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This paper presents a new algorithm that utilizes compressed sensing (CS) for reconstruction of wireless sensor networks (WSNs) data with spatial and temporal correlation. The proposed method utilizes a time‐varying sliding window mechanism that dynamically adjusts both the window size and the number of measurements. This flexibility allows the algorithm to exploit spatio‐temporal correlations effectively, ensuring that data within the window remains sparse and thus more compressible. By dynamically varying the number of measurements, the algorithm equitably distributes the sampling rate across different time slots, adapting to changes in signal characteristics and minimizing transmission costs. Simulation results demonstrate that our proposed algorithm outperforms other CS reconstruction methods by achieving higher reconstruction precision while requiring fewer transmissions. This is achieved through a decentralized data‐window framework that maximizes the use of prior signal information, leading to improved signal recovery performance in diverse WSN scenarios. [ABSTRACT FROM AUTHOR]

Summary: In wireless sensor networks (WSNs), trusted routing path needs to be determined for guaranteeing reliable data dissemination with maximized Quality of Service (QoS). But the sensor nodes may not exhibit a cooperative behavior for the objective of conserving energy and remaining active in the network. Trust management techniques are essential for alleviating the problem of packet dropping attacks of sensor nodes that intentionally deteriorates the performance of the network. In this paper, Hybrid ELECTRE and bipolar fuzzy PROMOTHEE‐based trust management (HEBFPTM) scheme is proposed for addressing the impact of packet dropping attacks that targets on improving QoS in WSNs. This is proposed as a multi‐criteria decision analysis solution for obtaining feasible number of parameters that could be derived from the sensor nodes of the network to determine its cooperation degree in the network. This HEBFPTM is proposed with the objective of integrating the ordinal evaluation of mobile nodes into a cardinal procedure using the method of PROMETHEE to attain quantitative and qualitative analysis that aides in identifying the weights of each criterion considered for cooperation determination using pairwise comparison. It adopted three preference models using partial, complete, and outranking through intervals. It handled the problem of uncertainty using the merits of bipolar fuzzy that helped in attaining the weight of the criteria and preference functions used for ranking the sensor nodes in the routing path. The experiments of the proposed HEBFPTM achieved using ns‐2 simulator confirmed its efficacy in improving the attack detection rate by 21.38%, reduced false positive rate by 15.42%, maximized packet delivery rate of 18.94%, and reduced energy utilization of 19.84% better than the benchmarked approaches used for investigation. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) offers substantial potential for enhancing real-time interaction between various smart components within a network. To reduce communication costs in the IoT infrastructure, wireless sensors can be employed as a cost-effective solution. The widespread applications of wireless sensor networks (WSNs) across various domains have significantly increased their adoption in recent years. A major challenge in these networks is the limited energy of nodes, which has driven efforts to improve energy management using more precise clustering techniques. Although numerous methods have been proposed to enhance clustering accuracy and reduce energy consumption, not all of them achieve optimal throughput. Addressing energy consumption challenges in IoT-based WSNs, this paper proposes an efficient clustering-based routing protocol. The protocol utilizes a multi-objective binary whale optimization algorithm (BWOA) for optimal cluster head (CH) selection, considering energy, node degree, and distance parameters. Additionally, a Mamdani-type fuzzy inference system (FIS) is employed for cluster formation to enhance energy efficiency. The FIS inputs include CH residual energy, neighborhood degree, and distance, with the output determining the connection probability of a sensor node to a CH. A multi-hop routing process based on the shortest path is implemented for data packet transmission. Simulations across various scenarios demonstrate the superior performance of the proposed BWOA based on V-shaped transfer function over the BWOA based on S-shaped transfer function and other related methods. Comparative analysis reveals that the proposed protocol effectively addresses key challenges in IoT-based WSNs, such as network lifetime and energy consumption, contributing to the development of more sustainable and efficient IoT infrastructures. When contrasted with the top-performing protocol, the proposed method exhibits substantial improvements in multiple crucial aspects. Notably, the FND metric has experienced a 4.5% increase, the HND measure has seen a 7.8% enhancement, and the LND benchmark has been elevated by 1.5%, indicating the potential impact of the proposed approach in the domain. [ABSTRACT FROM AUTHOR]

Accurate node localization is essential in wireless sensor networks (WSNs) for effective data analysis and the successful operation of applications like environmental monitoring and disaster management. Range-free methods like the distance vector-hop (DV-hop) algorithm are often used due to hardware and cost constraints, but they face challenges in accuracy and stability. The NP-hard nature of the localization problem has led to the integration of metaheuristic algorithms in previous studies to enhance performance. This paper presents EMGODV-Hop, a novel approach for node localization in multi-hop networks that combines the DV-Hop algorithm with the mountain gazelle optimization (MGO) algorithm to enhance localization precision. The EMGODV-Hop method operates in two phases: First, it uses an improved variant of the DV-Hop algorithm to more accurately estimate distances between unknown and anchor nodes by incorporating a correction factor. Next, it employs an enhanced version of MGO algorithm, referred to as EMGO, to determine the positions of WSN nodes. The improved DV-Hop version enhances accuracy by incorporating a correction factor for better estimation of hop distances, while the EMGO algorithm addresses the limitations of the original MGO algorithm and improves its search capabilities. Extensive simulations assessed the effectiveness of the proposed method across various factors, including anchor node ratios, total node count, and communication ranges. The results demonstrate significant accuracy improvements with the proposed algorithm, showing enhancements of 48.69%, 26.22%, 19.33%, 28.21%, and 40.47% compared to DV-Hop, MGODV-Hop, PSODV-Hop, WSODV-Hop, and SSADV-Hop, respectively. [ABSTRACT FROM AUTHOR]

