Design Of Sensor Networks Research Articles

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

Distributed Computing Approach for Wireless SensorNetwork Design in a Wire Harness Testing System

Distributed Computing Approach for Wireless SensorNetwork Design in a Wire Harness Testing System

Metallic Sensor: Architecture, Protocols, and Functions Of Wireless Network

Metals such as silver, gold, platinum, palladium, and rhodium are widely used in automotive sensors and communication devices Sensor frameworks are comprehensively conveyed in a collection of purposes going from military to environmental and helpful examination. In various applications, for instance, target following, disaster area perception and gate crasher area, metallic sensor network habitually work in unfriendly and unattended circumstances. Subsequently, there is a strong prerequisite for getting the distinguishing data and identifying readings. In remote circumstances, an enemy can see the radio traffic too as can catch or between rupt the exchanged messages. Thusly, various shows and estimations don't simply work in that frame of mind without having palatable wellbeing endeavours. Consequently, security winds up one of the critical worries while illustrating security shows in resource constrained wireless sensor networks (WsNs). The ebb and flow paper are a fair endeavour to draw in the consideration of the peruses towards the design, conventions and elements of remote sensor network on the grounds that comprehensively conveyed in a variety of purposes going from military to biological and helpful examination. The present paper is an honest attempt to attract the attention of the readers towards the architecture, protocols and functions of metallic wireless sensor network because it is broadly conveyed in an assortment of uses going from military to ecological and restorative research.

An energy-efficient irregular hexagonal tessellation-based approach for connected k-coverage in planar wireless sensor networks

In the design of planar wireless sensor networks (PWSNs), the preliminary tasks are to achieve both coverage and connectivity, which are essential for the correct operation of this type of network. A PWSN is said to be in perfect operational condition only when it is capable of guaranteeing both coverage of the field and connectivity among all the active sensor nodes. In order to achieve both coverage and connectivity in PWSNs, we intend to solve the problem of connected k-coverage in PWSNs, where each point in a field of interest is covered (or sensed) by at least k sensor nodes (k > 1) simultaneously and all the participating sensor nodes in the k-coverage process are connected to each other. In our study, we found an irregular hexagon, denoted by IrHx(rs/n), as the best polygon for tessellating a field of interest and allowing to deploy sensor nodes, where n > 1 is a natural number and rs is the radius of the sensing range of the sensor nodes. First, we construct our proposed irregular hexagon, IrHx(rs/n), which forms a tile, using the regular hexagon. Second, we compute the minimum sensor density required to k-cover a planar field of interest using our proposed IrHx(rs/n) tile. Third, we establish a relationship between the sensing and communication radii of the sensor nodes to ensure network connectivity in k-covered PWSNs. Finally, we substantiate our theory with various simulation results.

A novel secure communication method for optically designed sensor networks

A novel secure communication method for optically designed sensor networks

Sink Node Placement and Partial Connectivity in Wireless Sensor Networks.

This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources. Through mathematical modeling, theoretical analysis, and simulation evaluations, we derive, compare, and contrast the probabilities of partial and full connectivity within a random WSN, factoring in network parameters and the maximum allowable hop distance/count hmax. hmax captures the diverse range of delay-sensitive requirements encountered in practical scenarios. Our research underscores the significant impact of the sink node and its placement on network connectivity and the sensor connection rate. The results exemplify a noteworthy decline in the sensor connection rate, dropping from 98.8% to 72.5%, upon relocating the sink node from the network center to the periphery. Moreover, as compared with full connectivity, partial connectivity and the sensor connection rate are more suitable metrics for assessing the communication capability of random WSNs. The results illustrate that 1.367 times more energy is required to connect less than 4% of the remote sensors, based on the examined network settings. Additionally, to increase the sensor connection rate slightly from 96% to 100%, an additional 538% more energy is required in multipath fading based on the widely adopted energy consumption model. This research and its outcomes contribute to establishing appropriate performance metrics and determining critical network parameters for the practical design and implementation of real-world wireless sensor networks.

Convex optimization approach to design sensor networks using information theoretic measures

AbstractAccurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, the article aims at developing convex optimization formulations for designing the optimal sensor network using information‐theoretic measures in linear steady‐state data reconciliation. To this end, the estimation errors are characterized by a multivariate Gaussian distribution, and thus the analytical form for entropy and Kullback–Leibler divergences (forward, reverse, and symmetric) of estimation errors can be obtained to formulate the optimal sensor network design. The proposed information theoretic‐based optimal sensor selection problems are shown to be integer semi‐definite programming problems where the relaxation of binary decision variables results in solving a convex optimization problem. Thus, we use a branch and bound method to obtain a globally optimal sensor network design. Demonstrative case studies are presented to illustrate the efficacy of the proposed optimal sensor selection formulations.

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.

