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The Internet of Things (IoT) is a developing technology in the world of communication and embedded systems. The IoT consists of a wireless sensor network with Internet service. The data size of the sensor node is small, but the routing of the data and energy consumption are important issues that need to be advocated. The Mobile Adhoc Network (MANET) plays a very important role in IoT services. In MANET, nodes are moving within the network. So, routes are created dynamically on demand and do not have any centralized units. The route optimization method addresses issues like selecting the best routes in terms of overhead, loop free, traffic control, balancing, throughput, route maintenance, and so on. In this paper, IoT routes are created between sensors to sink through MANET nodes with WSN routing ideology. The Krill Herd and Feed Forward Optimization (KH-FFO)-based method discovers the routes. The Krill herd algorithm clusters the network. This method increases network speed and reduces energy waste. Feed-forward optimization involves learning all the nodes in the network and identifying the shortest and most energy-efficient route from source to sink. The overall performance of the KH-FFO protocol has improved the network's capacity, reduced packet loss, and increased the energy utilization of the nodes in the network. The ns-3 simulation for KH-FFO is tested in different node densities and observed energy utilization is increased by 28%, network life is increased by 7%, Packet delivery ratio improved by 7.5%, the End-to-End delay improved by 31% and the Throughput is 3%. These metrices are better than the existing works in the network. [ABSTRACT FROM AUTHOR]

The very large-scale integration implementation of a unique hardware-oriented image compression technique for wireless sensor networks (WSNs) is presented in this work. Networks of individually owned sensors spread out throughout an area that can detect, measure, and report changes in environmental variables are known as wireless sensor networks (WSNs). Color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, estimation, quantization, and enhanced Golomb-rice coding (EGRC) are all included in the proposed design. A unique improved BTC with an enhanced Golomb-rice coding (IBTC-EGRC) framework has been proposed in this paper. IBTC training framework has been developed using the fuzzy decision-based approach to achieve representative levels and satisfy WSN requirements to accomplish the cost-effective and power-efficient features. Two ideal reconstruction values and bitmap files have been obtained for every block. IBTC divides images into variable block sizes for mathematical translation and inter-pixel redundancy removal. The subsampling, estimation, and quantization stages have minimized redundant data. Finally, EGRC has been used to code the value with the highest likelihood. An EGRC module decreases memory use and computation complexity. The EGRC technique reduces hardware resource utilization by removing the need for the context module, a crucial part of lossless image compressor designs and its memory. Proposed method, Golomb-rice parameter forecasting and managment module is used to preserve pixel connection and improved compression ratio. A UMC 180 nm CMOS technology has been used to implement the suggested framework. This design has 5.8k synthesized gate counts and a core area of 56,000 µm2. 100 MHz and 3.01 mW were the operational frequency and energy consumption, respectively. The proposed method has a 9.37% reduction in gate count compared to the previous fuzzy BTC-based approach. [ABSTRACT FROM AUTHOR]

In large-scale industrial and agricultural production environments, many nodes of the Internet of Things (IoT) were deployed. These nodes are of various types and contain a wide range of domain-related themes, and their distribution is of theme and hierarchy. Starting from the thematic and hierarchical nature of the IoT, this paper proposes the concept of local thematic structure in wireless sensor networks (WSN), and establishes a hierarchical model of the thematic structure in WSN, presenting methods of thematic structure identification, hierarchical structure identification, and hierarchical aggregation of theme messages in WSN. A real-time monitoring system for WSN based on Kafka was established through simulation experiments on agricultural greenhouse WSN (including four themes: temperature, humidity, light intensity, and CO2 concentration). The experimental results have substantiated the objective existence of thematic structure in WSN, as well as the efficacy of the message hierarchy transmission models and algorithms derived from the thematic structure.The research results can be extended and applied to the management of graphically structured scenarios such as the urban brain, smart communities, and intelligent transportation, which is of universal significance to the development of the intelligent IoT in the era of the IoT. [ABSTRACT FROM AUTHOR]

This study presents an example of connecting relative humidity and temperature using three different Long Short-Term Memory (LSTM) algorithms that are learned and utilized to forecast WSN nodes data. The example predicts relative humidity values as a function of temperature using Stochastic Gradient Descent with Momentum (SGDM), Root Mean Square Propagation (RMSProp), and Adaptive Moment Estimation (ADAM). The results obtained from combining and averaging the three methods are utilized to provide the best possible predicted data. The Root Mean Squared Error (RMSE), which is utilized to enable verifying the correctness of predicted, is evaluated using two learning rate schedules. The two mathematical formulas that represent the expected and intended relationship between temperature and relative humidity are given. With a tiny intercept difference of 0.9, the predicted form function for the three associated LSTM algorithms demonstrated correct representation. This discrepancy is proportionate to the RMSE results that were achieved, allowing for the fine-tuning of the trained algorithms and the verification of the reduction in mathematical expressions that describe the behaviour of Wireless Sensor Network (WSN) nodes. Additionally, a mathematical model of the correlation between RMSE and epochs (number of iterations) is provided to allow for training cycle optimization for improved performance. The strategy of utilizing three distinct LSTM algorithms and comparing and correlating their outputs was demonstrated to be an effective way to lower prediction error and get the best solution. The general correlation mathematical model, which includes weights for each procedure before averaging to get the final value, is described in the discussion. Future advances could use this to incorporate each algorithm's strongest elements. [ABSTRACT FROM AUTHOR]

The paper uses WSN to monitor athletes' medical diagnosis information remotely. The aim is to strengthen the monitoring and management of athletes' medical diagnosis data. The method of wireless sensor node and IPv6 routing based on IPv6 is proposed. The terminal-to-terminal network structure based on IP can carry long-distance transmission and share athlete medical diagnosis information. The monitoring system's bottom structure design is realized using Contiki technology. At the same time, it also realizes the data acquisition and management in the monitoring system. The component interface is designed based on TinyOS. The design of the wireless client based on GPSR is completed based on GPSR. This paper uses the VanetMobiSim platform to complete a remote monitoring system's software development and simulation test based on wireless medical diagnosis information. The results show that this system's medical diagnosis information covers a wide range, and the information search efficiency is high. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have revolutionized data collection in diverse environments, from industrial settings to natural ecosystems. However, their decentralized nature and energy constraints pose unique security and operational challenges. Previous research provided foundational insights into WSN security but lacked comprehensive strategies for real-time intrusion prevention and efficient energy utilization. Our work employs a multi-layered approach, integrating network intrusion prevention systems (NIPS) with WSNs and leveraging machine learning for threat detection. We developed MinE-DT (minimum energy-direct transmission) hybrid routing an integrated WSN model that not only identifies and mitigates distributed denial-of-service (DDoS) attack but also optimizes energy consumption, ensuring prolonged network longevity without compromising security. The proposed model's distinctiveness lies in its fusion of NIPS with energy-saving algorithms, offering a dual advantage of enhanced security and energy efficiency. Utilizing a combination of simulations and theoretical analysis, our methodology yielded promising results, showcasing significant improvements in threat detection rates and energy conservation. [ABSTRACT FROM AUTHOR]