The various cyberattacks in wireless sensor networks (WSNs) have made confidentiality and data integrity as crucial principles in data aggregation. Therefore, several applications are presented to control the sharing of data and information as well as the associated cybersecurity aspects that must be preserved during data transfer. Most cybersecurity breaches that occur these days are categorized as cyberattacks. The WSN's resource-constrained architecture makes cybersecurity lapses and insider attacks possible. This study proposes a novel technique named multi-objective pigeon-inspired optimal long short-term memory (MPI-OLSTM) networks to develop the data aggregation in cybersecurity model. Initially, the WSN-detection systems (WSN-DS) dataset is collected and pre-processed using min-max normalization. For extracting features, the principal component analysis (PCA) is employed. The model's predictive power is assessed using the following metrics: accuracy (96.5%), precision (92.3%), and recall (90.4%). The findings demonstrate that, in comparison to existing techniques, our approach yielded more accurate results. [ABSTRACT FROM AUTHOR]

The issue of force misfortune in wireless sensor networks is one of the fundamental points and central defects that should be defeated in building any coordinated computer information trade and communications framework. Where numerous new examinations have given the idea that talk about this point and recommended various techniques and systems of their sorts, proficiency, and intricacy to take care of the issue of energy misfortune in far off sensors in advanced wireless sensor networks. The WSN networks rely upon the sixth-generation innovations by giving a better system than the pace of sending and getting data and giving permitting all over; likewise, the sixth generation crossing points embrace a smart technique for information transmission in WSNs. Sixth generation is the option in contrast to the fifth-generation cellular technique, where 6G frameworks can apply a larger number of frequencies than 5G frameworks and produce a lot higher transmission capacity with lower idleness. In this review, the hardships experienced in terahertz (THz) advances in wireless sensor networks will be demonstrated, including way obstacles that are viewed as the primary test; Additionally, the attention will be on tracking down answers for keep up with the best and least energy misfortune in the WSN networks by proposing machine learning systems that will show exceptional outcomes through effectiveness measures and ideal energy venture. [ABSTRACT FROM AUTHOR]

Summary: Clustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision‐making process. The design and development of an energy‐efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non‐deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end‐to‐end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), an efficient clustering technique is critical in optimizing the energy level of networked sensors and prolonging the network lifetime. While the traditional bee colony optimization technique has been widely used as a clustering technique in WSN, it mostly suffers from energy efficiency and network performance. This study proposes a Bee Colony Optimization that synergistically combines K-mean algorithms (referred to as K-BCO) for efficient clustering in heterogeneous sensor networks. This is to develop a robust and efficient clustering algorithm that addresses the challenges of energy consumption and network performance in WSNs. The K-BCO algorithm outperformed comparative clustering algorithms such as H-LEACH, DBCP, and ABC-ACO in average error rate (AER), average data delivery rate (ADDR), and average energy consumption (AEC) for transmitting data packets from sensors to cluster heads. The K-BCO outperformed other algorithms in terms of ADDR at 95.00% against H-LEACH (75.86%), DBCP (72.07%) and ABC-ACO (90.08%). The findings indicate that the K-BCO not only optimizes energy consumption but also guarantees more stable and robust solutions, thereby extending the network lifetime of WSNs. Thus, K-BCO is recommended to practitioners in wireless sensor networks as it paves the way for more efficient and sustainable wireless communication. [ABSTRACT FROM AUTHOR]

This research investigates the security challenges posed by worm propagation in wireless sensor networks (WSNs). A novel stochastic susceptible – infectious – vaccination – recovered model is introduced to analyse the dynamics of worm spread. Conditions for the existence of a unique global solution are examined, and necessary conditions for worm eradication are established. By incorporating random environmental fluctuations, the proposed model provides a more precise depiction of propagation dynamics than deterministic models. Empirical findings are presented to validate the model's predictive accuracy across diverse scenarios, underscoring its robustness. Numerical simulations affirm the effectiveness of the analytical approach in understanding worm propagation within WSNs. The study offers valuable insights into worm dynamics and proposes a methodological framework to enhance network security. The findings underscore the significant role of stochastic systems in modelling and provide strategic perspectives for designing resilient defensive frameworks against worm attacks in WSNs. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks have become an integral part of our lives with the advancement in the field of Internet of Technology. Multiple sensors operate together in Wireless Sensor Networks (WSNs) to collect data and communicate wirelessly with one another. For each sensor node's data collection to be useful, it is essential to explore precise localization technology for WSNs. DV-Hop, as an easily implementable range-free localization algorithm, has gained significant popularity in the research community. As a result, many enhanced DV-Hop variations have been put out in the literature. However, the challenges of poor location accuracy associated with DV-Hop continue to spark interest among researchers, leading to further investigations and making it a preferred area for research in localization algorithms. Research in this paper proposes an improved version of three-dimensional DV-Hop algorithm based on improved adaptive differential evolution (3D-IADE DV-Hop). The proposed method optimizes the estimated coordinates using an improved version of adaptive differential evolution by controlling offspring generation behaviour. Moreover, we have demonstrated the superiority of 3D-IADE DV-Hop compared to other algorithms under consideration. The simulation results serve to strengthen our observations, confirming that the proposed algorithm outperforms its counterparts with enhanced performance and superiority. [ABSTRACT FROM AUTHOR]

