Deployment Of Sensor Networks Research Articles

The node deployment of wireless sensor networks based on mobile edge computing

When deploying network nodes, there are many redundant nodes, low network coverage and high energy consumption of network nodes, the node deployment method of wireless sensor networks(WSN) based on mobile edge computing is studied. WSN nodes are divided into anchor nodes and unknown nodes. Taking the location information of anchor nodes as a reference, the specific location of unknown nodes is obtained by trilateral measurement. Minimizing the node distance error is taken as the objective function, and the cuckoo search algorithm is used to solve it to obtain the final location result of the node. The mobile edge computing method is used to design the node deployment method of WSN to complete the node deployment. Simulation results show that the number of redundant nodes in this method is 3, maximum network coverage is 89%, maximum energy consumption of network nodes is 34.3J.

Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory

Wireless sensor network deployment should be optimized to maximize network coverage. The D-S evidence theory is an effective means of information fusion that can handle not only uncertainty and inconsistency, but also ambiguity and instability. This work develops a node sensing probability model based on D-S evidence. When there are major evidence disputes, the priority factor is introduced to reassign the sensing probability, with the purpose of addressing the issue of the traditional D-S evidence theory aggregation rule not conforming to the actual scenario and producing an erroneous result. For optimizing node deployment, a virtual force-directed particle swarm optimization approach is proposed, and the optimization goal is to maximize network coverage. The approach employs the virtual force algorithm, whose virtual forces are fine-tuned by the sensing probability. The sensing probability is fused by D-S evidence to drive particle swarm evolution and accelerate convergence. The simulation results show that the virtual force-directed particle swarm optimization approach improves network coverage while taking less time.

Many-objective optimization of wireless sensor network deployment

Recently, the efficient deployment of wireless sensor networks (WSNs) has become a leading field of research in WSN design optimization. Practical scenarios related to WSN deployment are often considered as optimization models with multiple conflicting objectives that are simultaneously enhanced. In the related literature, it had been shown that moving from mono-objective to multi-objective resolution of WSN deployment is beneficial. However, since the deployment of real-world WSNs encompasses more than three objectives, a multi-objective optimization may harm other deployment criteria that are conflicting with the already considered ones. Thus, our aim is to go further, explore the modeling and the resolution of WSN deployment in a many-objective (i.e., optimization with more than three objectives) fashion and especially, exhibit its added value. In this context, we first propose a many-objective deployment model involving seven conflicting objectives, and then we solve it using an adaptation of the Decomposition-based Evolutionary Algorithm “ $$\theta$$ -DEA”. The developed adaptation is named “WSN- $$\theta$$ -DEA” and is validated through a detailed experimental study.

The Definitive Guide to Actual Sensor Network Deployments in Research Studies from 2013–2017: A Systematic Review

The research community has been working on sensor networks for more that seven decades and altogether more than a million research articles on sensor networks have been published, with this number growing every day. In this article, we try to provide a thorough and complete systematic review on the sensor network field resulting from representative subset of more than 3000 articles which include actual sensor network deployments. After a thorough analysis of these data, we provide a definitive overview of the trends in sensor network deployment described in published research articles.

Voltage, Current, Power Consumption Monitoring: A IOT Based System

Electricity is a basic human necessity that is extensively employed for home, industrial, and agricultural purposes. In this way, energy waste leads nations to lose revenue. Solutions based on technology, such as The Internet of Things (IOT) connects the physical and digital worlds, This IOT application ,In this scenario, manages and/or analyze energy consumption.. Furthermore, the advancement of micro and Nano-electronics has made it possible to the creation of connectivity modules like the XBEE that enables the rapid deployment of a wireless sensor network effectively, using the least amount of energy possible employed for responsibilities of monitoring and control. By developing a hardware and software solution, the given prototype takes advantage of the previously indicated features. It uses a scalable and modular platform with XBEE technology and a developed protocol for data exchange between the modules that make up the system to allow remote monitoring of electricity consumption in a home. The prototype's accuracy is shown when compared to readings acquired with a regular electric bill, according to the conclusions.