Providing an effective key management scheme to increase transaction security of homogeneous mobile wireless sensor networks

Network security is one of the key concerns with wireless sensor networks. Because sensor nodes employ radio channel frequencies, which are riskier than traditional networks, the attacker may be able to force the node to compromise, disrupt data integrity, eavesdrop, or insert false data into the network, wasting network resources. Creating dependable sensor networks that offer the highest level of security while using the fewest resources is thus one of the problems. The designers of wireless sensor networks can take into account providing an efficient key management system that can enhance any efficiency features such as communication overhead, calculation rate, memory demand, and energy consumption rate. The energy level of nodes is determined in this article using a novel approach based on fuzzy systems and simple to implement in both hardware and software. In this study, the memory needed to carry out the plan was decreased, and the search performance was raised by integrating elliptic curve cryptography with an AVL search tree and a LEACH model. Also, the frequency range of radio channels in this study is 2.4 GHz. Based on the theoretical analysis as well as the outcomes of the experiments, the suggested key management strategy for wireless sensor networks improves security while also reducing computational overhead by 23%, energy consumption by 14%, and memory consumption parameters by 14% . 18% of people have used the network. Additionally, it was demonstrated that the suggested approach is scalable and extendable. Because of this, the suggested technique has a wide range of applications in massive wireless sensor networks.

Design and Development of Hydro-Optical Communication-Based Invasive Wireless Sensor Networks

The selection and implementation of a hydro-optical communication connection for real-time position tracking of remotely operated vehicles (ROVs) in underwater settings are the main topics of this research article. For a variety of applications, including pipeline maintenance, environmental research, and undersea infrastructure inspection, effective monitoring and control of ROVs is essential. Due to signal attenuation and interference, conventional communication techniques are difficult to use in aquatic environments. To get over these restrictions, hydro-optical communication, which sends light messages through water, presents a viable option. The paper examines current hydro-optical communication methods and technology, pointing out their benefits and drawbacks. It deals with the technical needs of setting up a trustworthy communication channel for monitoring the real-time location of ROVs. To ensure accurate and timely data delivery, an appropriate communication protocol is created. The research also demonstrates the smooth operation of hydro-optical communication with the ROV's navigation system. The suggested method is put into practise and tested in safe underwater environments in order to assess how well it works at pinpointing the position of ROVs. Among the performance indicators evaluated are data rate, communication range, and energy efficiency. The results of this study help to advance the area of underwater communication and make it easier to monitor and manage ROVs in real-time for a variety of underwater applications.

Wearable Wireless Sensor Network for Mitigating COVID-19 Transmission Through Physical Distancing

The global community is currently facing a critical situation with the widespread transmission of the Covid-19 virus. The rapid increase in Covid-19 cases has necessitated immediate action and treatment. As the World Health Organization (WHO) recommended, maintaining a minimum distance of one meter between individuals is an effective preventive measure to avoid transmission through respiratory droplets. To address this issue, this paper introduces a cost-effective design and implementation of a wearable wireless sensor network using the ESP32 microcontroller and Bluetooth Low-Energy (BLE) technology that promotes physical distancing and contact tracing to mitigate the spread of COVID-19. In this research, we have determined that the safe distance between individuals is 1.5 meters. Proximity testing has indicated that an RSSI value of -62 dBm corresponds to a distance below 1.5 meters. Hence, we set -62 dBm as the distance threshold between wearable devices to ensure safe physical distancing. These wearable wireless sensor network devices are intended to assist individuals in maintaining a safe distance from others, thereby reducing the risk of COVID-19 transmission.

Feature Selection and Energy Management in Wireless Sensor Networks using Deep Learning

In wireless sensor networks, when the available energy sources and battery capacity are extremely constrained, energy efficiency is a major issue to be adressed. One of the main goals in the design of wireless sensor networks (WSNs) is to maximize longevity of battery life. Designers can benefit from the use of intelligent power utilization models to accomplish this goal. These models seek to decrease the number of chosen sensors used to record environmental measures in order to minimize power utilization while retaining the acceptable level of measurement accuracy. In order to simulate wireless sensor networks, we looked at real world datasets. Our simulation findings demonstrate that the suggested strategy can be used to accomplish significant goals by using the right number of sensors using deep learning, extend the lifespan of the wireless sensor networks.

Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks

Low energy consumption has always been one of the core issues in the routing design of underwater sensor networks. Due to the high cost and difficulty of deployment and replacement of current underwater nodes, many underwater applications require the routing protocol design to consider the network lifetime extension problem. Based on this, we designed a new routing protocol that takes into account both low energy consumption and balanced energy consumption, and achieves effective extension of the network lifetime, called adaptive power-controlled depth-based routing protocol for underwater wireless sensor networks (APCDBRP). The protocol consists of two phases: (1) the route establishment phase and (2) the data transmission phase. In the route establishment phase, the initial path is established by the sink node broadcasting beacon packets at the maximum transmission power. The receiving nodes update their routing tables based on the beacon information and forward the beacon packets. In the data transmission phase, APCDBRP introduces a novel forwarding factor that considers both energy efficiency and energy balance. It selects the optimal next hop based on high energy efficiency and relatively abundant energy, thus extending the network’s lifetime. Additionally, APCDBRP proposes a new data protection and route reconstruction mechanism to address issues such as network topology changes due to node mobility and data transmission failures. Our simulation is based on AquaSim–Next Generation, which is a specialized tool built on the NS3 platform for researching underwater networks. Simulation results demonstrate that, compared to other typical routing protocols, APCDBRP exhibits superior performance in reducing network energy consumption and extending the network’s lifetime. It also achieves a high packet delivery rate with lower energy consumption.

Communication about sensors and communication through sensors: localizing the Internet of Things in rural communities

AbstractInternet of Things (IoT) sensor networks are an emerging technology at the center of the datafication and optimization of far-reaching environmental infrastructures—from “smart cities” to workplace efficiencies. However, this low-power, low-cost technology is also well suited to local deployments in rural communities, which are often overlooked by digital development initiatives. Therefore, we used a social construction of technology approach to study how various U.S.-based IoT stakeholders—including designers and advocates as well as citizen stakeholders—understand and value sensor network technologies. Through observational methods, in-depth interviews, and participatory design research in a rural Upstate New York municipality, we worked to design sensor networks with rural community members to generate data about and for community members to further local knowledge. We found that designing rural sensor networks requires stakeholders to navigate obstacles of communication about sensors and communication through sensors to facilitate secure, ethical, and localized sensing in rural communities.

Wireless Sensor Network with Blockchain Technology using Elliptic Curve Discrete Logarithm Problem and its Applications

Wireless sensor network (WSN), a type of communication system, is normally deployed into the unattended environment where the intended user can get access to the network. The sensor nodes collect data from this environment. If the data are valuable and confidential, then security measures are needed to protect them from the unauthorized access. This situation requires an access control protocol (ACP) in the design of sensor network because of sensor nodes which are vulnerable to various malicious attacks during the authentication and key establishment and the new node addition phase. Nowadays, wireless sensor networks are being widely applied in many fields of human life such as civil and military applications. Although WSNs can bring a lot of benefits and conveniences. However, when applying the WSNs in the real world we have to face many challenges such as security, storage due to its centralized server/client model. Therefore, it is necessary to apply the distributed model in the WSNs system. One of the newest distributed systems today is Blockchain (BC). Blockchain is a decentralized technology that can help the computation and management processes as well as security in WSNs. In this paper, we propose a secured ACP for such WSN. This protocol is based on Elliptic Curve Discrete Log Problem (ECDLP) and double trapdoor chameleon hash function which secures the WSN from malicious attacks such as node masquerading attack, replay attack, man-in-the-middle attack, and forgery attacks. Proposed ACP has a special feature known as session key security. Also, the proposed ACP is more efficient as it requires only one modular multiplication during the initialization phase. This article provides an overview of Blockchain integration in WSN with highlighting the benefits and challenges of applying this technology to WSN. We can conclude that using Blockchain technology to solve the problem of security and distributed storage for WSN can be an effective approach. It could pave the way for new research directions and distributed applications.

Sensor network design for post-combustion CO[formula omitted] capture plants: Computational efficiency and robustness

State estimation is crucial for the monitoring and control of post-combustion CO2 capture plants (PCCPs). The performance of state estimation is highly reliant on the configuration of sensors. In this work, we consider the problem of sensor selection for PCCPs and propose a computationally efficient method to determine an appropriate number of sensors and the corresponding placement of the sensors. The objective is to find the (near-)optimal set of sensors that provides the maximum degree of observability for state estimation while satisfying the budget constraint. Specifically, we resort to the information in the sensitivity matrix calculated around the operating region of a PCCP to quantify the degree of observability of the entire system corresponding to the placed sensors. The sensor selection problem is converted to an optimization problem and is efficiently solved by a one-by-one removal approach through sensitivity analysis. Next, we extend our approach to study the fault tolerance of the selected sensors to sensor malfunction. The robust sensor selection problem is to find a sensor network that gives good estimation performance even when some of the sensors fail, thereby improving the overall system robustness. The robust sensor selection problem is formulated as a max–min optimization problem. We show how the proposed approach can be adapted to solve the sensor selection max–min optimization problem. By implementing the proposed approaches, the sensor network is configured for the PCCP efficiently.