In the world of wireless sensor networks (WSNs), optimizing performance and extending network lifetime are critical goals. In this paper, we propose a new model called DTLR-Net (Deep Temporal LSTM Regression Network) that employs long-short-term memory and is effective for long-term dependencies. Mobile sinks can move in arbitrary patterns, so the model employs long short-term memory (LSTM) networks to handle such movements. The parameters were initialized iteratively, and each node updated its position, mobility level, and other important metrics at each turn, with key measurements including active or inactive node ratio, energy consumption per cycle, received packets for each node, contact time, and interconnect time between nodes, among others. These metrics aid in determining whether the model can remain stable under a variety of conditions. Furthermore, in addition to focusing on stability and security, these measurements assist us in predicting future node behaviors as well as how the network operates. The results show that the proposed model outperformed all other models by achieving a lifetime of 493.5 s for a 400-node WSN that persisted through 750 rounds, whereas other models could not reach this value and were significantly lower. This research has many implications, and one way to improve network performance dependability and sustainability is to incorporate deep learning approaches into WSN dynamics. [ABSTRACT FROM AUTHOR]

ABSTRACT Currently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality. [ABSTRACT FROM AUTHOR]

In existing schemes, there has been no analysis or steps built for balancing data integrity and energy consumption of clusters in the network, and providing security in WSN is difficult. In this research work, to overcome these troubles, we have used a Query-based Location-Aware Secure Multicast Routing for Wireless Sensor Networks (QLAMSR) is proposed to attain energy efficiency and security. There are three modules involved here. The first module is about the network model and the system overview to obtain more network lifetime. In the second module, an advanced encryption standard and RC6 algorithms is used here to provide authentication and data integrity. In the third module, efficient energy routes are established and demonstrated to illustrate network reliability. Node trust and reliable route energy efficiency are calculated to produce efficient data integrity through the idea of a fuzzy decision model. The proposed protocol is simulated with the Network Simulator tool (NS 2.34) to analyze the network performance metrics and finally its shown that proposed QLAMSR attains route energy efficiency by 39–92% and data integrity rate by 18–89(packets/sec). [ABSTRACT FROM AUTHOR]

The quality of communication between any two users in multi-hop wireless sensor networks directly depends upon the path selection among the available paths between end-users. The issue of selecting the optimized path from source to a destination becomes the necessary criteria for effective communication between end-users. The art of work mainly focuses on the selection of the path which has the best available bandwidth however they do not consider other network parameters such as distance, energy, the intensity of traffic which plays a critical role in routing. In this paper, an algorithm is presented for the selection of optimized route from source to destination by considering different network parameters along with the bandwidth and rank is given to all the available routes from source to destinations per their weights. The paper is validated using network simulator. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks consist of several autonomous nodes that are outfitted with sensors, radio, processors, memory storage, and power sources. These nodes track, sense, and send data using radio. While establishing a network, the two most essential characteristics are coverage and connectivity. For better connectivity and a longer network life, it's important to make the coverage area as big as possible with the fewest number of sensor nodes. The goal of this research is to make a connected sensor network that uses less energy and can be used in situations where the sensors need to be placed in the best way to make the network last as long as possible. The probabilistic sensing model is used, and improved network lifetime is the goal of the research work by using problem‐specific intelligent optimization techniques like BAT, ACO, and JOA to maximize the coverage area with respect to energy and points of interest. This work introduces a novel approach that optimizes both coverage and connectivity. The modified binary bat algorithm overcomes computational complexities and local optima observed in existing methods. Uniquely, it models the three states of each sensor node and includes innovative features like a greedy initialization and a weighted cost function for improving network efficiency. After investigation, it was analyzed that the proposed solution significantly improves network lifetime by over 10% to 12% compared to existing methods like JOA and ACO. The proposed approach converges faster and performs more efficiently. [ABSTRACT FROM AUTHOR]

Mobility has ultimate applications in Wireless Sensor Networks (WSNs) including several areas such as wildlife intensive care, health monitoring, flood and fire detection, and much more. Mobile WSNs (MWSNs) have captivated customer attention in recent centuries because of their applications in numerous areas. Mobile WSNs are source constraints and request performance studies by many nodes' movement outlines. Usually, routing algorithms in MWSN have been examined for specified movement. However, for instantaneous network scenarios, designing an efficient routing protocol/algorithm and analysing changes in numerous movement outlines on routing approaches are important to provide efficient results. Hence, keeping in the assessment of the aforesaid problem, an Energy Proficient Mobile Routing (EPMR) protocol is proposed. All the simulations have been completed in MATLAB on varied constraints to assess the effectiveness of EPMR and state-of-the-art routing protocols. Simulation outcomes demonstrate that EPMR provides improved performance than Distributed Efficient Multi-hop Clustering (DEMC), Geographic Robust Clustering (GRC), Mobility Aware Routing (MAR), Distributed Efficient Clustering Approach (DECA), and Improved Energy Mobile Routing (ECMR). EPMR enhances packet delivery ratio by 13–15%, reduces packet loss percentage by 12–14%, extends throughput by 14–16%, decreases overhead by 13–14%, minimizes average end-to-end delay by 13–15%, minimizes energy consumption by 16–18%, and extends network lifetime 17–19% on sensors' mobility. EPMR achieves better outcomes with the Random Waypoint Mobility (RWPM) model than the Random Walk Mobility (RWM) model and Pathway Mobility (PM) model. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are increasingly being utilized in environments where human presence is limited or dangerous. The main goal is to enhance the data processing capabilities of these components to extend the overall lifespan of the design. Researchers have explored conventional energy-saving methods to address the energy constraints of sensor nodes. However, it became clear that traditional routing methods, specifically those based on packet grouping, were inadequate. The proposed system, known as Optimized Group-Centric Data Routing (OGC-DR), introduces an efficient method of data routing by utilizing the concept of grouping nodal points. This approach enhances data routing management by differentiating between routing within a nodal group and routing between adjacent nodal groups. Group Heading Nodes (GHN) are assigned to each group of sensory nodes according to fitness criteria. The implementation of a tree-based routing structure improves data routing by creating a "meeting-zone" and strategically selecting intermediary nodes between the source and destination node. To improve data privacy, a sender and receiver engage in an asymmetric secret-key exchange at nodal points. Data is then directed to its ultimate destination via predetermined intermediary nodes and Group Heading Nodes. Simulations of the proposed method indicate several advantages, such as lower end-to-end delays, reduced energy consumption, higher active node count, and enhanced packet delivery rates. Furthermore, it improves data privacy for all communication within the sensory architecture. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) is a network of connected devices, enabling the exchange and collection of data from various environments. The routing protocol for low power and lossy networks (RPL) is a protocol for routing IPv6 over low-power wireless personal area networks, commonly used in IoT applications. However, RPL has several security and privacy issues that make it vulnerable to various attacks, including rank attacks (RA), which can lead to denial-of-service (DoS) scenarios. This research aims to address the impact of RA on RPL networks by conducting simulations using the Contiki/Cooja simulator with two topology types, random and grid, along with three RA scenarios and a normal network scenario. The study compares the performance of RPL network OF0 and MRHOF in terms of throughput, packet delivery ratio (PDR), hop count (HC) and delay. The results demonstrate that RA significantly degrades network performance and reduces network lifetime, thus draining its limited resources. Some possible solutions are also suggested to mitigate these attacks by focusing on core components of the network like objective function (OF) and node behavior. Future work will focus on studying security mechanisms for RPL against RA. [ABSTRACT FROM AUTHOR]

