Deployment Of Sensor Networks Research Articles

Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research.

Low-cost urban carbon monitoring network and implications for china: a comprehensive review.

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented.

A study of deep neural networks for Newtonian noise subtraction at Terziet in Limburg—the Euregio Meuse-Rhine candidate site for Einstein Telescope

The Euregio Meuse-Rhine border region of Belgium, Germany and the Netherlands has been identified as a candidate site for hosting Einstein Telescope. Newtonian coupling of ground vibrations to the core optics of the detectors may limit the sensitivity of Einstein Telescope at frequencies below about 10 Hz. The contribution of Newtonian noise is site specific and depends on the ambient seismic field which in turn depends on the site’s geology and the distribution of surface and underground seismic-noise sources. We have investigated the application of machine learning in combination with the deployment of seismic sensor networks to predict seismic displacement noise at specific locations on the surface and underground. Moreover we have modeled a deep neural network (DNN) that allows to subtract Newtonian noise from the strain data measured by Einstein Telescope. The seismic-field model is based on a complete solution of the elastodynamic wave equations for a horizontally-layered soil structure. The geology features soft-soil layers on hard-rock and was shown to be effective in attenuating Newtonian noise from surface waves below the required sensitivity. In addition our model includes a random background of body waves with all possible angles of incidence. We show that a DNN is effective in predicting Newtonian noise. Application of our DNN allows Newtonian noise subtraction by a factor up to 4.7 at 1 Hz and 2.5 at 5 Hz.

A Deployment and Coverage Optimization Algorithm for Self-Powered Wireless Sensor Networks Based on Hybrid Swarm Intelligence

A Deployment and Coverage Optimization Algorithm for Self-Powered Wireless Sensor Networks Based on Hybrid Swarm Intelligence

Energy aware multilevel clustering scheme with Wake-Up Sleep Algorithm in underwater wireless sensor networks

For a wide range of reasons, researchers are interested in underwater wireless sensor networks (UWSNs). These include water quality and environment monitoring, disaster and earthquake prediction, surveillance by the military, and underwater navigation. Nevertheless, there are a lot of drawbacks and difficulties that come with the aquatic medium, including a high multipath delay, high levels of interference and noise, a harsh atmosphere, poor bandwidth, and short battery life for the sensor nodes. Yet, there are several obstacles to the successful deployment of underwater wireless sensor networks. The issue of sensor node energy depletion is the main cause of concern in the underwater sensor network. These problems require the use of good research methodologies and strategies to solve them. In this study, an Energy-Aware Multilevel Clustering with Wake-up Sleep Algorithm is developed to prolong the lifespan of the underwater wireless sensor network. The oceanic network region is thought to be a series of multi-level, 3D cylinders. High water pressure directed at the sea floor creates communication problems that are resolved by many layers of differing heights. The effectiveness of the suggested method is demonstrated by simulations, which demonstrate that it performs better in terms of an extended network lifespan, average residual energy, and many more factors. In comparison to the results of the simulation with current ones, the network lifespan has significantly increased.

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem.

In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme.

Multiobjective Optimized Deployment of Edge-Enabled Wireless Visual Sensor Networks for Target Coverage

In wireless visual sensor networks, the generation and transmission of huge amounts of image data consume much energy of sensor nodes, and their routing and processing take quite a long time. It is of great importance to shorten event reporting delay and prolong network lifetime, which can be achieved by the appropriate deployment of edge nodes that can not only collect but also process data. This work investigates how to jointly optimize sensor node deployment, edge node deployment, data routing, and data offloading to minimize the number of deployed sensor nodes, the number of deployed edge nodes, and event reporting delay and maximize network lifetime. We formulate this problem as a mixed integer nonlinear program and propose a multiobjective differential evolution algorithm to solve it. A large number of simulation results demonstrate that it can deliver a more accurate Pareto set than the nondominated sorting genetic algorithm III.

New key management scheme lattice-based for clustered wireless sensor networks.

