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With many practical applications, wireless sensor networks (WSNs) represent an important field of study. Real-world applications of WSNs include smart home automation, healthcare, agriculture, industrial automation, and environmental monitoring. WSNs present countless chances for creative solutions across various industries as they develop and become more sophisticated. But because they are unattended, we must devise ways to make them work better without using the sensor nodes' most important resource--battery power. A unique sink mobility model from a deployed WSN is proposed in this paper, based on constructing a minimal Spanning tree. The proposed approach derives a controlled movement model for the mobile sink based on minimal spanning tree (MST) features. Consequently, fixed nodes will be scheduled and visited to save routing overhead and improve network efficiency. Using the properties of the minimal spanning tree, the moving sink node can visit immobile sensor nodes, which is the most effective approach to gather data and send it to the base station. The effectiveness of WSNs was examined when implementing this mobility model, and we used the NS-2 simulator to run simulations to assess how efficiently the suggested strategy performed. Our findings demonstrate that WSN performance can be significantly enhanced by implementing the proposed method. [ABSTRACT FROM AUTHOR]

In wireless sensor network, mobile sink is used to collect data from sensor nodes by periodically traversing the network to prevent hotspot problem. However, when sensor nodes generate data non-uniformly, the efficiency of data collection is constrained by rendezvous points and network topology. It becomes more challenging under network energy consumption constraint. Thus, this paper investigates non-uniform data generation in wireless sensor network and proposes an innovative approach: Optimal Clustering and Network Topology for Mobile Sink-Driven Data Collection, called OCNTMS. It focuses on determining optimal rendezvous points and their associated clusters. Through innovative methods such as weight-balanced clustering, cost function optimization, load-balanced link construction, and node forwarding selection, the OCNTMS can efficiently construct link sets within clusters and accurately plan the mobile sink's traversal of rendezvous points. Simulation results show that the OCNTMS reduces energy consumption by 18% and increases network lifetime by 40% compared with existing approaches under the constraint of non-uniform data generation. This greatly improves the network energy efficiency and data transmission efficiency. [ABSTRACT FROM AUTHOR]

In the realm of Wireless Sensor Networks (WSNs), enhancing network longevity and accurately predicting mobile sink trajectories are critical challenges. This research introduces ALDS-Net (Adaptive Moment Estimation Long Short-Term Memory DeepSink Network), a novel model specifically designed to address these challenges by leveraging advanced deep learning techniques. The novelty of ALDS-Net lies in its unique integration of the ADAM optimizer with LSTM layers, enabling it to effectively capture and model complex mobility patterns of mobile sinks. The ADAM optimizer is utilized to manage noisy gradients, enhancing the stability and convergence of the model, while LSTM layers are employed to handle the long-term dependencies in sink mobility data. A key innovation of ALDS-Net is its three-stage simulation methodology, which includes initializing the simulation environment, training the predictive model using a simulated dataset, and deploying the trained model for real-time sink trajectory prediction in WSNs. This comprehensive approach ensures accurate modeling of sensor node interactions and sink mobility, resulting in improved data routing efficiency and extended network lifetime. Our experimental results demonstrate that ALDS-Net outperforms existing methods such as Adjacency Based Cell Score, Differential Moth Flame Optimization (DMFO), and Instantaneous Clustering Algorithm (ICP) in terms of energy efficiency and network stability. Specifically, ALDS-Net achieves a significant extension in network operational lifetime, reaching 498.5 seconds with 400 nodes over 750 rounds. This study highlights the potential of ALDS-Net to provide robust, efficient, and scalable solutions for data routing in dynamic WSN environments. [ABSTRACT FROM AUTHOR]

Abstract In wireless sensor network, mobile sink is used to collect data from sensor nodes by periodically traversing the network to prevent hotspot problem. However, when sensor nodes generate data non-uniformly, the efficiency of data collection is constrained by rendezvous points and network topology. It becomes more challenging under network energy consumption constraint. Thus, this paper investigates non-uniform data generation in wireless sensor network and proposes an innovative approach: Optimal Clustering and Network Topology for Mobile Sink-Driven Data Collection, called OCNTMS. It focuses on determining optimal rendezvous points and their associated clusters. Through innovative methods such as weight-balanced clustering, cost function optimization, load-balanced link construction, and node forwarding selection, the OCNTMS can efficiently construct link sets within clusters and accurately plan the mobile sink’s traversal of rendezvous points. Simulation results show that the OCNTMS reduces energy consumption by 18% and increases network lifetime by 40% compared with existing approaches under the constraint of non-uniform data generation. This greatly improves the network energy efficiency and data transmission efficiency.