One of the most important aspects of the effective functioning of wireless sensor network (WSN) is their security. Despite significant progress in WSN security, there are still several unresolved issues. Many review studies have been published on the problems of possible attacks on WSN and their identification. However, due to the lack of their systematic analysis, it is not possible to fully substantiate practical recommendations for the effective application of the proposed solutions in the field of WSN security. In particular, the creation of methods that provide a high degree of security while minimizing computational effort and costs, and the development of effective methods for detecting and preventing attacks on WSN. The purpose of this document is to fill this gap. The article presents the results of the study in the form of a systematic analysis of the literature with a targeted selection of sources to identify the most effective methods for detecting and preventing attacks on WSN. By identifying the security of WSN, which has not yet been addressed in research works, the review aims to reduce its impact. As a result, our extended taxonomy is presented, including attack types, datasets, effective WSN attack detection methods, countermeasures, and intrusion detection systems (IDS). [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) enable seamless data gathering and communication, facilitating efficient and real-time decision-making in IoT monitoring applications. However, the energy required to maintain communication in WSN-based IoT networks poses significant challenges, such as packet loss, packet drop, and rapid energy depletion. These issues reduce network life and performance, increasing the risk of delayed packet delivery. To address these challenges, this work presents a novel energy-efficient distributed neuro-fuzzy routing model executed in two stages to enhance communication efficiency and energy management in WSN-based IoT applications. In the first stage, nodes with high energy levels are predicted using a fusion of distributed learning with neural networks and fuzzy logic. In the second stage, clustering and routing are performed based on the predicted eligible nodes, incorporating thresholds for energy and distance with two combined metrics. The cluster head (CH) combined metric optimizes cluster head selection, while the next-hop combined metric facilitates efficient multi-hop communication. Extensive simulation results demonstrate that the proposed model significantly enhances network lifetime compared to EANFR, RBFNN T2F, and TTDFP by 9.48%, 25%, and 31.5%, respectively. [ABSTRACT FROM AUTHOR]

The spectacular concept of integrating OCR with the Internet of Things (IoT) and Wireless Sensor Networks (WSN), presented in this study, intends to increase the autonomy and social inclusion of people with visual impairments and deaf-blindness. This application converts text from photographs into real-time Braille representations by utilizing the capabilities of Optical Character Recognition (OCR) technology, Braille displays, and IoT. Braille is a tactile form of writing which assists the visually impaired by representing letters and numbers with raised dots. Text recognition and conversion through OCR helps people with vision issues by converting printed text into accessible formats like audio or Braille, assisting people in with recognizing things around them. Individuals with visual impairments are given autonomous access to critical information by smoothly integrating modern technologies, boosting their self-confidence and promoting active social involvement. This OCR-Braille approach's primary objective is to minimize the knowledge gap and promote social equality. Users' ease of access to labels, educational resources, and digital information about their environment fosters a sense of empowerment and independence. This ground-breaking study not only eliminates the accessibility gap but also provides the groundwork for a society that is better linked and accessible. Individuals have access to the tools they need for independently navigating their surroundings through simple IoT connectivity and WSN. This research lays the path for a future for assistive technology to catalyze equality and independence, independent of one's visual ability, by fusing assistive technology and IoT connectivity.

In the rapidly evolving landscape of the Internet of Things (IoT) and Wireless Sensor Networks (WSN), the need for secure and efficient data transmission is paramount. Block ciphers, which are fundamental cryptographic algorithms used for encrypting data, play a critical role in ensuring data security within these environments. This study delves into the implementation and performance evaluation of lightweight block ciphers, including PRESENT, Piccolo, RECTANGLE, SPARX, and LED, on various sensor motes. The research scrutinizes these cryptographic solutions within the resource-constrained environments characteristic of IoT and WSN deployments. Our investigation commences with a meticulous selection of sensor motes and platforms, enabling realistic simulations and practical evaluations. The Contiki Cooja framework, a comprehensive operating system (OS), development toolkit, and network simulator, was utilized to facilitate these assessments. Subsequently, we conduct a comprehensive performance assessment, rigorously analyzing each cipher's impact on memory usage, power consumption, and execution time. The results reveal that the SPARX cipher emerged as the most resource-efficient, with the lowest memory footprint, while the RECTANGLE cipher required more memory across platforms.

Research on wireless body area networks (WBAN), also known as wireless body sensor networks (WBSN), has been increasingly important in medical applications recently and is now crucial for patient monitoring. To create a dependable body area network (BAN) system, several factors need to be considered at both the software and hardware levels. One such factor is the designing and implementation of routing protocols in the network layers. Protocols for routing can detect and manage the routing paths in a network to facilitate efficient data transmission between nodes. Therefore, the routing protocol is crucial in wireless sensor networks (WSN) to provide dependable communication among the sensor nodes. Different clustering methods can be used in WBAN systems. However, these techniques often produce many cluster heads (CHs), which leads to higher energy consumption. Increased consumption of energy reduces the lifespan of WBANs and raises costs of monitoring. This research proposes a recent metaheuristic algorithm to select the optimal clusters to provide an energy-effective protocol for healthcare monitoring. This research aims to minimize the energy utilization of WBANs by choosing the most suitable CHs based on the BWO. The proposed BWO-based routing protocol demonstrates superior performance in WBANs based on energy consumption, packet loss, packet delivery ratio, network lifetime, end-to-end delay, and throughput. It optimizes energy consumption by effectively selecting CHs and routing paths, leading to balanced energy usage and prolonged network operation. The BWO model significantly reduces end-to-end delay by ensuring data packets follow the shortest and least congested routes, which is significant for real-time health monitoring. It achieves a high packet delivery ratio, typically between 95% and 98%, indicating reliable data transmission, while maintaining a low packet loss rate, generally between 1% and 5%. Additionally, the BWO-based routing protocol extends network lifetime by preventing early node depletion and enhances network throughput by reducing congestion and packet collisions, thereby supporting continuous and robust health data monitoring. [ABSTRACT FROM AUTHOR]

