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Internet of Things (IoT) facilitates connectivity in businesses and smart homes by integrating embedded technology, wireless sensor networks and data aggregation. Regular monitoring of energy usage in IoT networks is crucial due to the high energy consumption and delays in transmitting data to the Base Station (BS) by the sensor nodes. The most significant challenges in IoT include energy depletion and transmission delays. In this research, the proposed Differential Evolution with Sailfish Optimization (DESFO) model addresses large network handling, achieves maximum convergence rates, and reduces energy consumption. The Differential Evolution (DE) mutation and crossover operators enhance exploration capabilities, while SFO adaptive movement strategies improve the exploitation of the search space. Together, they achieve high convergence rates, prevent falling into local optima, provide iterative control and manage high-dimensional networks effectively. The DESFO method exhibits superior performance when compared to the existing methods, Firefly Optimization and Aquila Optimization (FF-AO), Fixed-Parameter Tractable Approximation Clustering (FPTAC), and Cluster based Reliable Data Aggregation-Sunflower Optimization (CRDA-SFO). The proposed DESFO method yields impressive results, achieving a Packet Delivery Ratio (PDR) of 96.12% at 250 nodes, a Delay of 3ms at 250node, Energy consumption of 12J at 250 respectively. [ABSTRACT FROM AUTHOR]

Water pollution is the most important factor affecting the environment. Appropriate monitoring is a big challenge to make sustainable growth by maintaining it for society. In recent times, water monitoring has turned into a smart monitoring system for water pollution (SMS-wp), with the advances on the Internet of things (IoT), machine learning (ML), and the improvement in current sensors. River Ganga is one of the major sources of water for drinking, irrigation, and industries in the northern part of India. Day by day, Ganga River is getting polluted, due to anthropogenic activities, such as the construction of dams, extensive use of fertilizers in agriculture, and untreated discharges from industries. Contamination in the river water is adversely affecting human health and river biota. Therefore, to improve the river ecosystem and to check infections and diseases, water quality assessment is very much important. The main aim of this study is to determine the Water Quality Index (WQI) of the River Ganges at the upper part of the Indo-Gangetic plain, just downstream of the Himalayan foothill using the last 3 years of data (2017–2019). Trend analysis for River Ganga water at considered locations is also a part of this study. Trend analysis is presenting the water quality of river Ganga in the coming years up to 2025. Twelve physicochemical parameters (TDS, chlorides, alkalinity, DO, temperature, COD, BOD, pH, magnesium, hardness, total coliform, and calcium) were analyzed to determine the water quality of River Ganga. As a result, WQI for next 5 years (from 2020 to 2025) is forecasted as an increment of 17.34% at Haridwar, 4.16% at Roorkee, and 21.63% at Dehradun. Results of the study indicated that WQI values just downstream of the Himalayan foothills in the upper reaches of the Gangetic plain are increasing every year. The authors have concentrated on how the advances in sensor innovation, the Internet of things, and machine learning techniques make water pollution monitoring a genuinely brilliant checking framework. Finally, the system of robust strategies for ML, denoising techniques, and advancement of appropriate guidelines for wireless sensor networks (WSNs) have been recommended. [ABSTRACT FROM AUTHOR]

The decision-making process in Industrial Wireless Sensor Networks heavily relies on the information provided by smart sensors. Ensuring the trustworthiness of these sensors is essential to prolong the lifetime of the network. Additionally, dependable data transmission by sensor nodes is crucial for effective decision-making. Trust management approaches play a vital role in safeguarding industrial sensor networks from internal threats, enhancing security, dependability, and network resilience. However, existing trust management schemes often focus solely on communication behaviour to calculate trust values, potentially leading to incorrect decisions amidst prevalent malicious attacks. Moreover, these schemes often fail to meet the resource and dependability requirements of IWSNs. To address these limitations, this paper proposes a novel hybrid Trust Management Scheme called the Multi-layered Assessment System for Trustworthiness Enhancement and Reliability (MASTER). The MASTER scheme employs a clustering approach within a hybrid architecture to reduce communication overhead, effectively detecting and mitigating various adversarial attacks such as Sybil, Blackhole, Ballot stuffing, and On–off attacks with minimal overheads. This multifactor trust scheme integrates both communication-based trust and data-based trust during trust estimation, aiming to improve the lifetime of industrial sensor networks. Furthermore, the proposed MASTER scheme utilizes a flexible weighting scheme that assigns more weight to recent interactions during both direct and recommendation (indirect) trust evaluation. This approach ensures robust and precise trust values tailored to the specific network scenario. To efficiently process and glean insights from dispersed data, machine learning algorithms are employed, offering a suitable solution. Experimental results demonstrate the superior performance of the MASTER scheme in several key metrics compared to recent trust models. For instance, when 30% of malicious Sensor Nodes (SNs) exist in a network comprising 500 sensor nodes, the MASTER scheme achieves a malicious behaviour detection rate of 97%, surpassing the rates of other models. Even after the occurrence of malicious SNs exceeding 30%, the False Negative Rate (FNR) in the MASTER scheme remains lower than other models due to adaptive trust functions employed at each level. With 50% malicious SNs in the network, the MASTER scheme achieves a malicious behaviour detection accuracy of 91%, outperforming alternative models. Moreover, the average energy consumption of SNs in the MASTER scheme is significantly lower compared to other schemes, owing to its elimination of unnecessary transactions through clustered topology utilization. Specifically, with 30% and 50% malicious SNs in the network, the MASTER scheme achieves throughput rates of 150 kbps and 108 kbps, respectively, demonstrating its efficiency in challenging network scenarios. Overall, the proposed MASTER scheme offers a comprehensive solution for enhancing security, trustworthiness, and collaboration among sensor nodes in IWSNs, while achieving superior performance in various metrics compared to existing trust models. [ABSTRACT FROM AUTHOR]

