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Summary In the last decades, wireless sensor networks (WSNs) have appeared as an active research area owing to their high applicability in different application areas like healthcare, automation, and the military. Several issues have been encountered in the WSNs, including energy consumption, deployment of sensor nodes, routing algorithms, energy efficiency, cluster head (CH) selection, and robustness. Cluster‐based routing is one of the energy‐efficient solutions in the WSN. At the same time, the hot spot problem remains a challenging problem, which needs to be addressed in the design of energy‐efficient solutions for WSNs. Motivated by the abovementioned discussion, c The comprehensive comparison study pointed out the betterment of the FLHBA‐CMHR technique in terms of different measures. The FLHBA‐CMHR technique obtains a maximum half‐node die (HND) of 1629 nodes, while the LEACH, FUCA, URBD, DEFL, and E‐FUCA techniques attain decreased HND of 793, 1085, 1092, 1139, and 1344 nodes correspondingly. [ABSTRACT FROM AUTHOR]

Data aggregation using the shortest path is a challenging issue in wireless sensor networks. Data aggregation slows the communication process and consumes many resources available to sensor nodes. So, while transmitting the aggregated data, it is crucial to identify the shortest and most reliable route for energy-efficient data aggregation from the source to the sink node. However, the nodes closer to the sink node are heavily utilized in multi-hop transmission and succumb to early death, which causes energy imbalance and an energy hole problem in the network. To overcome this problem, we propose an innovative node utilization index-based data routing and aggregation (NUIDRA) protocol. The NUIDRA protocol is designed and implemented in two phases. The first phase is to determine the shortest path from the source node to the sink node based on the amount of bandwidth utilized by each sensor node, minimum hop count, node's residual energy, and data aggregation factor. In the second phase, the selected shortest path is used for data transmission and aggregation using the dynamic selection of the aggregator node. The choice of aggregator node is based on the node's utilization index (UI) and adjacent node count from which it receives the data. The NUIDRA protocol is compared and analysed with I-LEACH and QADA protocols. The extensive simulation results show that in the proposed NUIDRA protocol, there is an increase in the average throughput of 70%, packet delivery ratio by 41.93%, and the average latency is reduced by 58.15% as compared to the I-LEACH protocol. Further, there is an increase in the average throughput of 24%, packet delivery ratio by 7.31%, and the average latency is reduced by 53.23% as compared to the QADA protocol for a data packet size of 512 bytes. [ABSTRACT FROM AUTHOR]

The development of tiny sensing nodes efficient for wireless communication in Wireless Sensor Networks (WSNs) can be attributed to the rapid advancements in processors and radio technology. Data transmission occurs through multi-hop routing in WSN, which relies on nodes’ cooperation. The collaboration between nodes has rendered these networks susceptible to various attacks. It is imperative to employ a security scheme to evaluate the dependability of nodes in distinctive malicious nodes from non-malicious nodes. In recent years, there has been a growing significance placed on security-based routing protocols with energy constraints as valuable mechanisms for enhancing the security and performance of WSNs. A novel solution called the Deep Learning-based Hybrid Energy Efficient and Security System (DL-HE2S2) is introduced to address these challenges. The research workflow encompasses various essential stages, namely the deployment of nodes, the creation of clusters, the selection of cluster heads, the detection of malevolent nodes within each group, and the determination of optimal paths intra- and inter-clusters employing the routing algorithm for efficient packet transmission. The design of the algorithm is focused on achieving energy efficiency and enhancing network security while also taking into account various performance metrics, including a mean network lifetime of 187.244 hours, a throughput of 59.88 kilobits per second, an end-to-end latency of 11.939 milliseconds, a packet loss of 14.9%, a packet delivery ratio of 99.194%, network security at 92.026%, and energy usage of 19.424 J. This research examines the algorithm’s scalability and efficiency across various network sizes using a Network Simulator (NS-2). DL-HE2S2 offers valuable insights that can be applied to practical implementations in multiple applications. [ABSTRACT FROM AUTHOR]

A collection of sensor nodes called a wireless sensor network is used to track and document the physical parameters of the surrounding area. The design of network clustering approaches has a big problem when it comes to extending the lifetime of wireless sensor networks (WSNs) and improving energy usage by making sure Both the processing speed and the batteries have a lengthy lifespan. This study presents a framework for machine learning-based channel property variation tracking and learning that is based on a Convolutional Neural Network (CNN)—Long Short-Term Memory (Convolutional-LSTM) network. Our hybrid technique improves sensor connectivity and lowers power consumption, Wireless sensor network longevity is increased. These algorithms are evaluated in a wireless sensor network: Harris Hawks Optimisation (HHO), Coyote Optimisation Algorithm (COY), Support Vector Machine (SVM), and Genetic Algorithm (GA). As for nodes analysis and energy consumption, the article concludes demonstrates the CNN-LSTM technique under consideration outperforms other algorithms. The learning authentication system's robustness and detection performance are thoroughly examined, and exhaustive simulations and testing reveal a notable improvement in the detection accuracy in time-varying scenarios. [ABSTRACT FROM AUTHOR]

Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol's methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415–443, representing 85–90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0–3.6 joules, representing 88–95% energy efficiency of the network; effected network range of 2.98, representing 72–89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72–89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT)-based limited WSN (wireless sensor network) has garnered much interest and advancement over the past several years to enhance resource utilization and service delivery. IoT demands a more robust network for communication and an appropriate location for an energy-efficient WSN for data transport across heterogeneous devices. In addition to the large area that needs to be covered and the restricted communication range of the sensors, WSNs with a single sink may not be appropriate in applications like smart cities. Therefore, Multi-sink WSN solutions appear appropriate for these kinds of applications. Although they boost network speed, the lifespan of the network, and energy usage, multi-sink WSNs are becoming more common. This study leverages deep learning architectures to create a unique resource allocation approach for wireless sensor IoT networks that is both energy-efficient and data-optimized. In this work, the integration of the network's energy efficiency, data optimization and optical wireless communication is processed. In this paper, we present Modified Bat for Node Optimization, a novel approach that uses reinforcement-based Q-learning approaches to improve energy efficiency and resource allocation in Multi-sink Wireless Sensor Networks (WSNs). Additionally, routing with multiple hops is necessary for the WSNS to gather information from sensor nodes and transmit it to the sinking node for decision-making. A reliable and energy-efficient way of communicating data between sensor nodes is provided by Optical Wireless Communication, which leverages RF technology. The best power distribution and relay selection are accomplished using the suggested strategy. Lowering total power transmission and achieving Quality of Service requirements improve resource allocation and relay selection. The simulation outcomes show that the proposed model outperforms traditional ones regarding throughput, energy efficiency, quality of service, frequency efficiency, and network longevity. [ABSTRACT FROM AUTHOR]

Recently, low-cost, low-power sensors have made wireless communication possible for Wireless Sensor Networks (WSNs), which are growing in popularity and bringing about tremendous improvement. Nevertheless, their autonomy eventually wanes because WSN nodes run entirely on batteries. The network lifetime is affected by this energy constraint. Therefore, the proposed method uses Quantum-based Optical sensors with the CUCKOO-ANN method for Enhanced Energy-Efficient Wireless Communication to overcome the existing issues. Initially, the Quantum-based optical sensors helped collect the data from the location and were also used for energy-efficient communication. It transfers the data very quickly and securely without any interruption. Next, the CUCKOO-ANN method optimises the real-time data and provides efficiency. Therefore, it helps to find an optimal route between the user and the Base station. The concept that has been presented exhibits excellent potential as it can efficiently fulfil customer service level agreements, lower average execution times, and improve data centre energy efficiency. Experimental results demonstrate that the proposed energy-efficient method minimises energy consumption, optimises data transmission, improves the network lifetime, and enhances the scalability of the network. [ABSTRACT FROM AUTHOR]

High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs' dynamic nature, which reflects in the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method's performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model. [ABSTRACT FROM AUTHOR]

Wireless transmission of information using water as a communication channel is one of the potential technologies for the progress of potential underwater observation systems including applications from aquaculture to the oil industry, submarine detection, instrument monitoring, and forecast of natural turbulence and study of marine life. Underwater wireless sensor networks applications are exhilarating but demanding at the same time. Some of the challenges faced by underwater networks are the movement of nodes in three-dimensional space because of which the network could not be well thought-out as a fixed topology, limited bandwidth, exceedingly low data rates, limited battery power, failures due to pollution, and corrosion, and a lot more. Additionally, as acoustic communications consume more power than terrestrial radio ones do, more sophisticated signal processing methods are needed at the receiver to make up for the channel's deficiencies. One of these difficulties is creating a routing protocol for the complexity of an underwater environment. The major aim of all these protocols is to advance data packets toward the sink to lessen the propagation delay, lessen energy expenditure, and utilize the least amount of bandwidth by restricting the broadcasting of multiple copies of the data packet. This article surveys various routing schemes and various challenges involved in the design and development of these routing schemes. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) have a wide variety of application areas and one of these areas is border crossing security. Unauthorized crossing of border areas, unauthorized arms and drug trafficking can be avoided at a lower cost and easier than conventional methods by monitoring the borders with the help of a WSN. In this study, we offer a mathematical model that guarantees the detection of possible intruders by scheduling the activities of the sensors whatever the route the intruder follows throughout the border zone or whatever the time the intruder enters to the route. To achieve the highest possible WSN management efficiency, we integrate coverage, routing, data routing, and sensor scheduling WSN design issues into the mathematical model. We first demonstrate the effectiveness of scheduling the sensors by the help of the offered mathematical model by comparing it against a random activity schedule of the sensors with respect to network lifetime and intruder detection ratio performance measures. We also develop a Lagrangean heuristic strategy to solve realistic sized instances of the proposed problem. We produce several random border zone instances with varying sizes and test the proposed solution strategy to illustrate the effectiveness of the offered solution strategy by comparing its performance against the performance of a commercial mixed-integer linear programming (MILP) solver. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to examine the difficulties encountered in underwater acoustic Sensor Networks (UASN) due to empty spaces in acoustic route routing. The presence of empty areas inside the network often results in the unsuccessful transmission of data packets in sparsely populated underwater acoustic sensor networks (UASNs). The current techniques used to identify and circumvent empty spaces are characterized by a significant expenditure of time and a reliance on location data. To tackle this issue, the study introduces a new protocol named Secure Opportunistic Vacant Hole Avoidance Routing (SOVHAR), which aims to enhance routing pathways by disregarding vacant spaces. The proposed approach guarantees the continuous transmission of packets to the intended destination by using a local neighbourhood search strategy on sensor nodes. This strategy considers several criteria, such as node depth, signal amplitude, and the number of intermediate hops. The protocol further considers variables such as salinity, losses, and noise level to enhance the precision of the localized search for energy-efficient packet transmission. A fitness function is established to assess the efficacy of a sensor node in transmitting data packets, therefore aiding in avoiding areas with limited coverage while minimizing additional costs. The efficacy of the proposed SOVHAR is assessed via simulations, demonstrating its energy efficiency and capability to avoid empty spaces while considering different communication factors. This evaluation highlights its enhanced resilience compared to current methodologies. [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]