Aiming at the quantum algorithm which can solve the problem of large integer decomposition and discrete logarithm in polynomial time, an anti-quantum computing key management scheme for clustered sensor networks is proposed in this paper. The lattice-based cryptosystem is used to achieve the anti-quantum performance of the key management scheme, and the security of the network is further improved through the mutual authentication of sensor network nodes. Due to the limited storage space of sensor nodes, this paper adopts the cluster management of wireless sensor networks, and most sensor nodes only need a small amount of storage space, thus reducing the deployment cost. Cluster management is suitable for medium and large-scale deployment of sensor networks. Because the data traffic is much larger than that of mutual authentication, the sensor nodes in wireless sensor networks use symmetric keys to communicate with each other after mutual authentication, which can effectively improve the communication efficiency in the case of frequent data communication. Experiments show that the authentication scheme based on lattice cryptosystem proposed in this paper will not improve with the continuous improvement of the security level, and its authentication scale will maintain a relatively stable state, while the algorithm scheme based on RSA will increase the authentication cost with the continuous improvement of the security level, so the scheme proposed in this paper is more suitable for application in the environment with high security level. This scheme can effectively reduce the cost of mutual authentication of sensor nodes, is conducive to the expansion of the network, and can ensure the security of authentication between sensor nodes even in the post-quantum era.

Radio Link Model for Node Deployment in Underground Mine Sensor Networks

This paper presents an experimental characterization of the proposed radio link model for an underground mine sensor network. Power efficiency and range are critical factors to consider when designing a wireless sensor network, particularly for low data rate applications where the goal is to have a long-lasting, low-maintenance network. A ‘deploy and forget’ strategy is desirable because it allows the network to operate autonomously without requiring frequent maintenance or intervention from network operators. DASH7 and IEEE 802.15.4f are both excellent choices for low-power, long-range wireless sensor networking applications. The proposed radio link model was developed and evaluated for 433 MHz DASH7 in underground mines, considering the practical electromagnetic properties of mine walls and the propagation medium, which helps in calculating accurate signal characteristics. Radio wave propagation is a critical factor that needs to be considered when designing a wireless sensor network for complex mine structures. The received signal strength indicator (RSSI) and packet error rate (PER) are two key parameters that are used to measure wave propagation and assess the quality of the radio link between sensor nodes. The radio link design has been optimized for complex mine structures by utilizing these parameters in a model, leading to improved performance and reliability. The measurements were carried out in the world’s second largest salt mine at Khewra, Pakistan, with representative irregular mine structures. The RSSI and PER were measured at different node positions and with variable separation between the nodes. The proposed model allows for the easy placement of nodes on either the rooftop or near the side walls of the mine corridors, with an average variation of 6% in RSSI and 1.9% in PER. The proposed model was validated using off-the-shelf wizzi sensor nodes received from Wizzi Lab, France, and was programmed to measure RSSI and PER while operating under the 433 MHz DASH7 protocol. An agreement between modeled and measured parameters has been noted, making the proposed model a decent method for efficient node deployment in underground mine sensor networks.

Research on the Optimal Deployment of Expressway Roadside Units under the Fusion Perception of Intelligent Connected Vehicles

At present, there is still a lack of relevant theoretical guidance on the deployment of roadside RSU on expressways. In the face of the coexistence of V2V and V2I communication in the future, the deployment adjustment after the penetration of intelligent vehicles is not considered. Therefore, this paper proposes a roadside RSU deployment income model in consideration of the influence of V2V and V2I communication. Based on the optimal income of roadside RSU nodes, it achieves the optimization of the RSU deployment range and determines the optimal deployment spacing through the forwarding and relaying role of V2V communication so as to achieve cost savings. In terms of RSU coverage of positive income, it considers the impact of intelligent vehicles and reconstructs the traditional information flow–traffic flow coupling theory to innovatively realize the modeling of income within the information life cycle. In terms of the information transmission deficit, the WSN node energy loss model is reconstructed with permeability. Also, in terms of the construction and maintenance costs, the cost models are constructed for different cluster lengths. In order to provide a basis for expressway sensor network deployment, MATLAB software (version R2016B) is used to analyze the three-dimensional relationship between expressway traffic density, intelligent vehicle permeability, and roadside RSU deployment spacing as well as to determine the optimal roadside RSU deployment spacing with the income model. Finally, the model reliability is validated by the Warshell algorithm and the Kmeans clustering algorithm.