The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model. [ABSTRACT FROM AUTHOR]

The efficient operation of smart farms relies on the precise monitoring of farm environmental information, necessitating the deployment of a large number of wireless sensors. These sensors must be integrated with their specific locations within the fields to ensure data accuracy. Therefore, efficiently and rapidly determining the positions of sensor nodes presents a significant challenge. To address this issue, this paper proposes a hybrid optimization DV-Hop localization algorithm based on the chaotic crested porcupine optimizer. The algorithm leverages the received signal strength indicator, combined with node hierarchical values, to achieve graded processing of the minimum number of hops. Polynomial fitting methods are employed to reduce the estimation distance error from the beacon nodes to unknown nodes. Finally, the chaotic optimization crested porcupine optimizer is designed for intelligent optimization. Simulation experiments verify the proposed algorithm's localization performance across different monitoring areas, varying beacon node ratios, and assorted communication radii. The simulation results demonstrate that the proposed algorithm effectively enhances node localization accuracy and significantly reduces localization errors compared to the results for other algorithms. In future work, we plan to consider the impact of algorithm complexity on the lifespan of wireless sensor networks and to further evaluate the algorithm in a pH monitoring system for farmland. [ABSTRACT FROM AUTHOR]

When wireless sensor networks are used in smart homes, the connection state will be unstable due to signal masking attenuation. This will cause low packet rate, high time delay and high cost in the network. In this paper, a network routing algorithm for wireless sensing based on connection conditions is designed. Secondly, the expected number of sends is proposed to evaluate the stability of links. Based on this, the following network signal delivery situation is forecasted in real time and quickly. According to the estimated expected number of transmissions, the path is dynamically corrected to effectively avoid attenuation in the channel and achieve optimal system performance. Experimental results show that the method proposed in this paper can improve the efficiency of message sending and reduce the routing cost under the condition of masking effect. [ABSTRACT FROM AUTHOR]

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes. [ABSTRACT FROM AUTHOR]

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One of the key elements of the internet of things (IoT) nowadays are wireless sensor networks (WSNs). Any IoT application uses a variety of sensor-based devices to collect data from real-world objects and send it to the base station (BS). In accordance with the sensor position, the BS evaluates this data. Any IoT-based application needs a high-performance intelligent WSN. IoT-enabled WSNs are high-performance intelligent WSNs, which are used throughout this article. Fault incidence likelihood in IoT-enabled WSNs is substantially higher than in conventional networks. The dependability of the IoT-enabled WSNs is affected by faulty nodes and broken connections. Incorporating multipath transmission, relay node location, and backup node selection, various faulttolerance techniques improve the network's dependability. These methods, however, have significant packet overhead, worse detection accuracy, and lengthy data transmission delays. For fault tolerance in IoT-enabled WSNs, a multi-objective red panda optimization method incorporating deep reinforcement learning (DRL) is suggested in this paper. This study's primary goal is to identify defective nodes using starting energy, transmission of packets rate, communication overhead, and packet delay measurements. In order to capture data in an energy-efficient manner and extend the network lifespan, a mobile sink (MS) is deployed. The suggested approach beat state-of-the-art techniques in terms of energy efficiency (EE), latency(L), packet delivery ratio (PDR) and network lifetime (NL) according to thorough simulations and theoretical research. [ABSTRACT FROM AUTHOR]

Railway bridges are crucial for transferring passengers and commodities in the transportation network. Railway bridges require continuous monitoring to observe their performance. A structural health monitoring system is one method for assessing the viability of a railway bridge structure. The functioning of railroad bridge structures has been extensively observed using wireless technology. This research aims to implement smart wireless sensors for monitoring the structural health of the railway bridge online in real-time during operation. Many sensors were installed on the railway bridge, including strain gauges, accelerometers, linear variable displacement transducers, and proximity sensors. Geometric modeling and numerical simulation were performed to find critical frame locations on the railway bridge where the instrumentation sensors would be placed. In this study, MONITA® is employed for data acquisition modules. The MONITA® system consists of a combination of hardware and software that functions to retrieve, send, store, and process data. This paper describes the result of establishing this method to comprehend the performance of the steel railway bridge structure in real-time via the human-machine interface display dashboard. As a result, the monitoring system can appropriately assess a structural railway bridge in real time. This study may be helpful to practicing engineers and researchers in future studies of steel railway bridge evaluation. This could be a valuable reference for future studies in implementing such systems as the railway bridge early warning system technique in detecting bridge damage. [ABSTRACT FROM AUTHOR]

Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless sensors while employed for electrocardiogram (ECG) anomaly detection. The technique involves extracting only relevant features for a given task directly from analog signals and conducting classification in the digital domain. Building on this approach, we extended the application of the proposed generic A2F converter to address a human activity recognition (HAR) task. The performed simulations include the training and evaluation of neural network (NN) classifiers built for each application. The corresponding results enabled the definition of valuable features and the hardware specifications for the ongoing complete circuit design. One of the principal elements constituting the developed converter, the integrator brought from the state-of-the-art design, was modified and simulated at the circuit level to meet our requirements. The revised value of its power consumption served to estimate the energy spent by the communication chain with the A2F converter. It consumes at least 20 and 5 times less than the chain employing the Nyquist approach in arrhythmia detection and HAR tasks, respectively. This fact highlights the potential of A2F conversion with NUWS in achieving flexible and energy-efficient sensor systems for diverse applications. [ABSTRACT FROM AUTHOR]

The power requirement of Smart Building-Wireless Sensor Network (SB-WSN) is achieved using the piezoelectric energy harvester. This article proposes a novel high-performance bimorph W-shaped Piezoelectric Energy Harvester (W-PEH) using different ceramic piezoelectric materials and proof mass material combinations [PZT-5A & Copper, PZT-5H & Copper, PZT-5J & Copper, PZT-5A & Platinum, PZT-5H & Platinum and PZT-5J & Platinum]. Three cases of W-PEH without proof mass, with single proof mass and with split proof mass are developed to improve the output performance. The electrical performance results under open and short circuit conditions are examined. The simulation is performed using COMSOL Multiphysics software. [ABSTRACT FROM AUTHOR]

Wireless sensor-based smart home systems are increasingly gaining prevalence, offering new opportunities for automation and an improved quality of life. However, the seamless integration of multi-layer wireless sensor networks remains a significant challenge. This article delves into a comprehensive approach to integrate wireless networks that employ distinct protocols, such as Zigbee, Z-Wave, Wi-Fi, Bluetooth, and LoRa, within the context of Home Assistant-based smart homes. The article explores architectural solutions and data transformation methods, and presents the outcomes of performance experiments, as well as the stability of multi-layered wireless sensor networks. These findings culminate in the identification of an optimal configuration. The formulation of this research problem sets the stage for novel perspectives in the development of efficient and adaptive smart homes. [ABSTRACT FROM AUTHOR]

The Aquasense project is a multi-site Innovative Training Network (ITN) in water and food quality monitoring using autonomous sensors and intelligent data gathering and analysis. This paper aims to study the most suitable communication system model regarding radio signal coverage and networking performance in this frame. This objective is achieved by evaluating the expected performance of three long-range communications technologies, 4G/LTE, NB-IoT, and LoRa, to support a wireless sensor network reporting data from the sensing devices to a remote central station where they are collected and analyzed. In order for the performance to be assessed, the WinProp software suite is adopted to reproduce the communications scenarios and estimate the network throughput. The results clearly show that LTE outperforms the other two technologies as it achieves the highest throughput but is limited in terms of coverage. On the other hand, the LoRa and NB-IoT technologies achieve outstanding connectivity for large areas far away from the base stations, making the NB-IoT the solution offering the most valuable trade-of between connectivity and throughput. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), sensor nodes were considered to be an integral part of IoT (Internet of Things) for sensing and monitoring the environment. The IoT-based applications need to be optimized regarding the changing requirements of users as everything is connected via the internet. In today's era, where every day new technologies were rebuilt where sensor nodes plays an important role on it. In every field, whether it is healthcare, smart agriculture, smart home appliances, smart traffic, or smart city sensors were deployed for sensing their environment, collecting data from them, and forwarding it to the servers. These sensor nodes were made up of non-rechargeable power batteries, as a fact efficient energy consumption of these batteries becomes vital. In WSN, efficient energy consumption is still an issue, and its solutions were given by many researchers among them, clustering is considered to be more effective in this domain. For efficient energy consumption, multi-attributes of cluster head selection need to be considered and proper coordination among the conflicting nature of multi-attributes needs to be done. In this paper, we have proposed PROMETHEE II and modified AHP together for cluster heads selection by considering multi-attributes. Twenty-one attributes were considered including connectivity, distance to the base station, residual energy, member nodes, and many more. Being conflicting in nature, proper coordination among these attributes has been done and optimal cluster heads were selected modified for data transmissions. In this paper, modified AHP has been compared with our proposed modified PROMETHEE II and AHP for understanding the significance of this integration. Results is evaluated in terms of energy consumption, network lifetime, and load balancing and it also validate that our proposed approach outperforms with modified AHP and other existing algorithms. Our proposed algorithm enriches network lifetime by balancing the load among sensor nodes which leads to efficient energy consumption. [ABSTRACT FROM AUTHOR]