The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being incorporated in various industrial applications, especially in electrical machines as a means of control, monitoring and preventive maintenance. This paper is focused on reviewing the use PMSM in lift systems, the application of various condition monitoring techniques and real-time data collection techniques using IoT technology. In addition, we focus on different categories of industrial sensors, their connectivity and the standards they should meet for PMSMs used in elevator applications. Finally, we analyze various secure ways of transmitting data on different platforms so that the transmission of information takes into account possible unwanted instructions from exogenous factors. [ABSTRACT FROM AUTHOR]

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes' lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system's network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network's performance. They improved the LoRa network's output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network's performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Internet of Things (IoT) has significantly increased the demand for efficient data collection, with Wireless Sensor Networks (WSNs) playing a crucial role in this process. However, WSNs face inherent challenges, such as limited resources, energy constraints, and fluctuating network conditions, negatively impacting network performance and longevity. Traditional Machine Learning centralized optimization approaches struggle to cope with these issues, highlighting the need for decentralized solutions. In response, this paper introduces a novel Federated Reinforcement Learning (FRL) framework designed explicitly for IoT-enabled WSNs. The proposed framework enables distributed model training across sensor nodes, allowing them to collaboratively optimize network operations without sharing raw data, thereby preserving privacy. Key contributions of this work include dynamic model updates, robust aggregation of heterogeneous data, and an energy-efficient federated averaging algorithm tailored to WSN environments. Extensive simulations demonstrate that the proposed FRL approach significantly improves WSN performance, yielding a 13% increase in packet delivery compared to Deep Q-Network (DQN) and a 30% improvement over Reinforcement Learning-Based Routing (RLBR). Additionally, energy efficiency is enhanced by 15% and 24% compared to DQN and RLBR, respectively. These findings underscore FRL's potential to overcome traditional optimization methods' limitations and substantially enhance the efficiency and longevity of IoT-enabled WSNs. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) employ multi-hop routing to efficiently transmit data, but energy consumption remains a significant challenge in ensuring effective communication. Optimizing network interactions and reducing energy consumption are crucial for the long-term viability of WSNs. Despite the advantages of multi-hop routing, energy constraints limit the effectiveness of sensor nodes (SNs) in transmitting data across the network. The challenge lies in finding the optimal route to minimize energy expenditure while maintaining reliable data transmission. To improve the efficiency of multi-hop routing in WSNs, we proposed an optimal way for Cluster Head (CH) selection in WSN using an Improved Q learning based Artificial Bee Colony Algorithm (IQ-ABC). This study introduces an improved version of the ABC algorithm, incorporating Q-learning to enhance both the exploration and exploitation phases. A modified Q-learning mechanism enhances the IQ-ABC’s exploitative capabilities. In the proposed system, every SN transfers data to the CH using the most energy-efficient route determined by the IQ-ABC algorithm. Additionally, a multi-objective fitness function balances key factors, such as energy efficiency, latency, and trust, to optimize the CH selection with weight assignment using Fuzzy Logic. Simulation outcomes demonstrate that the IQ-ABC algorithm significantly reduces energy consumption and extends the lifespan of SN compared to traditional routing algorithms. In Case 1, where SNs are positioned centrally, IQ-ABC achieves the lowest energy consumption, with only 0.253 units of energy used at 1200 rounds, outperforming Low-Energy Adaptive Clustering Hierarchy (LEACH) (0.38), Hybrid Energy-Efficient Distributed Clustering (HEED) (0.361), and Ant Colony Optimization (ACO) (0.6). Similarly, in Case 2 and Case 3, IQ-ABC continues to outperform, with energy usage of 0.30 and 0.33 units, respectively, significantly lower than ACO’s 0.72. [ABSTRACT FROM AUTHOR]

Clustering and routing approaches helps in addressing the design challenges which are primarily responsible for achieving energy efficiency and network lifetime in Wireless Sensor Networks (WSNs). Swarm-Intelligence (SI) based algorithms helps in determining optimal or near optimal solutions to the problems of Non-deterministic Polynomial (NP)-hard optimization related to clustering and routing. In this paper, Hybrid Termite Queen and Walrus Optimization Algorithm (HTQWOA)-based energy efficient cluster-based routing protocol with static sink node and mobile sink node for addressing the issue of hot spot and subsequent extension of network lifetime. Termite Queen Optimization algorithm (TQOA) is specifically used for determining optimal number of cluster heads (CHs). On the other hand, Walrus Optimization Algorithm (WOA) is adopted for achieving energy efficient routing between the selected CHs and the sink node for the objective of maximizing network lifetime and at the same time reducing the energy consumption. This proposed HTQWOA utilized sink mobility for preventing the multi-hop communication between the CHs and the sink nodes for sustaining necessitated energy in the network. It also included the derivation of multi-objective factors which are related to residual energy, inter-cluster distance, intra-cluster distance, packet drop rate, path loss, node degree, node centrality, link quality and restart number into account for achieving better CH selection. The experiments of HTQWOA conducted using ns-2 simulator revealed improved throughput of 16.21%, increased sustenance of alive nodes by 23.46%, and reduced residual energy of 25.12%, better than the baseline static and ACO-based mobile sink mobility approach (BACOSNM). Water strider algorithm (WSA) and ACO-based CH selection (WSACOCHS) and sink mobility approach, Cat Swarm Optimization algorithm-based CH selection and sink mobility technique (CSOCHSM) and Improved Squirrel Search Algorithm-based Optimized Fuzzy Clustering (ISSAOFC) approaches used for investigation. [ABSTRACT FROM AUTHOR]

Abstract This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol’s performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network’s robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol’s exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol’s prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications.