To extend the lifespan of Wireless Sensor Networks (WSNs), effective routing protocols are required to provide communication channels between the sources and sink. While nodes are arbitrarily distributed in a substantially unsafe situation, these steering protocols are susceptible to an extensive range of assaults. For WSNs, trust-based routing protocols are created, which employ a trusted route rather than the quickest path, to prevent these attacks. The artificial bee colony-based clustering technique is utilized because the conventional clustering algorithm reduces the energy usage of nodes. This allows it to increase the lifespan of the sensor network by evenly dividing energy use among all nodes. The artificial bee colony (ABC)-based grouping method was developed because the typical grouping technique minimizes the energy usage of nodes. By integrating diverse sensors and devices, Internet of Things (IoT) enhances the performance of WSN, by enabling efficient data collection, analysis, and communication. The creation of such traditional protocols does not guarantee the best global optimization for the lengthening of WSN life. Through simulation analysis, the suggested Artificial Bee Algorithm (ABC)-based Traffic-Aware Energy Efficient Routing (TEER) protocol's performance was evaluated and contrasted with the TEER protocols. The ABC-based TEER protocol's lifetime analysis, active node analysis is achieved and contrasted with those of other protocols. In terms of the number of rounds, the network performance for the ABC-based TEER scheme performs better than the TEER schemes. The Analysis of throughput of the ABC-TEER method, which reveals a 9.5% increase in performance in comparison to the TEER protocol. [ABSTRACT FROM AUTHOR]

Abstract A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR.

Abstract Black ice is responsible for dangerous road-related incidents that can cause collisions and harm vehicle drivers and pedestrians. Visual examination and weather forecasts are two standard traditional methods for detecting black ice on roads, but they are often inaccurate and may not deliver the vehicle driver with up-to-date information on road conditions. The evolution of Industry 4.0 enabling technologies such as wireless sensor network (WSN), Internet of Things (IoT), cloud computing, and machine learning (ML) has been capable of detecting events in real time. This study aims to analyse the integration of the WSN, IoT, ML, and image processing for black ice detection. The qualitative research method is followed in this study, where the problems of black ice detection are studied. Following this, the role of Industry 4.0 enabling technologies is analyzed in detail for black ice detection. According to the study, we can detect black ice using different methods, but some methods need to be refined if we talk about the prediction. By merging different technologies, we can improve the overall architecture and create an algorithm that works with images and physical variables like temperature, humidity, due point, and road temperature, which were responsible for black ice formation, and predict the chances of black ice formation by training the system.

Routing represents a pivotal concern in the context of Wireless Sensor Networks (WSN) owing to its divergence from traditional network routing paradigms. The inherent dynamism of the WSN environment, coupled with the scarcity of available resources, engenders considerable challenges for industry and academia alike in devising efficient routing strategies. Addressing these challenges, a viable recourse is applying heuristic search methodologies to ascertain the best path in WSNs. Ant Colony Optimization (ACO) is a well-established heuristic algorithm that has demonstrated notable advancements in routing contexts. This paper presents an altered routing protocol that is based on ant colony optimization. In this protocol, we incorporate the inverse of the distance between nodes and their neighbours in the probability equations of ACO, along with considering pheromone levels and residual energy. These formulation modifications facilitate the selection of the most suitable candidate for the subsequent hop, effectively minimising the average energy consumption across all nodes in each iteration. Furthermore, in this protocol, we iteratively fine-tune ACO's parameter values based on the outcomes of several experimental trials. The experimental analysis is conducted through a diverse set of network topologies, and the results are compared against well-established ACO algorithms and routing protocols. The efficacy of the proposed protocol is assessed based on various performance metrics, encompassing throughput, energy consumption, network lifetime, energy consumption, the extent of data transferred over the network, and the length of paths traversed by packets. These metrics collectively provide a comprehensive evaluation of the performance attainments of the routing protocols.

Abstract Wireless sensor networks' most prominent concern is energy optimization. It faces significant problems like high energy consumption, data loss, delay, and low network lifetime. To improve, it uses clustering. However, during clustering, coverage holes are most likely to appear near the network's edge, within the cluster, and between clusters. As a result, there are more energy holes and dead nodes; therefore, the goal of this work is to maximize node network lifetime and minimize energy consumption during data transmission in the wireless sensor network (WSN). The proposed work includes three entities: sensor nodes, an edge-assisted unmanned aerial vehicle (UAV), and a base station. It uses an edge-assisted unmanned aerial vehicle to provide additional resources to the UAV, which helps reduce energy consumption during data transmission. This research proposes using communication to enhance the speed and bandwidth of data transmission and reduce transmission latency. This work attempts to improve performance by increasing throughput.

One of the most significant issues in wireless sensor networks (WSNs) is security, which must be addressed to keep WSNs safe from malicious attacks. An intrusion detection system (IDS) is essential in analyzing network traffic and detecting abnormal events. However, these IDSs suffer from several drawbacks that affect their effectiveness and flexibility in accuracy, so they must overcome these drawbacks to improve the performance of IDS. These drawbacks include difficulties in determining the appropriate dataset, the problem of feature selection, and the issue of the imbalanced dataset and choosing the appropriate algorithms for the classification process in WSN. In this paper, a model for an anomaly‐based IDS in WSNs is proposed. This model applied mutual information (MI) for feature selection and the synthetic minority oversampling technique (SMOTE) for solving the imbalanced dataset problem. It used different machine learning (ML) algorithms, random forest (RF), decision tree (DT), support vector machine (SVM), and K‐nearest neighbors (KNNs) to analyze network traffic and binary classification or multiclass classification. To implement and evaluate the performance of the proposed model, the standard dataset NSL‐KDD is used. Python language is used to implement the proposed model in the Anaconda platform, and many evaluation metrics are also utilized to evaluate the performance of the proposed method. Experimental results show that the proposed model can detect intrusions using different ML algorithms with high accuracy. The results of the proposed model for different ML algorithms outperform the state‐of‐the‐art algorithms, and the maximum enhancement reached 15% in the accuracy metric. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR. [ABSTRACT FROM AUTHOR]

