Sensor Network Environment Research Articles

A novel energy efficient data gathering algorithm for wireless sensor networks using artificial intelligence

Energy efficiency is challenging task in wireless sensor network (WSN), it is the main barrier in extending network lifespan. In WSN, maximum energy is wasted during data gathering, hence energy efficient algorithms using artificial intelligence can be designed, that preserves energy while data gathering. Thus, our proposed methodology, A novel energy efficient data gathering algorithm using artificial intelligence for wireless sensor networks (NDGAI), uses novel artificial intelligence algorithms and addresses issue of energy consumption while gathering data. In our proposed work, mobile element is utilized to gather information from sensor nodes in the clusters, formed using amended-expectation-maximization. Each cluster should have a cluster leader and a virtual-point. These cluster leaders are formed utilizing fuzzy logic technique. Virtual-points are formed in the range of cluster leader, only when cluster leader has data. The mobile element reaches virtual point by taking the optimal path, that determined by the hybrid artificial intelligence algorithms, such as artificial-bee-colony (ABC) technique and particle swarm optimization (PSO) algorithms. Thus, by properly performing clustering, cluster leader selection, virtual-point selection and optimal path determination, lead to improved network lifetime and energy saving while gathering the data. Results are simulated and compared with scalable gridbased data gathering algorithm for environmental monitoring wireless sensor networks (SGBDN) and proposed algorithm performs better.

Optimizing Multi-Tier Scheduling and Secure Routing in Edge-Assisted Software-Defined Wireless Sensor Network Environment Using Moving Target Defense and AI Techniques

Software Defined Wireless Sensor Networks (SDWSN) enable flexibility in Wireless Sensor Network (WSN) environments by defining the controllable functions to WSN nodes by the Software Defined Network (SDN) controller. Due to the rapid evolution of SDWSNs, adverse effects also have occurred in terms of interference, energy consumption, and security issues. Several state-of-the-art works lend their utmost best to the SDWSN environment. However, the complete picture (i.e., relatability and security in SDWSN) poses severe challenges. The state-of-the-art issues is addressed in this research by proposing interference-aware Multi-Tier Scheduling for the SDWSN environment (MTS-SDWSN). First, we perform network construction in which the proposed network is constructed in a 2D hexagonal grid structure to resolve the connectivity issue. Upon constructing the network, the SDWSN nodes are clustered and managed to reduce the energy consumption using the Divide Well To Merge Better (DWTMB) algorithm in which the optimal Cluster Leader (CL) is selected based on adequate constraint. The data from the clustered nodes are sent to the Local Base Station (LBS) via CL in which they are scheduled in multi-tier format to diminish the complexity and interference issues. The first tier involved in scheduling among Cluster Members (CMs) and CL using adequate metrics, whereas the successive tiers (i.e., second and third) involved in scheduling among CLs to LBSs and LBSs to Sink Node (SN) are done using the Non-Cooperative Fuzzy Theory (NCFT) method. Last, the scheduled nodes are routed to appropriate destinations using Secure and Optimal Routing Protocol (SORP). The proposed SORP includes the Alibaba and Forty Thieves (AFT) and Multi Criteria Decision Making (MCDM) algorithms for selecting and ranking the optimal routes. Further, the security of the routes is enabled by adopting trust and Moving Target Defense (MTD) mechanisms. The MTD includes route switching among the SDWSN devices and active switch handling using Cycle Generative Adversarial Networks (CGAN) among the switches. The proposed work is implemented using a NS-3.26 simulation tool, and performance of the proposed model and existing works shows that the proposed work outperforms the existing works.

Remote Sensing and Assessment of Compound Groundwater Flooding Using an End-to-End Wireless Environmental Sensor Network and Data Model at a Coastal Cultural Heritage Site in Portsmouth, NH.

