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With the aim to enhance cloud security with higher data confidentiality rate and integrity, we propose a novel technique called Stochastic Deep Encryption Standard Algorithm (SDESA). After the successful registration of the cloud user with the Cloud Server, the authentication process is executed using a Stochastic Nesterov Gradient Piecewise Deep Belief Network model, which distinguishes between authorized and unauthorized users with the greatest accuracy and least time. Gestalt pattern matching method is used to find legitimate users. Stochastic Nesterov gradient approach is adapted to decrease the classification error, thus boosting the attack detection accuracy. Linear Congruential Ephemeral Advanced Encryption Standard Algorithm, which uses the Linear Congruential Ephemeral Secret Random Key for data encryption/ decryption to protect data by preventing unauthorized access, is finally introduced to guarantee the data confidentiality and integrity. The results indicate that SDESA outperforms conventional methods in terms of various performance metrics under different scenarios. [ABSTRACT FROM AUTHOR]

Feature selection (FS) plays a pivotal role in data mining, presenting an optimization challenge that seeks to reduce feature size and enhance model generalization simultaneously. The expansive search space involved in this task often leads conventional optimization methods to produce suboptimal outcomes, impeding the pursuit of the most optimal global solution. This study presents a novel hybrid optimization approach, AVOA-AOA, which integrates the Archimedes Optimization Algorithm (AOA) with the African Vultures Optimization Algorithm (AVOA) specifically for numerical optimization and FS purposes. AVOA algorithm, inspired by the foraging behavior of vultures in Africa, is known for its simple yet effective design. However, it has some drawbacks, such as limited exploration capacity and early convergence due to minimal search process exploration. These drawbacks lead to an unbalanced search with an inability to bypass local solutions. To address these issues, the proposed AVOA-AOA method integrates AOA into the AVOA framework, enhancing the exploration phase and facilitating a dynamic transition between exploration and exploitation. The in-depth investigation and analysis of the performance of the proposed AVOA-AOA method are carried out using (1) twenty-nine CEC2017 benchmark functions and (2) nineteen datasets for FS problems. The experimental findings show that AVOA's search tactics and convergence behavior have been vastly enhanced. The proposed AVOA-AOA achieves an average accuracy rate of 97.64% using the K-Nearest Neighbors (KNN) classifier and surpasses state-of-the-art results for 15 out of 19 datasets. [ABSTRACT FROM AUTHOR]

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

With the growing adoption of cloud computing in both public and private sector enterprises, the industry has experienced rapid expansion. To fully unlock the potential of cloud computing, efficient task scheduling becomes crucial. In cloud computing, task scheduling involves optimizing the allocation of tasks to a diverse range of resources, such as virtual machines, with the goals of reducing makespan, maximizing resource utilization, and minimizing response times. This challenge becomes even more pronounced for large-scale tasks due to the NP-hard nature of the problem. Consequently, the integration of metaheuristic algorithms into task scheduling has emerged as a solution to equitably distribute complex and diverse tasks across limited resources within acceptable timeframes. To enhance the quality of cloud computing services, this research introduces the modified white shark optimizer (mWSO) as an alternative task scheduling technique. The improved variant mWSO boosts the performance of the original WSO by introducing the following three enhancement steps: (1) introduce memory-based WSO to boost the exploitation phase, (2) propose an exploration-exploitation balance phase to enhance the exploration phase, and (3) introduce a control randomization parameter to balance exploration and exploitation properly. The mWSO is subjected to testing on both the global optimization problems from CEC2020 and cloud task scheduling problems. The experimental results of mWSO demonstrate high performance for CEC2020 competition benchmarks compared to other state-of-the-art and recent metaheuristic algorithms. In the case of the task scheduling problem, the mWSO achieved − 0.01 to 13.53% and 0.62–10.42% makespan and energy consumption reduction, respectively, for CEA-Curie workloads. For HPC2N workloads, mWSO achieved 7.27–29.53% makespan reduction and 3.52–26.08% energy savings over the compared metaheuristics. The statistical validity of the performance is also verified using Wilcoxon's rank-sum test. The experimental results and comparison analysis reveal the consistent and better performance of the proposed mWSO to solve optimization problems. [ABSTRACT FROM AUTHOR]