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements. [ABSTRACT FROM AUTHOR]

Research in the field of Wireless Sensor Networks aims to develop protocols ensuring minimal energy dissipation. In this work, we propose a lightweight congestion control based sink mobility solution to improve data gathering that considers jointly energy and memory constraints. Thus, we propose a mechanism to avoid and resolve situations when nodes may not receive new messages because of buffer saturation. This can causes data loss and energy waste while retransmitting non-received data by the source node. Indeed, our model moves the sink close to the congested nodes to alleviate their queues once a congestion risk was detected. The performances of our approach were approved by intensive simulations. Obtained results proved that the network lifetime achieved by our strategy is two to five times better than that reached by comparative approaches. We note also a considerable improvement in packet delivery ratio and a high adaptability to increased data traffic. [ABSTRACT FROM AUTHOR]

Summary: Artificial intelligence (AI)‐based wireless sensor network technology is the future of advancement for real‐time applications. With AI wireless sensor network technology, it is possible to collect data from any environment, analyze in real time, and use it to optimize processes and operations. AI wireless sensor network technology provides an unprecedented level of accuracy as well as the ability to detect even the slightest changes in a given environment. The AI‐based approach uses clustering‐based techniques with self‐organizing map (SOM) for energy conservation in resource‐constrained networks. By clustering the network, it becomes more energy efficient, as data can be shared among members of a cluster without needing to be transmitted across multiple nodes. The proposed AI cluster‐based routing approach outperforms in terms of energy consumption and computational challenges of the network. The results obtained demonstrate the proposed approach to achieve lower energy consumption than the existing algorithms while providing the same level of performance in terms of throughput and latency, as well as a comparison with traditional justification techniques. [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]

Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their wide range of applications, such as environmental monitoring, smart agriculture, and structural health monitoring. With the increasing volume of data generated by WSNs, efficient data analytics techniques are crucial for improving the overall performance and reducing energy consumption. This paper presents a novel distributed data analytics approach for WSNs using fuzzy logic-based machine learning. The proposed method combines the advantages of fuzzy logic for handling uncertainty and imprecision with the adaptability of machine learning techniques. It enables sensor nodes to process and analyze data locally, reducing the need for data transmission and consequently saving energy. Furthermore, this approach enhances data accuracy and fault tolerance by incorporating the fusion of heterogeneous sensor data. The proposed technique is evaluated on a series of real-world and synthetic datasets, demonstrating its effectiveness in improving the overall network performance, energy efficiency, and fault tolerance. The results indicate the potential of our approach to be applied in various WSN applications that demand low-energy consumption and reliable data analysis. [ABSTRACT FROM AUTHOR]

With the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor networks (WRSNs) are broadly utilized in numerous areas. However, the limited battery capacity of sensor nodes (SNs) is considered as a critical issue. To extend the battery life of SNs, mobile chargers (MCs) equipped with wireless power transfer (WPT) technology have been proposed as a key solution for charging SNs. Using directional antennas to focus energy within a specific area, as opposed to an omnidirectional antenna, increases the energy efficiency of an MC. In this paper, we focus on the travel path charging scheduling problem with a directional MC in on-demand WRSNs. Our goals are to develop a mechanism to reduce the changing delay time and boost the energy efficiency of MC. In this case, the MC receives the charging requests of SNs and responds to them by selecting appropriate stopping points (SPs) and the charging orientation angles in each SP. We propose a mobile directional charging scheduling (MDCS) solution based on a deep reinforcement learning technique. The simulation results demonstrate the superior performance of our method to existing studies in terms of the energy consumption of the MC, the number of dead SNs, and charging delay time. [ABSTRACT FROM AUTHOR]

One of the major significant problems in the existing techniques in Wireless Sensor Networks (WSNs) is Energy Efficiency (EE) because sensor nodes are battery-powered devices. The energy-efficient data transmission and routing to the sink are critical challenges because WSNs have inherent resource limitations. On the other hand, the clustering process is a crucial strategy that can rapidly increase network lifetime. As a result, WSNs require an energy-efficient routing strategy with optimum route election. These issues are overcome by using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm for Data Aggregation and Cluster-Based Routing in WSN (TFR-DLN-PMPOA-WSN) which is proposed to expand the network lifetime. Initially, Tasmanian Fully Recurrent Deep Learning Network (TFR-DLN) is proposed to elect the Optimal Cluster Head (OCH). After OCH selection, the three parameters, trust, connectivity, and QoS, are optimized for secure routing with the help of the Pelican Variable Marine Predators Optimization Algorithm (PMPOA). Finally, the proposed method finds the minimum distance among the nodes and selects the best routing to increase energy efficiency. The proposed approach will be activated in MATLAB. The efficacy of the TFR-DLN- PMPOA-WSN approach is assessed in terms of several performances. It achieves higher throughput, higher packet delivery ratio, higher detection rate, lower delay, lower energy utilization, and higher network lifespan than the existing methods. [ABSTRACT FROM AUTHOR]