Summary In this manuscript, a nature‐inspired optimization method, named transient search optimization (TSO), is proposed. Energy‐based monetary custom is a serious issue on the wireless sensor network (WSN). Here, the network clustering is an effectual technique to reduce node energy depletion and increased network lifetime. The proposed method aims to improve the efficiency of sensor nodes (SNs) by reducing their detachment, minimizing energy transmission, and protecting excessive energy stored in the nodes. This approach helps decrease delays, reduce traffic flow, and optimize network performance. The execution is implemented on the NS2 software. The experimental outcomes exhibit that the proposed system performs better based on two wireless sensor architectures, such as 50 nodes and 100 nodes. The parameter produces 52.24%, 54.38%, and 56.37% better network lifetime; 44.71%, 46.24%, and 49.45% higher alive node; and 39.26%, 36.26%, and 28.65% lesser dead SNs compared with existing techniques like multi‐objective cluster head (CH)–based energy‐aware optimized routing approach in wireless sensor network (MOCH‐ORR‐WSN), energy effective CH selection with improved sparrow search algorithm in WSN (ECH‐ISS‐WSN), and energy effectual cluster basis routing protocol under butterfly optimization along ant colony optimization algorithms for WSN (EEC‐BOA‐ACO‐WSN). [ABSTRACT FROM AUTHOR]

Summary: Wireless Sensor Networks (WSNs) find applications in diverse fields such as environmental monitoring, healthcare, and military surveillance. Nonetheless, one of the primary challenges encountered by WSNs is congestion. Congestion arises in WSNs when there is a high volume of traffic on the network, leading to significant repercussions. These repercussions encompass packet loss, heightened latency durations, and diminished network efficiency. This paper presents an innovative congestion control mechanism named Dual Indicator Random Early Detection (DIRED). DIRED leverages two indicators, namely queue length and packet loss rate, to dynamically adjust the dropping probability of packets, thereby mitigating congestion and enhancing network performance. For the successful execution of the DIRED model, a congestion control algorithm is introduced. This strategy efficiently prevents the deterioration of network performance that can commonly arise from extremely low packet drop probabilities. Simulations are conducted to evaluate the proposed DIRED model, comparing it with the KACO and KFOA techniques. The results demonstrate that DIRED outperforms KACO and KFOA in terms of network performance, achieving a more optimal balance between performance metrics and packet‐dropping probability. [ABSTRACT FROM AUTHOR]

Recent advances in wireless sensor networks (WSNs) have brought the sensor based monitoring developments to the surface in many applications. In such a scenario, the security of communication is a major challenge in the effective delivery of the collected data due to the presence of malicious nodes. Moreover, since security and minimization of energy consumption are critical factors in designing techniques for multi-hop secure routing in wireless sensor networks, it is necessary to address the issues of security in the routing process. Hence, this paper proposes a novel intrusion detection system for enhancing the security and Fuzzy based Ant Colony Optimization based Secured Quality of Service Routing Protocol (F-ACO-SQoSRP) for increasing the security of communication and network performance in WSNs. Using this proposed intrusion detection system (IDS), the proposed model identifies the distinct and malicious behaviours of nodes. Additionally, a clustering algorithm has been proposed in this work, wherein the cluster head selection (CHS) is based on quality of service (QoS) measures and the trust values of nodes are measured using the intrusion detection system results. The proposed ACO based routing framework also predicts the best optimum and secured path to allow for effective communication across each link. The simulation results obtained from this work proved that the proposed secured routing algorithm provides better performance in terms of security using robust trust values, increase in packet delivery ratio and network lifetime reduction in delay and energy consumption when this work is compared with the existing secured routing systems. [ABSTRACT FROM AUTHOR]

Design of effective algorithm for reliable and energy optimized secure routing protocol (SRP) for wireless sensor networks (WSNs) is a demanding design issue now. To handle this problem, we propose a trust and encryption-based SRP based on trust modelling with intrusion detection, elliptic curve cryptography (ECC), clustering, fuzzy rules and ant colony optimization (ACO)-oriented SRP for WSN routing. In this paper, an extended convolutional neural networks with Schrodinger equation and particle swarm optimization is proposed for developing and intrusion detection-based trust modelling. Moreover, a new node authentication scheme and an encryption-based secure routing protocol are also proposed in this work for increasing the security. This proposed secure protocol known as trust and ECC encryption-based ACO-SRP (TECC-ACO-SRP) performs authentication and trust analysis on the nodes using intrusion detection, and then, the data are communicated after data encryption using ECC encryption technique. This proposed system combines dominant set clustering with fuzzy rules to make clusters with similar type of nodes as members and then selects cluster heads (CHs) for every cluster. This SRP ensures improved security, reduced delay and energy usage with higher packet delivery ratio than other existing SRPs. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important approaches used to extend the lifetime of Wireless Sensor Networks (WSN). The fundamental metric taken by clustering algorithms is energy enhancement. Moreover, network coverage and load balance are two important approaches that play crucial roles in improving network lifetime and delivery since the former focuses on maximizing the use of all network resources, while the second is based on distributing the load between the nodes to enhance the energy consumption. As the challenge of clustering nodes in an energy-efficient way is an NP-Hard problem, firefly optimization algorithm is used to address this challenge. However, the proposed solutions focus on centralized processing of the algorithm, which makes them unsuitable for large-scale WSN. In this paper, a double firefly based efficient clustering solution is proposed for large-scale WSN which is implemented in a decentralized fashion to improve the lifetime and packet delivery. The first firefly algorithm is used by each node to move to the best initial Cluster Head (CH) by performing a balance of belonging between the clusters, while the second algorithm is used only between the initial CHs to eliminate membership redundancy and optimally construct balanced clusters. The simulation results show that our proposed solution significantly improves the network lifetime as well as the delivery rate. [ABSTRACT FROM AUTHOR]

Wireless communication offers significant advantages in terms of flexibility, coverage and maintenance compared to wired solutions and is being actively deployed in the industry. IEEE 802.15.4 standardizes the Physical and the Medium Access Control (MAC) layer for Low Power and Lossy Networks (LLNs) and features Timeslotted Channel Hopping (TSCH) for reliable, low-latency communication with scheduling capabilities. Multiple scheduling schemes were proposed to address Quality of Service (QoS) in challenging scenarios. However, most of them are evaluated through simulations and experiments, which are often time-consuming and may be difficult to reproduce. Analytical modeling of TSCH performance is lacking, as only one-hop communication with simplified traffic patterns is considered in state-of-the-art. This work proposes a new framework based on queuing theory and combinatorics to evaluate end-to-end delays in multihop TSCH networks of arbitrary topology, traffic and link conditions. The framework is validated in simulations using OMNeT++ and shows below 6% root-mean-square error (RMSE), providing quick and reliable latency estimation tool to support decision-making and enable formalized comparison of existing scheduling solutions. [ABSTRACT FROM AUTHOR]