The method of indeterminate monitoring of household appliances involves analyzing voltage and current signals detected at the power supply. Each electrical appliance exhibits a distinct consumption pattern (e.g., vacuum cleaners, ovens, etc.), including their switch-on and switch-off timings. The monitoring system determines these consumption patterns of electric appliances. This study introduces a wireless sensor network designed for subtle monitoring of household appliance consumption. This study addresses the challenges and issues concerning wireless sensor networks, alongside the notable advantages and capabilities in the areas mentioned above, such as network structure, performance methods, monitoring types, and energy consumption factor analysis. Through empirical investigation and scientific discourse, the study's findings indicate that a higher number of nodes and windows in a building correlates with a reduced rate of energy transfer. Conversely, employing fewer nodes and windows increases the speed at which energy is transferred. [ABSTRACT FROM AUTHOR]

The power grid is a constant target for attacks as they have the potential to affect a large geographical location, thus affecting hundreds of thousands of customers. With the advent of wireless sensor networks in the smart grids, the distributed network has more vulnerabilities than before, giving numerous entry points for an attacker. The power grid operation is usually not hindered by small-scale attacks; it is popularly known to be self-healing and recovers from an attack as the neighboring areas can mitigate the loss and prevent cascading failures. However, the attackers could target users, admins and other control personnel, disabling access to their systems and causing a delay in the required action to be taken. Termed as the biggest machine in the world, the US power grid has only been having an increased risk of outages due to cyber attacks. This work focuses on structuring the attack detection literature in power grids and provides a systematic review and insights into the work done in the past decade in the area of anomaly or attack detection in the domain. [ABSTRACT FROM AUTHOR]

The growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies. [ABSTRACT FROM AUTHOR]

The paper presents the results of developing and creating wireless sensor networks for agriculture and medicine, which the authors have obtained during the past five years. The paper considers wireless sensor networks for estimating the state of plants by the method of chlorophyll fluorescence induction on large areas of agricultural land. The above networks include original smart sensors, built according to flexible architecture and containing elements of artificial intelligence based on neural networks. This construction scheme increases their performance in determining the effect of stress factors of different origins. The authors have constructed a smart remote medical monitoring system based on the wireless sensor network, which employs several wireless data exchange protocols. They also have created a diagnostic system to assess the quality of life and the symptoms associated with the diseases and (or) treatment. It determines the patient's health state using international and national questionnaires. The operation of proposed smart measuring devices and systems is based on the knowledge-based principles of processing and analyzing the measuring data for making managerial decisions and providing recommendations. Most of the obtained results have been put into mass production. [ABSTRACT FROM AUTHOR]

The science of achieving a healthy mind, body, and spirit through objectives and activities is known as personal health (PH). We must be aware of our mental, bodily, and social well-being. The term "hygiene" refers to a wide variety of healthy behaviours. Individuals' healthcare costs and quality of life increased by avoiding or reducing the long-term effects of the disease through knowledge and skills. Biomedical apparel includes sutures, vascular grafts, and biodegradable clothes (BC). Biomedical clothing is anything implanted or incorporated into the human body and used near tissue, blood, or cells. Quick, dependable, and energyefficient connectivity between wireless sensor networks is necessary (WSNs). Physical layers, media access control, networking layers, and control requirements must be co-designed. For those with lesser means, health insurance will increase in cost. There are difficulties with privacy and cyber security, a higher chance of malpractice claims, and increased time and financial expenditures for doctors and patients. In this study, wireless sensor networks-based personal health biomedical clothing (PH-BC-WSN) was utilized to increase access to high-quality healthcare, increase food production through precision agriculture, and raise the standard of human resources. More effective healthcare and medical asset monitoring systems can be developed thanks to the Internet of Things. Eavesdropping on medical data, modification, fabrication of warnings, denial of service, user tracking and location, physical interference with equipment, and electromagnetic threats were extensively discussed. The article gives several instances of current technology, discusses design challenges including energy efficiency, security, and scalability, provides various demonstrations of current technology, and provides a complete analysis of all the advantages and disadvantages. Despite their many benefits, body sensor networks have several significant obstacles and unresolved research problems, which are described along with some potential answers. As a result, the experimental findings demonstrate that PH-BC-WSN enhances accuracy and reduces response time in inpatient health monitoring. [ABSTRACT FROM AUTHOR]

At present, there is little research on the application of wireless sensor networks in the agricultural field. In order to improve the operating performance and practical effects of the intelligent agricultural system, based on data mining technology, this paper uses ZigBee wireless sensor network as the networking technology to cover all aspects of crops under the guidance of the concept of sustainable agricultural development, and realizes the data collection and remote-control process of the agricultural production process, and conducts data analysis and processing through data mining. Moreover, in order to improve the performance of the model, this paper proposes to use the single-point crossover multiple-generation genetic algorithm to optimize the weights and thresholds of the BP neural network to establish the Multi-Generation Genetic Algorithm Back Propagation MGABP model. In addition, in application, the analytic hierarchy process is introduced as the guidance mechanism of neural networks. Finally, this paper designs experiments to analyze the performance of the system constructed in this paper, and uses mathematical statistics to perform statistics on experimental results. The experimental analysis and statistical diagrams of various parameters shows the outcome of this study. The research results show that the intelligent agricultural system model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