The method of intelligent wireless sensor to improve the water permeability of permeable asphalt concrete pavement

AbstractThis research aims to study the intelligent wireless sensor to improve the water permeability of permeable asphalt concrete pavement. The water permeability of pavement is extremely important for the traffic environment. This study first briefly introduces the background and significance analysis, and then exemplifies the deployment algorithm and model of wireless sensors on asphalt pavement. For example, the wireless sensor deployment strategy along the asphalt pavement based on linear dual sink nodes can be divided into linear wireless sensor node deployment, wireless sensor energy consumption model, and wireless sensor zone coverage principle. Another example is the wireless sensor network (WSN) deployment model along the asphalt road and the wireless sensor structure model, which are divided into WSN structure, sensor node structure, gateway interconnection of WSNs and other types of networks. Then this study conducts experiments and results analysis based on wireless sensors for improving the permeability of asphalt pavement and asphalt content. In this study, the factors affecting the water permeability of asphalt pavement are tested, and the application of sensors in testing its performance, and the optimal asphalt ratio is selected in the experiment to make its water permeability better. Finally, the study discusses and summarises the experiments, and it is concluded that the optimum asphalt content in the mixture is 5.48%–6.22%. Influencing factors include temperature and humidity, void ratio, cross slope gradient and so on. This experiment has a good reference prospect.

A Non-Dominated Sorting Genetic Algorithm Based on Voronoi Diagram for Deployment of Wireless Sensor Networks on 3-D Terrains

The deployment strategy for wireless sensor networks (WSNs) affects the quality of service (QoS). Adopting a reasonable deployment strategy can improve the QoS of WSNs. In this paper, the problem regarding sensor node deployment for WSNs on three-dimensional (3D) terrain is modeled as a multi-objective optimization problem. The coverage rate of the WSNs, their unbalanced energy consumption, and the number of sensor nodes are used as fitness functions for the optimization problem. We propose a non-dominated sorting genetic algorithm based on a Voronoi diagram (VNSGA) for solving the wireless sensor network deployment issue and improving the QoS of WSNs on 3D terrain. The proposed algorithm applies the Voronoi diagram to obtain the node-sensing radius and communication radius, which are suitable for 3D terrain with respect to calculating the fitness function of the optimization problem. The Pareto optimal solution is obtained by retaining the solution close to the reference point. The experiments compare the proposed algorithm with the Multi-Objective Particle Swarm Algorithm (MOPSO) and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) on two terrains with different ranges. The experimental results show that the proposed algorithm outperforms the comparison algorithm on both terrains with different range sizes. The proposed algorithm can improve the coverage to 97.95% and reduce the imbalance in energy consumption to 9.13% on large range terrain.

Propagation Measurements for IQRF Network in an Urban Environment.

Recently, IQRF has emerged as a promising technology for the Internet of Things (IoT), owing to its ability to support short- and medium-range low-power communications. However, real world deployment of IQRF-based wireless sensor networks (WSNs) requires accurate path loss modelling to estimate network coverage and other performances. In the existing literature, extensive research on propagation modelling for IQRF network deployment in urban environments has not been provided yet. Therefore, this study proposes an empirical path loss model for the deployment of IQRF networks in a peer-to-peer configured system where the IQRF sensor nodes operate in the 868 MHz band. For this purpose, extensive measurement campaigns are conducted outdoor in an urban environment for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Furthermore, in order to evaluate the prediction accuracy of well-known empirical path loss models for urban environments, the measurements are compared with the predicted path loss values. The results show that the COST-231 Walfisch–Ikegami model has higher prediction accuracy and can be used for IQRF network planning in LoS links, while the COST-231 Hata model has better accuracy in NLoS links. On the other hand, the effects of antennas on the performance of IQRF transceivers (TRs) for LoS and NLoS links are also scrutinized. The use of IQRF TRs with a Straight-Line Dipole Antenna (SLDA) antenna is found to offer more stable results when compared to IQRF (TRs) with Meander Line Antenna (MLA) antenna. Therefore, it is believed that the findings presented in this article could offer useful insights for researchers interested in the development of IoT-based smart city applications.

