Performance Of Sensor Networks Research Articles

Framework for Designing of Wireless Sensor Network Using OPNET and Effect of Transport Protocols on Simulation

A Wireless sensor network can be defined as a network of devices that can communicate the information gathered from a monitored field through wireless links. The data is forwarded through multiple nodes, and with a gateway, the data is connected to other networks like wireless Ethernet. It is intended for a general application and should be able to collect data from many nodes, every node connected to several sensors. One possible application for such a network is in the medical area. At first the network will be used to display the evolution of blood pressure; the body temperature, the ambient temperature and pressure and the level of the transfusion liquid can be collected as well. The network coordinator will transmit the data to a local server for further processing. The simulation will be used to evaluate the general parameters of the wireless network and to optimize it. In this paper, we aim to evaluate the performance of a ZigBee wireless sensor network using OPNET and also study the effects of TCP and UDP on the performance of the application based on OPNET simulation software. Keywords-Sensor, wireless sensor network, ZigBee, OPNET, TCP, UDP.

Wireless sensor network security management based on virtual resource scheduling model

ABSTRACT For lofting the performance of wireless sensor networks, a lightweight gradient elevator algorithm model is improved using a virtual resource scheduling model and sparrow search algorithm particle swarm optimization algorithm. A new network intrusion detection method (SSAPSO-LightGBM) has been proposed to improve detection accuracy and efficiency. Then, the data security transmission level and user historical priority resource scheduling model (SDT-HRU-VRSM) are used to schedule resources to ensure network resource revenue. The results show that the SSAPSO LightGBM algorithm has a detection accuracy of 99.61% for Normal, 98.42% for R2L, 97.03% for U2R, 96.01% for Probe, and 98.41% for DoS. The SDT-HRU-VRSM model not only meets the needs of secure data transmission, but also takes into account the stickiness of increasing high-quality users. And according to security requirements, the number of virtual resource scheduling is increased, reducing the number of service network slicing instances and improving security. This model can effectively increase the attack cost of malicious users and increase network revenue, which is crucial for ensuring the security and efficient operation of wireless sensor networks.

Deploying Wireless Sensor Networks in Multi-Story Buildings toward Internet of Things-Based Intelligent Environments: An Empirical Study.

With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system's performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research's findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments.

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN.

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm.

Design and Development of a Hybrid Cooperative- Based Routing Protocol avoiding the void nodes in WSNs using the Concepts of Minimum Energy Consumption with good Data Security features

This paper presents a novel cooperative-based routing protocol designed for underwater Wireless Sensor Network Cloud Applications (uWSNCA). The protocol aims to minimize energy consumption by avoiding void nodes, which are areas without sensor coverage. The proposed approach consists of two main steps: first, avoiding void nodes using an adaptive hop-by-hop vector method, and second, applying a cooperative rule-based adaptive hop-by-hop vector method to create a hybrid algorithm that is more efficient than existing approaches, such as the watch-dog protocol. The research focuses on developing an energy-efficient distributed cooperative routing protocol for wireless sensor networks, specifically tailored for cooperation-based uWSNCA. This is achieved by preventing data packets from being forwarded through void nodes. The proposed routing protocol utilizes cooperation-based adaptive hop-by-hop vector-based forwarding concepts, where sensor nodes forward data packets in a multi-hop fashion within a virtual pipeline. In the developed algorithm for the underwater WSN cloud application, data packets are forwarded in a multi-hop manner using a virtual pipelining mechanism.Nodes outside the virtual pipeline do not forward data packets, thereby reducing flooding in the cooperative WSN. To prevent data packets from being forwarded towards void areas in the WSN, 2-hop information is used at each hop. This approach improves the performance of the wireless sensor network in terms of various performance criteria compared to existing routing protocols. The simulation tool NS2 is used to observe the simulation results of the energy packets being transferred from the source to the sink. The simulation results demonstrate the effectiveness of the proposed methodology. Overall, this paper contributes to the field of underwater WSNs by introducing a novel routing protocol that improves energy efficiency and performance for cloud applications.