Summary: In the upcoming era, one of the emergent technologies is "Wireless Sensor Networks (WSNs)," which has provided various solutions in the field of defense, surveillance, and so on. But the main problems in the WSNs are security and energy‐efficient routing to the researchers. The deployment of security algorithms can monitor only the unauthorized signals to ensure security, but it lacks and leads to higher energy consumption, whereas the deployment of energy‐efficient routing algorithms is used only for the selection of routing paths, but it fails to ensure security. Hence, the main intent of this research work is to solve both problems by the following proposed models. Model 1 is "Identity‐Based Aggregate Signatures (IBAS)" that is the most effective security‐based framework protocol in this research work. Model 2 is the integration of IBAS and "Fuzzy Logic System (FLS)" that acts as an energy‐efficient algorithm, and here, FLS is used to find the optimal path from source to destination to transmit the data. Model 3 is the combination of IBAS and the "Whale Optimization Algorithm (WOA)" for higher energy efficiency and auto localization by analyzing the entire route in WSN. Model 4 is the combination of IBAS, WOA, and the "Reinforcement Theory of WOA (RTWOA)" energy‐efficiency model with a twofold and weight factor strategy for better speed and accuracy. These models ensure security, energy efficiency, and auto localization. The simulation parameters such as "Energy Efficiency," "Packet Delivery Ratio," "Average delay," "Throughput," and "Energy Consumption" are evaluated, and results show that the performance of energy efficiency and security is higher in Model 4 when compared to the other three models. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANET) is a wireless sensor network with high dynamic topologies that provides reliable routing for data packet transmission. Determining the optimal routing multipath along with the energy efficient multipath is a challenging task among the researchers and multiple routing protocols are initiated but have some limitations such as increased delay, high energy consumption and low reliability. Though recent studies highlight the working of the multipath routing protocols, the complexity of the protocols increases when there is an increase in the number of paths and hence, the selection of the path is a tedious and challenging task. In this research, the optimal multipath is determined using Gallus livia bird optimization (GLBO)-based routing, that helps in the determination of the more efficient, trustworthy and energy efficient path. The GLBO algorithm is developed by the standard hybridization of the characteristics of the Gallus and livia birds, where the foraging speed of the Gallus is enhanced that helps in obtaining an optimized routing path. The efficiency of the research is proved by measuring the number of alive nodes in final iterations, where the GLBO method consists of 44 alive nodes during the iteration 100, which tend to be more efficient. Similarly reduced delay of 0.557 sec is obtained during the transmission, which tends to reduce the energy and maximizes the lifetime of the network. The energy remained during the final iterations is measured in Joule, where the proposed method attained 0.073 Joules with the reliability of 48.520%. The overall analysis of the network shows that the GLBO based routing method works more efficiently compared to some state of art methods. [ABSTRACT FROM AUTHOR]

Radio‐Frequency Identification and Wireless Sensor Networks (WSN) are two basic pillars in the Internet of Things (IOT); they are used to identify and track objects, and collect information from the environment in various applications of the IOT. One of the architectures used for combining these two technologies is the combination of readers with wireless sensor nodes. In the combination of Radio‐Frequency Identification and WSN, we face issues concerning reader collision, tag rereading and load imbalance. In the dense environments of this hybrid network, due to congestion and the possible mobility of nodes, controlling access to the shared channel is one of the basic challenges. In addition, owing to the energy limitations of these networks, data redundancy and tag re‐reading are critical issues. An effective anti‐collision protocol suitable for RFID‐WSN networks is proposed to avoid the above‐mentioned issues. The Energy‐saving and Anti‐collision Protocol (EAP) method is a CSMA‐based multi‐channel protocol that not only solves the issue of readback collisions, but also, through tagging, it prevents the rereading of the tags. To evaluate the performance of the EAP method, this protocol is compared with the most prominent reader anti‐collision protocols ever presented. The results indicate that due to the better throughput and higher energy efficiency that the proposed protocol has, when compared to other multi‐channel protocols, it will be a better option for use in the IOT platform with dense conditions. [ABSTRACT FROM AUTHOR]

Wireless sensor networks consist of a large number of randomly distributed nodes in a given area. WSN nodes are battery-powered, so they lose all their energy after a certain period and this energy constraint affects the network lifetime. This study aims to maximize network lifetime while minimizing overall energy use. In this study, a novel Energy Efficient Cluster based Adaptive Routing (ECAR) approach has been proposed for large-scale WSNs. Initially, the Genetic Bee Colony algorithm (GBCA) is introduced, which provides an effective way for selecting cluster heads based on node degrees, node centralities, distances to neighbors, and residual energy. Consequently, the Quantum Inspired African Vulture Optimization algorithm (QIAVO) is utilized to find a routing path between the source and the destination over the cluster heads. To optimize the network performance, QIAVO considers multiple objectives, including residual energy, distance, and node degree. The proposed method is evaluated based on average packet delivery ratios, energy consumption, and average end-to-end delays. According to simulation results, the proposed protocol successfully balances the energy consumption of all sensor nodes and increases network lifespan. [ABSTRACT FROM AUTHOR]

In wireless-sensing networks (WSNs), the energy economy has lately emerged as the main problem. Since sensor networks run on batteries, they eventually run out of power. To increase the packet transmission ratio for sensing devices, it becomes more difficult to enhance data loss in an energy-efficient manner. In WSNs, the mobile drain causes high network energy usage and data delay. This paper suggests an Improved Ant Colony Clustering-Based Data Transmission Algorithm (EACODT) that first develops the network nodes' energy density function before allocating sensing nodes with higher residual energy as cluster leaders using the energy density function. The EACODT is thoroughly modeled for different WSN situations with variable numbers of sensing nodes and CHs, and the findings are contrasted with some recently developed meta-heuristic algorithms. As a consequence, it is discovered that EACODT gets 34% of energy usage, 98.8% of network lifespan, 95% of packet delivery ratio, 854 kbps of transmission, and a 98% convergence rate. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing "Region based Hierarchical Clustering for Efficient Routing (RHCER)"—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio. [ABSTRACT FROM AUTHOR]