Nowadays, Wireless Sensor Networks (WSN) face more security threats due to the increased service of data transmission at high speed in almost all applications. The security of the network must be ensured by identifying abnormal traffic and current emerging threats. The most promising model for safeguarding the core network from outside attacks is Intrusion Detection Systems (IDS). This work focuses on the introduction of clustering-based intrusion detection in WSN. Initially, clustering takes place, where the nodes are grouped under certain constraints via selecting the optimal Cluster Head (CH). The considered constraints are energy, delay, distance, risk, and link quality. This optimal selection takes place by a new hybrid optimization algorithm termed as Truncate Combined Bald Eagle Optimization (TCBEO) algorithm. The subsequent process is intrusion detection, where a hybrid detection model combining a Convolutional Neural Network (CNN) & Bi-directional Gated Recurrent unit (Bi-GRU) is employed, which is trained with features like improved entropy and correlation taking into consideration of constraints like energy and distance, respectively. Eventually, the suggested work's effectiveness is affirmed against existing techniques using various performance metrics. [ABSTRACT FROM AUTHOR]

The goal of a wireless sensor network (WSN) is to connect numerous nodes via a multi-hop self-organizing network. A variety of sensor nodes are arranged and positioned in relation to other nodes to provide safe data exchange. Sensor nodes watch as a sink node processes the data. The network includes a variety of assaults, including floods, wormhole, black hole, sinkhole, and so on. One of the most challenging tasks in WSN is secure routing because of the existence of attacks. The technology of attack detection is employed to identify attacks and enhances the security of data transmission. It provides a way to ignore network threats, enabling secure communication. Most recent efforts have been focused on implementing secure data communication in wireless sensor networks. However, the more accurate detection attack was not achieved. Numerous intrusion attackers are available to compromise network data packet communication. To provide better data distribution amongst soldiers in a combat environment, this wireless network invader node needs to be identified. Hence, in a WSN context, data transmission with a military scenario entails armed men engaged in combat, such as fighter aircraft, tankers, and shot ships. Finding intrusion attackers is a difficult task, especially in the highly dynamic military wireless sensor network. Therefore, different techniques are developed in WSN for achieving attack detection accuracy with minimal delay and maximum data delivery rate. [ABSTRACT FROM AUTHOR]

Black ice is responsible for dangerous road-related incidents that can cause collisions and harm vehicle drivers and pedestrians. Visual examination and weather forecasts are two standard traditional methods for detecting black ice on roads, but they are often inaccurate and may not deliver the vehicle driver with up-to-date information on road conditions. The evolution of Industry 4.0 enabling technologies such as wireless sensor network (WSN), Internet of Things (IoT), cloud computing, and machine learning (ML) has been capable of detecting events in real time. This study aims to analyse the integration of the WSN, IoT, ML, and image processing for black ice detection. The qualitative research method is followed in this study, where the problems of black ice detection are studied. Following this, the role of Industry 4.0 enabling technologies is analyzed in detail for black ice detection. According to the study, we can detect black ice using different methods, but some methods need to be refined if we talk about the prediction. By merging different technologies, we can improve the overall architecture and create an algorithm that works with images and physical variables like temperature, humidity, due point, and road temperature, which were responsible for black ice formation, and predict the chances of black ice formation by training the system. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors' data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) plays a crucial role in the current infrastructure of the Internet of Things (IoT) by enabling the continuous collection of data across various applications. One of the primary challenges in WSNs is effectively managing energy to prolong its lifespan while maintaining optimal performance. Therefore, there is a need for the synthesis of clustering and routing protocol as a method to achieve energy balance and enhance the lifespan of WSNs. By aggregating and processing data from a large number of sensor nodes, clustering techniques can significantly cut down on energy use. It intentionally selects Cluster Heads (CHs) to facilitate internal cluster communication and deliver consolidated information to the base station. Simultaneously, routing protocols optimize the paths through which data flows by considering factors such as energy levels of nodes in a network, network's topology and traffic demands of users. By implementing hierarchical communication structures WSNs can effectively address energy imbalances, prolong the network's lifespan and optimize the overall system's efficiency. This survey provides instances from various studies where clustering and routing protocols have been implemented to address energy balancing and extend network life in WSN. In addition, a comparative analysis of these studies demonstrates the advantage and disadvantages of various protocols. Possible future research topics are recommended to expand the capabilities of WSNs that include enhancing the durability of protocols, addressing security concerns. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are vital for applications such as environmental monitoring, surveillance, and healthcare, where comprehensive network coverage is essential for accurate data collection. However, achieving full coverage in WSNs presents significant challenges due to resource constraints, such as limited battery life, processing capabilities, and environmental factors like terrain and obstacles. To address these issues, coverage optimization techniques are employed to maximize spatial coverage while minimizing energy consumption and deployment costs. This paper provides a thorough overview of these coverage optimization techniques, categorizing them based on different deployment strategies, including static and dynamic sensor placement. It explores their respective advantages, limitations, and application scenarios, offering valuable insights for researchers and practitioners. The study is motivated by the need to better understand how to improve WSN coverage efficiency and ensure reliable data collection in diverse environments. The research aims to synthesize existing knowledge on WSN coverage optimization, identify gaps in current strategies, and guide future studies in this field. Key findings emphasize the effectiveness of various techniques in enhancing coverage, such as mobility-based approaches and energy-aware algorithms, while also addressing practical challenges like sensor redundancy and environmental unpredictability. Ultimately, this paper contributes to the ongoing efforts to develop more adaptive, scalable, and energy-efficient solutions for WSN coverage optimization. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are important for applications like environmental monitoring and industrial automation. However, the limited energy resources of sensor nodes pose a significant challenge to the network's longevity. Energy imbalances among nodes often result in premature failures and reduced overall network lifespan. Current solutions have not adequately addressed this issue due to network dynamics, varying energy consumption rates, and uneven node distribution. To tackle this, we propose a novel method using Prim's algorithm to construct minimum spanning trees (MSTs) that enhance energy balance in WSNs. Prim's algorithm effectively identifies optimal connections among network nodes to minimize energy consumption. Our methodology includes several key steps: network initialization, energy consumption modeling, MST construction using Prim's algorithm, and optimizing the movement of mobile sink nodes. Extensive experiments with diverse datasets show that our approach significantly improves energy equilibrium, demonstrating high sensitivity and moderate complexity. This research underscores the potential of Prim's algorithm to extend the lifespan of WSNs and enhance energy efficiency, contributing to sustainable and effective network deployments. [ABSTRACT FROM AUTHOR]

