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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]

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]

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]

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]

As a result of recent advancements in electronics technology, wireless sensor networks (WSNs) may now make use of a vast array of tiny sensors (WSN). WSN are viewed as problematic networks because of the environments in which they may operate and the technology they rely on. For instance, routing protocols need to be prepared for and capable of recovering from unexpected events like node failures and packet losses. The dynamic nature of these routing environments necessitates that every WSN routing protocol have mechanisms for adapting to new conditions. In many applications, security affects performance and energy efficiency. To provide such security services, we require resilient key management techniques. Security Power Aware Routing-Salp Swarm Optimization (SPAR-SSO) protocol uses a unique cryptosystem. SPAR-SSO uses connection quality estimation and power aware routing to reduce power consumption and delay while increasing packet delivery ratio. WSNs must conserve energy because the nodes are battery-powered. If the route with the greatest energy savings is constantly chosen for delivery, the nodes along this route will be overused and drained quickly, leading to network partitioning. In SPAR-SSO, an optimal path to the sink node is chosen. This technology is interesting because to its ease in enterprise and tractability in network transportation administration to deliver integrated services. It delivers a collision-free medium and minimizes entire energy expended in attainment the terminus, dropping energy each unit or packet. Moreover, Salp Swarm Optimization (SSO) is applied to optimize the results. The Packet Delivery Ratio (PDR) and End to End Delay is used as parameters to compare the performance of SPAR-SSO protocol. The results are compared with existing results and presented the success of the proposed method. Results show the proposed algorithm is suitable and compatibility with cyber-physical systems. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

Chatter vibration is an undesired phenomenon in machining operations. Chatter can lead to reduced machining quality, productivity, and tool life. The main cause of chatter is the dynamic instability between the cutting tool and the workpiece. Several attempts have been made to detect chatter. Some manual feature extraction methods used to detect the chatter involve wavelet packet transform (WPT), ensemble empirical mode decomposition (EEMD), local mean decomposition (LMD), and variational mode decomposition (VMD). These methods require human expertise for manual feature extraction. In recent time, convolution neural network (CNN) has been evolved as one of the techniques for automatic features extraction. However, CNN uses images and depends on the initial weights and hyperparameters. Creating images from acquired signals for CNN input is still cumbersome and adds one more step in signal processing making it computationally heavy. In this study, a simple, accurate, and robust online chatter detection method is proposed based on wavelet scattering network (WSN). This deep network iterates over standard wavelet transform, nonlinear modulus, and averaging operators of the acoustic signals. Experiments are performed to collect the acoustic signal during the turning operation to train and validate the algorithm. The automatic extracted chatter features are then used in supervised machine learning (ML) algorithms. The results clearly show that the WSN featurization method with SVM algorithm can significantly reduce the complexity of the existing online chatter detection systems without compromising the accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Privacy and security present significant challenges in wireless sensor networks (WSNs). In order to enhance security, the sensor network is equipped with high throughput. While the importance of both source node (SN) and base station (BS) location privacy and security is acknowledged, recent research has predominantly focused on location privacy. Addressing this gap, the geometric zigzag bidirectional tree effectively tackles privacy and security threats at both the SN and BS locations. Future iterations of WSN are anticipated to integrate additional functionalities to meet diverse requirements in real‐world applications. The absence of robust security constraints leads to an unpredictable sensor network setup. This article aims to reduce energy consumption (EC) while simultaneously enhancing network security and connectivity. To address security challenges in WSN, we propose an optimized meta‐heuristic clustering‐based privacy key‐agreement routing technique. In the suggested system, a gateway‐based network is constructed to devise a key arrangement protocol that promotes privacy during communication. The proposed routing strategy involves forming clusters of sensor nodes (Sn), facilitating the efficient selection of cluster heads (CHs) that prioritize nodes with the least modification. This effectively addresses the EC problem. A comprehensive performance evaluation is conducted, considering improvements in energy efficiency, packet delivery ratio (PDR), throughput, end‐to‐end delay (E2 delay), and EC. [ABSTRACT FROM AUTHOR]

