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Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. [ABSTRACT FROM AUTHOR]

In recent years, technological advancement characterized by heightened connectivity and digitization has revolutionized our interaction with the world. This evolution has been exemplified by the emergence of the Internet of Things, which has introduced a new era of digitization and automation, primarily propelled by devices based on the ESP32 microcontroller. This study seeks to comprehensively evaluate and characterize a fully functional sensor network built upon the ESP32 microcontroller. The evaluation covers three key aspects: network performance, the ESP32's built-in ADC peripheral, and the data acquisition system. The ESP32 device has demonstrated its capability to consistently sample sensors at a high rate with remarkable stability. The ESP32's built-in ADC exhibited undesirable behavior for analog devices, attributed to noise and non-linearity. As a result, in applications requiring precision with analog sensors, the utilization of a cost-effective external ADC, as employed in this study, is recommended. Finally, during the network analysis of the sensor network, no issues were identified. The data acquisition system exhibited no bottlenecks, and packet losses remained minimal, regardless of the sampling period. [ABSTRACT FROM AUTHOR]

Latency and energy are critical issues when working with time and power-constrained wireless sensor networks. To avoid wasting both time and energy, such systems require selecting optimal communication routes with minimum latency and energy. The energy and latency costs between sender and destination nodes are greatly affected by the occurrence of transmission holes (black hole and grey hole). Therefore, selecting the optimal path must consider the probability of transmission holes and investigate their impact on energy and latency costs. Based on these problems of silent failures, the current work, proposes i-OPC as an intelligent and effective system to address these problems by forecasting sources of such silent failures and resolving them before they occur. The proposed method uses a customized routing schedule and a multi-objective mathematical optimization approach to rank all candidate paths between the source and destination nodes. In addition, i-OPC implements a machine learning technique using deep learning to predict the energy and latency costs of the future location for mobile nodes (). Also, it determines if the future locations of mobile nodes can result in black holes. Experiments produces promising results in terms of delay, energy consumption, packet delivery rate and hole detection rate against existing methods. [ABSTRACT FROM AUTHOR]

Energy efficient routing has been the mainstay of research in wireless sensor networks. With rising utility of WSNs in a broad range of applications, a significant volume of research contributions are targeted towards obtaining an energy efficient routing with a good quality of service (QoS) metric. In recent times, the utility of evolutionary algorithm for such multi-objective problems has been on the rising trend as evolutionary models closely mimic the real time scenario of any problem optimization. In this research work, a modified ant colony optimization (M-ACO) has been proposed and implemented for optimization in terms of energy conservation and thereby to extend the network lifetime of the network [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are widely utilized in a variety of applications. Attackers might interfere with equipment, and eavesdrop on conversations. Attackers can also modify the data which has been communicated or connect unauthorized devices to the network if such devices are deployed in the physical environment. To ensure WSN security, messages exchanged between communicating nodes must be encrypted, and for encryption and decryption, the network must store a key. Consequently, Key management is of significant importance in achieving security in WSN. Obtaining such a key arrangement in a resource-constrained environment is required for secure and energy-efficient data transmission in WSN. Current algorithms have certain security issues, such as vulnerability to plaintext attacks, brute force attacks, side-channel attacks, and computational complexity. Cryptography along with clustering has been found to be a practical and efficient technique to improve the secure and energy-efficient data transmission in WSNs. In this article we have used an asymmetric Elliptic Curve Cryptography (ECC) method for key generation whereas encryption and decryption of data, has been done by a hybrid of Advanced Encryption Standard (AES) and ECC cryptography A novel algorithm has been developed that incorporates both techniques (Advanced Encryption Standard (AES) and ECC cryptography) along with clustering through standard LEACH protocol for improving energy efficiency, data security, and network lifetime. The proposed algorithm has been able to overcome the issue of key exchange that plagues AES, simpler than ECC and more reliable than AES. More significantly, it is able to encounter a variety of security threats including side-channel attacks. The proposed algorithm is better and has been compared to current techniques with respect to time complexity, encryption time and decryption time to demonstrate its efficacy. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel semi-dry electrode for collecting ECG signals of drivers is proposed. • The electrode solves the limitations of the traditional wet and dry electrodes. • This study presents the WSN method for driver fatigue detection. • This study promotes the development of safe driving techniques. A portable semi-dry electrode is proposed to address the problems of high impedance of traditional dry electrodes and inconvenient replenishment of conductive liquid in wet electrodes. The semi-dry electrode combines the advantages of conventional wet and dry electrodes. The impedance value of this electrode is relatively small, and it is easy to replenish the conductive liquid. In addition, the data collected by the semi-dry electrode and the traditional ECG electrode were compared and analysed. The results showed that fatigue characteristics in ECG acquired by the two electrodes were consistent. The novel electrode proposed can effectively collect ECG signals. Finally, this study uses a wavelet scattering network (WSN) to classify the driving fatigue. The results show that the average classification accuracy is 97.93%. The method has higher classification accuracy compared to traditional classification methods. And the running time is fast, which is convenient for real-time online monitoring of driver fatigue. [ABSTRACT FROM AUTHOR]

