Wireless Sensor Network Simulation Research Articles

Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors.

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques.

Experimental Design of Wireless Sensor Network Simulation Teaching Based on OBE Concept

In response to the problem that students cannot effectively master the theoretical knowledge of wireless sensor networks, this study proposes an outcome-based education (OBE) guided teaching method and designs a simulation experiment on wireless sensor networks using the NS3 simulation platform. In the experiment, access point (AP) and station (STA) nodes are set up to present different network environments, and the transmission layer protocols (TCP and UDP) are changed to analyze different network performance. Through the analysis of experimental results, students can gain a vivid understanding of the basic knowledge of wireless sensor networks and achieve the expected teaching goals. This teaching method helps improve students' understanding and application ability of wireless sensor network theory, and promotes their learning and research in related fields.

A secure worst elite sailfish optimizer based routing and deep learning for black hole attack detection

ABSTRACT The Wireless Sensor Network (WSN) is susceptible to two kinds of attacks, namely active attack and passive attack. In an active attack, the attacker directly communicates with the target system or network. In contrast, in passive attack, the attacker is in indirect contact with the network. To preserve the functionality and dependability of wireless sensor networks, this research has been conducted recently to detect and mitigate the black hole attacks. In this research, a Deep learning (DL) based black hole attack detection model is designed. The WSN simulation is the beginning stage of this process. Moreover, routing is the key process, where the data is passed to the base station (BS) via the shortest and finest route. The proposed Worst Elite Sailfish Optimization (WESFO) is utilized for routing. Moreover, black hole attack detection is performed in the BS. The Auto Encoder (AE) is employed in attack detection, which is trained with the use of the proposed WESFO algorithm. Additionally, the proposed model is validated in terms of delay, Packet Delivery Rate (PDR), throughput, False-Negative Rate (FNR), and False-Positive Rate (FPR) parameters with the corresponding outcomes like 25.64 s, 94.83%, 119.3, 0.084, and 0.135 are obtained.

Enhancing Quality of Service in WSN Through a Routing Algorithm Based on Self-Organizing Maps

Many scholars have focused their attention on Wireless Sensor Networks (WSN) within the last ten years. QoS control, Energy intake, MAC protocols, routing protocols, statistics aggregation, self-organizing net algorithms, Internet of Things (IoT), and so forth are among the research topics that have been thoroughly studied recently. Historically, the potential of artificial intelligence (AI) has not been fully realized due to constraints in data processing capabilities and energy efficiency. Nonetheless, the unique characteristics of neural networks can be harnessed for complex tasks, such as the role of travel advisors. This research aims to combine IoT and WSN technologies to enhance Quality of Service (QoS) parameters including reliability, energy, conservation, system scalability and response time. It provides an overview of the key components and techniques utilized in WSNs to achieve QoS. The objective of the proposed article is to compare the performance of two widely used route paradigms, Energy-Aware Routing and Directed Diffusion, with the proposed routing technique called Sensor Intelligence Routing (SIR). The foundation of Sensor Intelligence Routing (SIR) is the incorporation of neural networks into discrete sensor networks. WSN simulation, OLIMPO (an ad-hoc wireless sensor network simulator for optimal scada-applications) has been used in multiple simulations to examine how well neural networks perform within the system. The results obtained from every routing method have been compared and analyzed. The paper also aims at fostering the use of IoT-based synthetic intelligence techniques.

A novel approach for an energy-efficient traffic monitoring system using wireless sensor network and CupCarbon simulator (V 5.0) for a smart city

A novel approach for an energy-efficient traffic monitoring system using wireless sensor network and CupCarbon simulator (V 5.0) for a smart city

A novel approach for an energy-efficient traffic monitoring system using wireless sensor network and CupCarbon simulator (V 5.0) for a smart city

A novel approach for an energy-efficient traffic monitoring system using wireless sensor network and CupCarbon simulator (V 5.0) for a smart city

Collision Mitigation by Dolphin Ant Lion Optimizer Pre-Packet Scheduling Approach