Presented are real-world and simulation models, methods, and tools for simulating attacks on Wireless Sensor Networks (WSNs) intended for use in network vulnerability research. A comparative analysis was conducted to identify their advantages and disadvantages. The research demonstrated that integrating realworld and simulation approaches contributes to increased accuracy and reliability in attack detection. Recommendations are proposed for developing flexible and scalable simulation models, improving the efficiency of attack detection algorithms, and regularly updating models in accordance with changing WSN operating conditions and emerging threats. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT), WSNs are crucial components because they sense, acquire data and communicate with the base station. Because IoT connects devices with scarce resources, the energy needed for communication is viewed as one of the most challenging issues facing WSN assisted IoT. Clustering techniques have the potential to conserve energy and keep network nodes running for longer periods of time. Traditional hierarchical routing protocols are based on a random probability equation for cluster head (CH) selection. Moreover, there is scope to enhance the network lifespan by improving the CH selection approach. To address this, we present the hybrid K-means ant Lion optimization approach for Energy-efficient clustering based Routing (K-LionER) scheme for WSN supported by the IoT. The proposed K-LionER focuses on prolonging the network lifespan and improving energy efficiency. The clusters in WSN under investigation are created using K-means and each CH is chosen using ant lion optimization. CHs acquire the data from cluster members and transmit the agglomerated data to the base station. K-LionER selects the CH based on routing metrics, Remnant Energy (RE), distance between the CHs and Base station (CBD) and Intra-cluster Communication Cost (ICC). A comprehensive simulation is carried out on MATLAB 2017a. K-LionER's accomplishment is contrasted with LEACH, ECFU and GADA-LEACH. The simulation's outcome reveals gains in performance in various aspects, such as alive nodes, stability period, dead nodes and network lifetime metrics. In comparison to the aforementioned routing protocols, the proposed K-LionER protocol improves the network's lifetime by 10% to 48%. [ABSTRACT FROM AUTHOR]

The evolution of smart cities, driven by advancements in information transmission and the fusion of sensor technologies with IoT, faces challenges from substantial data traffic affecting Quality of Service factors like throughput and causing data loss and delivery delays. In order to improve traffic-aware data transmission in IoT-based smart city applications, a novel deep learning technique called the Multicollinearity Deming Regression-based Deep Perceptive NeurAl Learning Classifier (MERPAL) method is introduced. The proposed Deep Multilayer Perceptive Classifier consists of multiple layers such as input, output, and a number of hidden layers for assessing the given input. The number of nodes taken as input is passed to the primary hidden layer, where the energy and bandwidth availability of the nodes are assessed. The second hidden layer then uses the Multicollinearity Deming Regression to evaluate the estimated energy level and bandwidth of the nodes. After that, the analyzed results are given to the soft step activation function in the third hidden layer. The activation function identifies the best nodes based on energy level and bandwidth availability for efficient data delivery and helps minimize the network traffic. The simulation results indicate that MERPAL demonstrates superior performance compared to existing methods, exhibiting a 7% enhancement in data packet delivery ratio, a 15% increase in throughput, along with a reduction of 20% in packet loss and 14% in delay. [ABSTRACT FROM AUTHOR]