The diversity of applications supported by Underwater Sensor Networks (UWSNs) explains the success of this type of network and the increasing interest in exploiting and monitoring seas and oceans. One of the most important research fields is network deployment, since this deployment will affect all other research aspects in the UWSNs. Moreover, the initial random deployment resulting from scattering underwater sensor nodes on the network area's surface does not ensure this area's coverage and network connectivity. In this research, we propose a self-adjustment redeployment protocol that enhances network coverage and connectivity while reducing the energy consumed during network deployment. This protocol takes into account the peculiar dynamism of the underwater environment due to the water currents. First, we study the impact of these water currents on network deployment. Then, we exploit these water currents to adjust the nodes' positions to achieve total area coverage and reduce the energy consumed during the deployment by reducing the total distance traveled by the underwater sensor nodes. Simulation results show that the proposed protocol achieves a very high coverage rate (97%) and reduces the distance traveled by nodes during the deployment by 41%. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are widely recognized as a crucial enabling technology for Internet of Things applications. One of the primary challenges in designing WSNs is to ensure efficient energy management, which involves minimizing and uniformly distributing energy consumption among the wireless sensors to extend the network lifetime. In this paper, we propose a set of mathematical tools in the form of fitness functions that can be used to measure, compare, and control how the network manages energy consumption among wireless sensors. Furthermore, we present an Optimized Energy Balanced and Distributed Clustering (OEBDC) protocol for WSNs, which utilizes these fitness functions to manage energy resources more efficiently by promoting uniform energy consumption, minimizing communication overhead, and extending network lifetime. The proposed tools can be integrated with other WSN protocols to manage energy resources according to specific requirements that suited different applications. We have evaluated the performance of the proposed protocol against well-established routing protocols for WSNs and found that OEBDC achieves a notable advantage in extending network lifetime compared to other protocols, while also demonstrating robust control in managing energy resources. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are a group of devices/sensors which are connected as a network for transferring and receiving the data observed from the environment through intermediate links. Energy efficiency and security during data broadcasting are considered challenging tasks in the WSN. These challenging tasks are considered as a motivation of this research and the Multi-Objective - Trust Aware Average Inertia Weighted Cat Swarm Optimization (MO-TAIWCSO) is proposed for achieving secure reliable transmission over the WSN. Due to an effective velocity update of searching process, the AIWCSO is selected for discovering an optimal solutions. The developed MO-TAIWCSO is optimized by using the trust, energy ratio, communication cost, and degree of SCH. This MO-TAIWCSO performs optimal Secure Cluster Head (SCH) and secure path discovery for the secure transmission of data under malicious attacks. The main objective of this MO-TAIWCSO is to improve the data delivery while minimizing the energy usage of the nodes. The performance of the MO-TAIWCSO method is analyzed by using the throughput, Packet Delivery Ratio (PDR), energy consumption, network lifetime, Normalized Routing Load (NRL) and End to end delay (EED). The existing researches namely ETOR and TBSEER are used to evaluate the MO-TAIWCSO. The PDR of MO-TAIWCSO for 100 nodes is 99.97%, which is high when compared to the ETOR and TBSEER. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively. [ABSTRACT FROM AUTHOR]

Battery replacement or recharging is essential for sensor nodes because they are typically powered by batteries in wireless sensor network (WSN) applications. Therefore, creating an energy-efficient data transfer technique is required. The base station (BS) receives data from one sensor node and routes the data to another sensor node. As a result, an energy-efficient routing algorithm using fuzzy logic (EERF) represents a novel approach that is suggested in this study. One of the reasoning techniques utilized in scenarios where there is a lot of ambiguity is fuzzy logic. The remaining energy, the distance between the sensor node and the base station, and the total number of connected sensor nodes are all inputs given to the fuzzy system of the proposed EERF algorithm. The proposed EERF is contrasted with the current systems, like the energy-aware unequal clustering using fuzzy logic (EAUCF) and distributed unequal clustering using fuzzy logic (DUCF) algorithms, in terms of evaluation criteria, including energy consumption, the number of active sensor nodes for each round in the network, and network stability. EAUCF and DUCF were outperformed by EERF. [ABSTRACT FROM AUTHOR]

The maximization of the network lifespan is the main goal of designing SDN-enabled WSNs with limited network resource utilization. Clustering and routing techniques are widely used to balance the network load in SDN-enabled WSNs and achieve energy-efficient and stable network performance. However, one of the critical challenges in clustering is the selection of optimal control nodes (CNs). The CNs selection is challenging due to the high computational complexity during global optimization. Thus, it is necessary to optimize the CNs selection for the routing process with limited network resources. An intelligent and hybrid model of genetic mutation-based whale optimization (GM-WOA) is proposed to optimize the process of CNs selection in heterogeneous SDN-enabled WSN. In the GM-WOA approach, the self-adaptive inertia weights, fitness function and genetic mutation operation are used for dynamic CN selection and energy-efficient data transmission. The genetic mutation approach distributes CNs more evenly throughout the network to balance the network load. The GM-WOA performs at ONOS controller and simulated using ns-3 simulator. The simulation results demonstrate that the proposed nature-inspired GM-WOA approach is superior to existing techniques in terms of various performance metrics. These metrics include stability period, alive nodes per round, packets transmitted to control server, network residual energy, fitness value, average delay, and Jitter [ABSTRACT FROM AUTHOR]