A wireless sensor network (WSN) consists of distributed autonomous sensors deployed over a large geographical area. Wireless Sensor Networks (WSNs) are composed of large-scale sensors that are specifically assigned to do particular tasks, with a significant portion of these tasks involving reporting and monitoring activities. Nevertheless, because to the potential expansion of the network to several sensor nodes, the likelihood of collision is significantly increased. This research presents an innovative approach for conducting collision detection and mitigation in wireless sensor networks (WSN). The first step involves conducting a simulation of Wireless Sensor Networks (WSN), followed by the utilization of the Fractional Artificial Bee Colony (FABC) algorithm for the selection of cluster heads. In this context, the network-based parameter is derived by considering factors such as the Received Signal Strength Index (RSSI), priority level, delivery rate, and energy consumption. The Deep Recurrent Neural Network (DRNN) has been modified to suit the task of collision detection. The training of the deep recurrent neural network (DRNN) is conducted via the Lion Crow Search optimizer (LCSO). Following the completion of collision detection, the subsequent step involves the implementation of a collision mitigation process utilizing a pre-scheduling technique known as Dolphin Ant Lion Optimizer (Dolphin ALO). In this context, the evaluation of fitness encompasses many factors related to collision mitigation, including energy consumption, Sleep Index (SI), delivery rate, priority level, E-waste management, and E-save measures. The approach presented in this study demonstrated superior performance in terms of energy consumption, throughput, Packet Delivery Ratio (PDR), and collision detection rate. Specifically, it achieved the least energy consumption of 0.185, the greatest throughput of 0.815, the highest PDR of 0.815, and the highest collision detection rate of 0.930.

Technology Modules Providing Solutions for Agile Manufacturing

In this paper, we address the most pressing challenges faced by the manufacturing sector, particularly the manufacturing of small and medium-sized enterprises (SMEs), where the transition towards high-mix low-volume production and the availability of cost-effective solutions are crucial. To overcome these challenges, this paper presents 14 innovative solutions that can be utilized to support the introduction of agile manufacturing processes in SMEs. These solutions encompass a wide range of key technologies, including reconfigurable fixtures, low-cost automation for printed circuit board (PCB) assembly, computer-vision-based control, wireless sensor networks (WSNs) simulations, predictive maintenance based on Internet of Things (IoT), virtualization for operator training, intuitive robot programming using virtual reality (VR), autonomous trajectory generation, programming by demonstration for force-based tasks, on-line task allocation in human–robot collaboration (HRC), projector-based graphical user interface (GUI) for HRC, human safety in collaborative work cells, and integration of automated ground vehicles for intralogistics. All of these solutions were designed with the purpose of increasing agility in the manufacturing sector. They are designed to enable flexible and modular manufacturing systems that are easy to integrate and use while remaining cost-effective for SMEs. As such, they have a high potential to be implemented in the manufacturing industry. They can be used as standalone modules or combined to solve a more complicated task, and contribute to enhancing the agility, efficiency, and competitiveness of manufacturing companies. With their application tested in industrially relevant environments, the proposed solutions strive to ensure practical implementation and real-world impact. While this paper presents these solutions and gives an overview of their methodologies and evaluations, it does not go into their details. It provides summaries of comprehensive and multifaceted solutions to tackle the evolving needs and demands of the manufacturing sector, empowering SMEs to thrive in a dynamic and competitive market landscape.

Optimized Low-Powered Wide Area Network within Internet of Things

The Internet of Things (IoT) is rapidly becoming an integral part of everyday life. LPWANs have been introduced to support the billions of internet-connected devices and the data they produce. LPWANs are capable of providing reliable connectivity even in low-density areas and devices consuming a low amount of energy. The exponential increase in the use of IoT applications across the globe will continue to generate more and more data traffic within the IoT network. Hence, it will increase device battery usage that may reduce the battery life expectancy limits. Thus, End Devices (EDs) within the IoT network in the near future will rise up to billions of devices operating in public, industry, and personal networks, generating a necessity for more correct and reliable energy conservation technology. This prompted the research work on an optimized low-powered wide area network within IoT. This paper focuses on three different strategies: LoRa power consumption model design, simulation of IoT wireless sensor networks, and implementation of SF allocation across the wireless sensor network and results analysis. The experiment has been carried out in various stages: firstly running a simulation over a wireless sensor network without optimization using MATLAB Simulink and obtaining the following result of 6.3997e-17 joules power consumption. Secondly, the authors test the network with power optimization using particle swarm optimization algorithms and obtained a better result of 2.5230e-17 joules. The LoRa energy consumption is reduced by 60%. Lastly, different simulation tests of LoRaWAN protocols with respect to throughput, packet loss, delay, data transmission, buffer size, and network density. The results presented on the graph showed that the proposed model outperforms the existing models. Hence, appropriate spreading factor allocation has increased the power efficiency of LoRa end device battery.

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN.

Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.