The extensive utilization of IoT applications leads to the aggregation of a substantial volume of data, presenting a crucial challenge in terms of data routing within these networks. RPL intentionally surpasses the limitations sometimes observed in low-power and lossy networks, which are particularly prevalent in IoT networks. The RPL protocol is designed specifically for static networks that do not involve mobility or topological changes. The RPL protocol guarantees continuous connectivity between nodes and mitigates the risk of data loss in stationary IoT applications that do not involve mobility or alterations in network configuration. The article utilizes a mobility aid technology known as network performance stability using the intelligent routing protocol (nPSIR), which expands upon RPL. The Mobility Support Entity (nPSIR) facilitates the displacement of all nodes, with the exception of the root node, and ensures uninterrupted connection during mobility. Moreover, it deals with the situation where there is a physical barrier between two interconnected nodes in a changing environment. In order to achieve this objective, it employs a dynamic trickle timer that operates within two distinct ranges. Furthermore, it utilizes a neighbor link quality table, a mechanism for selecting the most beneficial parent node in the event of migration, a measure of confidence, the identification of crucial regions, and a blacklist. Multiple simulations validate that nPSIR effectively decreases hand-off delay and improves packet delivery, despite the minor drawbacks of increased signaling costs and power consumption. The delivery ratio decreases the quantity of lost data packets and surpasses both RPL as a responsive protocol and mRPL as a proactive protocol in relation to mobility. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents an overarching ideology that blurs the boundaries between the physical and virtual worlds, encompassing a broad spectrum of computing systems and information technologies. IoT devices, such as wearable devices like smart watches and implantable medical devices, are connected to the Internet. Some IoT devices are even integrated into the human body. Generally, wireless communication technologies facilitate connectivity among intelligent and autonomous objects. However, security concerns within IoT pose significant challenges that impede its widespread adoption and evolution. This paper introduces a lightweight and robust security protocol designed to authenticate objects, ensure data integrity during transmission, and detect intrusions in case of attacks on the system. [ABSTRACT FROM AUTHOR]

The advent of Internet of Things (IoT) devices has revolutionized our daily routines, fostering interconnectedness and convenience. However, this interconnected network also presents significant security challenges concerning authentication and data integrity. Traditional security measures, such as Public Key Infrastructure (PKI), encounter limitations when applied to resource-constrained IoT devices. This paper proposes a novel decentralized PKI system tailored specifically for IoT environments to address these challenges. Our approach introduces a unique "zone" architecture overseen by zone masters, facilitating efficient certificate management within IoT clusters while reducing the risk of single points of failure. Furthermore, we prioritize the use of lightweight cryptographic techniques, including Elliptic Curve Cryptography (ECC), to optimize performance without compromising security. Through comprehensive evaluation and benchmarking, we demonstrate the effectiveness of our proposed solution in bolstering the security and efficiency of IoT ecosystems. This contribution underlines the critical need for innovative security solutions in IoT deployments and presents a scalable framework to meet the evolving demands of IoT environments. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) has become widely used in applications such as smart homes, industrial automation, and transportation due to its affordable hardware and fast internet connectivity. However, the increase in IoT‐enabled gadgets, particularly those running on batteries or connected to other sources, is putting strain on the world's energy requirements. Therefore, this study focuses on a green routing solution for battery‐powered IoT‐enabled Software‐defined Wireless Sensor Networks (IoT‐SDWSN). Finding green solutions for IoT‐based networks to address this energy challenge has become crucial. This study focuses on developing a green routing solution for battery‐powered IoT‐SDWSN. Energy efficiency in IoT‐SDWSN is attained by the process of clustering nodes. The network is partitioned into small clusters, and a Control Node (CN) is set up by a Control Server (CS) to transmit the data packets sent by sensor nodes. Choosing a CN in these networks is a critical concern due to the substantial energy consumption involved in delivering data to the CS. This research focuses on the problem of energy‐efficient cluster routing in IoT‐based SD‐WSN. It introduces the Energy‐optimized Artificial Hummingbird Algorithm (EOAHA) as a green routing technique. EOAHA aims to extend the lifespan of IoT‐based SD‐WSNs by intelligently selecting (based on a new fitness function) CNs to distribute the network load and increase its overall longevity. To evaluate the performance of EOAHA, a comparative analysis is conducted against other state‐of‐the‐art algorithms. The results demonstrate that EOAHA outperforms these algorithms by a minimum of 13.5% in terms of network longevity. [ABSTRACT FROM AUTHOR]