Highlighting the back-action contribution of matter to quantum sensor network performance in multi-messenger astronomy

Highlighting the back-action contribution of matter to quantum sensor network performance in multi-messenger astronomy

Reply to: Highlighting the back-action contribution of matter to quantum sensor network performance in multi-messenger astronomy

Reply to: Highlighting the back-action contribution of matter to quantum sensor network performance in multi-messenger astronomy

Hybrid Crow Search and Particle Swarm Algorithmic optimization based CH Selection method to extend Wireless Sensor Network operation

In ad hoc wireless sensor networks, the mobile nodes are deployed to gather data from source and transferring them to base station for reactive decision making. This process of data forwarding attributed by the sensor nodes incurs huge loss of energy which has the possibility of minimizing the network lifetime. In this context, cluster-based topology is determined to be optimal for reducing energy loss of nodes in WSNs. The selection of CH using hybrid metaheuristic algorithms is identified to be significant to mitigate the quick exhaustion of energy in entire network. This paper explores the concept of hybrid Crow Search and Particle Swarm Optimization Algorithm-based CH Selection (HCSPSO-CHS) mechanism is proposed with the merits of Flower Pollination Algorithm (FPA) and integrated Crow Search Algorithm (CSA) for efficient CH selection. It further adopted an improved PSO for achieving sink node mobility to improve delivery of packets to sink nodes. This HCSPSO-CHS approach assessed the influential factors like residual energy, inter and intra-cluster distances, network proximity and network grade during efficient CH selection. It facilitated better search process and converged towards the best global solution, such that frequent CH selection is avoided to maximum level. The outcomes of the suggested simulation HCSPSO-CHS confirm better performance depending on the maximum number of active nodes by 23.18%, prevent death of sensor nodes by 23.41% with augmented network lifetime of 33.58% independent of the number of nodes and rounds of data transmission.

Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning

The recognition of energy harvesting techniques for enhanced operation in Wireless Sensor Networks (WSN) using IoT and deep learning entails a novel approach to improving the energy efficiency and operational durability of sensor nodes deployed in various environments. Energy harvesting refers to the process of collecting energy from diverse sources such as solar power, thermal energy, wind energy, and mechanical vibrations, and converting it into electrical energy to power electronic devices, including sensor nodes in a WSN. The integration of the Internet of Things with WSNs allows for interconnectivity, enabling enhanced communication, data exchange, and remote management capabilities. This integration facilitates the deployment of more intelligent and adaptive sensor networks, capable of making decisions based on collected information. Deep learning, a subset of machine learning, plays a vital role in enhancing the operation of WSNs by providing advanced data analysis and predictive modeling capabilities. Through the utilization of deep learning algorithms, the proposed system can forecast energy consumption patterns, identify optimal periods for energy harvesting, and optimize the energy efficiency of sensor nodes. This includes adjusting operational parameters such as data communication rates or sleep cycles based on the predicted availability of harvested energy. Deep learning also aids in identifying and prioritizing the most viable energy sources in the deployment environment of the WSN. By employing appropriate energy harvesting techniques based on environmental conditions, such as solar energy harvesting in summer or mechanical vibration harvesting in industrial settings, the proposed method ensures more efficient operation and sustainability of sensor networks across various applications.

Advancing Wireless Sensor Networks through Performance Evaluation of M-PDCH Routing Protocol for Enhanced Quality of Service

Objective: The decisive aim of this investigation is to improve the capabilities and limitations of PEGASIS with Double Cluster Head (PDCH), for enhancing the overall Quality of Service (QoS) in Wireless Sensor Networks (WSNs). Methods: This research is intended at the application of efficient routing protocol for wireless sensor networks. It works on the principle of the formation of cluster head and clustering. This is accomplished to achieve high QoS for WSN. Improving the efficacy of a WSN protocol is decisive for optimizing communication, resource utilization, and overall effectiveness. An innovative and novel protocol named Mobile-PDCH (M-PDCH) has been devised, developed, and tested. Here several parameters have been carefully investigated for enhancing the performance of a WSN protocol such as energy consumption, delay, and packet loss. A simulation environment has been created to evaluate, compare, and validate the performance and to suggest the desired applications of the proposed protocol. Findings: In the hierarchical category, PDCH performs better compared to all the other protocols. But it suffers from high packet drop, which decrease the performance of the protocol. To improve the performance of sensor networks, there is a need for adaptive routing protocol. The examination of simulation outcomes for the recommended protocol, M-PDCH, has been conducted in comparison with PDCH. The findings indicate that the protocol exhibits superior performance. The proposed algorithm improves the packet drop by 29.75%, reduces delay by 23.28%, and lowers energy consumption by 19.18% when compared to PDCH. Novelty: A novel adaptive routing protocol termed M-PDCH has been proposed for the enhancement of the WSN life span and durability of the sensor nodes. The simulation results show that the proposed algorithm produces better results compared to PDCH protocol. The various QoS parameter results are compared, verified, and validated with existing protocol PDCH. Keywords: WSN, PDCH, Design issues, Metrics, QoS, HRP