In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are applied in different domains such as agriculture, military, healthcare, and defense. The most critical issue in wireless sensor networks is achieving greater energy efficiency fairness measurement. This research proposes new evolutionary modeling using genetic algorithms with an energy-efficient clustering routing strategy to resolve this critical issue in healthcare networks of hospitals. This article develops a new model of homogeneous and heterogeneous network structures with the novelty of combining the consumption model of original energy to construct new evolutionary modeling to predict the optimal clusters count for minimizing the consumption of the total energy in wireless sensor healthcare applications. The simulation results show that the lifetime is maximum when the routing factor, α = 0.1 compared to other values of α. For the homogeneous healthcare networks instances, the death round value of the first node is for a smaller value of α. The better routing factor is obtained as α ranging from 0.2 to 0.5 for homogeneous and heterogeneous healthcare networks instances. For the range of energy levels that ranges from 0.4 to 0.8, the proposed evolutionary model achieves better measures for the rounds from 1800 up to 2400 compared to the recent methods. The proposed model evaluates optimal clusters based on the structure of the healthcare network, and then the clusters head is identified based on the clustering locations. The proposed method achieves a higher throughput percentage in all cases of homogeneous and heterogeneous instances where the energy lies between (0.4, 0.8). Simulation outcomes of the proposed evolutionary model result in the performance metrics mainly significant reduction of energy consumption decay rate of the network while maximizing the healthcare network's lifetime by improving the network's throughput compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Trustworthiness and energy efficiency are two important aspects of data gathering in Wireless Sensor Networks (WSNs). The first criterion can be fulfilled by adopting trustworthy nodes for data communication along with choosing watchdogs for monitoring. Additionally, the use of the clustering scheme reduces energy exhaustion substantially. Accordingly, effective data gathering requires trust-aware and energy-efficient clustering, data gathering tree construction, and watchdog selection. The previous data gathering algorithms did not include all of the clustering, tree construction, and watchdog selection phases. Furthermore, some studies proposed greedy schemes to solve the mentioned phases and had low performance. In this paper, we propose the Trust-aware and Energy-efficient Data Gathering (TEDG) algorithm to gather data more effectively. The proposed scheme comprises all the above-mentioned phases, including clustering, tree construction, and watchdog selection. These phases are modeled as optimization problems, and they are solved using Particle Swarm Optimization (PSO). The watchdog selection phase has variable-length particles because the number of watchdogs has not been unknown. Novel particle representation and initialization schemes are proposed to handle these particles. According to the performed simulations, TEDG improves consumed energy for data delivery to the sink, standard deviation of the residual energy of nodes, and network lifetime by 220%, 81%, and 129%, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a key technology to interconnect the real and digital worlds, enabling the development of smart cities and services. The timely collection of data is essential for IoT services. In scenarios such as agriculture, industry, transportation, public safety, and health, wireless sensor networks (WSNs) play a fundamental role in fulfilling this task. However, WSNs are commonly deployed in sensitive and remote environments, thus facing the challenge of jamming attacks. Therefore, these networks need to have the ability to detect such attacks and adopt countermeasures to guarantee connectivity and operation. In this work, we propose a novel clustering-based self-healing strategy to overcome jamming attacks, in which we denominate fairness cooperation with power allocation (FCPA). The proposed strategy, aware of the presence of the jammer, clusters the network and designates a cluster head that acts as a sink node to collect information from its cluster. Then, the most convenient routes to overcome the jamming are identified and the transmit power is adjusted to the minimum value required to guarantee the reliability of each link. Finally, through the weighted use of the relays, the lifetime of each subnetwork is extended. To show the impact of each capability of FCPA, we compare it with multiple benchmarks that only partially possess these capabilities. In the proposal evaluation, we consider a WSN composed of 64 static nodes distributed in a square area. Meanwhile, to assess the impact of the jamming attack, we consider seven different locations of the attacker. All experiments started with each node's battery full and stopped after one of these batteries was depleted. In these scenarios, FCPA outperforms all other strategies by more than 50 % of the information transmitted, due to the efficient use of relay power, through the weighted balance of cooperative routes. On average, FCPA permits 967,961 kb of information transmitted and 63 % of residual energy, as energy efficiency, from all the analyzed scenarios. Additionally, the proposed clustering-based self-healing strategy adapts to the change of jammer location, outperforming the rest of the strategies in terms of information transmitted and energy efficiency in all evaluated scenarios. [ABSTRACT FROM AUTHOR]

Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors' residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH's number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime. [ABSTRACT FROM AUTHOR]

In order to address the issue of spectrum scarcity, cognitive radio (CR) offers an effective usage of radio spectrum resources by offering dynamic spectrum access. One kind of wireless sensor networks having CR capabilities are wireless cognitive radio sensor networks (WCRSNs). The present work investigates throughput and energy-efficiency of WCRSN. More precisely, scenarios involving both noise and α - μ fading that affect the sensing (S) channels are taken into consideration. Each cognitive radio sensor (CRS) receives an unknown licensed signal information from the primary user (PU). The CRS uses an energy detection sensing technique and makes a local decision in one-bit binary form. The decisions from all the CRSs are forwarded to control center via reporting (R) channels and are combined based on the hard-decision fusion technique for making the final decision about active and inactive status of the PU. Throughput and energy-efficiency performances of WCRSN are assessed while taking into account the effects of pertinent network factors. To that end, first a brand-new mathematical probability of detection that is subject to noise plus generalized α - μ fading is established. Additionally, a computer based simulation and experimental setup are performed to verify the resulting expression. The analytical frameworks for assessing throughput and energy-efficiency performances under a variety of network and channel situations are then developed. Further, the effect of inaccurate S and R channels with channel error probability (q) on the general effectiveness of WCRSN is also examined. Finally, the effects of the α - μ fading parameters, the signal-to-noise ratio, the number of CRSs, and the detection threshold on the WCRSN are examined. For a number of network characteristics, throughput is maximized and energy-efficiency is up to 75% has been achieved. [ABSTRACT FROM AUTHOR]