Development of a wireless sensor network simulator using energy-efficient traffic routing methods

This document represents possible algorithms and methods for the analysis, optimization, and control of wireless sensor networks. The main goal of the work is to find the optimal implementation of the particle swarm optimization method for solving the problem of improving the energy consumption of such networks. The result of the work is a software package for simulating WSN (Wireless sensor network) networks using all the methods of routing and optimization of WSN networks presented in the work. The usage of a combination of routing schemes and different systems of device state management raises the wireless sensor network lifetime and reduces the power consumption of the network. The developed and presented in the work network simulator allows any developer to design and test different network topologies with various network parameters. Such software will make it easy to design such networks both for large enterprises and independent developers.

Linguistic Fuzzy Trust Model over Oscillating Wireless Sensor Networks

Simulation of the Linguistic Fuzzy Trust Model (LFTM) over oscillating Wireless Sensor Networks (WSNs) where the goodness of the servers belonging to them could change along the time is presented in this paper, and the comparison between the outcomes achieved with LFTM model over oscillating WSNs with the outcomes obtained by applying the model over static WSNs where the servers maintaining always the same goodness, in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length are also presented here. Also in this paper the comparison between the LFTM and the Bio-inspired Trust and Reputation Model for Wireless Sensor Networks (BTRM-WSN) in terms of the accuracy and the average path length suggested by each model is presented. Both models give quite good and accurate outcomes over oscillating WSNs. Also it must be mentioned that the evaluation environment used here is Trust and Reputation Model Simulator for WSN.

Quad-Interleaving Routing Algorithm for Performance Enhancement of Oil and Gas Pipeline Network Monitoring

The pipeline is one of the main modes oftransportation for the oil and gas industry that could be laid offshore and onshore. Pipeline transportation requires close monitoring as a failure in pipelines causes disruption of supply, loss of precious commodities and not to mention irreversible environmental damage will be an essential topic. The major issue in AODV and DSDV is when the network gets more extensive, which causes high packet traffic resulting in high packet loss, energy consumption and passive nodes. This research aims to establish a routing algorithm with better performance focusing on throughput, fairness index, delivery ratio and passive nodes and energy consumption. The study is confined to wireless sensor network simulation on Network Simulator Tool. A technique of quad-interleaving Ad-hoc On- demand Distance Vector (QAODV) routing algorithm has been developed where predefined Alpha, Beta, Charlie and Delta nodes will communicate with the nodes which belong to their family. The results of the developed routing protocol have been compared with standard AODV, DSDV and OLSR routing protocols. The developed routing algorithm has produced a massive improvement in the delivery ratio (25% more), throughput (44% more), passive nodes (71 less), fairness index (0.06 more) and energy consumption (0.22J less).

ZigBee-Based Wireless Sensor Network Topology Design and Comparison in Residential Areas

When designing a wireless sensor network based on ZigBee, it is very important to choose the right network topology, especially in networks with many nodes such as residential areas. Choosing the wrong topology will have an impact on the performance of the wireless sensor network as a whole because it will cause a large delay value. To overcome this, in this study a ZigBee-based wireless sensor network simulation was carried out using an environmental model consisting of several residential units, with one ZigBee device for each house. In addition, three ZigBee network topologies namely mesh, star, and tree are used in this simulation. This is done to determine which topology model will work best in a residential environment. The housing used for the simulation in this study is Citraland Waterfront City Housing which is located in Palu City, Central Sulawesi Province, Indonesia. By using simulations in the Opnet Modeler 14.5 application, it is known that the star topology on the ZigBee network is suitable for application in residential areas with a large number of nodes. This can be seen from the highest throughput values and the lowest media access delay, end-to-end delay, hop number, and packet dropped values compared to tree and mesh topologies.

IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management

The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency.

Traffic Clearance for Ambulance during Pandemic Situation and Road Accidents using LORAWAN Network

Intelligent Transportation System (ITS) plays an important role in handling pandemic situation and disaster management. Due to rapid urbanization, there is a requirement for implementing an effective traffic control system not only to avoid heavy congestion but also to make a better solution for ambulance clearance which would help to save the human life. The proposed work intends to implement an effective traffic control system using Long-Range Wide Area Network (LoRaWAN) that provides seamless traffic clearance for ambulances, so that they reach the hospitals without any delay. Cupcarbon, a Wireless Sensor Network (WSN) simulator, is used to evaluate the performance of the proposed work. The simulation involves a case study considering an accident zone in Coimbatore city and the performance of the proposed system is compared with that of existing systems. The simulation results prove that LoRaWAN can be used to effectively control the traffic lights with a wider coverage range, as compared to existing systems.