Data collection using Mobile Sink (MS) is one of the best approaches to address the hot spot issue resulting from multihop data collection and extend the lifetime of Wireless Sensor Networks wherein the MS tours a few specific locations called sojourn locations that serve as data collecting points (DCPs). The best choice of these locations is an NP-hard problem, and the optimum or nearly optimum results can be achieved by applying meta-heuristic optimization methods. It is challenging to create an effective algorithm that allows MS for data collection irrespective of the network topology changes caused by node failures since these changes affect node coverage, data transmission, and network lifespan. Hence, an effort must be made to ensure a trade-off between the MS trajectory and the number of hops. Different MS-based techniques have been proposed; however, most of them fell short of addressing the above goals. With this inspiration, we propose iCapS-MS, which is an integrated approach that utilizes an improved Capuchin Search Algorithm (iCapSA) to determine the best set of DCPs and enhanced Ant Colony Optimization (e-ACO)-based MS trajectory design. Using iCapSA, the best DCPs are selected such that almost every node is served in one-hop communication with the shortest feasible hop distance and minimum coverage intersection between DCPs. The best trajectory for MS is established using e-ACO method. The results demonstrate that iCapS-MS outperforms existing methods based on several performance metrics. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) hold the promise of delivering new intelligent, cost-effective, and collaborative applications with the potential to have a great impact on our daily life. WSNs are often employed for detecting and tracking a wide range of entities involved in realistic scenarios where security is of vital importance. While selecting energy-efficient Cluster Heads (CHs) is the primary focus of the majority of clustering approaches currently in use in WSNs, researchers have not given adequate consideration to the security aspects of CHs when developing a CH selection strategy. Estimating the trust between the nodes not only makes the WSN secure, but also improves communication between nodes and makes the WSN more reliable. In this paper, we develop a secure and energy-aware clustering approach (SEACDSC) for WSNs by adapting sand cat swarm optimization algorithm (SCSO). SEACDSC incorporates a novel mechanism for determining secure and energy-efficient CHs among the WSN nodes. In particular, we propose a Discrete SCSO method, a variant of the traditional SCSO, to facilitate the secure and efficacious selection of CHs. The fitness function is designed by considering nodes' remaining energy and trust values for choosing CH efficiently. Furthermore, the exponential weighted moving average (EWMA) is used for dynamically updating the predefined threshold values following the network state. As demonstrated by the simulation results, SEACDSC outperforms the existing BAT-Based, MG-LEACH, Enhanced-LEACH, Improved-Leach, and RCH-LEACH techniques in terms of network stability, number of alive nodes, energy efficiency, reliability, average trust value of CHs and network lifetime. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has led to the development of a new network paradigm called the Internet of Vehicles (IoV), which has evolved from the traditional Vehicular Ad hoc Networks (VANETs). Vehicle communication becomes incredibly challenging in the IoV network due to high vehicle speed and rapid network topology changes. In a large network setting, the complicated road conditions and the huge number of vehicles place greater demands on IoV communication performance. Vehicle clustering is an effective way to enhance communication capability among vehicles. However, choosing stable cluster heads (CHs) and reducing the number of clusters are challenging while organizing the network by clusters. A clustering algorithm that integrates the Genetic Algorithm and Sparrow Search Algorithm called Enhanced Sparrow Search Algorithm for IoV (ESSAIoV) is proposed. ESSAIoV has the ability to operate in the IoV high mobility nodes scenario and produces optimized clusters for effective communication. It uses a fitness function that combines the benefits of weight-based and mobility-based clustering approaches. The employed fitness function considers mobility metrics along the cluster distance to construct the fewest clusters with stable CHs. The effectiveness of the proposed ESSAIoV algorithm is assessed through simulations. The simulation results show that ESSAIoV outperforms the state-of-the-art algorithms in terms of the number of clusters and the average cluster lifetime and so it enhances the network performance and subsequently reduces the vehicular network's routing cost. [ABSTRACT FROM AUTHOR]

As a result of technological growth, WSNs enable creative practical solutions for anything from basic communication to sophisticated applications, which are not confined to a particular domain. In addition to the advances, there is an ongoing issue concerning the frequent threats or attacks on WSNs. The sinkhole attack, which jeopardizes the accuracy and trustworthiness of information in a WSN, is one of the most devastating attacks. Moreover, WSN routing schemes are extremely vulnerable to sinkhole attacks. To address the security challenges faced by WSNs, timely and reliable intrusion detection is of utmost importance. In this paper, a novel intrusion detection scheme, called Time Synchronized Multivariate Regressive Convolution Deep Neural Network Model (TSMR-CDNN), is introduced for sinkhole attack detection. TSMR-CDNN incorporates a reverse time synchronization scheme for its potential to provide precise clock skews and offsets, effectively reducing detection delays. The Broken-stick regression method is applied for the analysis of multivariate data such as clock features, RSSI, LQI, and energy by setting the threshold value, for enhancing its detection capabilities. The steepest gradient function is used to update the weight parameter in order to reduce the false positive rate. This approach minimizes the error rate, resulting in superior classification performance. The experimental results confirm that TSMR-CDNN outperforms the existing methods in terms of various metrics, and demonstrates 5% improvement in attack detection accuracy, 3% enhancement in precision, recall, and F-measure, and 10% reduction in detection time, respectively. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an emerging technology that can be integrated with the Internet of vehicles (IoV) to enhance vehicle services with low latency and optimize task offloading efficiency. MEC technology enhances the battery life of mobile devices by bringing storage and computational services closer to the edge of the network. However, challenges arise in areas with limited MEC server coverage. To address these challenges, this paper proposes a multi-hop task offloading model in MEC-IoV utilizing the modified sparrow search algorithm (MSSAMTO-IoV). The MSSAMTO-IoV model consists of two phases: candidate vehicle selection and task offloading. In the candidate vehicle selection mechanism, the model takes into account the k-hop wireless communication range. It identifies and selects candidate vehicles from neighboring vehicles for task offloading. The task offloading problem is then formulated as an optimization problem, aiming to minimize the delay. To solve this problem, MSSA is utilized, with modifications introduced to enhance the initial population quality and diversity of the standard SSA using the logistic map. Furthermore, an inertia weight is introduced to improve search speed, convergence rate, and exploration capabilities. The mean termination criterion is employed to avoid unnecessary iterations and minimize run-time. Additionally, a mutation strategy is employed to prevent falling into local optima. The simulation results demonstrate that MSSAMTO-IoV achieves faster convergence and outperforms BAT and SSA by approximately 2 times and 3 times, respectively. Moreover, MSSAMTO-IoV effectively reduces latency across different task sizes. For a 500 KB task, it reduces latency by approximately 5, 9, and 13% compared to basic SSA, BAT, and greedy algorithms, respectively. [ABSTRACT FROM AUTHOR]

The intelligent collaboration of Unmanned Aerial Vehicles (UAV) and Wireless Sensor Networks (WSN) can bring about increased data collection efficiency and overall network performance in numerous applications. Despite the success of different studies, UAV-WSN collaboration still faces many open challenges including trajectory planning, data collection and obstacle avoidance strategies. In this paper, we propose an Enhanced Evolutionary Scheme for Obstacle-Aware Data Gathering in UAV-assisted WSNs (OaD-UAV). Different from the existing studies, we include different factors that influence the energy efficiency and delay in WSN data gathering to define an efficient UAV trajectory. We examine the UAV path feasibility and introduce different evolutionary operations to derive efficient path that considers both the path length and UAV angle changes in an obstacle-present environment. An improved heuristic evolutionary approach based on genetic algorithm is designed to determine the best trajectory for the UAV to hover and collect data from the selected rendezvous points. The results reveal the effectiveness of the OaD-UAV scheme against the other related approaches in terms of path cost and data collection efficiency, energy consumption and network lifetime. [ABSTRACT FROM AUTHOR]