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications.

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol's performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network's robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol's exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol's prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications. [ABSTRACT FROM AUTHOR]

Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on "Cybersecurity with WSN in Smart Grids," presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles. [ABSTRACT FROM AUTHOR]

This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML's potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are characterized by heterogeneous traffic types (audio, video, data) and diverse application traffic requirements. This paper introduces three traffic classes following the defined model of heterogeneous traffic differentiation in WSNs. The requirements for each class regarding sensitivity to QoS (Quality of Service) parameters, such as loss, delay, and jitter, are described. These classes encompass real-time and delay-tolerant traffic. Given that QoS evaluation is a multi-criteria decision-making problem, we employed the AHP (Analytical Hierarchy Process) method for multi-criteria optimization. As a result of this approach, we derived weight values for different traffic classes based on key QoS factors and requirements. These weights are assigned to individual traffic classes to determine transmission priority. This study provides a thorough comparative analysis of the proposed model against existing methods, demonstrating its superior performance across various traffic scenarios and its implications for future WSN applications. The results highlight the model’s adaptability and robustness in optimizing network resources under varying conditions, offering insights into practical deployments in real-world scenarios. Additionally, the paper includes an analysis of energy consumption, underscoring the trade-offs between QoS performance and energy efficiency. This study presents the development of a differentiated services model for heterogeneous traffic in wireless sensor networks, considering the appropriate QoS framework supported by experimental analyses. [ABSTRACT FROM AUTHOR]

The security of Wireless Sensor Networks (WSNs) is of the utmost importance because of their widespread use in various applications. Protecting WSNs from harmful activity is a vital function of intrusion detection systems (IDSs). An innovative approach to WSN intrusion detection (ID) utilizing the CatBoost classifier (Cb-C) and the Lyrebird Optimization Algorithm is presented in this work (LOA). As is typical in ID settings, Cb-C excels at handling datasets that are imbalanced. The lyrebird's remarkable capacity to imitate the sounds of its surroundings served as inspiration for the LOA, a metaheuristic optimization algorithm. The WSN-DS dataset, acquired from Prince Sultan University in Saudi Arabia, is used to assess the suggested method. Among the models presented, LOA-Cb-C produces the highest accuracy of 99.66%; nevertheless, when compared with the other methods discussed in this article, its error value of 0.34% is the lowest. Experimental results reveal that the suggested strategy improves WSN-IoT security over the existing methods in terms of detection accuracy and the false alarm rate. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) represent the collection of restricted energy sensor nodes that are deployed in an area of target for gathering potential environment data for decision‐making with respect to their objective of application. However, the implementation of energy‐effective data gathering strategies in large‐scale WSNs is the most challenging due to the limited energy resources. Clustering‐based data gathering strategies are identified to be quite effective for energy saving that directly attributes to extended network lifetime. Moreover, optimal path amid the cluster head (CH) and sink node needs to be selected for sustaining energy efficiency and improving network lifespan. In this article, Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)‐based clustering scheme is proposed for improving the network lifetime and routing optimization with maximized energy efficacy. It integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade‐off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra‐cluster distance, inter‐cluster distance, and node centrality into account during the process of fitness evaluation. It performed well by constructing an optimized number of clusters, such that energy stability and network lifetime are maintained in the network. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) play a vital role in modern research and applications due to their potential to gather data from various environments. Because sensor nodes (SNs) within WSNs have limited battery life, those in close proximity to the sink often experience rapid power depletion, leading to the emergence of hotspot issues. To address this, the concept of a mobile sink (MS) has emerged as a potential solution, effectively mitigating power usage in SNs and thereby extending the network's overall lifespan. Furthermore, many sensor-based applications necessitate specific data collection timeframes, underscoring the necessity of effective strategies. Leveraging rendezvous points (RPs) to enhance network efficiency becomes imperative in enabling the MS to efficiently collect data from all SNs within designated time periods. A sophisticated cost function is employed to strategically determine RPs, considering multiple factors that influence the efficacy of each RP. This process culminates in the selection of RPs, optimizing for the longest path with minimal delays. Through the proposed hybrid mobile vehicle (HMV) method, compared against the prevailing MOOVor method, significant enhancements are observed in terms of sensor coverage and reduced hop count within the network. [ABSTRACT FROM AUTHOR]