• This study is the first to identify the number of leaks in water supply pipes. • The hybrid model of WSN and Bi-LSTM is proposed to identify the number of leaks. • The Bayesian optimization algorithm is used to optimize the hyperparameters of the model. Water supply pipeline leaks have significant impacts on urban areas and daily life. Acoustic techniques are widely applied in its leak detection, but there is a lack of research on quantifying multiple leaks in pipelines. This study presents a novel approach using a Wavelet Scattering Network (WSN) and Bi-directional Long and Short-Term Memory (Bi-LSTM) model with Bayesian optimization to identify leak quantities accurately. The WSN autonomously extracts signal features, which are subsequently processed by the Bi-LSTM for classification. Through Bayesian optimization, the model's hyperparameters are refined to enhance performance. The results exhibit an impressive 90.8 % classification accuracy, showcasing the model's effectiveness in accurately identifying leak quantities. In comparison with existing models, the proposed method stands out for its superior recognition accuracy, shorter training time, and enhanced noise immunity. Ablation experiments further confirm the distinct roles and contributions of the WSN, Bayesian optimization, and Bi-LSTM components in achieving these exceptional results. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a heterogeneous, distributed network composed of tiny cognitive, autonomous sensor nodes integrated with processor, sensors, transceivers, and software. WSNs offer much to the sensing world and are deployed in predefined geographical areas that are out of human interventions to perform multiple applications. Sensing, computing, and communication are the main functions of the sensor node. However, WSNs are mainly constrained by limited resources such as power, computational speed, memory, sensing capability, communication range, and bandwidth. WSNs when shared for multiple tasks and applications, resource management becomes a challenging task. Hence, effective utilization of available resources is a critical issue to prolong the life span of sensor network. Current research has explored various methods for resources management in WSNs, but most of these approaches are traditional and often fall short in addressing the resource management issues during real-time applications. Resource management schemes involves in resource identification, resource scheduling, resource allocation, resource utilization and monitoring, etc. This paper aims to fill the gap by reviewing and analysing the latest Computational Intelligence (CI) techniques, particularly Machine Learning (ML) and Artificial Intelligence (AI). AIML has been applied to countless humdrum and complex problems arising in WSN operation and resource management. AIML algorithms increase the efficiency of the network and speed up the computational time with optimized utilization of the available resources. Therefore, this is a timely perspective on the ramifications of machine learning algorithms for autonomous WSN establishment, operation, and resource management. [ABSTRACT FROM AUTHOR]

• Exploited spatial correlation in clustered sensor networks after event occurrence. • Determines minimum reporting nodes (4–1) for correlation threshold (0.1–1). • Correlation threshold variation from 0.1 to 1 extended lifetime by 66 %. • Presented neural network based technique for ideal correlation threshold selection. • Impressive model accuracy (R = 0.97813) and act as personalized correlation advisor. Wireless sensor networks comprise autonomous nodes monitoring environments while managing energy consumption. Extending network lifespan involves minimizing redundant transmissions by filtering identical information from correlated sensor nodes. Correlation depends on spatial locations and it quantifies sensor relationships. It allows nodes classification as correlated or uncorrelated using a threshold value between 0 and 1 which enhances network lifespan by 60–65 %. Selecting the optimal threshold considering network metrics remains a challenge. This study proposes a neural network-guided correlation thresholding technique (NNCT). NNCT aims to identify the optimal threshold for filtering redundant reports by training a neural network on varied parameters like node density, threshold, coverage, and events using different clustering methods. The process involves implementing spatial correlation models, virtual clustering, event detection, and iterative threshold-based node selection. The neural network achieves a high accuracy of R = 0.97813, offering a promising adaptive solution for automated threshold selection in correlation-based filtering. [ABSTRACT FROM AUTHOR]

Due to high maintenance costs and inaccessibility, replacing batteries regularly is a major difficulty for Wireless Sensor Nodes (WSNs) in remote locations. Harvesting energy from multiple resources like sun, wind, thermal, and vibration is one option. Because of its plentiful availability, solar energy harvesting is the finest alternative among them. The battery gets charged during the day by solar energy, and while solar energy is unavailable, the system is powered by the charge stored in the battery. Hence, in this paper, a highly efficient Solar Energy Harvesting (SEH) system is proposed using Leadership Promoted Wild Horse Optimizer (LPWHO). LPWHO refers to the conceptual improvement of the standard Wild Horse optimization (WHO) algorithm. This research is going to focus on overall harvesting efficiency which further depends on MPPT. MPPT is used as it extracts maximal power from the solar panels and reduces power loss. The usage of MPPT enhances the extracted power's efficiency out of the solar panel when its voltages are out of sync. At last, the supremacy of the presented approach is proved with respect to varied measures. • A highly efficient Solar Energy Harvesting (SEH) system is proposed using Leadership Promoted Wild Horse Optimizer (LPWHO). • The developed method LPWHO is the conceptual improvement of the standard Wild Horse optimization (WHO) algorithm. • The proposed technique focus on overall harvesting efficiency which further depends on MPPT. • The developed LPWHO obtained efficient performance using Convergence, Error and high efficiency. [ABSTRACT FROM AUTHOR]

Data aggregation involves the integration of correlated data generated by various wireless sensors and devices in WSN and IoT networks, in order to arrive at meaningful interpretation of the data sensed. It also serves as an efficient mechanism to optimize the resources like node energy, communication bandwidth and computation time, in WSN and IoT networks. The paper presents a brief overview of basic concepts related to WSN & IoT networks, data aggregation, and various optimization parameters used by the data aggregation protocols. A major contribution of the paper is to provide a comprehensive survey of various data aggregation protocols developed to address issues related to network topology, interference, fault-tolerance, mobility and security in WSN and IoT networks. Due to resource constraints, WSN and IoT networks are besieged with several competing requirements like latency, energy efficiency, data accuracy, data freshness, temporal correctness etc. The paper presents an in-depth discussion on these requirements, and the tradeoff strategies to be followed by data aggregation approaches to optimize these requirements. The paper highlights some of the gap areas in existing data aggregation approaches and suggests research solutions to amicably plug these gaps. [ABSTRACT FROM AUTHOR]