Recognizing critical nodes in complex networks has emerged as a challenging task across several application areas. The critical node detection problem (CNDP) is an optimization challenge that entails determining the subset of nodes whose removal adversely affects network connectivity and performance based on certain predetermined criteria. The problem of recognizing critical nodes has received significant consideration since it is a vital challenge in a multitude of application areas. As a result, many variants have been proposed on the basis of numerous metrics. In this survey, we discuss different applications, challenges, and solutions to single- and multi-objective CNDP. We review and classify different recent advancements and obtained outcomes for each variant, proposed from 2017 to 2022. To our best knowledge, this is the first survey on the heuristic optimization-based solutions for CNDP that have been developed in recent years. This study also provides researchers with future insight into filling gaps in the critical nodes research field and identifying emerging research trends in this area. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of an extensive number of low-power sensor nodes to gather information from their environment and monitor physical activities. This makes node localization a crucial aspect in most WSN applications since measurement data is worthless unless the location from where the data is acquired is known precisely. The majority of localization solutions rely on anchor nodes for estimating the node locations with different localization accuracy, complexity, and hence different applicability. But, the cost and complexity in the localization of large-scale WSNs are not significantly reduced. In this paper, a novel Hybridized Dragonfly and Jaya Optimization technique (HyDAJ) is introduced for improving localization accuracy and performance of mobile WSNs. The proposed hybrid technique combines the advantages of Dragonfly algorithm and Jaya algorithm to localize the sensor nodes in a more efficient way and overcomes the limitations of the original algorithm. The hybrid algorithm verifies that all target nodes are precisely localized with higher accuracy. Simulation results reveal that HyDAJ outperforms existing methods under multiple metrics including localization efficiency, mean localization error, computation time, and energy consumption. [ABSTRACT FROM AUTHOR]

Although researchers have investigated multiple facets of fault tolerance, majority of them have overlooked fault tolerance in face structured WSNs. Motivated by this, we propose a Fault-Tolerant Coverage Preserving Strategy for Face Topology-based WSNs (FtCFt). Unlike existing methods of recovering failures by merging the adjacent faces, we propose a coverage-aware node replacement method to replace the failing node with a suitable alternate node. This is significant because a mobile target will go undetected, and no evidence of it can be acquired until it leaves the hole region and is sensed by a node. FtCFt offers fault tolerance by incorporating node self-check and link-check strategies that works in conjunction with one of its mobile target tracking applications. Unlike existing works, the proposed restoration algorithm effectively repairs and restores the face structure to ensure network coverage and connectivity. Simulation results reveal that FtCFt improves coverage, quality of service and WSN liferime. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, a Mobile Collector (MC) is used to gather data by periodically traversing the network to avoid hotspot or energy-hole issues. Although the MC's data collection process and network performance can be enhanced by determining suitable set of Stop Points (SPs), it is challenging to find the best set of SPs and schedule an effective MC trajectory. Much attention has been received to MC's path planning through SPs in a static environment where the path is determined during the initial phase, but they do not emphasize the nodes' coverage rate and cannot be adapted to network topology changes. In this context, we propose an Efficient Trajectory Planning method for Coverage Enhanced Data collection in WSN (ETP-CED). We introduce an enhanced method based on integrated Particle Swarm Optimization and Ant Colony Optimization for selecting the best set of SPs and planning efficient MC trajectory. ETP-CED is adaptive to node failures, allowing the nodes to reposition themselves to patch up coverage holes in the network. MC readjusts its planned path when there are less nodes in the network due to node failures, thereby shortening the trajectory length and speeding up data delivery. The results show that ETP-CED outperforms existing methods in the aspects of nodes' coverage and data collection efficiency. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes' remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network. [ABSTRACT FROM AUTHOR]

A huge number of sensor nodes collect information about the environment around them in wireless sensor networks (WSNs), but this information is not valuable until the precise location where it was collected is revealed. No infrastructure exists to estimate the locations of deployed nodes, since global positioning system (GPS) receivers are too expensive to be included with every sensor. Hence, localization of sensor nodes plays a key role in a number of WSN applications, such as health, whether, industrial and military. Sensor node localization is one of the most significant challenges in WSNs, that aims to determine the coordinates of unknown nodes based on the coordinates of anchor nodes. The researchers are designing new localization schemes that are suitable for WSN implementation, as traditional localization algorithms (eg., GPS) are not suitable. There are a variety of meta-heuristic algorithms used to solve optimization problems in WSNs. Rat swarm optimizer (RSO) is a recently developed algorithm with competitive performance and remarkable different results from other meta-heuristic algorithms. In this work, we propose a modified rat swarm optimizer (MRSO) based nodes localization problem in wireless sensor networks (WSNs). To evaluate the proposed work comparative study is done with the original RSO and other meta-heuristic based approaches. The proposed MRSO outperforms the original RSO algorithm and other existing optimization algorithms in terms of different localization error metrics. The proposed MRSO reduces the ALE by 68.52 % , 71.75 % , 70.58 % and 66.81 % comparing to RSO, bat optimization algorithm (BOA), BOA variant 1 and BOA variant 2, respectively. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) have advanced significantly over the past years as an incredible technology for averting congested traffic and enhancing traffic safety. Recent researchers show that incorporating Wireless Sensor Networks (WSN) into ITS can decrease the necessary investment and permits the creation of intelligent collaborative applications that enhance traffic efficiency and driver safety. In this paper, we propose a Trust Aware Clustering Technique based on Rat Swarm Optimizer for WSN-based Intelligent Transportation System (TACTIRSO), which is a secure method for selecting cluster heads (CHs) based on nodes' trust value. We employed the Rat Swarm Optimizer (RSO), one of the most recent swarm-based optimization methods, to efficiently choose CHs. For the selection of CH, the proposed fitness function takes into account the node remaining energy and trust value. Moreover, the exponential moving average model is employed to dynamically change the predefined threshold values according to the network state. In order to enhance the performance of RSO, we applied different local search strategies in addition to an energy and trust-aware method of initializing the rat population. The simulation results reveal that TACTIRSO outperforms existing studies in terms of energy efficiency, selection of most trustworthy nodes, and average network lifetime. Numerical results indicate that TACTIRSO improves average network stability by at least 1.13 times, average trust value by at least 3.31 times, and reliability by at least 4.45 times over other schemes in a heterogeneous network, while these improvements are by 1.02 times, 0.33 times, and 3.52 times, respectively, in a homogeneous network. [ABSTRACT FROM AUTHOR]