By the 2010s, BIM is highly popular research area that gathers researchers from various domains. We tackle the major challenge of BIM and the main barrier for the application of BIM in smart environments: integration of BIM and wireless sensor networks. Existing studies in the literature have various dependencies and propose limited solutions. These solutions are dedicated to specific applications and domains. However, it is highly required to have a generic solution for real time indoor and outdoor environmental monitoring with full integration with the digital model of the building. To fulfill this need, in this study, we propose a novel approach to monitor and fulfill user requirements by a numerical model of the building, a novel framework and a BIMWSN integrated architecture based on the BIM principles for smart building environments. All the experiments are performed on a real testbed, physical sensor devices deployed on the classrooms of the university building. Such numerical model presents the digital model of the environment by gathering acquired data. Besides, data is gathered on a big data platform where it may be served to various other applications such as machine learning studies. [ABSTRACT FROM AUTHOR]

The underwater acoustic sensor network (UASN) plays a crucial role in collecting real-time data from remote areas of the ocean. However, the deployment of UASN poses significant challenges due to the demanding environmental conditions and the considerable expenses associated with its implementation. Therefore, it is essential to design an appropriate routing protocol to effectively address the issues of packet delivery delay, routing void, and energy consumption. In this paper, an adaptive support vector machine (SVM)-based routing (ASVMR) protocol is proposed for the UASN to minimize end-to-end delay and prolong the network lifetime. The proposed protocol employs a distributed routing approach that dynamically optimizes the routing path in real time by considering four types of node state information. Moreover, the ASVMR protocol establishes a "routing vector" spanning from the current node to the sink node and selects a suitable pipe radius according to the packet delivery ratio (PDR). In addition, the ASVMR protocol incorporates future states of sensor nodes into the decision-making process, along with the adoption of a waiting time mechanism and routing void recovery mechanism. Extensive simulation results demonstrate that the proposed ASVMR protocol performs well in terms of the PDR, the hop count, the end-to-end delay, and the energy efficiency in dynamic underwater environments. [ABSTRACT FROM AUTHOR]

The launching of 5G technology provides excellent opportunity for the prosperous development of Internet of Things (IoT) devices and intelligent wireless sensor nodes. However, deploying of tremendous wireless sensor nodes network presents a great challenge to sustainable power supply and self‐powered active sensing. Triboelectric nanogenerator (TENG) has shown great capability for powering wireless sensors and work as self‐powered sensors since its discovery in 2012. Nevertheless, its inherent property of large internal impedance and pulsed "high‐voltage and low‐current" output characteristic seriously limit its direct application as stable power supply. Herein, a generic triboelectric sensor module (TSM) is developed toward managing the high output of TENG into signals that can be directly utilized by commercial electronics. Finally, an IoT‐based smart switching system is realized by integrating the TSM with a typical vertical contact–separation mode TENG and microcontroller, which is able to monitor the real‐time appliance status and location information. Such design of a universal energy solution for triboelectric sensors is applicable for managing and normalizing the wide output range generated from various working modes of TENGs and suitable for facile integration with IoT platform, representing a significant step toward scaling up TENG applications in future smart sensing. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of a wireless control area network (CAN bus) protocol for communication between the smart NOx (nitrogen oxide) sensor on diesel engines and the engine control unit (ECU). In this research, the approach taken is based on a case study of Wärtsilä's smart NOx sensor on a W4L20 diesel engine with the objective of replacing the wired CAN protocol with a wireless CAN communication node. In the current setup, the smart NOx sensor is connected to the engine control unit (ECU) with a wired CAN bus connection. The XBee module, which uses the ZigBee (IEEE 802.15.4) technology was used in the design and implementation of the wireless CAN prototype. With the emergence of 5G networks and the era of IoT, the topic of wireless industrial automation becomes essential in the modern industry. In addition to the great advantages and opportunities that the use of wireless nodes has in automation systems, there are many real challenges. The practical design challenges have been addressed in this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling. [ABSTRACT FROM AUTHOR]