In WSN sensor nodes are connected to their neighbor nodes by a wireless channel and these all nodes are operated on a battery to perform any operations. So improving the lifetime of WSN is a major research challenge by effective utilization of each and every node's energy. The lifetime of WSNs can be improved in three steps: path creation, path selection, and power saving. In existing works, path selection has the major role to utilize and enhance lifetime. In the proposed PStack algorithm, path generation is a major concern, and routing is applied. It takes advantage of and solves the limitations of existing algorithms using the stack concept in WSN. The proposed algorithm shows a better result than the improved LEACH protocol because it uses the data structure with graph theory. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is often utilised in many different industries for a variety of purposes. The improved network access area and quick network setup make it an excellent technology. However, certain eradicating problems will also be present, slowing the expansion of the network. The forecasting of connection failure is still thought to be a mystery in the WSN area. The suggested method provides a thorough understanding of connection failure. This method introduces the proficient trustworthy Node ID dependent resource reservation protocol (PTN-RRP). Then, using technique weighted on end-to-end latency, the shortest path is identified. Major issues with WSN include data link failure and intrusions. This method might increase the rate of detection since it determines the shortest route from a particular preliminary point to the target. The hop address and sequence number are added to the protocol by determining the shortest path. Following the discovery of the shortest path, the likelihood of connection failure is calculated. The established trustworthy etiquette adopted has a link failure structure that can foresee and resolve connection failure-related problems. After that, a posterior probability evaluation approach is used to determine the type of assault in order to determine the attack reason intended for the connection failure. Finally, a simulation study is used to evaluate the performance of the suggested strategy. The simulation's results show that the proposed technique is extremely successful in identifying link breaks, and the developed algorithm for recognising the shortest path will shorten the time required to find the shortest way. To demonstrate the usefulness of the suggested methodology, the performance result is contrasted with currently used procedures. • It is a promising technology due to its higher range of the network connectivity and the fast set up of the network. • The proposed technique offers a detailed idea on the failure of link. • The shortest path from a specifi preliminary position to the traget is identified thus enhacing detection rate. • Highly effective in the link breakage detection and impletemented algorithm for shortest path recognition. [ABSTRACT FROM AUTHOR]

Efficient resource use is a very important issue in wireless sensor networks and decentralized IoT-based systems. In this context, a smooth pathfinding mechanism can achieve this goal. However, since this problem is a Non-deterministic Polynomial-time (NP-hard) problem type, metaheuristic algorithms can be used. This article proposes two new energy-efficient routing methods based on Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO) algorithms to find optimal paths. Moreover, in this study, a general architecture has been proposed, making it possible for many different metaheuristic algorithms to work in an adaptive manner as well as these algorithms. In the proposed methods, a new fitness function is defined to determine the next hop based on some parameters such as residual energy, traffic, distance, buffer size and hop size. These parameters are important measurements in subsequent node selections. The main purpose of these methods is to minimize traffic, improve fault tolerance in related systems, and increase reliability and lifetime. The two metaheuristic algorithms mentioned above are used to find the best values ​​for these parameters. The suggested methods find the best path of any length for the path between any source and destination node. In this study, no ready dataset was used, and the established network and system were run in the simulation environment. As a result, the optimal path has been discovered in terms of the minimum cost of the best paths obtained by the proposed methods. These methods can be very useful in decentralized peer-to-peer and distributed systems. The metrics for performance evaluation and comparisons are i) network lifetime, ii) the alive node ratio in the network, iii) the packet delivery ratio and lost data packets, iv) routing overhead, v) throughput, and vi) convergence behavior. According to the results, the proposed methods generally choose the most suitable and efficient ways with minimum cost. These methods are compared with Genetic Algorithm Based Routing (GAR), Artificial Bee Colony Based routing (ABCbased), Multi-Agent Protocol based on Ant Colony Optimization (MAP-ACO), and Wireless Sensor Networks based on Grey Wolf optimizer. (GWO-WSN) algorithms. The simulation results show that the proposed methods outperform the others. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is built with the wireless interconnection of Sensor Nodes (SNs) generally deployed to monitor the changes within the environment of hostile, rugged, and unreachable target regions. The optimal placement of SNs is very important for the efficient and effective operation of any WSN. Unlike small and reachable regions, the deployment of the SNs in large-scale regions (e.g., forest regions, nuclear radiation affected regions, international border regions, natural calamity affected regions, etc.) is substantially challenging. Present paper deals with an autonomous air-bone scheme for the precise placement of SNs in such large-scale regions. It uses an Omni-directional Circular Glider (OCG) per SN. After being aerially dropped, SN pilots the OCG to glide itself to the predetermined locations (PL) within a target region. The major advantage of using OCG is its capability to quickly update the direction, during the flight (with turning radius = 0) toward its PL. The proposed uses a recursive path correction model to maintain the orientation of the gliding SN towards the PL. The simulation results, and the hardware implementation, indicate that the proposed model is effectively operational in the environmental winds. It is time-efficient and more accurate in the deployment of the SNs in comparison to existing state of art SN deployment models. [ABSTRACT FROM AUTHOR]

Wireless sensors are backed by their battery life. The battery depletion creates a communication hole in the network. The replacement of certain energy-depleted sensor nodes necessitates their detection. The work in this article presents a novel solution for the detection of coverage hole in the WSN. An undirected graph was employed in this study to divide the WSN region into two parts: the outer and inner nodes. The Gaussian Mixture Model (GMM) clustering detects the outer/edge nodes at the coverage hole area. Node degree, closeness, betweenness, and page rank are the four graph metrics that GMM uses for clustering in this work. GMM's clustering performance is governed by the static mean and covariance. A novel combination of social spider optimization and GMM is then leveraged to fine-tune the cluster to decrease false detection rates of holes. Distinct holes, such as round, E-shape, and rectangular, are used to conduct the analysis. Using the SSO-EM GMM approach, the comparison shows that it outperforms the current methods of coverage hole identification. [ABSTRACT FROM AUTHOR]

This paper examines the lifetimes of alternative system configurations for a wireless sensor network (WSN) in a circular region. The coverage region is divided into hexagonal cells, and the information from each cell is combined and transmitted to the sink or to other cells. Sensors are equipped with directional antennas that improve transmit/receive power efficiency and potentially reduce interference. Simultaneous transmission to multiple cells is permitted, and the power level of different transmissions can be controlled to different levels. Under the assumptions of uniform information density and uniform sensor distribution, we show that for larger systems (with 800 cells or more) lifetime can be prolonged by more than 10 times by introducing a limited number of transmission lines or line sinks to facilitate information transfer to the main sink. Alternatively, introducing up to six secondary sinks can increase the system lifetime by a factor of about 4.5 for the same size region. Our results imply that incorporating line or secondary sinks may provide simple, low-cost solutions for extending system lifetime for larger WSN's. [ABSTRACT FROM AUTHOR]