There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN's reliability is essential during the design process and when evaluating WSNs' performance. Current research uses the reliability block diagram (RBD) technique, based on component functioning or failure state, to evaluate reliability. In this study, a new methodology-based RBD, to calculate the energy reliability of various proposed chain models in WSNs, is presented. A new method called D-Chain is proposed, to form the chain starting from the nearest node to the base station (BS) and to choose the chain head based on the minimum distance D, and Q-Chain is proposed, to form the chain starting from the farthest node from the BS and select the head based on the maximum weight, Q. Each chain has three different arrangements: single chain/single-hop, multi-chain/single-hop, and multi-chain/multi-hop. Moreover, we applied dynamic leader nodes to all of the models mentioned. The simulation results indicate that the multi Q-Chain/single-hop has the best performance, while the single D-Chain has the least reliability in all situations. In the grid scenario, multi Q-Chain/single-hop achieved better average reliability, 11.12 times greater than multi D-Chain/single-hop. On the other hand, multi Q-Chain/single-hop achieved 6.38 times better average reliability than multi D-Chain/single-hop, in a random scenario. [ABSTRACT FROM AUTHOR]

Data collection is an important task in many mobile wireless sensor network (MWSN) applications. The energy of sensor nodes around the sink depletes rapidly due to transmitting large amounts of data from neighboring nodes. This problem can be mitigated through the use of intelligent mobile vehicles to collect the data. While traditional data collection methods focus on maximizing data acquisition or reducing network energy consumption, they do not take into account the actual sensor nodes' coverage of the region of interest (ROI). To the best of our knowledge, most research on data collection focuses on path planning for the mobile collector in a static environment. During the lifetime of the network, coverage holes may appear due to node energy depletion. We propose a coverage aware topology adaptive path planning algorithm (CAPP) for path planning for WSNs where all sensor nodes are coverage aware and respond by moving to better locations to improve coverage of the network and compensate for the failed nodes. First, the path planning algorithm determines the number of Stop Points (SPs) where it will stop to gather data. Then, Particle Swarm Optimization is used to find the best location for these SPs. Finally, the shortest path through these SPs is determined by Ant Colony Optimization. Through extensive simulation, we show that CAPP performs efficiently in data collection while also allowing the nodes to move for coverage hole repair. The result shows improvement in area coverage and reduced delay in data collection, with no increase in energy consumption. [ABSTRACT FROM AUTHOR]

Maintaining prolonged service lifetime and adequate quality of sensing coverage are the key challenges in constructing Wireless Sensor Network (WSN) based applications. As such networks usually operate in inhospitable and hostile environment, failures are ineludible and providing resilience is a necessity. However, it is challenging to satisfy the conflicting problems of enhancing energy efficiency and fault tolerance simultaneously. Fault-tolerance is a significant requirement while designing WSN. It is crucial to detect the failures in advance and take necessary measures to maintain durable and efficient functioning of the network. Generally, in the existing face structured WSNs, node faults and failures can induce the formation of coverage holes, disrupt the face structure and consequently curtail the application performance. The coverage quality will affect the monitoring effectiveness of tracking applications, e.g., a moving target tracking. Moreover, node failures can cause the network to be partitioned, further reducing the accuracy in tracking. In this paper, we propose a robust fault-tolerance scheme with coverage preservation using a face structured WSN topology ( F CAFT ). The key objective of the proposed F CAFT scheme is to sustain the performance of the network by timely healing the faults in the network, to enhance the durability and reliability of the WSN. The results of simulation and comparison with existing methods reveal that F CAFT is efficacious in enhancing the service lifetime of WSN by about 14% and sustains about 96% of coverage even when the failure rate is more than 20%, which is a necessity for critical monitoring and tracking applications of WSNs. [ABSTRACT FROM AUTHOR]

Despite the obvious benefits and growing popularity of Machine Learning (ML) technology, there are still concerns regarding its ability to provide Financial Distress Prediction (FDP). An accurate FDP model is required to avoid financial risk at the lowest possible cost. However, in the Internet era, financial data are exploding, and they are being coupled with other kinds of risk data, making an FDP model challenging to operate. As a result, researchers presented several novel FDP models based on ML and Deep Learning. Time series data is are important to reflect the multi-source and heterogeneous aspects of financial data. This paper gives insight into building a time-series model and forecasting distress far in advance of its occurrence. To build an efficient FDP model, we provide a hybrid model (GALSTM-FDP) that incorporates LSTM and GA. Unlike other previous studies, which established models that predicted distress probability only within one year, our approach predicts distress two years ahead. This research integrates GA with LSTM to find the optimum hyperparameter configuration for LSTM. Using GA, we focus on optimizing architectural aspects for modeling the optimal network based on prediction accuracy. The results showed that our algorithm outperforms other state-of-the-art methods in terms of predictive accuracy. [ABSTRACT FROM AUTHOR]