In the context of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) for smart agriculture, ensuring efficient resource utilization, prolonged network lifespan and robust security mechanisms is paramount. This paper addresses these challenges by introducing an optimized secure cluster-based routing protocol with blockchain. The algorithm initiates with node ID assignment, followed by the use of Distributed Fuzzy Cognitive Maps (DFCM) to select Cluster Heads (CHs) based on energy, proximity to the Base Station (BS) and neighbor count. DFCM aims for balanced CH distribution to optimize energy usage. The secure routing protocol, employing Earthworm-based Deer Hunting Optimization Algorithm (EW-DHOA) and blockchain, ensures reliable data transmission. Through extensive comparative analyses with existing techniques, including GA-PSO, CI-ROA, ACI-GSO and P-WWO, our approach consistently outperforms in critical parameters. At varying node densities, the proposed method demonstrates a substantial improvement in network lifetime, achieving a 60% increase over GA-PSO and maintaining a superior average of 3200 rounds. Energy consumption is notably reduced, with a 33.3% improvement compared to GA-PSO at a density of 100 nodes. The packet delivery ratio reaches 98%, showcasing a 4% enhancement over the best-performing existing technique P-WWO. Throughput at a density of 500 nodes achieves an impressive 33.3% increase, reaching 0.8 Mbps. Notably, our methodology excels in preserving active nodes, sustaining a network lifetime of 66.7% more than competing techniques at the 3500th round. The proposed approach demonstrates a higher detection rate, ranging from 75% to 90% and exhibits a significantly higher convergence rate. Therefore, our Optimized Secure Cluster-Based Routing Protocol with Blockchain-Based Integrity Checking presents a comprehensive and superior solution for enhancing the efficiency, resilience and security of WSNs in smart agriculture. [ABSTRACT FROM AUTHOR]

With the increasing security issues of data transmitted from wireless sensor network terminals, the traditional cryptographic algorithms cannot meet the different security requirements of different types of data in wireless sensor networks (WSN). In this paper, we propose a new lightweight block cipher algorithm RAB that supports variable key length based on Feistel and Substitution-Permutation Network (SPN) structure. To address the inherent slow diffusion in the Feistel structure, the RAB algorithm uses a diffusion layer consisting of MDS matrix. This enhancement ensures that even a 3-round implementation of the RAB algorithm exhibits the ideal avalanche effect, enhancing its security. To handle keys of different lengths and provide different security strengths, the RAB algorithm applies a key schedule function that can receive different lengths of keys. This paper performs differential and linear cryptanalysis of the RAB algorithm using Mixed Integer Linear Programming (MILP) techniques. The results confirm that the RAB algorithm can meet the security demands of resource-constrained devices. Furthermore, the results of the efficiency analyses of the software and hardware performance of the RAB algorithm show that the RAB algorithm has a high software performance efficiency and the hardware performance meets the requirements of lightweight block ciphers. The algorithm is applied to encrypt the data on the sensor nodes, which can correctly encrypt and decrypt the sensor data, and runs smoothly. It indicates that the proposed algorithm is suitable for securing data in resource constrained nodes. [ABSTRACT FROM AUTHOR]

Pipeline infrastructure plays a critical role in the transportation of vital resources, including oil, gas, and water. However, pipeline failures and leaks can have devastating consequences, resulting in environmental damage, economic losses, and risk to human life. Traditional methods of leak detection, such as visual inspection and pressure testing, are often time-consuming, labor-intensive, and unreliable. With the advent of wireless sensor networks (WSNs), there is an opportunity to revolutionize pipeline monitoring and leak detection. In this paper, we present a system that can monitor and detect leakage early, to enable engineers carry out prompt maintenance. This is made possible by the use of a network of nodes in a WSN, placed along a pipeline, each of which is capable of measuring and reporting varying flow rates, indicative of possible leakages. The system design consists of three major layers namely, the nodes layer, the cloud layer (for data logging), and the reporting layer. Tests were conducted under various conditions. The results show that with no leakages, the average flow rates for nodes 1, 2, 3, and 4 were 16.89747978, 16.89935602, 16.90978163, and 16.93380634 respectively. Furthermore, percentage flow rate differences of -0.02550353, 29.959675, and 30.3944134 were recorded for nodes 2, 3, and 4 respectively, after leakages occurred. The high values of the percentage difference for nodes 3 and 4 indicate a significant discrepancy in flow rate, worthy of physical inspection. The system is capable of detecting faults and leakages, even in the event of sensor failure, or network disruption. [ABSTRACT FROM AUTHOR]

Clustering strategy in wireless sensor networks (WSNs) affects the lifetime, adaptability, and energy productivity of the wireless network system. The low-energy adaptive clustering hierarchy (LEACH) protocol is a convention used to improve the lifetime of WSNs. In this paper, a novel energy-efficient clustering algorithm is proposed, with the aim of improving the energy efficiency of WSNs by reducing and balancing the energy consumptions. The clustering-based convention adjusts the energy utilization by allowing an equal opportunity for each node to turn them into a cluster head (CH). Two-level clustering (TLC) is introduced by adopting LEACH convention where CH selection process undergoes first and second level of clustering to overcome boundary problem in LEACH protocol. The TLC method structures nodes within the scope of the appointed CHs, in order to extend the lifetime of the system. The simulation results show that, in comparison with state-of-the-art methodologies, our proposed method significantly enhanced the system lifetime. [ABSTRACT FROM AUTHOR]

Remote sensing technology is a vital component of disaster management, poised to revolutionize how we safeguard lives and property through enhanced prediction, mitigation, and recovery efforts. Disaster management hinges on continuous monitoring of various environments, from urban areas to forests and farms. Data from these observations are relayed to servers, where sophisticated processing algorithms forecast impending disasters. Remote sensing technology operates through a layered framework. The sensing layer acquires raw data, the network layer facilitates data transmission, and the data processing layer extracts meaningful insights. The application layer then leverages these insights to make informed decisions. Elevating the intelligence of remote sensing technology necessitates advancements across these layers. This paper delves into disaster management concepts and highlights the pivotal role played by remote sensing technology. It offers a comprehensive exploration of each layer within the remote sensing technology framework, detailing foundational principles, tools, and methodologies for enhancing intelligence. Addressing challenges inherent to this technology, the paper also presents future-oriented solutions. Furthermore, it examines the influence of wireless network infrastructure, alongside emerging technologies like the Internet of Things, cloud computing, virtual machines, and low-power wireless networks, in nurturing the evolution and sustainability of remote sensing technology. [ABSTRACT FROM AUTHOR]