The distributed secure filtering issue of Takagi-Sugeno (T-S) fuzzy systems (TSFSs) under hybrid attacks is investigated in the framework of finite-time boundedness (FTB). The state is estimated via a wireless sensor network (WSN) in a distributed way, where each sensor collects information from the system independently, and transmits it to the remote local estimator through the measurement information transmission (MIT) network. The estimator interacts with others through the local information transmission (LIT) network. Firstly, to describe the dynamic changes of the system under hybrid cyber-attacks, a switched model with nine subsystems is introduced. Then, the conditions of the FTB of the filtering dynamic system are given by the switching system analysis method. Moreover, the quantitative relationship between the average dwell time of attack modes and the stability boundary is established. Finally, to demonstrate the efficiency of the designed filtering scheme, the simulation study on the tunnel diode circuits is provided. • A comprehensive system model containing hybrid attacks is established. • Design of the distributed H ∞ filtering algorithm based on the finite-time stability. • The quantitative relationship between the switching parameter and the stability boundary is established. [ABSTRACT FROM AUTHOR]

• Flexible system to monitor different physical magnitudes in marine environments. • Stand-alone profiler for several weeks deployment. • IoT technologies allowing real-time collection of profiles of reliable data. • Low-cost, small and reproducible mechanical design underwater profiler. • Remote configuration ability for flexible sensor sampling. Marine pollution is a complex issue that has prompted extensive research. The Mar Menor lagoon, a singular Mediterranean ecological feature, is under pressure from various economic activities, leading to episodes of hypoxia and fish mass mortality. The lagoon is covered by an extensive meadow of submerged vegetation that release oxygen during daylight hours but consumes it during the night. Under exceptional conditions, the water column becomes stratified, with higher oxygen consumption in the deeper layers due to organic matter decomposition. Therefore, continuous high-frequency monitoring of critical parameters affecting the metabolic behaviour of the lagoon is essential. This marine environment has traditionally been monitored with traditional techniques from vessels on a weekly basis, incurring in high costs and yielding limited results. This article introduces the s-Nautilus, a submersible profiler that enables near real-time data transmission in shallow marine environments. It showcases an operational autonomy of two months and significantly reduces installation costs by eliminating the need for on-site infrastructure and personnel. Weighing 21 kg, makes it easy to transport, and it can be equipped with different commercial sensors to monitor various parameters as required. Currently, several s-Nautilus profilers are deployed in the Mar Menor lagoon, collecting data during critical hours, and providing valuable information to the managers of this emblematic environment. [ABSTRACT FROM AUTHOR]

• A new Markov clustering algorithm for wireless sensor networks. • Sensor abundance and residual energy for cluster-head and queried sensor selection. • A new protocol that handles non-uniform sensor distribution appropriately. • Network energy consumption reduction and network lifetime enhancement. • Comparison with other clustering protocols based on multiple criteria. Interest has increased in wireless sensor networks (WSNs) in fields such as healthcare, industrial control and environmental monitoring. More recently, WSNs have been widely deployed in Internet of Things (IoT) based applications. They are considered as an essential part of IoT networks. The expanded use of WSNs raises daunting technical challenges, not the least of which is sensor battery energy conservation. The design of efficient sensor clustering strategies to reduce energy consumption by data transmission throughout the WSN has become crucial. Several applications of WSNs induce a random deployment of sensors such as the monitoring of conflict zones, the study of natural phenomena in hostile zones or rescue operations. In this context, energy conservation should be ensured according to the non-uniformity of the resulting sensor distribution in different areas of the network. In this study, a novel weighted Markov clustering protocol that considers sensor abundance for cluster-head and queried sensor selections is presented. The new protocol aims to decrease intra-cluster energy consumption by reducing the sending of redundant data in sensor-dense regions. In addition, it attempts to prolong sensor-sparse regions lifetime by limiting the number of queried sensors. This protocol combines a Markov clustering of sensors with a sensor weighting based on residual energy and sensor abundance in the network. The proposed protocol is a significant improvement of an existing unweighted Markov clustering protocol. The unweighted Markov clustering protocol is based on sensor residual energy and sensor location without taking into account the sensor abundance in different areas of the network. Simulations affirm that the new protocol handles more appropriately the non-uniformity of sensor distribution and enhance the durability of wireless sensor networks. Indeed, simulation results show that the proposed clustering protocol outperforms its unweighted ancestor and other well-known clustering protocols in terms of energy conservation and network lifetime. The number of expired sensors and the average dissipated energy are reduced, whereas, the average sensor lifetime is prolonged compared to the unweighted ancestor, or HEED, LEACH, PEGASIS, and TEEN. [ABSTRACT FROM AUTHOR]

Energy-efficient resource-optimization for multiple application scenarios requires appropriate clustering-based network-fractioning that skillfully removes unbalanced network connectivity, which is a stimulating bottleneck in WSN-performance. This paper proposes a new Uniform Connectivity-based clustering Protocol, called Lines-of-Uniformity based Enhanced-Threshold (LUET) to provide energy-efficient coverage in three-tier heterogeneous WSN. This protocol considers the node's remnant-energy and its proximity from either of the lines of uniformity for lowering down average isolated node count in WSN. This paper also proposes a rotation epoch-based LUET variant, (LUET|R) that incorporates static epoch for initial clustering rounds until First-Node-Death to overcome the rapid fall after the death of the first node. The simulation model demonstrates the superiority of LUET and its variants over other established clustering protocols in terms of network-lifetime, power-efficiency, net death-rate, average isolated-nodes, throughput. [ABSTRACT FROM AUTHOR]