Summary: In wireless sensor networks (WSNs), routing based on cluster construction is highly preferred as it greatly supports reliable data communication, load balancing, and fault tolerance with extended network lifetime. In specific, metaheuristic approach‐based dynamic cluster heads (CHs) selection has the possibility of enhancing the lifespan of network and at the same time is capable in reducing the energy consumption. In this paper, hybrid grasshopper and Harris hawk optimization algorithm‐based energy efficient routing protocol (HGHHOA) is propounded for optimal CH selection. This proposed HGHHOA approach adopted a fitness function that incorporated the factors of residual energy, distance between CH and cluster members, distance between selected CHs and the sink, node centrality, and node degree into account. The fitness function values of optimality facilitate a potential CH selection with significant cost‐effective routing. It is proposed with primary objective of improving the network lifespan through optimized selection of CHs that balances the available energy in a predominant way. It is proposed with significance of handling premature convergence with minimized energy consumption and network lifetime through the possibility of establishing an ideal balance between number of alive and dead nodes. The results of this proposed HGHHOA approach with varying rounds of implementation exhibited better results in throughput and residual energy which is 23.98% and 29.21%, better than the bassline CH selection mechanisms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are challenged by the need for optimized Energy Consumption (EC), efficient Data Aggregation (DA), and reliable routing due to their dynamic topologies and limited resources. Existing solutions like TEAMR and DDQNDA address these concerns but face significant drawbacks--TEAMR lacks adaptability to rapidly changing topologies, while DDQNDA suffers from high computational overhead and delayed convergence, hindering its effectiveness in real-time scenarios. To overcome these limitations, this paper introduces the Adaptive Reinforcement Learning (RL)-Based DA and Routing Optimization (ARL-DARO) algorithm. The proposed methodology follows a systematic approach, beginning with cluster formation and Cluster Head (CH) selection (CHS) using the Grey Wolf Optimizer (GWO), which ensures Energy-Efficient (EE) clustering and optimal CH selection. In the next step, trust factors such as Node Connectivity (NC), Residual Trust (RT), and Cooperation Rate (CR) are integrated into Quality of Service (QoS) metrics as part of the Fitness Function(FF) to enhance route reliability and security. Finally, the ARL-DARO algorithm is employed to dynamically optimize both data aggregation and routing. It leverages Q-learning to select optimal routes based on energy efficiency, security, and link reliability, further reducing data redundancy and improving adaptability to realtime network changes. Performance is assessed using parameters such EC, packet delivery ratio (PDR), end-to-end latency (E2E delay), throughput, and network lifetime (NL) across networks with 100, 200, 300, 400, and 500 nodes. Results show that ARL-DARO significantly reduces energy consumption by up to 45%, increases throughput by 30%, and extends network lifetime, proving its effectiveness over existing methods. [ABSTRACT FROM AUTHOR]

Mango fruit is one of the main fruit commodities that contributes to Taiwan's income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks ("WSNs")-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: "optimal," "sub-optimal," or "not-optimal" conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system. A value function-based will be employed to perform DRL model called deep Q-network (DQN) which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior. The WSNs experiment result indicates the system's accuracy by capturing the real-time environment parameters is 98.39%. Meanwhile, the results of comparative accuracy model experiments of the proposed DQN, individual Q-learning, uniform coverage (UC), and Naïve Bayes classifier (NBC) are 97.60%, 95.30%, 96.50%, and 92.30%, respectively. From the results of the comparative experiment, it can be seen that the proposed DQN used in the study has the most optimal accuracy. Testing with 22 test scenarios for "optimal," "sub-optimal," and "not-optimal" conditions was carried out to ensure the system runs well in the real-world data. The accuracy percentage which is generated from the real-world data reaches 95.45%. From the results of the cost analysis, the system can provide a low-cost system compared to the conventional system. [ABSTRACT FROM AUTHOR]

This review article explores jamming attacks, a critical security threat disrupting communication in wireless sensor networks (WSNs). We begin by introducing WSNs and highlighting the detrimental effects of jamming. The article then delves into various research directions for mitigating jamming in WSNs. This includes exploring different jamming techniques, their impact, and potential countermeasures. We categorize jamming techniques and analyze existing detection and localization mechanisms. Furthermore, we examine current security approaches for combating jamming attacks in WSNs. We identify unresolved research challenges in this area and compare our survey with previous work on jamming in WSNs. This review provides a comprehensive overview of jamming threats in WSNs, outlining existing solutions and future research directions for robust network security. [ABSTRACT FROM AUTHOR]

To establish ubiquitous and energy-efficient wireless sensor networks (WSNs), short-range Internet of Things (IoT) devices require Bluetooth low energy (BLE) technology, which functions at 2.4 GHz. This study presents a novel approach as follows: a fully integrated all-digital phase-locked loop (ADPLL)-based Gaussian frequency shift keying (GFSK) modulator incorporating two-point modulation (TPM). The modulator aims to enhance the efficiency of BLE communication in these networks. The design includes a time-to-digital converter (TDC) with the following three key features to improve linearity and time resolution: fast settling time, low dropout regulators (LDOs) that adapt to process, voltage, and temperature (PVT) variations, and interpolation assisted by an analog-to-digital converter (ADC). It features a digital controlled oscillator (DCO) with two key enhancements as follows: ΔΣ modulator dithering and hierarchical capacitive banks, which expand the frequency tuning range and improve linearity, and an integrated, fast-converging least-mean-square (LMS) algorithm for DCO gain calibration, which ensures compliance with BLE 5.0 stable modulation index (SMI) requirements. Implemented in a 28 nm CMOS process, occupying an active area of 0.33 mm2, the modulator demonstrates a wide frequency tuning range of from 2.21 to 2.58 GHz, in-band phase noise of −102.1 dBc/Hz, and FSK error of 1.42% while consuming 1.6 mW. [ABSTRACT FROM AUTHOR]