The cluster technique involves the creation of clusters and the selection of a cluster head (CH), which connects sensor nodes, known as cluster members (CM), to the CH. The CH receives data from the CM and collects data from sensor nodes, removing unnecessary data to conserve energy. It compresses the data and transmits them to base stations through multi-hop to reduce network load. Since CMs only communicate with their CH and have a limited range, they avoid redundant information. However, the CH's routing, compression, and aggregation functions consume power quickly compared to other protocols, like TPGF, LQEAR, MPRM, and P-LQCLR. To address energy usage in wireless sensor networks (WSNs), heterogeneous high-power nodes (HPN) are used to balance energy consumption. CHs close to the base station require effective algorithms for improvement. The cluster-based glow-worm optimization technique utilizes random clustering, distributed cluster leader selection, and link-based routing. The cluster head routes data to the next group leader, balancing energy utilization in the WSN. This algorithm reduces energy consumption through multi-hop communication, cluster construction, and cluster head election. The glow-worm optimization technique allows for faster convergence and improved multi-parameter selection. By combining these methods, a new routing scheme is proposed to extend the network's lifetime and balance energy in various environments. However, the proposed model consumes more energy than TPGF, and other protocols for packets with 0 or 1 retransmission count in a 260-node network. This is mainly due to the short INFO packets during the neighbor discovery period and the increased hop count of the proposed derived pathways. Herein, simulations are conducted to evaluate the technique's throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

One of the major challenge in Wireless Sensor Networks (WSN's) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime. [ABSTRACT FROM AUTHOR]

In recent years, chronic diseases, an aging population, and high healthcare costs have become global concerns. The Internet of Things (IoT) is transforming society by enabling physical objects to sense and collect data about their surroundings. It has evolved to encompass a wide range of sensing strategies, and it continues to improve in terms of sophistication and cost reduction. IoT can play an important role in enhancing human health through remote healthcare. The application of advanced IoT technology in healthcare is still a significant challenge due to a number of issues, such as the shortage of cost-effective and accurate smart medical sensors, the absence of standardized IoT architectures, the heterogeneity of connected wearable devices, the multidimensionality of data generated, and the need for interoperability. In order to provide insight into the advance of IoT technologies in healthcare, this paper presents a comprehensive discussion on IoT device capabilities, focusing on the hardware and software systems, as well as the processing abilities, operating systems, and built-in tools. [ABSTRACT FROM AUTHOR]

Introduction: The echo states that networks in the Internet of Things (IoT) are currently being implemented in the widest sense. Echo state networks are fast and efficient recurrent neural networks. This consists of an input layer, a reservoir with many sparsely connected neurons, and an output layer. Issues: In the existing wireless sensor networks, strong mobility may disrupt an existing link between two communicating nodes. There is an inconvenience in data communication, and then it searches for a new node to build a better connection. Methods: To overcome these issues, the recently introduced echo state network (ESN) model opened the way to an extremely efficient approach for designing neural networks for temporal data. The study focuses on the ESN-enabled Intelligent Smart Sensor Design (IS2D) for creating the robotic nervous system with a smart healthcare Digital Nervous System (DNS) using the techniques of IoT, DNS, and Smart Sensor Design and Strain Sensor Fabrication (SSF). Results: Experimental results demonstrate the training set testing against the IS2D, the confusion matrix for ESN outcome, the real-time healthcare monitoring for the DNS, the IS2D sensor accuracy, and the DNS intensity calculation. Discussion: The performance analysis of the proposed model in realistic environments attests to the benefits of energy-centric metrics such as energy consumption, network lifetime, delay, and throughput. Finally, we discuss the challenges and opportunities by summarizing the study and proposing possible future works. The training set testing against the IS2D is based on time count, and the voltage result is estimated. The first portion of the data set should be 11.46% at the initial level. Further, this will increase from 1% to 5%, from 6% to 10%, and from 16% to 28% at the consecutive data set. The confusion matrix for ESN outcome is based on accuracy 28.45% higher than the existing strategies. In this part, the initial accuracy is 8.45% while accessing the initial stage. This value should increase with consecutive data sets from 18.45% to 28.45%. [ABSTRACT FROM AUTHOR]

This paper intends to apply the Intelligent Wireless Sensor Network (IWSN) to the archaeological excavation site and make the dynamic site of archaeological excavation can be monitored intelligently in real-time. This paper firstly discusses the structure and function of Wireless Sensor Network (WSN). Secondly, according to the problem of limited dynamic monitoring of WSN, an IWSN is established. The dynamic environmental monitoring of archaeological excavation sites is achieved by using Geographic Information System (GIS) and remote sensing technology combined with IWSN. Finally, this paper analyses the environmental monitoring results of the archaeological excavation site, the dynamic environmental monitoring location of the archaeological excavation site of the IWSN, and the simulation results of the Intelligent Wireless Sensor Network-Geographic Information System (IWSN-GIS) in the dynamic environmental monitoring of the archaeological excavation site. The results show that the Inverse Distance to a Power (IDP) algorithm in temperature and humidity monitoring data on the overall downward trend, the algorithm performance is relatively stable, and the algorithm performance is better than the other two algorithms. The monitoring performance of IWSN-GIS system is better than that of IWSN system. It shows that with the increase in the communication distance of sensor nodes, the relative error of different monitoring systems for the dynamic monitoring position of the archaeological excavation site is decreasing. IWSN-GIS can monitor the environment before, during, and after the archaeological site. This paper realizes the intelligent, scientific, and technological archaeology of archaeological excavation through IWSN, GIS, and remote sensing image technology, and provides technical support for digital archaeology in archaeological research. [ABSTRACT FROM AUTHOR]