An access control mechanism plays a critical role in new node deployment within a resource-constrained Wireless Sensor Networks. The deployment of a new node is inevitable either due to the outage of power or nodes getting compromised due to the adversary's attacks. The access control scheme prevents malicious node deployment and also allows a new node to establish a shared key with its neighbors for secure communication. Besides having low communication and computational overheads, an access control mechanism must suffice to specific security and functional requirements for their practical implementations. In this paper, a provable and practical access control scheme based on Elliptical Curve Cryptography (ECC) has been presented. The proposed access control scheme supports node privacy while addressing all other major functional and security requirements. The formal validation of the proposed scheme has been carried out using automated validation of internet security protocols and applications (AVISPA) and Scyther tools. A comparative study of the proposed scheme with the existing schemes has been carried out on various security and functional requirements suggesting a better trade-off. Finally, practical experimentation on TinyOS and MICAz motes has been carried out to provide detailed energy analysis and test-bed implementation of the proposed scheme. [ABSTRACT FROM AUTHOR]

In recent years, data generated by Internet of Things (IoT) devices has become increasingly massive. The amount of data stored in the cloud is also enormous and needs to be processed in a timely, efficient, secure, and private manner. While data stored on the cloud is guaranteed to be available, this does not mean that the secrecy and privacy of the sender are guaranteed. Moreover, sharing data on the cloud may sometimes lead to unpleasant surprises, such as undeniable disputes due to the shared content. Our certificateless signcryption (CLSC) scheme is proposed to address the above issues. It provides unforgeability, indistinguishability, non-transferability, and privacy of sender identity to ensure secrecy and privacy of the sender. Our contributions are not only limited to solving the aforementioned issues. The security model of non-transferability and privacy of sender identity for the signcryption scheme is first proposed. This is followed by a concrete simulation of the proof. In addition, our CLSC scheme does not use bilinear pairing, which is significantly lowers its computational cost and makes it suitable for IoT devices in wireless sensor networks (WSNs). [ABSTRACT FROM AUTHOR]

The advancements of Wireless sensor network (WSN) in large number of applications made it common. However, the energy is a major challenge in the WSN environment as the battery-operated sensor nodes in the network consumes huge amount of energy during transmission. This work addresses the energy issue and provides an energy efficient multi-hop routing in WSN named Taylor based Cat Salp Swarm Algorithm (Taylor C-SSA) by modifying C-SSA with Taylor series. This method undergoes two stages for attaining multi-hop routing, which includes selection of cluster head (CH), and transmission of data. Initially, the energy-efficient cluster heads are selected using the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for effective data transmission, the sensor nodes sends data over the CH, which transmits the data to the base station through the selected optimal hop. The optimal hop selection is done using the proposed Taylor C-SSA. Moreover, the security aware multi-hop routing is performed by introducing trust model that involves indirect trust, integrity factor, direct trust, and data forwarding rate. The proposed Taylor C-SSA algorithm shows best performance in terms of energy, number of alive nodes, delay, and throughput values of 0.129, 42, 0.291, and 0.1, respectively. [ABSTRACT FROM AUTHOR]

Although wireless sensor networks (WSNs) have been utilized for over one decade, it is now heavily used by many modern applications such as medical observance, disaster management and environmental monitoring. This type of network suffers from limited energy and a short lifetime in addition to the low channel bandwidth. Bandwidth represents the major challenges of such systems due to the great impact of communication cost on the consumption of nodes power. Clustering has been proven to be one of the best techniques to conserve the energy of WSNs. LEACH (low energy adaptive clustering hierarchy) protocol is one of the most fundamental works of WSN clustering. However, this protocol suffers from some drawbacks, especially in the setup phase where CH is selected randomly. This work aims to enhance LEACH by identifying a cluster head according to the lowest degree of consuming energy. The results clarify the ability of this work to enhance LEACH while prolonging the lifetime and improving the performance of WSN. [ABSTRACT FROM AUTHOR]

In a Wireless Sensor Network (WSN), one of the most important issue is to minimize the energy consumption without losing accuracy during faster data transmission. During information broadcast, message communication is to be sent in an optimized way to increase energy efficiency in the networks. By applying various techniques and methodology in cluster WSN the network lifetime is increased and delay is minimized with the load balanced network. To accomplish load balance, Adelson-Velskii and Landis (AVL) tree rotation clustering algorithm is simulated considering the cluster sensor node. A single large area network is divided into multiple clusters using modified K-means clustering algorithm. Computational complexity is reduced through the construction of Minimum Connected Dominating Set with Multi-hop Information (MCDS-MI) and Bi-Partite Graph (BG) technique. Cluster Head (CH) assortment mechanism is implemented to find maximum cover set count of the sensor nodes. In addition, the enactment of the anticipated design is established through simulations during scalable data transmission in a WSN. Hypothetical investigation and experimental simulations are studied by measuring various performance evaluation metrics namely Virtual Dominators, Size Reduction, Network Lifetime and Residual Energy. The results shows that the proposed MSDS-MI system has maximum reduction in network size of 50%, maximum increase in network lifetime of 60% and saved maximum residual energy consumption of 47.76%. The results are encouraging and our proposed method is found to be more efficient than Connected Dominating Sets (CDS), Pseudo Dominating Sets (PDS), Dynamic Cluster Head Genetic Algorithm (DCH-GA) and Distributed Self-Healing Approach (DSHA). [ABSTRACT FROM AUTHOR]