The effects of climate change in the forms of rising sea levels and increased frequency of storms and storm surges are being noticed across many coastal communities around the United States. These increases are impacting the timing and frequency of tidal and rainfall influenced compound groundwater flooding events. These types of events can be exemplified by the recent and ongoing occurrence of groundwater flooding within building basements at the historic Strawbery Banke Museum (SBM) living history campus in Portsmouth, New Hampshire. Fresh water and saline groundwater intrusion within basements of historic structures can be destructive to foundations, mortar, joists, fasteners, and the overlaying wood structure. Although this is the case, there appears to be a dearth of research that examines the use of wireless streaming sensor networks to monitor and assess groundwater inundation within historic buildings in near-real time. Within the current study, we designed and deployed a three-sensor latitudinal network at the SBM. This network includes the deployment and remote monitoring of water level sensors in the basements of two historic structures 120 and 240 m from the river, as well as one sensor within the river itself. Groundwater salinity levels were also monitored within the basements of the two historic buildings. Assessments and model results from the recorded sensor data provided evidence of both terrestrial rainfall and tidal influences on the flooding at SBM. Understanding the sources of compound flooding within historic buildings can allow site managers to mitigate better and adapt to the effects of current and future flooding events. Data and results of this work are available via the project's interactive webpage and through a public touchscreen kiosk interface developed for and deployed within the SBM Rowland Gallery's "Water Has a Memory" exhibit.

Distributed Observer for Linear Systems with Multirate Sampled Outputs Involving Multiple Delays

This paper focuses on the design of a continuous distributed observer for linear systems under multirate sampled output measurements involving multiple delays. It is mathematically proved that the continuous distributed observer can achieve estimation in a sensor network environment, where output measurements from each sensor are available at different sampling instants, whether these times are periodic or aperiodic, and despite the presence of multiple time-varying delays. Each sampled and delayed measurement represents a node of the network, necessitating a dedicated observer for each node, which has access to only part of the system’s output and communicates with its neighbors according to a given network graph. The exponential convergence of the error dynamics is ensured by Lyapunov stability analysis, which accounts for the influence of the sampled and delayed measurements at each node. To demonstrate the effectiveness of the proposed observer, simulation tests were conducted on the tracking control of chasing satellites in low Earth orbit (LEO), encompassing both small and large sampling rates and delays. The continuous distributed observer with sampled output measurements exhibited convergence in scenarios with different sampling intervals, even in the presence of time-varying delays, achieving asymptotic omniscience, as demonstrated in the convergence analysis.

An energy‐efficient Chebyshev fire hawks optimization algorithm for energy balancing in sensor‐enabled Internet of Things

SummarySensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models.

Simulation of Optical Sensors in IoT Motion Training Systems in Wireless Sensor Networks and Cloud Technology Environments

Simulation of Optical Sensors in IoT Motion Training Systems in Wireless Sensor Networks and Cloud Technology Environments

Optimized intelligent 3D localization in wireless sensor networks for better data sharing

WSNs are networks of small, autonomous devices called sensors or nodes that are equipped with sensors, processing capabilities, and wireless communication capabilities. These networks are designed to monitor and collect data from their surrounding environments and transmit that data wirelessly to a central location for analysis. However, challenges such as packet loss and limited throughput have impeded their efficiency. To address these issues, this research presents an innovative approach known as Squirrel-based Elman Neural Localization (SbENL) to optimize 3D localization in WSNs. Squirrel fitness tracks the node's location in the sensor network environment. This study initially configures WSN nodes with constrained energy resources, and data rates are continually monitored and predicted. The outcome is improved data sharing with higher throughput rates. The research assesses the performance of SbENL against existing localization techniques, demonstrating its effectiveness in minimizing data loss, reducing energy consumption, and maximizing data transfer rates. This optimized intelligent 3D localization approach holds promise for enhancing data sharing and communication efficiency in WSNs, benefiting a wide range of applications. Finally, the communication metrics were measured and valued with recent existing approaches. A SbENL has minimized the data loss energy consumption and maximized the data transferring rate.

Multiscale feature-based robust secure diffusion estimation with noisy input over adversarial networks

This paper studies secure distributed estimation over wireless sensor networks in adversarial and noisy environments where a subset of sensors may be invaded by malicious attacks. We build a system model for the problem and prove that the bias-compensation method can resist noisy input but fails in attack defence. To alleviate the impact of attacks, a multiscale feature-based attack detection algorithm is proposed consisting of two main steps. Firstly, a binary discrimination mechanism is proposed to split neighbors of each node into two groups by their relative-state whose perception explores and exploits delicate element-wised features of attack vector. To reduce computational costs, the classifier is designed in a tree shape. Specifically, at each internal tree-node, a generalized correntropy based similarity metric function is developed and compared with an optimal threshold. Secondly, based on features of the classified groups, an absolute-state decider is presented to detect the trust neighbors and thus secure information sharing can be achieved. Simulation results reveal the effectiveness and robustness of our proposed method compared with some state-of-the-art algorithms under various attack forms.