One of the major challenges in realizing a reliable wireless sensor network (WSN) that can survive under the emerging applications is the constrained energy of the sensors. Hence, extending the lifetime of WSN is a major concern, which directly impacts the performance of various WSN-based applications. In this regard, various methods have been developed that either investigate the energy consumption or lifetime enhancement of WSN. A promising method to conserve the energy of the sensors is to use sleep–awake scheduling by choosing disjoint groups of nodes called dominating set (DS). By distributing the data collection duties among these DSs, one DS handles these tasks for a specified period of time before being replaced by another group, extending the lifespan of the network. This problem becomes challenging in WSN with heterogeneous energy. Despite the success of the algorithms in determining the DS, none of the existing methods consider the node's energy while creation or selection of DS. This motivates us to utilize the DSs concept to control and maintain sleep/awake schedule of WSN nodes with heterogeneous energy. Toward this goal, we propose an energy-aware algorithm known as proposed initializer for whale optimization algorithm-based operator (PI-WOA-BO) to construct disjoint DSs that work as collector nodes for data gathering in each round and extend the total WSN lifetime. An energy-aware fitness function is introduced for selecting the best DSs that can maximize the WSN lifetime. Simulation results reveal that PI-WOA-BO exhibits enhanced performance over baseline techniques under various metrics including energy, stability, reliability and lifetime of WSN. PI-WOA-BO outperforms FUZZY-DS-ACO, CDS-FOR, BEE-VBC and CDS-LEACH by (17.4%, 40.1%, 31.1% and 53.6%), (7.7%, 33.5%, 23.4% and 48.5%) and (7.9%, 33.5%, 22.9% and 47.8%) in terms of First, Half and Last node dies, respectively. [ABSTRACT FROM AUTHOR]

This study aims to develop a better Financial Statement Fraud (FSF) detection model by utilizing data from publicly available financial statements of firms in the MENA region. We develop an FSF model using a powerful ensemble technique, the XGBoost (eXtreme Gradient Boosting) algorithm, that helps to identify fraud in a set of sample companies drawn from the Middle East and North Africa (MENA) region. The issue of class imbalance in the dataset is addressed by applying the Synthetic Minority Oversampling Technique (SMOTE) algorithm. We use different Machine Learning techniques in Python to predict FSF, and our empirical findings show that the XGBoost algorithm outperformed the other algorithms in this study, namely, Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), AdaBoost, and Random Forest (RF). We then optimize the XGBoost algorithm to obtain the best result, with a final accuracy of 96.05% in the detection of FSF. [ABSTRACT FROM AUTHOR]

Past studies reveal the benefits of using Mobile Sink in Wireless Sensor Networks to bring about increased data collection efficiency and overall network performance in numerous applications. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suitably in response to node failures. In this paper, we propose a Modified Sparrow Search Algorithm-based Mobile Sink Path Planning for WSNs (MSSPP) to create shorter travel route for MS and minimize data gathering latency. The proposed method helps in improving the performance of basic SSA by enhancing the quality of initial sparrow population, population diversity and search ability through modified strategies and is adaptive to node failure scenarios. In the first phase, we introduce a modified Sparrow Search-based algorithm to select a set of RPs that maximizes the coverage of nodes and minimizes the overlap in RP coverage. Then, an ACO-based path planning algorithm is utilized to determine the shortest tour through the RPs. The results reveal the effectiveness of MSSPP against other related approaches in terms of number of RPs, data gathering time, MS path, energy utilization and network lifetime. [ABSTRACT FROM AUTHOR]

The increasing relevance and significant acceptance of Wireless Sensor Network (WSN) solutions have aided the creation of smart environments in a multitude of sectors, including the Internet of Things, and offer ubiquitous practical applications. We examine current research trends in WSN using Artificial Intelligence (AI) technologies and the potential application of these methods for WSN improvement in this study. We emphasize the security, fault detection and tolerance, and quality of service (QoS) concerns in WSN, and provide a detailed review of current research that used different AI technologies to satisfy particular WSN objectives from 2010 to 2022. Specifically, this study's purpose is to give a current review that compares various AI methodologies in order to provide insights for tackling existing WSN difficulties. Furthermore, there has been minimal existing related work concentrating employing AI approaches to solve security, fault detection and tolerance, and quality of service (QoS) concerns associated to WSN, and our goal is to fill the gap in existing studies. The application of AI solutions for WSN is the goal of this work, and we explore all parts of it in order to meet different WSN challenges such as security, fault detection and tolerance, and QoS. This will lead to an increased understanding of current AI applications in the areas of security, fault detection and tolerance, and QoS. Secondly, we present a comprehensive study and analysis of various AI schemes utilized in WSNs, which will aid the researchers in recognizing the most widely used techniques and the merits of employing various AI solutions to tackle WSN-related challenges. Finally, a list of open research issues has been provided, together with considerable bibliographic information, which provides useful recent research trends on the topics and encourages new research directions and possibilities. [ABSTRACT FROM AUTHOR]

Financial distress occurs when individuals or companies struggle to meet financial obligations, often due to factors like high fixed costs, illiquidity, or revenue sensitivity to economic downturns. While various machine learning and deep learning approaches have been developed for predicting financial distress by analyzing financial data to assess the likelihood of a company facing financial difficulties, challenges persist in achieving enhanced prediction accuracy and managing time complexity. To address this, a novel method known as the Chinese Whisper Clustered Stochastic Gradient Descent Federated Learning method (CWCSGDFL) is introduced. It aims to improve the efficiency of financial distress prediction by grouping data samples using the Hamann Similarity Indexed Chinese Whispers clustering process, validating them with a matching coefficient, and addressing data imbalance. Also, Kaiser-Meyer-Olkin correlative targeted projection model is employed to choose the pertinent features for prediction. By achieving balanced clustering results with these selected features, CWCSGDFL reduces time complexity in financial distress prediction. Stochastic Gradient Gaussian Kernel Federated Learning approach is employed to preserve privacy while predicting distress, aided by kriging regression for model analysis. Stochastic gradient descent momentum optimizes the global objective function, ensuring accurate classification with minimal loss. Experimental results demonstrate that CWCSGDFL achieves an average accuracy of 96%, precision of 94%, recall of 98%, F-measure of 98%, and a time complexity of 36 ms, outperforming the existing techniques. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the most important requirements for effective UAV–WSN operations is to perform data collection in timely and safe manner. Identifying an effective path in an environment with various obstacles and ensuring that the path may efficiently cover the selected stop points for effective data collection are both necessary and difficult. We propose a UAV-based effective data gathering scheme for wireless sensor networks with obstacles, where a UAV is employed as a mobile sink to collect data from the ground sensor nodes (EDGO). The main novelty includes a UAV–WSN collaborative approach for data gathering, which incorporates a convenient method for UAV trajectory design in a three-dimensional environment with obstacles. We propose an improved heuristic evolutionary approach based on genetic algorithm to determine the optimized trajectory for the UAV to gather data. In contrast to existing methods, the proposed method focus to reduce the length and angle cost of the path, minimize the energy consumption, and delay, and includes different evolutionary operations to generate a collision free path for UAV. Our approach retains the infeasible path through sufficient modifications, which improves the diversity of paths, so that it is possible to jump out of the local optima. The results reveal the effectiveness of the EDGO scheme against the other related approaches in terms of path cost and data collection efficiency. The network lifetime is extended by approximately 11% and offers a reduction of 42% and 35% in the UAV path length and travel time, respectively, when compared to the existing schemes. [ABSTRACT FROM AUTHOR]