ReLeC: A Reinforcement Learning-Based Clustering-Enhanced Protocol for Efficient Energy Optimization in Wireless Sensor Networks

Wireless sensor networks (WSNs) are a widely studied area in the field of networked embedded computing. They are made up of several sensor nodes, which keep track of a variety of physical and environmental parameters, like temperature and humidity. The nodes are autonomous, self-configuring, and wireless. A significant problem in WSNs is that sensors in these networks consume a lot of energy. Energy consumption is a big issue when it comes to the deployment of sensor networks. The reason for this is the cost of operating a sensor node and the cost incurred due to energy consumption. Energy optimization is based on intelligent energy management. This paper presents a reinforcement learning-based and clustering-enhanced method. Reinforcement learning is a set of algorithms inspired by operant conditioning in animal behavior, and clustering-based methods have been extensively used for devising energy-efficient protocols. The proposed method is able to plan and schedule the nodes to ensure an extended network lifetime. In this work, we aim to assess and increase the efficiency of power consumption and reduce sensor node energy loss. The simulation results prove that the presented protocol effectively reduces the energy consumption of sensor nodes and ensures a prolonged lifetime of the sensor network.

Double attribute based node deployment in wireless sensor networks using novel weight based clustering approach

Double attribute based node deployment in wireless sensor networks using novel weight based clustering approach

Programmable timer triggered energy harvesting wireless sensor-node using long range radio access technology

<span>Despite widespread deployment of wireless sensor networks (WSN) in remote and inapproachable locations, energy consumption/storage of WSN hindered its adoption. Similarly, the battery-powered sensor nodes are of no use once the battery is depleted. To overcome this limitation, energy harvesting is one of the key techniques. In this paper, an almost perpetual self-powered sensor node is proposed. This sensor node uses a solar panel to harvest energy while the entire energy management is accomplished by BQ25570. Similarly, a super-capacitor is used as an energy storage unit with long range radio access (LoRa) as a transceiver unit. We measured the power generated/consumed continuously for 15 days with a transmission interval of 10 minutes. The result shows that this sensor node can potentially last for more than 7 days even at a low illuminance. Considering periodic wakeup at every 10 seconds with a sleep interval of 3 sec, a timer-triggered mechanism saves approximately 595 milliwatts of energy in one day compared to a deep-sleep mechanism. Furthermore, it is found that the application of the novel idea of external timer-driven technology in sensor node reduces energy consumption and provides a much efficient power optimization mechanism compared to the deep sleep mechanism that prevailed in WSNs technology.</span>

An Extensive Research on Acoustic Underwater Wireless Sensor Networks (AUWSN)

It is a well-known fact that water covers nearly 71% of the total earth's surface. This makes the extraction of most valuable information from the seafloor and underwater resources extremely difficult for humans. As a result, in order to meet the requirements of underwater exploration, researchers have focused their efforts on developing new technologies for establishing underwater communication. The recent advancements in wireless acoustic underwater sensing and communication technology has resulted in an upsurge in the exploration of abundant underwater natural resources. This research study intends to provide a comprehensive overview of acoustic underwater sensor networks, including their implementation techniques, routing algorithms and applications. This study also provides a comparative analysis on various acoustic wireless sensor networks deployment in order to find the existing research gaps. Furthermore, some real-time AUWSN applications were reviewed, providing diverse and insightful information about the AUWSN approach. Finally, the study discusses about some potential future research directions for designing the next-generation AUWSNs.

Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application

Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman–Reingold graph layout, we propose the Fruchterman–Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors’ hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future.

Optimizing Node Deployment in Rechargeable Camera Sensor Networks for Full-View Coverage

Full-view coverage realized by camera sensor networks (CSNs) is highly demanded for monitoring and recognizing objects appearing at target points. However, it aggravates the energy shortage in CSNs as caused by the need to generate and process much sensed data. Undoubtedly, enabling CSN nodes to be rechargeable and harvest energy from their surroundings is an effective method to overcome the energy limitation of a CSN and ensuring its perpetual operation. Moreover, using rechargeable nodes can avoid the replacement of batteries, and thus can reduce network maintenance cost. In this article, we investigate how to design and deploy a rechargeable CSN with the fewest nodes to achieve full-view coverage of all target points while guaranteeing its connectivity and perpetual operation. We first formulate the problem as an integer linear program and prove its NP-hardness, and then propose a greedy heuristic and a differential evolution algorithm to solve it. Extensive simulation results reveal that the latter is able to achieve a larger success rate and higher solution quality but spends more time than the former.