The study introduces the Group Acknowledgement Circular Shift (GACS) algorithm, aimed at improving fairness in the current consumption of End Devices (EDs) in a time-slotted LoRa-based Wireless Sensor Network (WSN). This algorithm manages the rotation and scheduling of data transmission and reception times for nodes based on the number of nodes (SN) within each group slot. Employing MATLAB for simulations, performance evaluation utilizes Jain's Fairness Index. Two scenarios are explored: one with Group Acknowledgement (GA) excluding Circular Shift (CS) and another with Group Acknowledgement incorporating Circular Shift (GACS). Simulations involve a network with ten (10) EDs and a gateway across ten transmission cycles. Results indicate that under GA without CS, fairness is 98.32%, significantly improving to 99.75% with GACS. This improvement persists even with a transmission cycle of 100. Additional simulations maintain same transmission parameters but change payload to 54 bytes, set nodes to 50, and establish a transmission cycle of 50. In the GA without CS scenario, Jain's fairness index is 94.22%, while the GACS scenario shows a higher index of 98.68%. Overall, the study emphasizes the robust fairness index of the GACS algorithm, irrespective of the SN within each group slot, with nodes consistently exhibiting uniform current consumption at every SNth cycle. [ABSTRACT FROM AUTHOR]

The presence of the malicious internet of things (IoT) devices in wireless sensor networks (WSNs), in the underlying work, is identified using a private blockchain and interplanetary file system (IPFS) based system. In the proposed model, the registration of IoT devices is being done via the IoT device manager. After registration, the IoT devices send a request to get services from the IoT device manager. At this stage, authentication is performed through a machine learning technique, known as logitboost (LB), which helps in identifying the IoT devices as malicious or legitimate. A verification report is forwarded to the IoT device manager upon detection. If the IoT device is found to be acting maliciously, it is penalized and vice versa. Thus, ensuring the IoT device manager provides the required service only to the legitimate IoT device. The data used in the proposed work is taken from the WSN dataset (WSN-DS), which is balanced via the synthetic minority oversampling technique. Additionally, the miner nodes' verification is performed via verifiable byzantine fault tolerance (VBFT). Also, IPFS is used to store the transaction data being shared between the IoT devices. The simulation results show that VBFT surpasses the proof of authority consensus mechanism by 15–20% in terms of transaction throughput. Furthermore, the LB classifier is found to surpass decision tree, nearest centroid, quadratic discriminant analysis and weighted average ensemble by 2%, 24%, 71%, 5% in terms of accuracy. [ABSTRACT FROM AUTHOR]

Optimization of the routing represents an important challenge when considering the rapid development of Wireless Sensor Networks (WSN), which involve efficient energy methods. Applying the effectiveness of a Deep Neural Network (DNN) and a Gaussian Mixture Model (GMM), the present article proposes an innovative method for attaining Energy-Efficient Routing (EER) in WSN. When it comes to dealing with dynamic network issues, conventional routing protocols generally conflict, resulting in unsustainable Energy consumption (EC). By applying algorithms based on data mining to adapt routing selections in an effective procedure, the GMM + DNN methodology that has been developed is able to successfully address this problem. The GMM is a fundamental Feature Extraction (FE) method for accurately representing the features of statistical analysis associated with network parameters like signal frequency, the amount of traffic, and channel states. By learning from previous data collection, the DNN, which relies on these FE, provides improved routing selections, resulting in more efficient use of energy. Since routing paths are constantly optimized to ensure dynamic adaptation, where less energy is used, networks last longer and perform more efficiently. Network simulations highlight the GMM + DNN method's effectiveness and depict how it outperforms conventional routing methods while preserving network connectivity and data throughput. The GMM + DNN's adaptability to multiple network topologies and traffic patterns and its durability make it an efficient EER technique in the diverse WSN context. The GMM + DNN achieves an EC of 0.561 J, outperforming the existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are used to monitor the real‐time parameters of remote/restricted access areas of automation industries which are tightly sealed. In such scenarios, the main challenge is signal transmission in sealed environments. An omnidirectional radiation pattern is preferred as a control link for receiving packets or periodic updates from its neighbouring nodes. However, the gain and directivity of omnidirectional antenna should be sufficient enough to transfer the data to the collecting node. This paper proposes a high‐gain horizontally polarised omnidirectional radiation pattern planar antenna resonating at the 2.45 GHz. The developed microstrip planar antenna offers a very simple and compact structure printed on either side of FR4 epoxy substrate (ε = 4.4) suitable for WSN nodes. A printed dipole director is arranged orthogonally and offers horizontal polarisation. A pair of parasitic elements is placed parallel to dipole radiators to improve the total gain of the antenna. A parallel pair of parasitic elements is placed on an inclination of 45° in antenna design plane for uniform distribution of surface current to achieve the omnidirectional antenna. The symmetrical structure of the radiating element on either side of the substrate avoids the design of the traditional balun structure to reduce the complexity of feeding network. The developed antenna is simulated, fabricated, measured and tested using WSN. The measured radiation (directivity = 3.9 dBi) is maximum at Ⴔ = 0° and Ⴔ = 90°. The frequency response of the antenna indicates a −10 dB fractional bandwidth of 380 MHz (2.24 GHz–2.62 GHz). The measured far‐field response shows a stable average gain of 3.9 dBi over the ISM band. The real‐time received signal strength indicator (RSSI) of the antenna installed in the sensor node observed a success rate of 79.46% with a transmission error of 10%. The observed response of an antenna makes it well‐suitable for WSN in industrial automation. [ABSTRACT FROM AUTHOR]