The problem of Data acquisition in large distributed Wireless Sensor Networks (WSNs) scale is a hindrance in the growth of the Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted great interest from researchers. An open question of this approach is how to effectively integrate these technologies for specific tasks. The objective of this paper is two parts. First, we propose an effective deterministic clustering scheme based CS technique (EDCCS) for data collection in IoT based homogeneous and heterogeneous WSN to deal with the data acquisition problem, reduce the consumption of energy and increase the lifetime of network. Second, we propose random matching pursuit (RMP) as an effective CS reconstruction algorithm to improve the recovery process by reducing the error average at the base station (BS). The simulation results show that our proposed novel EDCCS scheme reduces at least 60% of the average power consumption and increases the network lifetime at least 1.3 times of the other schemes in homogeneous network while, it increases the network lifetime and residual energy by 1.9 times and 1.3 times respectively, compared to the other schemes in heterogeneous network. Also, our proposed RMP algorithm reduces the error average of reconstruction at least 35% compared to other reconstruction algorithms. [ABSTRACT FROM AUTHOR]

While several Mobile Sink (MS)-based data gathering methods have been proposed for Wireless Sensor Networks, most of them are less adaptive to changes in network topology, and the planned MS trajectory cannot be refined to accommodate node failures. Hence, a KD-Tree-based scheme (KDT) is proposed, which is an adaptive and robust algorithm that reduces network energy consumption and data gathering delay. In contrast to many existing algorithms, the number of Rendezvous Points (RPs) are assigned dynamically by prioritizing the nodes' coverage. Overlapping coverage of RPs is minimized, while guaranteeing 100% coverage of nodes. KDT is adaptive to network topology changes, and the planned MS trajectory can be refined to accommodate node failures. The shortest MS path is found using Ant Colony Optimization. Simulation results show that KDT requires approximately half the number of RPs and about 13% reduction in MS travel time compared to existing schemes. [ABSTRACT FROM AUTHOR]

An emergent solution to overcome the limitations of traditional multi-hop routing in wireless sensor networks (WSNs) is to use mobile collectors (MCs) for data gathering, thereby reducing energy consumed in internode communications. Most of the existing data collection approaches emphasize data gathering or network lifetime extension, without taking into account sensor node area coverage or how to handle sensor node failures through node mobility. It is desirable to utilize node mobility as a key functionality for WSN coverage optimization. We propose a robust coverage-aware multiple path-planning algorithm (CAMP) for WSN data gathering using MCs. CAMP works in tandem with any coverage hole-repair algorithm to heal coverage holes created by dying nodes, if any, and can plan efficient paths for MCs. CAMP initially selects polling points using Particle Swarm Optimization, and then divides the area into radial sections based on the number of available MCs. The size of subsection is adjusted to balance the estimated trip times within an acceptable margin and each MC traverses its assigned section following the shortest path determined by Ant Colony Optimization. Performance is analyzed in terms of coverage, energy consumption, data delivery delay, and network lifetime. Results reveal that CAMP provides above 90% coverage of nodes. Moreover, it is robust to failures and covers over 70% of the area even when more than half of the nodes fail. CAMP also saves a considerable amount of nodes' communication energy, and the network lifetime is increased by 2.5 times when compared to a similar state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Copyright of AIN Journal is the property of Arab Institute of Navigation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of AIN Journal is the property of Arab Institute of Navigation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wireless Sensor Networks based on Mobile Sinks (MS) are becoming more popular because of their benefits in enhancing performance and minimizing unequal energy usage of the nodes. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suitably in response to node failures. Also, it is difficult to plan an efficient path for data gathering by MS using a set of Rendezvous Points (RPs) that could reduce the MS travel time while reducing the WSN energy usage. In this paper, we propose an Enhanced Sparrow Search based Adaptive and Robust Data Gathering Scheme for WSNs (ESRP-MP) to create shorter travel route for MS and minimize data gathering latency. For effective utilization of RPs, the best RP locations that maximize the coverage of nodes and minimize the overlap in coverage with adjacent RPs are considered. Also, the algorithm is adaptive to node failures by updating the RPs and refining the planned MS path when necessary. In the first phase, we introduce an enhanced Sparrow Search based algorithm to select a set of RPs that maximizes the coverage of nodes and minimizes the overlap in RP coverage. Then, the shortest tour through the RPs is obtained using Ant Colony Optimization (ACO). The results reveal the effectiveness of ESRP-MP against other related approaches in terms of number of RPs, data gathering time, MS path, energy utilization and network lifetime. [ABSTRACT FROM AUTHOR]

The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT). However, a large portion of the household devices and appliances already in use, are not IoT enabled, and therefore, requires their default control mechanisms for the devices to operate. This paper proposes a smart appliance controller that uses Augmented Reality, MQTT, and other up-to-date platforms to control aftermarket home appliances in the most efficient manner. The proposed work integrates mobile AR with IoT, to control household appliances, with the help of infrared (IR) signals. The characteristics of the system are evaluated through a series of tests and performance measures. The results of the test highlight the quick response time of MQTT for the implementation of a Home Automation System, when compared to the request–reply protocol: CoAP (4 times as fast). [ABSTRACT FROM AUTHOR]