The localization problem of unknown nodes in wireless sensor networks (WSN) has drawn increasing scholarly attention along together the popularity of meta-heuristic algorithms. To overcome the shortcomings of low accuracy that traditional least square method (LSM) inevitably produces, this paper introduces an improved adaptive genetic algorithm (IAGA) to handle the aforementioned problem and uses a modified evaluation function to reduce the error of distance measurement in a topological structure. The experimental results prove that the IAGA algorithm based on DV-Hop has superior performance in comparison of original DV-Hop and other meta-heuristic algorithms. The conclusion can be drawn that meta-heuristic algorithms have an better superiority over DV-Hop in locating nodes in WSN and the IAGA is more promising than other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm has various demerits like picking of cluster head during each round which depletes energy of the node, non consideration of residual energy to name a few. Due to this, researchers are focusing on ways to boost the LEACH algorithm and making it more efficient for practical use. To address the issues of LEACH like the non uniformity of the amount of cluster heads and disregard of the unconsumed power of the nodes, this paper proposes an enhanced algorithm called ESO-LEACH. In this work, meta-heuristic particle swarm enhancement is utilized for initially clustering the sensor nodes. The concept of advanced nodes and enhanced set of rules for CH election is used to minimize the random nature of the algorithm in the proposed ESO-LEACH. Python-based recreation outputs show that ESO-LEACH outflanks conventional LEACH, and enhances the network's life span. The python based results obtained for the proposed ESO-LEACH algorithm and by performing its comparison with an existing LEACH algorithm shows that lifespan of network using ESO-LEACH is coming out to be almost double than the lifespan of network using LEACH protocol which indicates that the enhanced proposed algorithm is successful in extending network lifespan adequately. [ABSTRACT FROM AUTHOR]

The rapid increase in Internet users has made web applications essential for daily services, rendering them targets for various cyber-attacks like path traversal, zero-day attacks, and injection attacks. While traditional security measures can prevent many familiar attacks, they are often ineffective against OPTIONS attacks, which involve injecting malicious code via hyperlinks to obstruct user access to legitimate webpage content. To address this novel challenge, we propose the OAD-WSN-MMRCNN technique, leveraging an Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network for OPTIONS attack detection in Wireless Sensor Networks (WSNs). This approach begins by selecting a CPU parameters dataset for attack detection, followed by pre-processing with a Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter to remove redundant data. Key features such as handles, threads, processor, context switch, deferred procedure call (DPC), interrupt delta, CPU socket, and core are extracted using a variable velocity strategy particle swarm optimization algorithm. The MMRCNN, optimized with the Tyrannosaurus Optimization Algorithm, is then employed to detect normal and OPTIONS attacks. Implemented in Python, the efficacy of OAD-WSN-MMRCNN is evaluated using metrics such as energy consumption, target window, accuracy, precision, F-measure, recall, and CPU utilization. Experimental results demonstrate that OAD-WSN-MMRCNN outperforms existing techniques, achieving a 20 % improvement in detection accuracy and a 25 % reduction in energy consumption, highlighting its effectiveness and potential for enhancing web application cyber security. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) is an emerging networking technology that allows for the low-cost, unattended monitoring of a variety of environments, thus WSN technologies have become increasingly important. Simultaneously, lack of security and poor energy efficiency is considered the major concerns in many routing protocols in various existing WSN protocols. Security and energy efficiency concerns are mainly due to the emergence of various kinds of malicious attacks because of advancements in networking technology. To mitigate these issues secured transmission is essential in any routing protocol. In order to provide safe data transfer in WSN-based smart environment applications, the TABEESR protocol (Trust Analysis Based Energy Efficient and Safe Routing) was developed. The proposed routing protocol encompasses four phases such as network creation and node deployment, grading for trust evaluation, route identification process and data transmission. The trust evaluation is performed with the aid of sensor packet features to categorize the positive nodes and negative nodes. The path identification process includes two processes such as cluster head selection through red fox optimization and relay node selection using a fuzzy interference system for energy efficient and secure path selection. A public static agreement key based cryptography technique is introduced for secure data transmission. The proposed TABEESR is tested with MATLAB software to validate its performance. SMEER, SHER, SRPA and AFSA are the prior routing protocols in WSN that are considered to compare the performance of the proposed routing protocol. The proposed TABEESR algorithm attained 95(%) of Average Residual Energy, 91 (%) of packet delivery ratio, 1350 (sec) network lifetime, 0.131 (Mbps) of throughput and 7(%) Packet Loss. Moreover, the proposed PSAKC algorithm attained 5.8 (%) information loss, 3.8 (sec) encryption time, 2.3 (sec) decryption time, 6 (sec) execution time, and 6.5 (J) energy consumption which is better than the prior methods. The proposed routing algorithm is applicable for providing security in both server to device and device to device in various smart applications. [ABSTRACT FROM AUTHOR]

Internet Protocol version 6 (IPv6) address configuration is needed to meet the needs of current portable devices and wearable applications, where each device needs its own unique identification number. High complexity in Wireless Sensor Network (WSN) and Internet of Things (IoT) environments has increased challenges, such as low energy consumption and high overhead in IPv6 address configuration. The proposed IPv6 generation is based on a hybrid mathematical approach that retains the IP address's confidentiality and prevents Denial of Service (DoS) attacks from new and existing nodes in the network. In this paper, the proposed hybrid protocol for DoS attacks from new and existing nodes is analyzed. It is evident from the results that the rate of successful configuration, overhead, and energy consumption is found to be better than the existing approach, namely, Programming Protocol-independent Packet Processors Duplicate Address Detection (P4DAD), match prevention technique, etc. • Focuses on IPv6 using a hybrid Duplicate Address Detection (DAD). • Reduces the energy consumption of the IPv6 as compared to P4DAD, etc. • Reduces the long wait time compared to EUI-64. • ASR is investigated for the Hybrid DAD under three different scenarios [ABSTRACT FROM AUTHOR]

The climate and weather dynamics in the past few years has driven a massive increase in the number and intensity of flood disasters, which severely claim casualties in human, goods and properties. Aimed to reduce these casualties, emerging software-defined internet protocol-based communication technologies in the form of Internet of Things (IoT) have attracted strong interests from disaster mitigation stakeholders to rapidly locate victims and acquire their relevant information, which in turn can boost up the efficiency and effectiveness of Search and Rescue (SAR) missions. In order to capture state-of-the-art development and technological challenges, this paper presents an extensive review on the flood SAR systems, highlighting some of the key emerging IoT technologies that prove or are potentially useful in improving the SAR operation by the rescuers. Furthermore, a comprehensive study on different existing communication technologies for SAR is provided, covering the system architecture, communication network compositions and applications. Based on the critical analysis of existing works, this paper puts forward a proposal on an IoT-aided integrated flood management framework to support SAR in the flood-catchment areas, leveraging upon three-domain (ground, water and air) collaborative wireless networks. [ABSTRACT FROM AUTHOR]