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications. [ABSTRACT FROM AUTHOR]

The proliferation of mobile devices and the spread of IoT devices have increased the tendency to use wireless sensor networks (WSNs), especially since the implementation of the 5G communication system has begun in most countries. This type of network does not require any infrastructure or additional cost, making it a good alternative for use in disasters, environmental monitoring, military, and rescue operations. However, WSNs suffer from some limitations, such as mobility and battery lifetime. Significant research has been conducted to overcome the limitation of battery lifetime by developing routing methods and reducing the required communications among wireless mobile nodes. In this research, we utilize the low-energy adaptive clustering hierarchy (LEACH) concept to minimize communication between the nodes and the base station. A new approach has been developed to form clusters in WSN nodes and select the optimal cluster head by facilitating the election of a new cluster head (CH). When the current cluster head's energy depletes, a new one is selected, ensuring continuous operation. The simulation results demonstrate that the proposed algorithm outperforms the existing LEACH clustering algorithm in terms of energy consumption, packet delivery ratio (PDR), and latency time. [ABSTRACT FROM AUTHOR]

Enhancing the security of Wireless Sensor Networks (WSNs) improves the usability of their applications. Therefore, finding solutions to various attacks, such as the blackhole attack, is crucial for the success of WSN applications. This paper proposes an enhanced version of the AODV (Ad Hoc On-Demand Distance Vector) protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs, thereby avoiding them when delivering packets. The proposed version employs a network-based reputation system to select the best and most secure path to a destination. To achieve this goal, the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation system. To minimize the network overhead of the proposed approach, the paper uses reputation aggregator nodes only for forwarding reputation tables. Moreover, to reduce the overhead of updating reputation tables, the paper proposes three mechanisms, which are the prompt broadcast, the regular broadcast, and the light broadcast approaches. The proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes, including blackholes, move continuously, which is considered a challenge for other protocols. Using the proposed enhanced protocol, a node evaluates the security of different routes to a destination and can select the most secure routing path. The paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different zones. Furthermore, the paper discusses the proposed approach's overhead analysis to prove the proposed enhancement's correctness and applicability. [ABSTRACT FROM AUTHOR]

The paper presents a new routing scheme using the information on the locations of nodes to create a routing region that controls the region of packet routing to achieve route optimization. The proposed scheme aimed to reduce the occurrence of packet detours or other routing overheads caused by the undirected packet transmission. The strength of this approach is that it can improve the lifetime of nodes in the network while decreasing the time taken for a packet to arrive at its destination or base station (BS). The proposed scheme used a self-adaptive algorithm that dynamically adjusted the routing region based on the BS’s calculation of the network layer parameters to achieve energy efficiency while satisfying data quality. The routing region limits the area of routing and restricts data flooding in the entire network, which potentially will waste resources and cause data redundancy. The simulation showed that the proposed scheme outperformed, the original fitness scheme and SPEED, according to energy consumption, transmission delay, throughput, and reliability (packet delivery ratio) under different congestion levels. The proposed scheme offered double the throughput and shortened packet delay by 20%. Furthermore, it had a longer lifetime, exceeding other schemes by approximately twofold when the traffic was not too congested. However, the gap decreases when the network becomes worse.

The limited energy possessed by each individual sensor node makes the process of designing and developing an efficient wireless sensor network as a herculean task. The process of clustering and successive cluster head (CH) process with energy efficiency is essential for extending network lifetime and sustaining the existence of alive sensor nodes in the network. In this paper, a multi-objective optimization algorithm based on Hybrid Bald Eagle Search Optimization Algorithm (HBESAOA) is presented as the energy clustering solution for targeting on the process of extending the sensor nodes' network lifetime. This multi-objective optimization HBESAOA algorithm included the fitness evaluating factors of energy, delay, distance, node centrality and node degree into account during the process of CH selection. It specifically adopted the well-balanced exploration and exploitation capabilities of Bald Eagle Search (BES), such that potential search process during clustering and subsequent CH selection is attained in the network. It is prevented with the potential of attaining maximized solution diversity with prevented premature convergence problem. The results of this HBESAOA scheme is identified to be significant in increasing the mean network lifetime by 26.78%, compared to the baseline CH selection schemes independent to the position of the sink node. The energy sustenance in the network is also predominantly improved by 21.98%, independent to the scalable increase in the number of sensor nodes on comparison with the competitive approaches used for evaluation. [ABSTRACT FROM AUTHOR]

Secrecy Diversity Analysis is to improve Physical Layer Security (PLS) in Wireless Sensor Networks (WSNs). Wireless Sensor Networks (WSNs) are more susceptible to security risks owing to the broadcast characteristics of wireless communication, necessitating the protection of sensitive data. The research examines the application of diversity strategies to enhance secrecy performance in wireless communication networks. Utilizing variety, including antenna diversity and channel diversity, the system can bolster resilience against eavesdroppers and augment the overall secrecy capacity. Employing MATLAB alongside Simulink as the simulation instrument, multiple secrecy performance measures are assessed, including secrecy capacity and secrecy outage probability. The simulation results indicate that the incorporation of diversity approaches at the physical layer markedly enhances the security of wireless sensor networks, diminishing the probability of eavesdropping and augmenting system resiliency. This research emphasizes the significance of integrating PLS with diversity approaches to improve security in resource-limited wireless systems such as WSNs. [ABSTRACT FROM AUTHOR]