Wireless Sensor Network with Mobile Collectors (MCs) enable increased flexibility and convenience in data gathering for numerous large-scale applications. However, introducing MCs also brings a new set of challenges to overcome. To reduce the data delivery latency of the application, it is required to select the minimum number of Rendezvous Points (RPs) that allow most sensors to forward data in single-hop and best path must be planned for each MC to provide uniform path length and round-trip time for all MCs. In contrast to existing schemes, we propose a Fuzzy C-Means based multi-layered RP Clustering and ACO-based Route-Planning scheme (FCM-RP) which is a robust method to determine RP-positions and MC-assignments. More particularly, the existing works have given priority to either reducing network energy consumption or minimizing data gathering delay. In our paper, along with these priorities, we consider the robustness and adaptability of the algorithm; meaning that, our algorithm can modify the planned trajectories of deployed MCs in response to sensor node failures. This allows to adapt to any changes in network topology caused by node failures or external factors. Also, to maximize utilization of each RP and provide greater coverage for nodes, the ideal positioning of RPs with minimum coverage overlap with neighboring RPs is considered. Simulation analysis shows that the FCM-RP scheme defines better routes for MCs in terms of total path length and delay, and significantly outperforms the existing algorithms in terms of total energy consumption and network lifetime. [ABSTRACT FROM AUTHOR]

The prime numbers have attracted mathematicians and other researchers to study their interesting qualitative properties as it opens the door to some interesting questions to be answered. In this paper, the Random Matrix Theory (RMT) within superstatistics and the method of the Nearest Neighbor Spacing Distribution (NNSD) are used to investigate the statistical proprieties of the spacings between adjacent prime numbers. We used the inverse χ 2 distribution and the Brody distribution for investigating the regular-chaos mixed systems. The distributions are made up of sequences of prime numbers from one hundred to three hundred and fifty million prime numbers. The prime numbers are treated as eigenvalues of a quantum physical system. We found that the system of prime numbers may be considered regular-chaos mixed system and it becomes more regular as the value of the prime numbers largely increases with periodic behavior at logarithmic scale. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) lend themselves to a wide variety of applications in our daily lives, such as environmental monitoring, safety, health-care, animal monitoring, etc. However, one of the key issues in WSN is energy constraints. This makes energy-conservation one of the major keys to the efficient functioning and lifetime of WSN. In this paper, given a network of nodes with heterogeneous energy, our goal is to determine energy-aware disjoint dominating sets (DSs) that work as data collection nodes in each round, to improve overall WSN lifetime. In order to accomplish this goal, we propose an intelligent data collection technique with two phases, the collector nodes selection, and the data gathering path formation and collection phases. In the collector nodes selection phase, an energy-aware algorithm based on swarm intelligence is proposed to construct disjoint dominating sets that work as collector nodes in each round. In the data gathering path formation and collection phase, data gathering path is determined for achieving maximal data collection efficiency and reduced energy consumption. The efficiency of our proposed technique is proved mathematically and through simulations. [ABSTRACT FROM AUTHOR]

The emerging Internet of Things (IoT)‐based systems that integrate diverse types of sensors, mobiles and other technologies to physical world is becoming increasingly popular for use in wide varieties of applications. Compressive sensing (CS)‐based information acquisition and in‐network compression provide an effective method for accurate data recovery at the base station (BS) with reduced cost of communication. In this study, how CS can be combined with routing protocols for gathering data in IoT‐based Wireless Sensor Networks (WSNs) in an energy‐efficient manner are examined. A novel chain‐routing scheme with CS based data acquisition is introduced that includes the following new algorithms: (1) Seed Estimation Algorithm (SEA) to find the best measurement matrix by selecting the best‐estimated seed, (2) Chain Construction Algorithm (CCA) to organise the network nodes during transmitting and receiving process, (3) Compression approach with reduced consumption of energy that improves the lifetime of the network by minimising the local data traffic, and (4) Reconstruction Algorithm (RA) that reconstructs the original data with minimum reconstruction error. Here, extensive simulation and analysis results prove the performance of our proposed method in improving the network lifetime 35% better than the ECST algorithm and 93% better than the PEGASIS algorithm. In addition, the proposed reconstruction algorithm exceeds the other reconstruction algorithms performance. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is a new sampling theory used in many signal processing applications due to its simplicity and efficiency. However, signal reconstruction is considered as one of the biggest challenge faced by the CS method. Therefore in this paper, we aim to address this challenge by proposing an Adaptive Iterative Forward–Backward Greedy Algorithm (AFB). AFB algorithm is different from all other reconstruction algorithms, as it depends on solving the least squares problem in the forward phase, which increases the probability of selecting the correct columns better than other reconstruction algorithms. In addition, AFB improves the selection process by removing the incorrect columns selected in the previous step. To evaluate the AFB's reconstruction performance, we used two types of data: computer-generated data and real data set (Intel Berkeley data set). The simulation results show that AFB outperforms Forward–Backward Pursuit, Subspace Pursuit, Orthogonal Matching Pursuit, and Regularized OMP in terms of reducing reconstruction error. [ABSTRACT FROM AUTHOR]

Minimizing the quantity of transmitted data and protecting the devices from the adversary environment are the main challenges hindering the advancement of Internet of Things (IoT) network. Based on the theorem of unique prime factorization that any natural number can be expressed as the product of powers of prime numbers, we introduce a new scheme in this paper for secure and efficient gathering of the IoT data. The proposed scheme consists of three phases: Setup, Secure data gathering, and Data reconstruction. In the Setup phase, the properties of the prime numbers to create an efficient clustering and routing tree algorithm are utilized to reduce the power consumption during data transmission process. The Secure data gathering phase includes secure intra and inter communication procedures. During secure intra-communication procedure, the Compressive Sensing (CS) based method of data compression and encryption is used. Moreover, the prime number properties and the Bezier curve function are used to solve the CS problems. In the secure inter-communication procedure, the security performance of the IoT based Wireless Sensor Networks (WSNs) is improved by combining Private and Public key algorithms with the consideration of the power limitation of IoT devices. Finally, the proposed scheme introduces an adept reconstruction algorithm which is a hybrid algorithm of Bees and Genetic Algorithm to successfully restore the actual sensor data from the compressed samples, which improves the reconstruction process. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are decentralized electronic counterparts of government‐issued money. The first and best‐known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine‐learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight. [ABSTRACT FROM AUTHOR]