We present a novel technique to detect an intrusive attack that occurs in the network due to the presence of a compromised node. These intrusive attacks last for a long time in the network due to the existence of compromised nodes this also affects the sensor reading. As the time span of the attack in longer in the network, it affects the system and can cause a system failure. Hence, we propose a technique that uses the combination of multi-varying kernel density estimation with distributed computing. This combination analyzes the individual probability of the existence of data and calculates the global value of the Probability Density Function (PDFs). Pearson's divergence (PE) is applied for efficient in-network detection and estimation of intrusion at low False Positive Rate (FPRs). The approximation of PE divergence is carried out using different techniques of distributed computing. The value of PDFs is calculated for a successive period of time in order to provide efficient performance. We also propose an entropy-based method that uses a centralized computing approach. Results obtained using PE divergence and entropy-based method are compared in order to judge the robustness. Finally, the proposed algorithms are evaluated using real-world based datasets, and the results are compared using Accuracy and FPRs. • A novel distributed density estimation based divergence technique is proposed. • Divergence technique is proposed for anomaly based intrusion detection in WSN. • Datasets i.e. NSL-KDD, AWID, RSS measurement, Anomaly detection are utilized. • Entropic Graph based method as centralized computing technique is utilized. • Performance measure i.e. Detection Accuracy, FPR, TPR, Precision, etc. are utilized. [ABSTRACT FROM AUTHOR]

IEEE 802.11 standards adopted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) to witness improvements in performance. Supporting, CSMA/CA is found to enhance performance when the transmitters are located within the neighborhood of one another. But, in the presence of multiple flows, the channel state creates an asymmetric distribution of throughput for different flows. Some flows experience high throughput while others suffer with insufficient bandwidth. To address this problem, Receiver Based Contention Management Scheme (RBCMS) approach is proposed based on collisions sensed by the receiver. RBCMS makes use of RRTS (Request for RTS) packets and also ensures that only the required number of RRTS packets are used to minimize the control overhead. In RBCMS, the current channel status is estimated by Success Ratio (SR) parameter by which contention and congestion are effectively controlled. The potential receiver sends SR value via packets and that is utilized by the sender to estimate network condition in and around the receiver. Accordingly, Contention Window (CW) size in MAC layer and the procedures of congestion avoidance and slow start in transport layer are intensified. The NS3 simulation results obtained in terms of fairness, throughput, delay etc. reveal the performance enhancements of RBCMS over Advanced Backoff Algorithm (ABA) and BEB. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) paradigm is one of the most exciting innovations of recent years. However, with each connected object the number of possible attacks will increase exponentially. This article presents a comparative study between the different solutions against Iot attacks, we will first present a security architecture that can be divided into 3 main layers: perception layer, network layer, and application layer. Each layer is divided by hierarchies that allow us to guarantee security, i.e. confidentiality, integrity, authenticity, and data acquisition. We then focused on the attacks of the perception layer which is extremely vulnerable to security attacks while classifying these attacks according to the two wireless technologies (WSN, RFID). Finally, we discuss the different approaches provided against these attacks. [ABSTRACT FROM AUTHOR]

• Maximizing network lifetime will be cope with by resolving energy hole problem and preserving energy. • The energy hole problem is relieved by gradient deployment algorithm. • Based on the Choquet integral and Entropy, entropy-driven abnormal event monitoring algorithm is proposed. • Entropy-driven aggregation tree-based routing algorithm is designed for energy consumption. Data aggregation is one of the essential and fundamental processes in Wireless Sensor Networks (WSNs). When and how to gather data from the sensors to the sink has a direct impact on the lifetime of the WSNs because the energy consumption is proportion to the frequency of data transmission. In general, sensors in a WSN are randomly distributed for creating a massive coverage WSN environment within a short period. Because the sensors nearby the sink are responsible for more data forwarding tasks, they normally have much shorter lifetime than those located away from the sink. Once all sensors nearby the sink run out of energy, the data collected from the terminal sensors can not reach the sink because all established connections to the sink are broken, which is called the termination of a WSN lifetime. This paper proposes a strategy with multiple algorithms for deploying sensors aiming at maximizing a WSN lifetime. The proposed Entropy-driven Data Aggregation with Gradient Distribution (EDAGD) deployment strategy contains three algorithms: (1) multihop tree-based data aggregation, (2) entropy-driven aggregation tree-based routing algorithm and (3) gradient deployment algorithm. The numerical and experimental results indicating that the proposed EDAGD method outperforms the conventional algorithm with the random deployment strategy. [ABSTRACT FROM AUTHOR]

IEEE 802.15.4 protocol is intended to accomplish the characteristics of low power and low rate Wireless Sensor Network. It employs carrier sense multiple access with collision avoidance (CSMA/CA) protocol to access the network using either unslotted or slotted mode. This paper concentrates on the unslotted CSMA/CA where the node tends to wait for a very limited backoff exponent value, and cannot perform clear channel assessment until backoff procedure is completed. Therefore, the collision probability and the average delay are high. To enhance the unslotted CSMA/CA performance, this paper proposes a modified unslotted CSMA/CA that divides the backoff delay into Main Backoff and Secondary Backoff. The Markov model for the modified CSMA/CA is presented to assess a single hop IEEE 802.15.4 behavior, and derive expressions for average delay, energy consumption and reliability. An optimization problem is also presented to minimize the delay while guaranteeing energy consumption and reliability constraints for the modified CSMA/CA. OPNET simulation tool is applied to compare the modified to the standard unslotted CSMA, and to validate the Markov model proposed. The results demonstrate that the modified CSMA improves the reliability, while reducing the average delay significantly. [ABSTRACT FROM AUTHOR]