Energy-Efficient Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

Transmission path optimization Based on Efficiency Communication System

Routing is the core of wireless sensor network deployment. This paper points out the research focus of the wireless sensor network transmission path and discusses the optimization problem of the transmission based on grid model. In this paper, routing attribute table that is maintained by the nodes, the density and relative distance of node distribution, is used to determine the clustering method. At the same time, the developed algorithm combines the relative position of the cluster and the base station to further improve the routing method of the sub-cluster head, Finally, the algorithm analyzes the influence of different parameters on the transmission path, and use the simulation experiments to evaluate the conclusions.

An Enhanced Black Widow Optimization Algorithm for the Deployment of Wireless Sensor Networks

In order to solve the deployment problem, which is considered a major issue that faces the design of efficient Wireless Sensor Networks (WSNs), a novel deployment algorithm based on an Enhanced Black Widow Optimization algorithm (EBWO) is proposed. The EBWO algorithm aims to determine the optimal number of sensors and their locations for optimizing both the coverage and the deployment cost. The BWO algorithm is adapted to solve the deployment problem by introducing a set of enhancements, which improves the search capability and the run time of the algorithm. A chaotic initialization is employed in the EBWO algorithm to strengthen the exploration capability of the initial population. Moreover, a modified reproduction mechanism is designed to assist the algorithm in optimizing the number of deployed sensors. Comparisons with modern state-of-the-art deployment methods show that the EBWO algorithm can deliver excellent solutions, where it is ranked first during all the simulations with a coverage difference varying between 3.34% and 7.94% from the other competitors.

The Challenges that Frustrate the Deployment and use of Wireless Sensor Networks for Oil Pipeline Monitoring in the Niger Delta Region of Nigeria

The Challenges that Frustrate the Deployment and use of Wireless Sensor Networks for Oil Pipeline Monitoring in the Niger Delta Region of Nigeria

Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments

The large volume of data generated with the increasing development of Internet of Things applications has encouraged the development of a large number of works related to data management, wireless communication technologies, the deployment of sensor networks with limited resources, and energy consumption. Different types of new or well-known algorithms have been used for the processing and analysis of data acquired through sensor networks, algorithms for compression, filtering, calibration, analysis, or variables being common. In some cases, databases available on the network, public government databases, data generated from sensor networks deployed by the authors themselves, or values generated by simulation are used. In the case that the work approach is more related to the algorithm than to the characteristics of the sensor networks, these data source options may have some limitations such as the availability of databases, the time required for data acquisition, the need for the deployment of a real sensors network, and the reliability or characteristics of acquired data. The dataset in this article contains 4,164,267 values of timestamp, indoor temperature, and relative humidity acquired in the months of October and November 2019, with twelve temperature and humidity sensors Xiaomi Mijia at the laboratory of Control Systems and Robotics, and the De La Salle Museum of Natural Sciences, both of the Instituto Tecnológico Metropolitano, Medellín—Colombia. The devices were calibrated in a Metrology Laboratory accredited by the National Accreditation Body of Colombia (Organismo Nacional de Acreditación de Colombia—ONAC). The dataset is available in Mendeley Data repository.

Agent-based Localization using Mobile Sink in Wireless Sensor Networks

Aims: This paper presents agent-based localization using the mobile sink in wireless sensor networks. This proposed scheme is less expensive than GPS. It has a longer lifetime and is more energy-efficient. The mobile sink has a large and easily replenishable energy reserve and is movable within the sensor network's deployment area. Objectives: In this work, a mobile sink is proposed that traverses inside the network's boundary and gathers information at a low energy cost. Methods: The use of a mobile sink in localization introduces a new way to reduce energy consumption in WSNs. The location of a mobile sink beacon signal is known to all the sensor nodes and is also communicated periodically to all the sensor nodes. The distance measurements of the three beacon signals broadcasted by the mobile sink moving in a predetermined path and time slot are considered in this scheme, which uses the trilateration method to compute the position of the node. For isolated nodes, location estimation is performed for non-GPS equipped nodes to derive from the network their locations by using the reference node beacon signals and performing multilateration. For nodes that receive only one beacon signal from the sink, position estimation is performed by considering the reference node beacon signal by iterative multilateration technique. Results: In this scheme, reference nodes are those nodes that are localized by the sink, and no GPS is included. The proposed algorithm is simulated using C language, and some of the performance parameters used for the evaluation of the scheme are localization time, localization error, data gathering time, and communication overhead. Conclusion: The proposed work is compared to the centralized algorithm and the proposed work has been found to outperform the existing networks.