A smart city has a complex hierarchical communication system with various components. It must meet the requirements of fast connection, reliability, and security without data compromise. Internet of Things technology is widely used to provide connectivity and control solutions for smart sensors and other devices using heterogeneous networking technologies. In this paper, we propose a routing solution for Wireless Sensor Networks (WSN) and Mobile Ad hoc NETworks (MANET) with increasing speed, reliability, and sufficient security. Many routing protocols have been proposed for WSNs and MANETs. We combine the Secret Sharing Schemes (SSS) and Redundant Residual Number Systems (RRNS) to provide an efficient mechanism for a Distributed dynamic heterogeneous network Transmission (DT) with new security and reliability routing protocol (DT-RRNS). We analyze the concept of data transmission based on RRNS that divides data into smaller encoded shares and transmits them in parallel, protecting them from attacks on routes by adaptive multipath secured transmission and providing self-correcting properties that improve the reliability and fault tolerance of the entire system. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) enabled wireless sensor network (WSN) is now widely employed in various sectors like smart city and vehicle transportation for their expanded capabilities such as data storage, access, and monitoring. The use of smart sensors that continuously collect data from the smart environment makes these possible. Furthermore, these facilitate the easy access of stored data over a secure IoT-gateway for mobile users. This device mobility that allows shifting to multiple locations, makes it challenging to route data across many access points. In this regard, it induces packet loss and improper node selection, which could result in connection failure and network unreliability. This study proposes a new data routing protocol called as Fuzzy Logic Nodes Distributed Clustering for Energy-Efficient Fault Tolerance (F-NDC-EEFT). It can be deployed on any network platform, including mobile and non-mobile nodes. It considers performance metrics such as delivery rate, withstand node aliveness, communication delay, and energy efficiency to find an optimized path for the better performance of IoT enabled WSNs. The clustering approach is applied to the instant data load, which divides it into the distinct node groups. When proposed algorithm is tested alongside existing routing protocols for performance, it is found to save energy, minimize the number of connection failures, boost the throughput, and increase the network's lifetime. [ABSTRACT FROM AUTHOR]

The current autonomous driving vehicles cannot collaborate with each other due to the disconnection of sensors between vehicles, which leads to poor performance in traffic efficiency and safety. Recently, with the development of wireless intelligent sensors, vehicular sensor network (VSN) is emerging to provide information sharing between vehicles, so that cooperative autonomous driving (CAD) can be enabled. This paper especially investigates the VSN-assisted straight-going CAD at urban intersections under the rule of traffic signals. First, the function of VSN is presented, and the information sharing mechanism is designed. Then distributed stop-and-go strategies with maintaining the optimal safety spacing between adjacent vehicles are proposed for multiple traffic cases. With the proposed strategies, every vehicle can get an exact time to decelerate or accelerate at signalized intersections. Finally, we use the model predictive control (MPC) scheme to actuate our strategies, and it is demonstrated that vehicles perform well in both passage safety and efficiency as expected. [ABSTRACT FROM AUTHOR]

The stationary hierarchical network faces considerable challenges from hotspots and faster network breakdowns, especially in smart monitoring applications. As a solution to this issue, mobile sinks were recommended since they are associated with huge and balanced ways to transfer data and energy across the network. Again, due to the mobile sink node advertisement around the network latency and the energy utilization overheads introduced across the network, ring routing reduces the control overhead while preserving the benefits of the mobile sink, thereby optimizing the energy and improving the network life span. Consequently, we suggested a novel, distributed advanced ring routing strategy, in this work, for the mobile wireless sensor network. Extensive simulations and performance evaluation, in comparison to previous distributed mobile approaches, reveal a 37% and 40% boost in the network throughput and end-to end delay, respectively. Additionally, the lifespan of a network is determined by the control overhead and energy demand. [ABSTRACT FROM AUTHOR]

With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society, such as healthcare, industry, farming, and housing. Accordingly, in recent years, attention to localization has increased significantly since it is one of the main facets of a WSN. In a nutshell, localization is the process in which the position of a sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracies, complexities, and, hence, applicabilities. The localization accuracy is limited by fundamental limitations, theoretical and practical, which restrict the localization accuracy regardless of the technique employed in the localization process. In this article, we pay special attention to such limitations from theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. In addition, a discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives is presented. [ABSTRACT FROM AUTHOR]

For the monitoring and processing of network data, wireless systems are widely used in many industrial applications. With the assistance of wireless sensor networks (WSNs) and the Internet of Things (IoT), smart grids are being explored in many distributed communication systems. They collect data from the surrounding environment and transmit it with the support of a multi-hop system. However, there is still a significant research gap in energy management for IoT devices and smart sensors. Many solutions have been proposed by researchers to cope with efficient routing schemes in smart grid applications. But, reducing energy holes and offering intelligent decisions for forwarding data are remain major problems. Moreover, the management of network traffic on grid nodes while balancing the communication overhead on the routing paths is an also demanding challenge. In this research work, we propose a secure edge-based energy management protocol for a smart grid environment with the support of multi-route management. It strengthens the ability to predict the data forwarding process and improves the management of IoT devices by utilizing a technique of correlation analysis. Moreover, the proposed protocol increases the system's reliability and achieves security goals by employing lightweight authentication with sink coordination. To demonstrate the superiority of our proposed protocol over the chosen existing work, extensive experiments were performed on various network parameters. [ABSTRACT FROM AUTHOR]