The use of mobile sinks (MSs) can address several challenges for traditional wireless sensor networks (WSNs), such as energy holes, meanwhile it also introduces some new issues. Especially, for the path constraint MS, the fixed trajectory and uncontrollable moving speed results in significant delay and even buffer overflows. Existing research mainly focused on delay-tolerant applications which are unsuitable for delay-sensitive data or adopted the greedy policy to collect data which is inefficient in energy. To minimize the energy consumption and guarantee delay requirement simultaneously, we propose a delay-sensitive and energy-efficient routing scheme. Firstly, to obtain the MS position, we propose a K-nearest neighbor-based prediction model, which can reduce the energy and delay overhead of the location update. Based on the prediction result, an efficient cluster routing scheme is designed. To ensure both search speed and the optimality of the cluster head (CH), we improve the Reed-Deer algorithm (RDA) as IRDA, and use the IRDA to find out CHs. To remove the redundancy of the CH, a removal method is proposed. After constructing clusters, a method is used to determine the next-hop of the CH by using the predication position of the MS, the received timestamp and the delay requirement of the data. A fault tolerance mechanism is supplied to deal the predication error and other unexpected situations. By applying IRDA and the prediction model, the proposed routing scheme avoids the frequent construction of new routes, reduces the hops of the end-to-end path, minimizes network energy consumption, and ensures timely data transmission. The comparison results indicates that the proposed protocol can effectively improve the network performance while guarantee delay requirement. Especially in realistic scenario, the proposed algorithm can effectively reduce energy consumption and the end-to-end delay and improve the packet delivery ratio. [ABSTRACT FROM AUTHOR]

• The number of packets is dynamically adjusted based on the moving speed of nodes. • Time on Air (ToA) is used as a selection criterion for modulation parameters. • Distance, moving speed and SNR are used to determine the network margin. Wireless sensor network (WSN) is an important research area in smart agriculture. As an emerging technology, LoRa is used for communication between wireless sensor nodes. When LoRa is used as a mobile terminal node (mobile robot, drone, etc.), the Quality of Service (QoS) of LoRa network will be greatly reduced, which will have an impact on the quantitative decision-making and intelligent control in the future. Therefore, a novel Adaptive Data Rate (ADR) algorithm for agricultural mobile sensor nodes is proposed in this paper. The channel condition changes are effectively sensed based on three LoRa signal characteristics of SNR, RSSI, and frequency offset. It could respond quickly to the complex and changeable radio channel environment, and configured the modulation parameters dynamically and accurately. The Mobile LoRaSim tool and Arduino + SX1278 hardware platform were adopted for evaluation and analysis. The results showed that compared with the standard ADR algorithm and the Blind ADR algorithm, the data extraction rate of the Mobile ADR algorithm was increased by 15 %, the data collision rate was reduced by 45 %, the network energy consumption was reduced by 8 %, and the time on air was reduced by 50 %. The proposed algorithm improves the adaptability and robustness in terms of QoS of LoRaWAN networks. [ABSTRACT FROM AUTHOR]

Recently, the precise location of sensor nodes has emerged as a significant challenge in the realm of Internet of Things (IoT) applications, including wireless sensor networks (WSNs). The accurate determination of geographical coordinates for detected events holds pivotal importance in these applications. Despite the DV-Hop gaining popularity due to its cost-effectiveness, feasibility, and lack of additional hardware requirements, it remains hindered by a relatively notable localization error. To overcome this limitation, our study introduces three new approaches of localization that combine the DV-Hop with the chicken swarm optimization approach. The primary objective is to improve the precision of DV-hop-based approaches. Through extensive simulations, we compare the efficiency of our novel localization algorithms with competitive approaches considering random network topologies. The results derived from the simulation validate the efficiency of our proposed algorithms. The proposed algorithm, the HW-DV-HopCSO, achieves a considerable improvement regarding positioning accuracy. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs often face limitations in terms of their energy resources, making energy efficiency a critical concern when designing WSNs. Clustering and multihop routing are effective methods for maximizing the lifetime of WSNs. The Graph Neural Network (GNN) is an emerging architecture in neural networks that has recently gained attention for solving problems in various domains, including wireless networks. In this paper, we propose a method based on GNN to create static clusters of equal size. This approach aims to balance energy consumption among the nodes, which is an important sub-goal in designing an energy-efficient WSN clustering protocol. Additionally, we introduce a distributed cluster head selection scheme that operates independently within each cluster. Another crucial aspect of energy efficiency in WSNs is multi-hop routing, which helps avoid long-distance communication that consumes significant amounts of energy. Our proposed centralized routing protocol establishes exclusive routes to the base station for each cluster. This ensures even energy consumption for relay nodes and prevents the hotspot problem. To evaluate our proposed protocol, we conducted simulations comparing it with several state-of-the-art counterparts. The empirical results demonstrate that our approach outperforms other protocols in terms of lifetime and coverage metrics. [ABSTRACT FROM AUTHOR]

Wireless sensor networks is made up of many tiny nodes that has the ability to measure physical phenomena and send the collected measurements to the sink node using multiple-hop protocol with short range transmission. One of the main factors that can be considered to increase the sensor network life is routing protocol. The main goal of routing protocol is to select the most optimal path and make decision of sending the gathering data to the sink node. Inefficient routing protocol leads to more re-transmission of data which consume more energy.Thus, a careful and comprehensive performance analysis of the routing protocols used in any wireless sensor network is required. The main goal of this paper is to study the performance of the RPL protocol under three different MAC protocols named; ContikiMac, CXMAC, NullRDC in terms of end-to-end delay, packet delivery ratio and power consumption to find the best behaviour of the RPL routing protocol under the MAC protocols effect. The results show that NullRDC is better in terms of latency while the ContikiMac outperforms the others in terms of power consumption. [ABSTRACT FROM AUTHOR]