The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network's lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have had many problems up until now because they are open, adaptable, and limited in resources. These problems have included privacy, effectiveness, and consumption of energy. Sensitive information should always be transmitted over wireless networks with extreme caution because public communications on these networks are sometimes unreliable. Although hierarchical routing methods may handle many applications, there are difficult problems with cluster head (CH) selection and network overload distribution. The secure low energy adaptive clustering hierarchy (SLEACH) protocol Cryptographic n-RSA method (SLEACH-n-RSA) is introduced in this work to improve network longevity, reduce energy consumption, and guarantee high security. The initial step of the SLEACH-n-RSA protocol is to use the improved LEACH protocol, which is based on the estimated remaining energy (ERE) and depleted energy (DE) for setting the threshold function value that will decide who will be the CH and how the cluster will form. In the second step, the suggested n-RSA encryption algorithm has been used to ensure the confidentiality of the transmitted data. The performance analysis of the proposed SLEACH-n-RSA protocol shows better performance results when compared with other currently used protocols in terms of network lifetime, packet delivery ratio, energy consumption, and execution time. The experimental results show that the proposed protocol outperforms other existing protocols. [ABSTRACT FROM AUTHOR]

Since heterogeneous wireless sensor networks consist of sensor nodes of varying capacity and energy-constrained, effective routing techniques are essential to ensure the proper functioning of the systems. Most traditional routing techniques fail to dynamically adjust to varying network conditions, leading to ineffective use of energy and poor performance. Therefore, deep Q-Networks implementation of reinforcement learning provides a beneficial approach to the problem due to adaptive routing decisions depending on the environmental signals and systems' performance. Therefore, the suggested approach integrates Deep Q-Network into the data routing framework for different Wireless Sensor Networks to improve energy-efficiency and ensure data delivery. The DQN agent is designed to pick routing functions that maximize total rewards which depend on energy consumption, packet delivery, and network stability. Hence, the decentralized learning allows each sensor node to develop its routing policy based on the local environment under the interactions with their neighbors. Therefore, the approach promotes the ability to adapt and learn, crucial for changing network conditions. Thus, extensive simulation was conducted to assess the applicability of the DQNbased routing for different WSNs, proving the significant reducing of energy consumption compared to traditional routing approaches with an average of 25% regardless of the network formation and traffic conditions. This approach also demonstrates lower packet loss of 15%, revealing enhanced data transfer reliability. In particular, the existing on demand routing protocols, only forward the request that arrives first from each route discovery process. The attacker exploits this property of the operation of route discovery. The network lifetime was extended by 30% showing growing energy efficiency for a long-term run. In general, the integration of Deep Q-Networks into data routing provides an excellent opportunity to improve energy-efficiency in different types of wireless sensor networks. Hence, the proposed approach effectively optimizes the routing solutions in real-time, using adaptive lenience, also showing enhancing data delivery, and improving the systems' lifetime. Hence, the presented results prove the capability of reinforcement learning methods to address the growing challenges of WSNs and leave space for further research in autonomous WSN improvement. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network. [ABSTRACT FROM AUTHOR]

We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications, including military, environment monitoring, and surveillance and body area sensor networks. A wireless sensor network builds the core part for IoT. Besides this, lifetime maximization is the biggest challenge in the wireless sensor network. Also, In a wireless sensor network, it is difficult to find an optimal node deployment approach that would minimize costs, be robust to node failures, decrease computing overhead and communication, and maintain a high degree of coverage and network connectivity. There is numerous literature addressed this challenge which is discussed in this paper; still there are lot many challenges yet to be addressed. Considering this scenario, in this paper, we propose a scheduled-based node deployment algorithm using Firefly Optimization (FA) to offer a circumstance where we have a group of target points that satisfy p-coverage and sensor nodes that satisfy q-connectivity, with subject to the selection of the optimal number of a sensor node that has the highest energy and minimum distance. The multiple parameters as no. of sensor nodes, distance, survivability factors, coverage, and connectivity of the sensor nodes are considered for designing the fitness function. A comprehensive statistical analysis is done using the simulation results to prove the proposed scheme's efficiency with other existing state-of-the-art methods under various p-coverage and q-connectivity configurations. [ABSTRACT FROM AUTHOR]