Summary: For accurate environmental monitoring, a wireless sensor network (WSN) uses a collection of autonomous sensor nodes (SNs) that are dispersed at random across a given area. WSNs are used for various applications that require real‐time data. While the SNs in a WSN are limited in their ability to generate energy, the routing process is seen as a vital means of improving the network's ability to conserve resources and prolong its useful life. In this research work, we present sink mobility‐based energy‐optimized routing (SMEOR) in energy harvesting (EH)‐enabled WSN. While following the cluster‐based routing, the election of cluster head (CH) is executed using our proposed extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy‐hole problem, four data‐collection energy‐unlimited nodes are used around the periphery of the network. The sink moves from one EH‐enabled node to another to collect data. Further, security in SMEOR is ensured by encrypting the data using a stream cipher and a stochastically generated security key. It is evident from the simulation analysis that SMEOR delivers network longevity and supreme performance. [ABSTRACT FROM AUTHOR]

Agriculture not only plays a vital role in human survival but also contributes to the nation's greater economic development. With the use of technologies like IoT, WSNs, remote sensing, camera surveillance, and many more, precision agriculture is the newest buzzword in the field of technology. Its primary goal is to lessen the labour of farmers while increasing the output of farms. Many machine learning techniques are designed to improve the productivity and quality of the crops, but the improper irrigation and disease prediction of the existing techniques leads to loss of productivity and quality. Hence, the IoT based wireless communication system is designed for smart irrigation and rice leaf prediction using ANN and ResNeXt-50 model. In this designed model, smart irrigation is controlled by collecting the temperature and moisture of the soil in the agricultural field by using the WSN. Likewise, a surveillance camera is placed in the agricultural field to capture the rice leaf to find the disease such as rice blast, leaf smut, brown spot and bacterial blight. These collected data are processed and classified for smart irrigation and rice leaf disease prediction. For evaluating the performance of both the ANN and ResNeXt-50 trained model, the performance metrics such as accuracy, sensitivity, specificity, precision, error etc. The performance metrics for the ANN and ResNeXt-50 model are 0.9427, 0.925, 0.903, 0.86, 0.0573 and 0.967, 0.935, 0.943, 0.939 and 0.033. Thus, the evaluation of the designed model results that the proposed approach is performing better compared to the current techniques. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for relocating sensors in a Wireless Sensor and Robots Network (WSRN) using a mobile robot. The goal is to repair coverage holes using redundant sensors that are caused by an initial random deployment. The holes are repaired without prior knowledge of their positions or that of the redundant sensors. The existing solutions mainly focus either on how to optimally repair holes by determining to where relocate redundant sensors, or how to build a repair path with assumption that the positions of holes and redundant sensors are known. In both scenarios, the literature lacked the optimization of the robot’s path for its initial exploration to identify both the holes and redundant sensors. Our proposed solution introduces an efficient robot trajectory that utilizes stochastic paths that adhere to the principles of light reflection. This trajectory serves the dual purpose of identifying redundant sensors and detecting as well as repairing coverage holes. We achieve this by incorporating the law of large numbers into the light reflection principal, enabling the robot to move randomly while adhering to the pathways of light reflection to efficiently relocate the redundant sensors. This approach results in a highly efficient and effective sensor relocation process. The effectiveness of the proposed solution is assessed across multiple parameters, including relocation time, the length of the relocation path, the robot average moves, and the total energy consumption required to cover holes with varying carrying capacity, dimensions of region of interest, coverage ratio and exit angles of reflection. Through a series of extensive simulations, we provide compelling evidence that our proposed solution distinctly surpasses the existing state-of-the-art approaches. This notable advantage becomes evident in multiple aspects: from reduced relocation time and shorter relocation path length to minimized total energy consumption. These combined enhancements underscore the effectiveness of our solution in tackling the challenge of sensor relocation. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have emerged as a one of the most fascinating areas of lookup in the past few years principally due to its massive quantity of workable applications. However limited energy, node deployment strategy, routing techniques has considerable impact on the overall performance of network. This paper proposes sustainable honey bee ratel based hybrid wireless sensor network (SHHWSN) layering framework for improving load balancing and scalability issues in network. In SHHWSN tessellated hexagonal shape clusters are formed to cover entire geographical region without any gaps. Hybrid routing schema constrained with redundant data identification policy used in this, seeks to optimize energy utilization. The proposed SHHWSN was evaluated and validated by simulation, and the results were compared with earlier schematics where mostly one sided stochastic node deployments were followed. The experimental results show that SHHWSN has superior performance in terms of network alive nodes, delay, energy and throughput. The proposed SHHWSN obtained values of 50 nodes is 3, 0.061, 0.14, 0.89, 100 nodes is 3,0.037, 0.19, 0.551, 150 nodes is 13, 0.081, 0.23, 0.415, and 200 nodes is 23, 1.01, 0.25, 0.356, which is significantly more effective than current techniques. [ABSTRACT FROM AUTHOR]

The implementation of ultra-wideband technology for accurate indoor location in industrial and public environments has recently been the basis of numerous research studies. Wireless sensor networks must take localization into consideration. Localization processes improve in various ways when mobility is established. As research progresses on range-free localization with UWB, enhancements in methods and optimal techniques need to be evaluated for the present scenario. Within a single localization zone, they focus is on accuracy. The objective of work is to improve the location accuracy by decreasing localization error with hybrid optimal techniques and algorithms. However, many large-scale applications will benefit from technologies developed for line-of-sight conditions. TDOA (Time Difference of Arrival) is a method for calculating the distance between two or more radio transmitters and one receiver. Distance vector routing protocols measure distance by the number of routers a packet has to pass through; one router counts as one hop. These protocols send the entire routing table to each neighbor every 30 s. DV-Hop method has been adopted for the present work, and hybridization has been considered as a means of enhancing the results. Accuracy with initial measurement techniques lies in 2D and 3D with Kalman filter linked with DV-Hop to reduce initial errors. Localization error in nodes' dynamic momentum was observed, and the deployment of beacons in the range-free selected area was estimated with an improved technique. Ensemble method (EM)-PSO-based optimization with hybrid HOP algorithms is proposed in the present work. The results of the present approach show a significant difference in localization error reduction compared to conventional DV-HOP. Finally, an error below 2% is observed using multiple iterations, and a variation of 15% is observed compared with other state-of-the-art existing techniques. Using hybrid algorithms, this increased accuracy can then be used to reduce the localization error in network for the application users in range-free conditions. [ABSTRACT FROM AUTHOR]

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput. [ABSTRACT FROM AUTHOR]

A network of wireless sensors is a self‐infrastructure approach with many sensory nodes. The distributed sensory nodes communicate with each other via sensory points. In wireless sensor network (WSN), the sensory nodes collect information for healthcare, military and monitoring systems. Such networks require an exclusive arrangement of the nodes to challenge inherent limitations and energy deficiency. The conventional design of a communication system consumes more energy with high latency causing degraded performance. This study provided a machine learning‐based path optimization mechanism using the least energy resources in designing an effective wireless network system with enhanced three measures of network performance, including throughput, packet delivery efficiency and energy usage. The proposed methodology is validated through network simulation tools. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters. [ABSTRACT FROM AUTHOR]