Sequential Deployment of Mobile Radiation Sensor Network Using Reinforcement Learning in Radioactive Source Search

Ever since the attack on the World Trade Center on September 11, prevention of nuclear terrorist attacks in urban environments has been a major focus for homeland security. To that end, mobile radiation sensor networks that are deployed within a specific area to acquire consecutive measurements are a first line of defense against the illicit movement of nuclear threats. However, sensor network deployment is a complex process imposed on physical and financial constraints and dynamically varying conditions. In this work, reinforcement learning (RL) is applied to control the sequential deployment of a mobile radiation sensor network within a specific geographic area. RL is utilized for dynamically learning of the environment and subsequent decision making on the optimal position of the network sensors driven by shared mutual information. RL has the benefit of allowing the network to learn and update a deployment strategy online from an initially unknown state. The performance of the RL method is demonstrated through self-contained exploration and interaction between sensors in a source search scenario for detecting a radioactive source with a set of mobile detectors within the space of the University of Texas at San Antonio campus. Results exhibit the efficiency and efficacy of (a-sequential) RL in comparison to the sequential placement of the mobile sensors, showcasing optimality in accuracy and efficiency in source detection.

Ultra-Low Power IoT Solution based on WSSN for Monitoring Infrastructures

In recent years, one of the emerging technologies that have had big impact on the field of research is Wireless Smart Sensors Networks (WSSN) given its diversity of features and applications. The accelerating growth in IoT for data communication, for sensing and monitoring applications (ranging from biomedical to environmental targets) has originated a high demand not only for low-power autonomous devices but also for ultra- low cost combining single chip radio with baseband signal and digital data processing [1]. Target applications, such as Smart Grids, Cyber-Physical Systems, Body Area Networks, Internet of Things (IoT) and high-density environmental sensing networks, will be massively available if it is removed the requirement of explicit human action for energy recharge. A third concern for the deployment of large numbers wireless sensor networks is the cost of each individual remote device.

Enhanced energy efficient sleep awake aware intelligent sensor network routing protocol for wireless sensor network

One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime.

IARMTS: Design of an Interference-Aware Routing Model with Time Synchronization Capabilities for Dense Wireless Sensor Network Deployments

Performance of dense wireless sensor networks is often degraded due to communication interference and time synchronization issues. Existing machine learning & deep learning models that propose bioinspired & pre-emptive packet-analysis solutions for these tasks either have high complexity, or high deployment costs. Moreover, these models cannot be scaled for heterogeneous node & traffic types, which limits their applicability when applied to real-time scenarios. To overcome these issues, this text proposes design of an interference-aware routing model with time synchronization capabilities for dense wireless sensor network deployments. The network initially collects temporal clock states & packet delivery performance of different nodes on heterogeneous traffic scenarios. These traffic patterns are converted into frequency, entropy, Gabor, and Wavelet components. The converted components are used to train an ensemble set of Naïve Bayes (NB), k Nearest Neighbour (kNN), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) classifiers. These classifiers assist in identification of optimal clock deviations and set of routing paths. These routing paths are further fine-tuned via use of a Bacterial Foraging Optimization (BFO) Model, which assists in identification of interference-aware paths. The BFO Model uses a temporal fitness function that fuses throughput, communication delay, energy levels, and packet delivery performance for different set of contextual communications. Due to which, the model is able to showcase lower end-to-end delay, higher throughput, lower energy consumption, and higher packet delivery performance when compared with existing routing methods under high density nodes & heterogeneous network scenarios. The model showcases 99% PDR, 18.3% lower delay, 19.5% higher energy efficiency and 10.4% lower delay levels when compared with existing methods.

Embedding antennas with tilted beam patterns into parabolic wave equation‐based models for tunnel propagation

AbstractAccurate radio wave prediction models are necessary for the innovation of mobile communication technology and the deployment of wireless sensor networks in railways. The parabolic wave equation (PWE) method is one commonly used method to predict the electromagnetic characteristic of long railway tunnels. However, the advent of novel antennas and technologies (e.g., beam steering, beamforming) has posed new challenges to PWE‐based models such as effective means of taking tilted beam patterns into account. This study addresses such a challenge by using tilted Gaussian beams to embed antennas with oriented patterns or tilted beam directions into PWE‐based models. The proposed approach does not reduce the simulation efficiency of PWE itself. The validity and practicability of the technique are demonstrated by comparing against ray‐tracing as well as experimental measurements in various tunnel environments.