Direct Link Interference Suppression for Bistatic Backscatter Communication in Distributed MIMO

Backscatter communication (BC) is a promising technique for future Internet-of-Things (IoT) owing to its low complexity, low cost, and potential for energy-efficient operation in sensor networks. There are several network infrastructure setups that can be used for BC with IoT nodes. One of them is the bistatic setup where typically there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader. In this paper, we investigate a bistatic BC setup with multiple antennas. We propose a novel transmission scheme, which includes a protocol for channel estimation at the carrier emitter (CE) as well as a transmit beamformer construction that suppresses the direct link interference between the two ends of a bistatic link (namely CE and reader), and increases the detection performance of the backscatter device (BD) symbol. Further, we derive a generalized log-likelihood ratio test (GLRT) to detect the symbol/presence of the BD. We also provide an iterative algorithm to estimate the unknown parameters in the GLRT. Finally, simulation results show that the required dynamic range of the system is significantly decreased, and the detection performance of the BD symbol is increased, by the proposed algorithm compared to a system not using beamforming at the CE.

Minimal-Overlap Centrality for Multi-Gateway Designation in Real-Time TSCH Networks

This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design . To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering . Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector , closeness , betweenness , and degree . Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods.

Outage performance and energy efficiency optimization of wireless-powered millimeter-wave sensor networks

AbstractWith the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme.

Operation of a mesh grid optic-fiber sensor network with self-reconfigurable function

This paper presents a mesh grid topology for an optical fiber sensor network, which has an efficient structure for intelligent monitoring and reconfigurable protection operations. The integration of light sources and detectors within the network plays a crucial role in achieving comprehensive monitoring of the entire sensor network. Moreover, we have designed multiple nodes for sensing channels with distinct function to maximize the benefits of the topology in terms of network resilience. Finally, centralized and discrete signal processing schemes for network control systems have been demonstrated to be effective under various application conditions.

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical Wireless Sensor Networks Using Long Short-Term Memory

The multiple objective optimisation (MOO) challenges encountered in the context of wireless sensor networks (WSNs) present a formidable NP-hard problem. These issues primarily arise from the constraints imposed by critical factors such as connectivity, coverage, and, most notably, energy consumption. Simultaneously fulfilling these three requirements is no longer considered the standard approach for enhancing system dependability. To illustrate, a prospective solution may optimise one or two of these requirements while bolstering overall network energy efficiency. Nonetheless, prior endeavours documented in the extant literature reveal unexplored avenues for enhancement. Hence, this paper introduces a new methodology aimed at alleviating MOO concerns and thereby enhancing the quality of service (QoS) in WSNs. A long short-term memory (LSTM) model is proposed as an analytical tool to deliver an energy-efficient scheduling solution that aligns and optimises WSN parameters, striving to attain the most favourable system performance. The LSTM algorithm’s effectiveness is assessed through the iterative application of periods, confirming the desired QoS levels. The unique feature of LSTM lies in its capability to observe specific event sequences and subsequently establish them as the system’s default configuration for its entire operational lifespan. Once these favourable parameters are identified, LSTM automatically ensures consistent service availability and reliability throughout the network’s lifespan. The results obtained demonstrate the superiority of the proposed LSTM-based scheduling algorithm in comparison to the self-organising map (SOFM)-based node scheduling algorithm. The LSTM-based approach outperforms the SOFM-based alternative by a remarkable 75% in terms of coverage and exhibits a 20% enhancement in network lifetime, all while maintaining equivalent levels of connectivity (i.e., 99%) in both algorithms.