With the help of wireless power transfer (WPT) technology, the mobile charger (MC) can transfer energy to the sensor nodes. This technology provides a new solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). However, most of the existing studies focused on improving the charging efficiency or minimizing the latency, while very few studies improved the data accuracy of the network. This article proposes an efficient-energy recharging schedule for MC, aiming to maximize the data accuracy of the given network. Initially, the data accuracy of each sensor is measured by considering the spatial and temporal qualities. The proposed recharging schedule considers the spatial quality contribution of each recharging requested sensor. In addition, an energy management strategy is proposed for each requested sensor to locally adjust the sensing time sequence, aiming to improve the temporal quality. Each sensor might have a different energy consumption rate; therefore, this study also formulates an adaptive recharging request threshold for the sensor nodes, which is suitable for real applications. The experimental study shows that the proposed algorithm outperforms the literature in terms of data accuracy and recharged sensor’s spatial quality contributions. [ABSTRACT FROM AUTHOR]

Modern boom and understanding of our growing needs, applications based on the Mobile Internet of Things (MIoT) are becoming important for future internet technologies. MIoT will force to connect billions of intelligent devices such as sensors connected with the internet. This increase in a mobile application for long-lasting high throughput network sensor coordination plays a crucial role in the design of energy-efficient protocols, specifically in the Medium Access Control (MAC) layer. In this survey, we first propose a taxonomy for classifying mobility-aware MAC protocols in MWSNs. Then we classify the Mobility MAC protocols into the synchronous and asynchronous-based mechanism. For each category, relevant protocols are thoroughly reviewed with the design, and the selected mobility MAC is evaluated under different mobility models. Finally, we discuss open issues associated with the design of mobility MAC protocols gives a start-up for the design of mobile MAC solutions for future large-scale Mobile IoT applications. [ABSTRACT FROM AUTHOR]

As one of the most crucial scenarios of the Internet of Things (IoT), wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for remote environments. The area coverage reflects the perception of WMSNs to the surrounding environment, where a good coverage effect can ensure effective data collection. Given the harsh and complex physical environment of WMSNs, which easily form the sensing overlapping regions and coverage holes by random deployment. The intention of our research is to deal with the optimization problem of maximizing the coverage rate in WMSNs. By proving the NP-hard of the coverage enhancement of WMSNs, inspired by the predation behavior of army ants, this article proposes a novel swarm intelligence (SI) technology army ant search optimizer (AASO) to solve the above problem, which is implemented by five operators: army ant and prey initialization, recruited by prey, attack prey, update prey, and build ant bridge. The simulation results demonstrate that the optimizer shows good performance in terms of exploration and exploitation on benchmark suites when compared to other representative SI algorithms. More importantly, coverage enhancement AASO-based in WMSNs has better merits in terms of coverage effect when compared to existing approaches. [ABSTRACT FROM AUTHOR]

A close correlation between CO2 concentration and aerosol enables the wide utilization of CO2 concentration as a good representation of Severe Acute Respiratory Syndrome-Coronavirus-2 infection airborne transmission. On the other side, many indoor air-quality monitoring devices have been developed for indoor monitoring applications. However, most of them are multiparameter air-quality sensor systems and tend to consume relatively high power, are relatively large devices, and are fairly expensive; therefore, they not meet the requirement for indoor monitoring applications. This paper presents a smart wireless sensor node that can measure and monitor CO2 concentration levels. The node was designed to meet the requirements of indoor air-quality monitoring applications by considering several factors, such as compact size, low cost, and low power, as well as providing real-time, continuous, reliable, and remote measurement. Furthermore, the commercial off-the-shelf and low-power consumption components are chosen to fit with the low-cost development and reduce energy consumption. Moreover, a low-power algorithm and cloud-based data logger also were applied to minimize the total power consumption. This power strategy was applied as a preliminary development toward an autonomous sensor node. The node has a compact size and consumes low energy for one cycle of CO2 measurement, accompanied by high accuracy with very low measurement error. The experiment result revealed the node could measure and monitor in real-time continuous, reliable, and remote CO2 concentration levels in indoor and outdoor environments. A user interface visualizes CO2 concentration graphically and numerically using the Adafruit platform for easy accessibility over the Internet of Things. The developed node is very promising and suitable for indoor CO2 monitoring applications with the acquired data that could be utilized as an indicator to minimize the risk of indoor Severe Acute Respiratory Syndrome-Coronavirus-2 airborne transmission. [ABSTRACT FROM AUTHOR]

With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways. [ABSTRACT FROM AUTHOR]