Sensor networks, as a special subtype of wireless networks, consist of sets of wirelessly connected sensor nodes often placed in hard-to-reach environments. Therefore, it is expected that sensor nodes will not be powered from the power grid. Instead, sensor nodes have their own power sources, the replacement of which is often impractical and requires additional costs, so it is necessary to ensure minimum energy consumption. For that reason, the energy efficiency of wireless sensor networks used for monitoring environmental parameters is essential, especially in remote networking scenarios. In this paper, an overview of the latest research progress on wireless sensor networks based on LoRa was provided. Furthermore, analyses of energy consumption of sensor nodes used in agriculture to observe environmental parameters were conducted using the results of real measurements in the field, as well as simulations carried out based on collected data about real equipment. Optimization methods of energy consumption, in terms of choosing the appropriate data collection processes from the conducted field measurements, as well as the settings of network radio parameters imitating real conditions used in conducted simulations were highlighted. In the analyses, special emphasis was placed on choosing the optimal packet size. Unlike in other papers analyzing energy efficiency of LoRa communication, in this paper, it was proven that the adjustment of the transmission speed to the actual size of the packet is important for better energy efficiency of communication and that it can reduce energy consumption considerably. Moreover, in the paper, the contents of a packet that can be used in precision agriculture is suggested in order to prove that the 6-bit packet is sufficient for energy-efficient collection of parameters from the environment, in contrast to the 11-bit packets used in standard commercially available equipment. [ABSTRACT FROM AUTHOR]

Recently, research into Wireless Body-Area Sensor Networks (WBASN) or Wireless Body-Area Networks (WBAN) has gained much importance in medical applications, and now plays a significant role in patient monitoring. Among the various operations, routing is still recognized as a resource-intensive activity. As a result, designing an energy-efficient routing system for WBAN is critical. The existing routing algorithms focus more on energy efficiency than security. However, security attacks will lead to more energy consumption, which will reduce overall network performance. To handle the issues of reliability, energy efficiency, and security in WBAN, a new cluster-based secure routing protocol called the Secure Optimal Path-Routing (SOPR) protocol has been proposed in this paper. This proposed algorithm provides security by identifying and avoiding black-hole attacks on one side, and by sending data packets in encrypted form on the other side to strengthen communication security in WBANs. The main advantages of implementing the proposed protocol include improved overall network performance by increasing the packet-delivery ratio and reducing attack-detection overheads, detection time, energy consumption, and delay. [ABSTRACT FROM AUTHOR]

Introduction-- Smart irrigation systems require reliable and energy-efficient communication between the sensors in a Wireless Sensor Network (WSN) and the control system. Objective-- To address this challenge, this paper presents several cluster-based selection protocols, based on the Stable Election Protocol (SEP) and Distributed Energy-Efficient Clustering (DEEC). Methodology-- The presented protocols divide the agriculture field into sub-fields to reduce energy consumption between far sensors and the Base Station (BS). Results-- Comparison with traditional protocols using evaluation metrics such as network throughput, stability, instability period, and lifetime, shows that the presented protocols outperform in terms of all metrics. Conclusions-- The results indicate the effectiveness of the proposed protocols in prolonging the network's lifetime and improving energy efficiency in heterogeneous WSNs, thus supporting the performance of smart irrigation systems. Numerically, using the proposed protocols, the network lifetime increased by 23% compared to conventional SEP. [ABSTRACT FROM AUTHOR]

Recently, unmanned aerial vehicles (UAVs) have emerged as a viable solution for data collection from remote Internet of Things (IoT) applications. However, the successful implementation in this regard necessitates the development of a reliable and energy-efficient routing protocol. This paper proposes a reliable and an energy-efficient UAV-assisted clustering hierarchical (EEUCH) protocol designed for remote wireless sensor networks (WSNs) based IoT applications. The proposed EEUCH routing protocol facilitates UAVs to collect data from ground sensor nodes (SNs) that are equipped with wake-up radios (WuRs) and deployed remotely from the base station (BS) in the field of interest (FoI). During each round of the EEUCH protocol, the UAVs arrive at the predefined hovering positions at the FoI, perform clear channel assignment, and broadcast wake-up calls (WuCs) to the SNs. Upon receiving the WuCs by the SNs' wake-up receivers, the SNs perform carrier sense multiple access/collision avoidance before sending joining requests to ensure reliability and cluster-memberships with the particular UAV whose WuC is received. The cluster-member SNs turn on their main radios (MRs) for data packet transmission. The UAV assigns time division multiple access (TDMA) slots to each of its cluster-member SNs whose joining request is received. Each SN must send the data packets in its assigned TDMA slot. When data packets are successfully received by the UAV, it sends acknowledgments to the SNs, after which the SNs turn off their MRs, completing a single round of the protocol. The proposed EEUCH routing protocol with WuR eliminates the issue of cluster overlapping, improves the overall performance, and increases network stability time by a factor of 8.7. It also improves energy efficiency by a factor of 12.55, resulting in a longer network lifespan compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Moreover, EEUCH collects 5.05 times more data from the FoI than LEACH. These results are based on simulations in which the EEUCH protocol outperformed the existing six benchmark routing protocols proposed for homogeneous, two-tier, and three-tier heterogeneous WSNs. [ABSTRACT FROM AUTHOR]