A Novel Adaptive Random Access Window (RAW) Slot Adjustment Model for IoT Network Using an Integrated Scheme

The RAW (Restricted Access Window) component of the IoT network is deployed to reduce traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands schedules, collision analysis and send/receive matrices to accurately predict the future of stations’ interactions in an IoT environment. Thus systematically adjusting its operations to reduce contention among the stations and the Access Point(AP), thereby ensuring a flexible transmission even in a dense network environment. This paper critically investigated, analysed and proposed a novel approach to RAW size adjustment. The new approach will invoke data mining instruments and optimisation algorithms to improve performance of stations in an IoT environment and thereafter simulated (with respect to some specified QoS parameters) to ascertain it’s (output) performance index with the already existing models

GTR: GAN-Based Trusted Routing Algorithm for Underwater Wireless Sensor Networks.

The transmission environment of underwater wireless sensor networks is open, and important transmission data can be easily intercepted, interfered with, and tampered with by malicious nodes. Malicious nodes can be mixed in the network and are difficult to distinguish, especially in time-varying underwater environments. To address this issue, this article proposes a GAN-based trusted routing algorithm (GTR). GTR defines the trust feature attributes and trust evaluation matrix of underwater network nodes, constructs the trust evaluation model based on a generative adversarial network (GAN), and achieves malicious node detection by establishing a trust feature profile of a trusted node, which improves the detection performance for malicious nodes in underwater networks under unlabeled and imbalanced training data conditions. GTR combines the trust evaluation algorithm with the adaptive routing algorithm based on Q-Learning to provide an optimal trusted data forwarding route for underwater network applications, improving the security, reliability, and efficiency of data forwarding in underwater networks. GTR relies on the trust feature profile of trusted nodes to distinguish malicious nodes and can adaptively select the forwarding route based on the status of trusted candidate next-hop nodes, which enables GTR to better cope with the changing underwater transmission environment and more accurately detect malicious nodes, especially unknown malicious node intrusions, compared to baseline algorithms. Simulation experiments showed that, compared to baseline algorithms, GTR can provide a better malicious node detection performance and data forwarding performance. Under the condition of 15% malicious nodes and 10% unknown malicious nodes mixed in, the detection rate of malicious nodes by the underwater network configured with GTR increased by 5.4%, the error detection rate decreased by 36.4%, the packet delivery rate increased by 11.0%, the energy tax decreased by 11.4%, and the network throughput increased by 20.4%.

Load aware active low energy adaptive clustering hierarchy for IoT-WSN

Clustering is a primary process that takes place in an IoT based wireless sensor network environment commences from the deployment phase. Due to the heterogeneity and resource constrained nature of internet of things (IoT) networks, dynamic clustering, cluster head selection, and routing are required to optimize the network and to improve the overall network performance. Load aware active low energy adaptive clustering hierarchy (LAALEACH) work is an attempt to introduce novel components to the standard LEACH protocol. The main objective of LAALEACH work is to achieve a load aware active routing in IoT based wireless sensor network environments. Rapid load estimator, load pattern tracker, and load aware active routing are the contributed modules introduced in this LAALEACH work. Most recent related works are analyzed and the proposed modules are devised in a way to overcome the issues in the existing methods. Standard network performance parameters such as throughput, packet delivery rate, communication delays, and energy consumption are measured by the OPNET based simulation during the experiments. Obtained improvements in the overall performance is the accomplishment of LAALEACH work.

Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Cluster based Wireless Sensor Network

Wireless Sensor Network highly exposed to certain security attacks due to various uncertainties of the network which lead to node compromise. Hence considerable attention has been made by researcher during scheduling of the mobile sink to data collection of sensed information from the sensor nodes. Many secure data aggregation mechanism has been proposed to withstand the network against these kind of attack. Despite of many advantageous, those architectures will lead to several limitations such as large propagation delay and high energy consumption and spatial and temporal uncertainties. In order to mitigate those challenges, Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Wireless Sensor Network is designed. It should be capable achieving energy efficiency and collision avoidance due to uncertainty in transmission and reception. Proposed model eliminates the packet collision and poor channel utilization challenges. Initially, node is partitioned and cluster head is selected based on the highest energy density of the node in the particular location. Employing Full duplex channel easily examines the spatial and temporal characteristic of the node through utilization cache information of routing table. It further helps to build the light weight scheme with behavioral strategies of the nodes for secure propagation of the sensed data to the mobile sink. Light weight scheme deployed in mobile sinks estimate the node trustworthiness and simultaneously collects the secure aggregated data packets of the sensor node through cluster head against dynamic time slots concurrently .Comparing with traditional secure routing architecture, simulation results demonstrates that proposed architecture can reduce the propagation delay against various uncertainties and maximizes the life time of the network on increasing the networking performances with respect to packet delivery ratio, throughput and propagation delay. Finally proposed model obtains the cooperative communication of sensor node information to the base station through mobile sink on heterogeneous wireless sensor network environment. DOI: https://doi.org/10.52783/pst.387

Emerging Technologies for Automation in Environmental Sensing: Review

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.

Path-Loss Model for Wireless Sensor Networks in Air Pollution Environments Leveraging of Drones

Path-Loss Model for Wireless Sensor Networks in Air Pollution Environments Leveraging of Drones

AI-driven environmental sensor networks and digital platforms for urban air pollution monitoring and modelling

Recent advances in Artificial Intelligence (AI) research have opened up new opportunities for leveraging AI research for societal impacts. AI research offers novel ways of tackling societal problems including environmental, health, and education challenges. Despite the potential, there are limited documented use cases and methodologies for translating AI research to societal impact at a large scale. This paper presents AirQo, an AI and advanced technology-driven use case for urban environmental pollution monitoring and modelling and the resulting societal impacts that have been realised. The research outputs include a set of digital solutions for the environmental air pollution challenges including (1) custom-designed low-cost air quality monitors that are premised on IoT technology (2) a methodology for deploying a high-resolution and citizen-driven air quality monitoring (3) AI-powered digital tools for air quality information modelling and analysis for citizens and city leaders, and (4) a framework for engagement for citizens and leaders. The AirQo project has been deployed and scaled out in cities in Eastern, Western, and Central African countries. The societal impacts resulting from the implementation of the AirQo research project include policy and regulations, education and awareness, and research around air quality issues.

The Evolution of Wireless Sensor Networks through Smart Radios for Energy Efficiency

This article investigates the significant influence of smart radios on the energy efficiency of Wireless Sensor Networks (WSNs). It explores the incorporation of intelligent radios as a crucial element to enhance WSNs’ sustainability, providing a novel approach to tackle energy-related challenges. Smart radios are essential for prolonging the lifespan of sensor devices and reducing the environmental effect of WSNs by autonomously adjusting communication protocols and optimizing energy consumption. This paper also highlights the differences between 5G and 6G technologies within the WSNs framework. While 5G brought improvements in data speed and connectivity, 6G represents a significant progress by prioritizing energy efficiency as a fundamental goal. This shift represents a fundamental change, with a primary focus on achieving extremely low energy consumption and ensuring the sustainable operation of WSNs. The paper examines crucial technological factors enabling 6G to outperform its predecessor, establishing it as a revolutionary force in the field of wireless communication for sensor networks. This research illuminates the crucial function performed by smart radios in connecting the desire for energy efficiency with the changing environment of wireless sensor networks.