Summary The cluster networking protocols are the roots that embed intelligent decision‐making and enhance the lifespan of wireless sensor networks (WSNs). Wireless sensors with limited capabilities face several challenges due to the heterogeneous application environments. Especially, the mobility‐incorporated sensors in most situations trouble the cluster network's robustness. Many cluster networking protocols have been presented in the past to enhance the network lifespan and data delivery ratio. However, they lack a dedicated and efficient mechanism for mobility assistance, an adequate cluster management process and cluster head selection criteria. To overcome these issues and for the uniform energy load distribution, we propose a mobility‐compatible cache controlled cluster networking protocol (MC‐CCCNP) in this paper. It is an energy‐efficient cluster networking protocol that supports sensor movement. Network resource management and routing are controlled distributively by an optimal number of cache nodes. It defines a new strategy for cache node deployment based on neighbour density as well as a weight formula for cluster head selection and cluster formation based on the residual energy, the distance to the base station and the node velocity. It also includes techniques for detaching and reconnecting a mobile node to an appropriate cluster cache if it crosses the cluster boundary. We simulate and compare the performance of our protocol with the centralised energy‐efficient clustering routing, energy‐efficient mobility‐based cluster head selection protocol and dual tier cluster‐based routing protocols over different network configurations with varying mobility, scalability and heterogeneity. The MC‐CCCNP showed remarkable improvements in energy utilisation uniformity and energy consumption. With the improved network lifespan, it also maintains a higher data throughput rate of 95% or more in almost all network configurations. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]

Summary: Environmental disasters such as earthquakes, tsunamis, floods, and landslides disrupt natural resources, human casualties, and so on. Early warning and alert of such events will help for preparedness to avoid human and economic causalities. Managing a disaster or emergency scenario is a difficult endeavor. These episodes of mass devastation, whether caused by natural disasters or man‐made disasters, result in massive losses of money, property, and lives as a result of inadequate response by the government and management authorities. As a result, actions must be taken to avoid these circumstances from occurring by discovering the causes of these catastrophes in advance and providing swift rescue procedures once the crisis happens. In a disaster situation, communication plays a vital role, and a lot of people turn towards it as a source of information conveyance/delivery for controlling the situation. Even these communication networks are damaged and incur tremendous losses during disasters. All the communication networks, including mobile wireless sensor networks (WSNs) deployed for communication, also suffer huge damage and face many challenges during disasters. Nature appears to deal with these issues well. WSN is inspired by several biological systems and processes that have intrinsically attractive properties such as adaptation, self‐organization, and self‐healing. In this paper, we proposed an enhanced bio‐inspired energy‐efficient localization (EBEEL) routing for Mobile WSN. The main focus of the proposed EBEEL algorithm is to overcome the challenges faced by peer‐existing routing protocols such as data aggregation, limited bandwidth, scalability, data redundancy, energy consumption, packet drops, load balancing, and Quality of Service (QoS). [ABSTRACT FROM AUTHOR]

Localization has emerged as an important and critical component of research in Wireless Sensor Networks (WSNs). WSN is a network of numerous sensors distributed across broad areas of the world to conduct numerous activities, including sensing the data and transferring it to various devices. Most applications, like animal tracking, object monitoring, and innumerable resources put in the interior as well as outdoor locations, need to identify the position of the occurring incident. The primary objective of localization is to identify the locality of sensor nodes installed in a network so that the location of a particular event can be traced. Different optimization approaches are observed in the work for solving the localization challenge in WSN and assigning the apt positions to undiscovered sensor nodes. This research employs the approach of localizing sensor nodes in a 2D platform utilizing an exclusive static anchor node and virtual anchors to detect dynamic target nodes by projecting these six virtual anchors hexagonally at different orientations and then optimizing the estimated target node co-ordinates employing Whale Optimization-based Naked Mole Rat Algorithm (WONMRA). Moreover, the effectiveness of a variety of optimization strategies employed for localization is compared to the WONMRA strategy concerning localization error and the number of nodes being localized, and it has been investigated that the average error in localization is 0.1999 according to WONMRA and is less than all other optimization techniques. [ABSTRACT FROM AUTHOR]

One of the networking technologies is called a wireless sensor network, and what it does is link sensor nodes by using wireless communication as the channel of connection. A haphazard and random distribution of nodes is carried out throughout the deployment process within the network area. As a result, the distances between the nodes do not remain constant. The nodes have a high sensing capacity, but their range of perception and battery life are also severely limited. The wireless data may be sensed by all of the nodes inside their respective sensing regions. Consequently, there are a variety of new applications that make use of WSN applications, particularly in surveillance monitoring applications such as those used in hospitals, buildings, forests, and airspace monitoring. The transmission of the data to the appropriate server or data centre that is linked with the cloud or the internet is experiencing greater difficulties as a result of the growing amount of monitoring data. This indicates that a significant quantity of data is lost as a result of the fact that wireless transmission is affected by environmental factors such as precipitation, humidity, and wind storms. It is vital to create and execute an innovative routing protocol to deliver greater throughput and raise the overall Quality of Service of the WSN applications in order to solve the difficulties that have been outlined above. This study paper presents a metaheuristic algorithm based optimal routing protocol for WSN application, which boosts the overall quality of service of any surveillance monitoring applications. To achieve this goal, the protocol was designed for wireless sensor networks. It does so in an effective manner, resulting in decreased delay and energy consumption as well as enhanced throughput and PDR. Based on the findings of the experiments, it has been hypothesised that the proposed metaheuristic algorithm-based routing protocol is capable of performing better routing for any type of surveillance monitoring and remote monitoring, as well as successfully transmitting any type of data, including numerical, video, and image data. [ABSTRACT FROM AUTHOR]

WSNs (Wireless sensor networks) are used in multiple applications including IoT (Internet of Things) applications like intelligent control, prediction, tracking, and other communication network services (Internet-of-Things). Due to their limited design for two-dimensional space, high computing costs, or sensitivity to measurement errors, the typical localization frameworks might not perform well in real-world settings. Location information of deployed sensor nodes in their surrounding environments is important for algorithmic three-dimensional localizations. But there are drawbacks to current 3D localization algorithms in many parameters including complexity, positional precisions, and excessive energy consumption. Hence, this work proposes Enhanced 3D-DV-Hop (3D-Distance Vector Hop) localizations based on PSO (Particle Swarm Optimization) and GAs (Genetic algorithms) for the aforementioned issues. To further increase the diversity and accuracy of the DV-Hop outputs, a learning technique is employed. The learning technique leads to an improvement in search effectiveness, convergence speed, and result in quality. The simulation results demonstrate that the suggested strategy can improve positioning coverage while maintaining positioning accuracy. [ABSTRACT FROM AUTHOR]