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting constraints and, thus, scaling is difficult. In the related research literature, various solutions are proposed that attempt to address near-optimal behavior in polynomial time, the majority of which relies on heuristics. In this paper, we formulate a topology control and lifetime extension problem regarding sensor placement, under coverage and energy constraints, and solve it by applying and testing several neural network configurations. To do so, the neural network dynamically proposes and handles sensor placement coordinates in a 2D plane, having the ultimate goal to extend network lifetime. Simulation results show that our proposed algorithm improves network lifetime, while maintaining communication and energy constraints, for medium- and large-scale deployments.

Channel Modeling and Characteristics Analysis under Different 3D Dynamic Trajectories for UAV-Assisted Emergency Communications.

This study involved channel modeling and characteristics analysis of unmanned aerial vehicles (UAVs) according to different operating trajectories. Based on the idea of standardized channel modeling, air-to-ground (AG) channel modeling of a UAV was carried out, taking into consideration that both the receiver (Rx) and the transmitter (Tx) ran along different types of trajectories. In addition, based on Markov chains and a smooth-turn (ST) mobility model, the influences of different operation trajectories on typical channel characteristics-including time-variant power delay profile (PDP), stationary interval, temporal autocorrelation function (ACF), root mean square (RMS) delay spread (DS), and spatial cross-correlation function (CCF)-were studied. The multi-mobility multi-trajectory UAV channel model matched well with actual operation scenarios, and the characteristics of the UAV AG channel could be analyzed more accurately, thus providing a reference for future system design and sensor network deployment of sixth-generation (6G) UAV-assisted emergency communications.

A High-Reliable Wireless Sensor Network Coverage Scheme in Substations for the Power Internet of Things

With the construction of the Power Internet of Things (PIoT) in full swing as well as the development of wireless communication technology, the deployment of wireless sensor networks is the key to the intelligent transformation of power systems. This paper proposes an asymmetric double-layer wireless sensor network coverage scheme for the intelligent substation. We conducted field measurements in the substation to record the received power at different locations, which were compared with the prediction results of the simulation model established by Winprop to verify the effectiveness of the simulation method. Based on the simulation platform, two different simulation models of substation scenarios are built, including a 220 kV outdoor substation and a 110 kV GIS room. The coverage of received power and line-of-sight (LOS) transmission are analyzed for various nodes of different layers. Several node distribution planning methods are proposed and proven to be feasible according to the simulation results. The wireless coverage scheme can provide useful references for the implementation of PIoT in substations.

Charging Protocol for Partially Rechargeable Mobile Sensor Networks.

Wireless sensor networks (WSNs) have wide applicability in services used in daily life. However, for such networks, limited energy is a critical issue. The efficiency of a deployed sensor network may be subject to energy supply. Wireless rechargeable sensor networks have recently been proposed and discussed. Most related studies have involved applying static rechargeable sensors to an entire rechargeable environment or having mobile chargers patrol the environment to charge sensors within it. For partially rechargeable environments, improving the recharge efficiency and extending the lifetime of WSNs are considerable challenges. Scientists have devoted attention to energy transmission technologies and mobile sensor network (MSN) applications. In this paper, we propose a flexible charging protocol in which energy can be transmitted from certain energy supply regions to other regions in an MSN. Mobile rechargeable sensors are deployed to monitor the environment. To share energy in a certain region, the sensors move to replenish their energy and transmit energy to sensors outside the energy supply region. The efficiency of the proposed protocol is also discussed in the context of various situations. The evaluation results suggest that the flexible protocol is more efficient than other charging protocols in several situations.

A multi-sensor dataset with annotated activities of daily living recorded in a residential setting

SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the ‘SPHERE House’ in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities of daily living from multi-modal sensor data in real-world environments. A subset of this dataset was released as a machine learning competition in association with the European Conference on Machine Learning (ECML-PKDD 2016).