % for throughput, 1.86 % to 7.40 % for network lifetime, and 9.65 % to 37.95 % for residual energy. These advancements are attributed to the innovative modifications introduced in both ZFO and SHO algorithms, ultimately contributing to the enhanced performance of the entire system.

Coverage optimization of wireless sensor networks based on bottle sea sheath optimization algorithm

To tackle the problem of inadequate placement of wireless sensor networks’ sensor nodes, a proposed algorithm aims to enhance network coverage through a more optimal approach. Recognizing limitations in the Salp Swarm Algorithm (SSA) optimization, specifically its insufficient local search capability and susceptibility to local extreme values, a hybrid strategy is introduced in the search step. This strategy involves adaptive weight factor adjustments and incorporates Levy flight, achieving a better balance between global search, convergence speed, and computational accuracy. Consequently, this refinement contributes to improved effective network coverage, enhancing the overall network coverage in wireless sensor networks and facilitating faster coverage of the nodes.

Coverage Optimization of Robotic Sensor Networks in Nonconvex Environments via Rotary Pointer Partitions

Coverage Optimization of Robotic Sensor Networks in Nonconvex Environments via Rotary Pointer Partitions

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