The ability to foresee financial distress has become an important subject of research as it can provide the organization with early warning. Furthermore, predicting financial distress is also of benefit to investors and creditors. In this paper, we propose a hybrid approach with Multi-Layer Perceptron and Genetic Algorithm for Financial Distress Prediction. There are numerous hyperparameters that can be tuned to improve the predictive performance of a neural network. We focus on genetic algorithm-based tuning of the main four hyperparameters namely Network depth, Network width, Dense layer activation function, and Network optimizer, which can make a difference in the algorithm exploding or converging. The main objective of this study is to tune the hyperparameters of the Multi-Layer Perceptron (MLP) model using an improved genetic algorithm. The prediction performance is evaluated using real data set with samples of companies from countries in MENA region. All the experiments in this study apply the technique of resampling using k-fold evaluation metrics, to get unbiased and most accurate results. The simulation results demonstrate that the proposed hybrid model outperforms the classical machine learning models in terms of predictive accuracy. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) integrates diverse types of sensors, mobiles and other technologies to physical world and IoT technology is used in a wide range of applications. Compressive sensing based in-network compression is an efficient technique to reduce communication cost and accurately recover sensory data at the base station. In this paper, we investigate how compressive sensing can be combined with routing protocols for energy efficient data gathering in IoT-based wireless sensor networks. We propose a new compressive sensing routing scheme that includes the following new algorithms: (1) seed estimation algorithm to find the best measurement matrix by selecting the best-estimated seed, (2) chain construction algorithm to organize the network nodes during transmitting and receiving process, (3) compression approach to reduce the energy consumption and prolong the network lifetime by reducing the local data traffic, and (4) reconstruction algorithm to reconstruct the original data with minimum reconstruction error. The simulation results reveal that the proposed scheme outperforms existing baseline algorithms in terms of energy consumption, network lifetime and reconstruction error. [ABSTRACT FROM AUTHOR]

The resource-constrained nature of WSNs require efficient use of resources, especially energy, to prolong their lifetime. Clustering is one of the popular approaches to allocate the resources efficiently among the WSN nodes. In this work, we analyze the problem of round length determination in cluster based WSN which has severe impact on the energy efficiency. This problem is very important since round length determines how often the cluster head (CH) rotates or re-clustering process occurs. A longer round length will cause the CH nodes to operate for a long time and drain their energies faster than other nodes resulting in uneven energy consumption in the network, while a shorter round length results in considerable wastage of energy due to frequent running of the setup phase. Hence, we propose an adaptive and dynamic mechanism for round length determination in cluster based WSNs by adapting Behavior Curve Function modeled by quadratic Bezier curves, where we associate the remaining energy level of the cluster to its round operation length and to its assigned criticality which is defined based on network energy level. This helps to determine the number of frames in a round or how many times the data collection occurs in a cluster in a round and the criticality of the energy in the WSN. Simulation results reveal that the proposed mechanism has effectively reduced the energy consumption and improved the WSN lifetime in both homogeneous and heterogeneous network settings. [ABSTRACT FROM AUTHOR]

Providing effective sensing coverage of an observation area with reduced set of working nodes for maximum duration of time is an important concern for the development of durable and energy efficient WSN applications. A well-organized network structure can greatly promote such requirements. Motivated by the use of computational geometry in network design, we propose a coverage-aware and efficient planar face topology structure (CAFT) for WSN in this paper. Also, a distributed target tracking algorithm is proposed to run on the proposed face structure. Most of the existing works utilize the face based WSNs which are built by generating planarized graphs using Gabriel graph or Relative neighborhood graph in which all the deployed nodes become a part of the created toplogy. In contrast to this, our proposed distributed topology construction method selects and organizes a subset of nodes into faces, ensures coverage and connectivity while retaining the remaining nodes in sleep mode which can reduce redundant communication that may result in extra energy consumption and cost. The sleep nodes can promote durable service time for the WSN as such nodes can act as replacement nodes in case of node faults and failures, reducing coverage hole formation in the WSN, which is crucial in critical tracking applications. The simulation results and comparison with existing techniques prove that the proposed design is effective in reducing the energy consumption and thereby improves the WSN lifetime. [ABSTRACT FROM AUTHOR]

Sensor node energy constraint is considered as an impediment in the further development of the Internet of Things (IoT) technology. One of the most efficient solution is to combine between compressive sensing (CS) and routing techniques. However, this combination faces many challenges that makes it an attractive point for research. This paper proposes an Efficient Multi-hop Cluster-based Aggregation scheme using Hybrid CS (EMCA-CS) for IoT based heterogeneous wireless sensor networks (WSNs). EMCA-CS efficiently combines between CS and routing protocols to extend the network lifetime and reduces the reconstruction error. EMCA-CS includes the following: a new algorithm to partition the field into various hexagonal cells (clusters) and based on multiple criteria, selects a node from each cluster as cluster head (CH). Each CH will then compress its cluster data using hybrid CS method. Also, a new Grey Wolf based algorithm to create optimal path for CHs to deliver the compressed data to base station (BS) and a CSMO-GWO algorithm to optimize the CS matrix construction process is introduced. Moreover, a new Grey Wolf and reversible Greedy based Reconstruction Algorithm is proposed to recover the actual data. The simulation results indicate that the performance of the proposed work exceeds the existing baseline techniques in terms of prolonging WSN lifetime, reducing the power consumption and reducing normalized mean square error. [ABSTRACT FROM AUTHOR]

Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime. [ABSTRACT FROM AUTHOR]