State of the Art Systematic Literature Review on Selection of Top Ten WSN Simulators – Part A

The proliferation of technology has led to numerous advancements in various fields, including the field of simulation. Simulators, in particular, have proven to be a valuable tool for researchers and students as they eliminate the need for physical hardware, reduce costs, and increase accessibility for conducting experiments. One of the most significant innovations in this realm is the use of simulators, which are specially designed software programs that mimic real-world scenarios. However, with hundreds of simulators available for various fields, the selection of the appropriate one can be a daunting task. This work presents a systematic literature review on wireless sensor network (WSN) simulators and their selection methodology. A total of 31 literature works since 2016 were reviewed, with the majority of the works conducted in the past 4 years. The study found 52 unique simulators studied about 182 times with repetitive sets in different literatures. Most of the works presented basic and general information about the simulators which does not assist readers in selecting the appropriate and best suitable simulator for their work. Additionally, about 55% of the works did not define any selection criteria or present comparisons among the studied simulators. The majority of literature work either selected simulators based on their frequency of appearance in their studied literature subset or based on their popularity. The results of this study can aid researchers in selecting the appropriate WSN simulator for their experimental work.

Design of WSN Model with NS2 for Animal Tracking and Monitoring

Tracking and monitoring wild life is a significant ecological issue that cannot be overlooked. Especially getting fundamental indications of wild animal like Tigers is critical and evaluating their wellbeing without harming their habitat is another important task and recording their vital signs by sedating causes stress and injuries in animal. This paper proposes a design of wireless based tracking and health monitoring system for Tigers using WSN and Network simulator 2. The important physiological parameters like temperature of heart beat rate, sound signal, respiration rate, accelerometer signal and GPS data is transmitted and monitored through wireless sensor network (WSN).A sensor network is composed of different sensors which remotely communicate with each other and the information of each node are separated. Each node has ability to communicate among each other or to a specific node to communicate with sink node A Network simulator which acts as simulation tool for network of sensor nodes in tracking and monitoring system is employed to simulate the location and health of tigers. This system empowers veterinarians and forest guards to precisely and accurately take an animal's vital signs and analyses the health and location. We analyze the performance of simulation model by using metrics, such as average throughput of all nodes, end to end delay, packet received, packet dropped and packet delivery ratio (PDR), average good put and energy model.

Virtual Infrastructure based Routing Algorithm for IoT enabled Wireless Sensor Networks with Mobile Gateway

The IoT technology is seeking the attention of industry and researcher day by day due to its large number of applications and is off monitoring and controlling the object from the remote location. The very basic and important element of IoT is wireless sensor network where sensor nodes are attached with the object and generates the sensory data related to the object an actuator can provide the movement to the object. To make it a success reliable and efficient communication model is the main requirement. To fulfil the objective related to IoT application success in this paper a communication protocol called virtual infrastructure based routing algorithm is proposed. In this proposal a virtual cross region structure is formed in the central of the sensor network, which is called meeting region. In this meeting region the sensor nodes send their generated data and the gateway node receives those data. The problem of energy hole, the mobile gateway node is considered in this proposal. The proposed algorithm is implemented using the standard wireless sensor network simulator tool and compared the performance of the proposed algorithm with the existing algorithm based on some standard performance metrics. The simulation outcome exhibits that the proposed algorithm outperformed the existing algorithms in different performance metrics.

Virtual Infrastructure based Routing Algorithm for IoT enabled Wireless Sensor Networks with Mobile Gateway

The IoT technology is seeking the attention of industry and researcher day by day due to its large number of applications and is off monitoring and controlling the object from the remote location. The very basic and important element of IoT is wireless sensor network where sensor nodes are attached with the object and generates the sensory data related to the object an actuator can provide the movement to the object. To make it a success reliable and efficient communication model is the main requirement. To fulfil the objective related to IoT application success in this paper a communication protocol called virtual infrastructure based routing algorithm is proposed. In this proposal a virtual cross region structure is formed in the central of the sensor network, which is called meeting region. In this meeting region the sensor nodes send their generated data and the gateway node receives those data. The problem of energy hole, the mobile gateway node is considered in this proposal. The proposed algorithm is implemented using the standard wireless sensor network simulator tool and compared the performance of the proposed algorithm with the existing algorithm based on some standard performance metrics. The simulation outcome exhibits that the proposed algorithm outperformed the existing algorithms in different performance metrics.