Quality-based irregular area coverage algorithm in aerial sensor networks

Wireless communication technology is increasingly gaining popularity, with aerial wireless sensor networks being highly regarded for their high mobility, ability to handle special environments, and wide coverage. To enhance the performance of aerial wireless sensor networks, the study of area coverage algorithms is crucial. However, most existing coverage algorithms primarily focus on the coverage area without considering coverage quality. To address this limitation, this paper proposes a coverage algorithm based on coverage quality for regional coverage by unmanned aerial vehicles (UAVs). The algorithm incorporates the importance and priority of the coverage area, utilizes an adaptive spatial distribution function for weight distribution, and employs horizontal and vertical control strategies to strike a balance between maximum coverage and optimization of coverage quality. Simulation experiments are conducted to validate the effectiveness of the proposed algorithm. This research contributes to the field by emphasizing the significance and impact of considering coverage quality in aerial wireless sensor networks, thereby enhancing their overall performance.

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.

Application and performance of a Low Power Wide Area Sensor Network for distributed remote hydrological measurements

Communication distances of wireless sensor networks (WSNs) are greatly limited in settings where vegetation coverage is moderate or dense, and power consumption can be an issue in remote environmental settings. A newer innovative technology called “Low Power Wide Area Sensor Networks” (LPWAN) is capable of greater communication distances while consuming less power than traditional WSNs. This research evaluates the design and in-field performance of a LPWAN configuration in headwater catchments to measure environmental variables. The performance of the Beta LPWAN deployment indicate reduced signal strength in topographic valleys, but better actual than modelled data transmission performance. System performance during extreme cold temperatures (below – 15 ºC) resulted in increased sensor down time. The configuration of antennae combinations provides the greatest improvement in signal strength and system performance. This technology facilitates remote collection of physically-based, spatially-distributed information within regions with limited accessibility, ultimately advancing data collection capabilities into areas that are not feasible to visit regularly.

Self-Configuration Management towards Fix-Distributed Byzantine Sensors for Clustering Schemes in Wireless Sensor Networks

To avoid overloading a network, it is critical to continuously monitor the natural environment and disseminate data streams in synchronization. Based on self-maintaining technology, this study presents a technique called self-configuration management (SCM). The purpose is to ensure consistency in the performance, functionality, and physical attributes of a wireless sensor network (WSN) over its lifetime. During device communication, the SCM approach delivers an operational software package for the radio board of system problematic nodes. We offered two techniques to help cluster heads manage autonomous configuration. First, we created a separate capability to determine which defective devices require the operating system (OS) replica. The software package was then delivered from the head node to the network’s malfunctioning device via communication roles. Second, we built an autonomous capability to automatically install software packages and arrange the time. The simulations revealed that the suggested technique was quick in transfers and used less energy. It also provided better coverage of system fault peaks than competitors. We used the proposed SCM approach to distribute homogenous sensor networks, and it increased system fault tolerance to 93.2%.

Enhancing the Performance of 6LoWPAN-Based Wireless Sensor Networks (WSNs) with IEEE 802.11AH (Wi-Fi HaLow)

6LoWPAN allows IEEE 802.15.4 standard-based wireless sensor networks (WSNs) to be connected to the Internet through the Internet protocol IPv6; however, its performance decreases as the network grows in size due to complications such as the bottleneck problem; therefore this paper aims to improve the performance of 6LoWPAN-based WSNs by using the IEEE 802.11AH standard as a backbone in these networks, since this emerging new technology is suitable for IoT applications, as it can provide a coverage range up to 1,000 m and data transmission rates up to 78 Mbps. The IEEE 802.11AH standard can be used as a backbone by utilizing its characteristics to improve some of the performance parameters of the 6LoWPAN networks such as end-to-end delay, frames delivery ratio, and the network throughput. This paper proposes a heterogeneous network infrastructure consisting of 6LoWPAN and the IEEE 802.11AH standard to enable bidirectional data transmission between the network components and the Internet via the Internet protocol IPv6 with two types of gateways, the edge gateways to connect the entire network to the Internet and the intermediate gateways to connect the 6LoWPAN clusters to the edge gateways. The simulation results demonstrate that the heterogeneous network approach can provide significant improvement gains compared to the 6LoWPAN homogeneous networks, since it shows clear improvements to the packets delivery ratio, the end-to-end delay mean value and the network throughput, subsequently leading to a distinct enhancement in the network’s overall reliability.