The use of the Internet of Things (IoT) is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices. In critical infrastructure domains like oil and gas supply, intelligent transportation, power grids, and autonomous agriculture, it is essential to guarantee the confidentiality, integrity, and authenticity of data collected and exchanged. However, the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques. Consequently, designing a lightweight secure data transmission scheme is becoming essential. In this article, we propose lightweight secure data transmission (LSDT) scheme for IoT environments. LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol. We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs. Security and performance analysis indicates that the LSDT scheme is secure, suitable for IoT applications, and performs better in comparison to other related security schemes. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), maintaining connectivity is crucial due to their dynamic nature. To address this challenge, this paper proposes an "improved schema," a novel approach aimed at enhancing the robustness and efficiency of Mobile Ad Hoc Networks (MANETs). A core advancement in the "improved schema" is its sophisticated neighbor selection algorithm, which considers factors such as node stability, link quality, and proximity. This algorithm ensures the selection of reliable neighbors, thereby improving overall network stability. Another significant improvement in the "improved schema" is the dynamic transmission range adjustment. By adapting transmission ranges in real-time based on current network conditions, the scheme optimizes communication ranges, enhancing network performance in varying environments. Additionally, the scheme leverages knowledge of network topology for pathfinding. By incorporating awareness of the network structure, the "improved schema" identifies more efficient paths for connectivity restoration, thereby minimizing resource utilization and latency. This study conducted extensive simulations to evaluate the performance of the "improved schema" against established schemes, including the Restoring Connectivity through Inward Motion (RIM), Nearest Neighbour (NN), and Survivability-Aware Connectivity Restoration (SACR) strategies. The results consistently demonstrate that the "improved schema" outperforms existing schemes in terms of connectivity robustness, adaptability to dynamic scenarios, and overall network efficiency. These findings underscore the effectiveness of the "improved schema" in addressing the inherent challenges faced by WSNs and MANETs, making it a promising solution for improving network performance in dynamic environments. [ABSTRACT FROM AUTHOR]

Outstanding progress in unsolicited intrusions along with security threats, which interrupt the normal operations of wireless sensor networks (WSNs), have been attracted by the proliferation of WSNs and their applications. In WSNs, this demands an intrusion detection system (IDS), which can detect such attacks with higher detection accuracy. Designing an effective model for IDS using the SDK-LSHB-based SHAKE-ESDRL algorithm to improve accuracy and lessen training time and response time is the goal of this work. At first, duplicate removal, missing data removal, and data transfer are the steps through which the dataset was processed. From the processed data, by providing the extracted attributes as input to the entropy-based generalized discriminant analysis (E-GDA) method, the number of attributes is reduced. After that, the LogSwish-based deep reinforcement learning algorithm (LS-DRLA) method wielded the reduced attributes for intrusion detection (ID). By utilizing the SHAKE 256 algorithm, the attributes that fall into the attacked class label are hashed and stored in the hash table during this process. Next, to test the real-time data with the trained IDS, the WSN nodes are initialized. For this, by utilizing the supremum distance (SD-K-Means) algorithm, the sensor nodes (SNs) are clustered centered on the cluster heads (CHs) selected by the linear scaling-based honey badger optimization algorithm (LS-HBOA) method. At last, utilizing real-world-based datasets, the proposed algorithms are evaluated and the results are compared using statistical metrics. [ABSTRACT FROM AUTHOR]

Clustering is one of the most promising approaches to reducing energy usage in Wireless Sensor Networks, where power remains a bottleneck (WSNs). The vast majority of advances in energy efficiency result in exorbitant computing costs. Because of its superiority in simulating human decision making and its capacity to turn several inputs into a single output, the fuzzy-logic based clustering algorithm is preferred over others. This paper proposes a technique to energy-efficient clustering based on fuzzy logic (FLEE). Energy consumption is a significant barrier for geographically dispersed wireless sensor networks (WSNs). Because of the rapid improvement of energy harvesting technology, a novel approach known as simultaneous wireless information and power transmission (SWIPT) has emerged as a hot research topic due to its use in tackling the energy scarcity problem in WSNs. The bulk of existing SWIPT systems rely on time switching (TS) or power splitting due to their high complexity, lack of flexibility, and implementation issues (PS). In this paper, we provide a novel Fuzzy Logic-based Energy Efficient—SWIPT approach for adaptively dividing data packets into energy data packets and information data packets using fountain codes and real-time node decoding status information. The modulation switching thresholds and transmit power allocation at the SWIPT transmitter, as well as the power splitting ratios at the SWIPT receiver, are jointly changed to maximise the spectrum efficiency of wireless information transfer in wireless sensor networks. The numerical demonstration of the FLEE-SWIPT performance of many fixed modulation schemes under different fading situations. The FLEE-SWIPT transceiver with adaptive modulation has been proved to be useful. The results show an increase in Network Lifetime of 12.342 s, an increase in Network Throughput of 1579.41 kbps, a decrease in Energy consumption of 23.789 J, a decrease in Packet Loss Ratio of 38.61%, a decrease in Packet Delivery Ratio of 96.33%, a decrease in Encryption Time of 2.987 s, a decrease in Decryption Time of 3.567 s, and a decrease in Execution. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: "Lightweight Trust Management based on Bayesian and Entropy (LTMBE)", "Beta-based Trust and Reputation Evaluation System (BTRES)", and "Lightweight and Dependable Trust System (LDTS)". To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs. [ABSTRACT FROM AUTHOR]

In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. InWSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs subsequently transmit packets to neighboring RPs. The unique determination of this study is the shortest path to reach RPs. As the mobile sink (MS) concept has emerged as a promising solution to the energy consumption problem in WSNs, caused by multi-hop data collection with static sinks. In this study, we proposed two novel hybrid algorithms, namely" Reduced k-means based on Artificial Neural Network "(RkM-ANN) and "Delay Bound Reduced kmeans with ANN" (DBRkM-ANN) for designing a fast, efficient, and most proficient MS path depending upon rendezvous points (RPs). The first algorithm optimizes the MS's latency, while the second considers the designing of delay-bound paths, also defined as the number of paths with delay over bound for the MS. Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage. In addition, a method of using MS scheduling for efficient data collection is provided. Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators. [ABSTRACT FROM AUTHOR]