Summary: The demand for the Internet of Things (IoT) has significantly increased in the current scenario; specific sectors that require IoT include industrial automation, home control, health care applications, military and surveillance applications, habitat monitoring, and nanoscopic sensor applications. The use of optimal wireless sensor networks (WSNs) in transmission techniques has resulted in their involvement. A WSN is made up of thousands of randomly distributed sensor nodes that sense and transmit environmental data such as temperature, pressure, humidity, light, and sound. One of the most important requirements when using these sensor nodes is energy. As a result, it has become a major area of research in recent years; additionally, several design techniques and protocols have been presented in the last decade, particularly for IoT‐based applications. As a result, the systemization of an energy‐optimized WSN in dynamic functional conditions with automatic self‐configuration of sensor nodes is a critical goal. This paper proposed an opportunistic energy‐efficient dynamic self‐configuration routing (OEDSR) algorithm for IoT‐based applications. Initially, the optimal route to the base station (BS) is calculated by using the residual energy and mobility factor of the sensor nodes obtained through a routing tree model based on graph theory. To reduce the number of connections, an optimal path is determined based on dynamic cluster generation through a hierarchical tree architecture. Finally, the network‐related parameters, such as throughput, delay and packet delivery ratio (PDR), are compared with the peer existing routing protocols to depict the efficiency of the OEDSR protocol. [ABSTRACT FROM AUTHOR]

This paper deals with an electrothermal model of a thermoelectric converter dedicated to performing simulations of coupled thermal and electrical phenomena taking place in harvesting processes. The proposed model is used to estimate the electrical energy gain from waste heat that would be sufficient to supply electronic circuits, in particular autonomous battery-less nodes of wireless sensor networks (WSN) and Internet of Things (IoT) devices. The developed model is not limited to low-power electronic solutions such as WSN or IoT; it can also be scaled up and applied to simulations of considerably higher thermal power conversion. In this paper, a few practical case studies are presented that show the feasibility and suitability of the proposed model for complex simultaneous simulation processes in both the electrical and thermal domains. The first example deals with a combined simulation of the electrothermal model of a thermoelectric generator (TEG) and an electronic harvester circuit based on Analog Devices' power management integrated circuit LTC3108. The second example relates to the thermalization effect in heat sink-less harvesting applications that could be mitigated by a pulse mode operation. The unique contribution and advancement of the model is the hierarchical structure for scaling up and down, incorporating the complexity of the Seebeck effect, the Joule effect, heat conduction, as well as the temperature dependence of the used materials and the thermoelectric pellet geometries. The simulations can be performed in steady as well as transient states under changing electrical loads and temperatures. [ABSTRACT FROM AUTHOR]

Conventional wireless sensors have difficulty solving the problem of energy limitation, especially in sensor networks in hard-to-reach extreme areas. In order to solve the problem that it is difficult to charge wireless sensors in the field using conventional energy sources, the energy harvesting wireless senor is designed to use renewable energy sources for power supply. Considering the uncertainty and unknown nature of renewable energy generation, and the need for effective energy management of the sensor. In this paper, an Node Power Control Optimization (NPCO) power allocation algorithm is proposed to adjust the power allocation problem of wireless sensor nodes within each time slot. In addition, to address the unknown and random nature of energy arrival, this paper proposes a CLSTM model based on deep learning to predict the energy arrival. The continuous autonomous energy management of wireless sensor nodes is achieved by combining the CLSTM prediction results using the NPCO algorithm. The algorithm is applicable to continuous states and is able to show good performance in the verification of real solar data. The algorithm achieves better performance in terms of long-term average net bit rate compared to the current DDPG algorithm, AC algorithm, and Lyapunov optimization algorithm. [ABSTRACT FROM AUTHOR]