Security is one of the major concerns while designing robust protocols for underwater sensor networks (UWSNs). The underwater sensor node (USN) is an example of medium access control (MAC) that should control underwater UWSN, and underwater vehicles (UV) combined. Therefore, our proposed method, in this research, investigates UWSN combined with UV optimized as an underwater vehicular wireless network (UVWSN) that can completely detect malicious node attacks (MNA) from the network. Thus, MNA that engages the USN channel and launches MNA is resolved by our proposed protocol through SDAA (secure data aggregation and authentication) protocol deployed in UVWSN. SDAA protocol plays a significant role in secure data communication, as the cluster-based network design (CBND) network organization creates a concise, stable, and energy-efficient network. This paper introduces SDAA optimized network known as UVWSN. In this proposed SDAA protocol, the cluster head (CH) is authenticated through the gateway (GW) and the base station (BS) to guarantee that a legitimate USN oversees all clusters deployed in the UVWSN are securely established for providing trustworthiness/privacy. Furthermore, the communicated data in the UVWSN network guarantee that data transmission is secure due to the optimized SDAA models in the network. Thus, the USNs deployed in the UVWSN are securely confirmed to maintain secure data communication in CBND for energy efficiency. The proposed method is implemented and validated on the UVWSN for measuring reliability, delay, and energy efficiency in the network. The proposed method is utilized for monitoring scenarios for inspecting vehicles or ship structures in the ocean. Based on the testing results, the proposed SDAA protocol methods improve energy efficiency and reduce network delay compared to other standard secure MAC methods. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have proven to be a promising paradigm due to their wide applicability in the real‐world scenarios. Because of the resource constraint, type‐variant and battery‐powered features of the sensor nodes, the key challenge is how to improve the energy usage and network life in the heterogeneous sensor networks. In this paper, the Mahalanobis distance based K‐Means algorithm is integrated with a novel evolutionary approach for clustering called calf search optimization algorithm (K‐CSOA) that creates energy efficient clusters. The routing in the network is performed using a power efficient ant colony optimization algorithm (ACO). The extensive and multiple simulations are performed to validate the effectiveness of the proposed method. It is observed that the running time of the proposed scheme is lowered due to the implementation of hybrid clustering. The results are validated for various performance metrics wherein the proposed method shows 41.6% and 4.3%–12% reduction in energy expenditure and end‐to‐end delay respectively and around 60% enhancement in the throughput of the network when compared with other similar state‐of‐the‐art methods. In addition to this, statistical analysis is also carried out to identify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) comprises sensor networks, intelligent things, devices, and humans for heterogeneous services and applications. Energy constraints in conventional wireless networks impact IoT performance resulting in service failures. For reducing the adverse impact of energy, this article introduces a commissioned energy‐efficient resource virtualization (CE2RV) scheme. This proposed scheme classifies the sensor nodes as fast and slow‐depleting for identifying service failures. The fast‐depleting nodes are discontinued from the service replications, and the remaining energy high‐node‐connected resources are identified. Such resources are virtualized for thwarting the existing energy failures over various services. The node classifications are performed using a tree‐learning algorithm. The classifications are performed for node replacement and service virtualization under different energy depletion rates. This is required for preventing sensor network disconnections between the users and service providers. The classification is required for overcoming multiple virtualizations between common nodes across different service providers. The proposed scheme's performance is analyzed using the metrics of service disconnections, energy utilization, energy efficiency, and service delay. [ABSTRACT FROM AUTHOR]

Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil medium, while the channel quality of WUSNs is greatly impacted by the underground environment, such as soil moisture and composition. Due to the precipitation and harsh weather, the underground environments change frequently, which make wireless communication in WUSNs much complicated than that in terrestrial over-the-air wireless sensor networks. To achieve reliable and energy-efficient data gathering in dynamic WUSNs, this article proposes an optimal transmission policy, where path loss of sensory data transmission, energy constraint, and network load balancing are the factors to be considered. Specifically, we capture the effect of underground environments on wireless communications, and evaluate path probability, energy consumption, and load balancing factor with respect to reliability and efficiency of transmission paths. The transmission topology can be reduced to a multi-objective and multi-constrained optimization problem and solved through an improved maximum flow minimum cost algorithm. By using reinforcement learning, we derive an adaptive transmission policy for underground sensors to efficiently use their energy and avoid transmitting sensory data in unreliable paths under a dynamic environment. Through simulations and comparison upon publicly available real data, our technique achieves more reliable wireless communication with significant reduction of packet loss, and enables more energy-efficient data gathering than other techniques, especially when soil moisture varies frequently. [ABSTRACT FROM AUTHOR]

A measurement matrix is essential to compressed sensing frameworks. The measurement matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand, and enhance the stability and performance of the recovery algorithm. Choosing a suitable measurement matrix for Wireless Multimedia Sensor Networks (WMSNs) is demanding because there is a sensitive weighing of energy efficiency against image quality that must be performed. Many measurement matrices have been proposed to deliver low computational complexity or high image quality, but only some have achieved both, and even fewer have been proven beyond doubt. A Deterministic Partial Canonical Identity (DPCI) matrix is proposed that has the lowest sensing complexity of the leading energy-efficient sensing matrices while offering better image quality than the Gaussian measurement matrix. The simplest sensing matrix is the basis of the proposed matrix, where random numbers were replaced with a chaotic sequence, and the random permutation was replaced with random sample positions. The novel construction significantly reduces the computational complexity as well time complexity of the sensing matrix. The DPCI has lower recovery accuracy than other deterministic measurement matrices such as the Binary Permuted Block Diagonal (BPBD) and Deterministic Binary Block Diagonal (DBBD) but offers a lower construction cost than the BPBD and lower sensing cost than the DBBD. This matrix offers the best balance between energy efficiency and image quality for energy-sensitive applications. [ABSTRACT FROM AUTHOR]