A Survey on Computational Techniques in WSN Networks

A network made up of many tiny, inexpensive sensors that use wireless communications is now technically and economically possible because to considerable advancements in hardware manufacturing technology and the creation of effective software algorithms, this is known as a wireless sensor network. The use of sensor networks in mission-critical environments like conflict zones as well as in more commonplace security and business applications like building and traffic surveillance, habitat monitoring, and smart homes, among others, has considerable promise. Wireless sensor networks, however, provide particular security difficulties. Because WSNs are used for so many security-sensitive applications, security is now a top issue for protocol designers working on WSNs. We have attempted to list all known security flaws in wireless sensor networks in this study, which also examines a broad range of WSN attacks and their categorization processes. The various security measures that may be used to counter them, as well as the difficulties encountered. In this paper, we've taken up the problem and put forward a proposal for an integrated, all-encompassing security that would provide security services for all sensor network services. It is lucrative to be utilised in significant quantities in the future incorporating the wireless connectivity, computational power, and sensor technologies it combines. There are several different security risks associated with wireless communication technologies

Effective Data Aggregation Moel for the Healthcare Data Transmission and Security in Wireless Sensor Network Environment

Data aggregation is the process of collecting and combining information from multiple sources to provide a unified view or summary. In various fields such as statistics, economics, and data analysis, aggregating data helps reveal patterns, trends, or general insights that may not be apparent when examining individual data points. Aggregated data can provide a more comprehensive perspective, facilitating decision-making and strategic planning. This paper explores the application of Sugeno Fuzzy Model Monkey Swarm Optimization (SFMMsO) in healthcare, specifically focusing on data aggregation and security within Wireless Sensor Networks (WSN). The study demonstrates SFMMsO’s efficacy in aggregating patient health data, generating comprehensive Aggregated Health Indices. It also highlights SFMMsO’s optimization capabilities, refining decision variables to enhance healthcare algorithm performance. The paper addresses the critical dimension of security, showcasing SFMMsO’s adaptability in optimizing security parameters and assessing its vulnerability to various attack types. The findings underscore SFMMsO’s potential as a robust tool for healthcare applications, emphasizing the need for proactive security measures to fortify its resilience against potential threats. This study contributes valuable insights into the intersection of optimization algorithms, data aggregation, and security in healthcare, paving the way for advancements in utilizing SFMMsO for secure and comprehensive healthcare systems.

Zigbee Based Mobile Sensing for Wireless Sensor Networks

Wireless sensor networks, have drawn a lot of interest because of their adaptability and range of uses in different industries. WSNs face significant challenges when it comes to energy efficiency because sensor nodes are usually battery-powered and have limited resources. Several energy-efficient methods and protocols, such as duty cycling, data aggregation, and topology management, have been put forth to address this problem. Moreover, new potential for mobile wireless sensor networks are presented by the integration of WSNs with mobile and static devices, such as drones, tablets, and smartphones. In this study, we suggest utilizing Zigbee technology to establish a robust and flexible monitoring system for both stationary and mobile sensors. Numerous industries, including healthcare, smart agriculture, asset tracking, energy management, smart home automation, and industrial monitoring and control, have made extensive use of Zigbee. By leveraging Zigbee's capabilities, we hope to improve the protocol's performance while establishing dependable communication links between nodes, analyzing the communication range, and assessing the influence of environmental conditions. In this study, a system model for Zigbee deployment in mobile robots will be presented. It will address the basics of Zigbee, communication difficulties, networking with Zigbee, and simulations or real-world outcomes. We will learn about the strengths and weaknesses of Zigbee-based systems in terms of creating reliable communication links in mobile wireless sensor networks by looking at their architecture and functionality. The results of this study will help us comprehend Zigbee's potential to improve monitoring systems and make better decisions across a range of industries. The study's emphasis on mobile monitoring systems signifies a step forward in addressing the evolving needs of wireless sensor networks in dynamic environments.

PM2.5 forecasting under distribution shift: A graph learning approach

We present a new benchmark task for graph-based machine learning, aiming to predict future air quality (PM2.5 concentration) observed by a geographically distributed network of environmental sensors. While prior work has successfully applied Graph Neural Networks (GNNs) on a wide family of spatio-temporal prediction tasks, the new benchmark task introduced here brings a technical challenge that has been less studied in the context of graph-based spatio-temporal learning: distribution shift across a long period of time. An important goal of this paper is to understand the behavior of spatio-temporal GNNs under distribution shift. We conduct a comprehensive comparative study of both graph-based and non-graph-based machine learning models under two data split methods, one results in distribution shift and one does not. Our empirical results suggest that GNN models tend to suffer more from distribution shift compared to non-graph-based models, which calls for special attention when deploying spatio-temporal GNNs in practice.