Ensuring Data Freshness in Wireless Monitoring Networks: Age-of-Information-Sensitive Coverage and Energy Efficiency Perspectives

With the development of IoT, the sensor usage has been elevated to a new level, and it becomes more crucial to maintain reliable sensor networks. In this paper, we provide how to efficiently and reliably manage the sensor monitoring system for ensuring data freshness. A sensor transmits its sensing information regularly to the data center (DC), and the sensed information generally has the correlation over the space and the time. Hence, the accuracy of the estimated information from the sensed information decreases as the target location for information estimation is further away from the sensor or the AoI, the elapsed time from the information generation, increases. Therefore, by considering the effect of the AoI and the distance from the sensor on the estimation error, we newly define an error-tolerable sensing (ETS) coverage as the area that the estimated information at the DC is with smaller error than the target value. To analyze the impact of the AoI on the ETS coverage, we derive the η-coverage probability, which is the probability that the ETS coverage is greater than η ratio of the maximum sensor coverage, and the average ETS coverage. We also provide the optimal transmission power of the sensor, which minimizes the average energy consumption while guaranteeing certain level of the η-coverage probability. Numerical results validate the theoretical analysis and show the tendency of the optimal transmission power according to the maximum number of retransmissions. This paper can pave the way to efficient design of the AoI-sensitive sensor networks.

Design of the energy-balanced wireless sensor networks for 3D seismic exploration

Wireless Sensor Networks for 3D Seismic Exploration are large-scale and long-term networks to ensure high resolution. Energy balance is essential to avoid interruption of the whole network. In this paper, the hybrid wireless network architecture is designed for low-power monitoring system. An energy-balanced clustering method is developed to prolong network lifetime. The partial energy factor is introduced to optimize scheduling of cluster head nodes. An improved ant colony algorithm for energy-efficient clustering and routing network (IACA-EECR) is proposed to find optimal path. The results show the proposed architecture outperforms the existing platform. Extensive tests validate energy efficiency and network performance.

Elastic wave propagation in thick-walled hollow cylinders for damage localization through inner surface sensing

Thick-walled hollow cylinders (TWHCs) are widely used in engineering structures and transportation systems, exemplified by train axles. The real-time and online health monitoring of such structures is crucial to ensure their structural integrity and operational safety. While elastic-wave-based structural health monitoring (SHM) shows promise, the development of feasible methods strongly relies on a good understanding and exploitation of the wave propagation properties and their interaction with structural defects. TWHCs usually bear multiple wave modes, which is a less investigated and explored topic as compared with thin-walled structures. This work examines this issue and proposes a dedicated damage localization strategy by using the selected waves captured on the inner surface of a TWHC. It is shown that, alongside the quasi-surface-waves on the outer surface, longitudinal waves converted from the thickness-through shear bulk waves are generated to propagate along the inner surface. Their propagation characteristics are exploited for damage localization based on hyperbolic loci methods through inner surface sensing. Numerical studies are conducted to validate the method and assess different transducer configurations, alongside experimental verifications on a benchmark TWHC containing a notch-type defect. Studies provide guidance on damage detection in TWHCs and sensor network design.

MODEL OF A CHAOTIC ULTRA-WIDEBAND INFORMATION TRANSMISSION SYSTEM FOR WIRELESS SENSOR NETWORKS

Distributer measuring systems, consisting of interconnected wireless sensor nodes with autonomous supply, have plenty of applications in different areas of engineering end technology. Therefore, wireless sensor network design and optimization is a vital problem in informational technologies and computer engineering. Regardless of particular application and architecture, a wireless sensor network consists of several small sensor nodes somehow distributed in an area of inspection. Such a design ensures a distributed parameter measuring, according to which, each node performs preprocessing procedures for the data, which are to be aggregated by a so-called sink node and sent via other networks, e.g., wired or wireless local area network. For healthcare solutions, it is important to ensure a stable connection with the network and server back-end to provide a high-level analysis based on predicting methods of machine learning algorithms. This paper describes an application of an ultra-wideband communication model based on chaos synchronization as a low-power and efficient alternative solution for building wireless sensor networks. Thus, using chaotic signals as information carriers in wireless communication has several advantages, including a wide smooth spectrum, high information capability, cybersecurity, and low power consumption. The synchronization problem is one of the most vital tasks to be solved to design a chaos application for ultra-wide-band communication. Being well-studied for periodic signals, modern synchronization theory contains plenty of solutions for classical telecommunication and radio engineering systems, however, it is not developed enough for chaotic systems. Hence, a one-directional dissipative synchronization between two Chen systems is studied in the first section. The second section is devoted to the computer simulation of the model, described in the previous section. All the models built and the simulations performed have been done using MATLAB/Simulink software. The negative impact of channel noise and inequality of system parameters is considered. The possible way how to improve technical characteristics is also provided. Proposed models are to be used to design and develop low-cost wireless sensor networks for multi-channel healthcare solutions.