Nowadays, wireless sensor networks (WSNs) are the central means for data gathering in the edge-based internet of things era. Mobile edge nodes, such as mobile sinks (MSs), are the most reasonable way to gather the sensed data in these WSNs by travelling through rendezvous points (RPs) across the region of interest. Nevertheless, effective path planning for the MS is a significantly challenging issue, considering the questions: (1) How many RPs are optimal, and what should be their locations, (2) what is the sequence of RPs followed by the MS to form a path? According to the existing literature, this problem is NP-hard in nature, and no existing solution provides the best solution so far. In this paper, we address the above-mentioned questions and design an efficient scheme for path planning of the MS. The proposed scheme determines an optimal number of RPs using a variable dimension shark smell optimization-based approach. It uses the concept of adaptive search space dimension. The optimization procedure begins with a randomly chosen problem dimension, adapts as the swarm progresses, and then chooses an optimal dimensional space. The scheme has been extensively simulated to show superior performance over other competing schemes in terms of various parameters. Particularly, the proposed scheme achieved 16.79% and 6.29% improvement over the competing schemes in terms of energy consumption and network lifetime, respectively. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are crucial in collecting environmental information through sensor nodes. However, limited energy resources pose a challenge, necessitating efficient routing algorithms to minimize energy consumption. Failure to address issues can consume energy and reduce network lifespan and overall efficiency. This research paper presents a cutting-edge approach for minimizing the consumption of energy within WSN through the implementation of an optimal routing method. The approach involves two steps: first, clustering sensor nodes using the pond skater algorithm (PSA) to select cluster head (CHs) for routing; second, by leveraging the ant colony optimization (ACO) algorithm, this study introduces an innovative technique that empowers a mobile sink to gather packets from given CHs and transmit effectively, send them back to the base station (BS). Notably, the authors make a significant contribution by introducing a different variant of the PSA algorithm to select CH. This novel approach aims to curtail the consumption of energy within WSN significantly. The authors also present an ACO-based head traversal for cluster method, resembling the traveling salesman problem coding, for minimized energy consumption. The study's primary objectives include reducing energy consumption, minimizing packet delivery ratio, and prolonging the lifetime of the WSN. The assessment efficacy of the proposed method was achieved by regressive simulations using MATLAB on diverse scenarios. Through meticulous comparative analyses with several efficient algorithms, the method proposed here has shown significant performance in network lifetime comparison of PSACO in terms of Alive nodes with number of rounds PSO: 17.65%, GWO: 25%, CS: 33.33%, CBR-ICWSN: 66.66%, CCP-IC: 17.65%. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are critical in numerous applications due to their ability to sense and transmit data. However, energy limitations of sensor nodes, powered by finite batteries, significantly impact network longevity. Traditional routing methods involving multi-hop transmissions and cluster formation can result in substantial energy consumption, particularly by Cluster Heads (CHs) involved in data aggregation and transmission. This research addresses the problem by optimizing energy-efficient routing using a Deep Belief Network (DBN) with Long Short-Term Memory (LSTM) for routing and CH selection. A mobile sink moving in a linear path minimizes energy consumption by reducing cluster formation and promoting single-hop transmissions. The proposed method utilizes LSTM-based CH selection to ensure that nodes with the highest residual energy are chosen, enhancing network lifetime. Experimental results demonstrate that the proposed method reduces energy consumption by up to 25% compared to circular path sink movement and multi-hop data transmission, resulting in a 40% increase in network lifetime. Performance was evaluated on a 100-node network with varying sink velocities, achieving an energy efficiency of 15% over traditional models. [ABSTRACT FROM AUTHOR]

The paradigm of sensor networks involves connecting wireless electronic devices through small sensor nodes to gather and sense surrounding information. As these networks have limited resources, it is crucial to optimize their usage to enhance network performance. To achieve this, software-defined network technology is integrated into wireless sensor networks to efficiently utilize network resources. Furthermore, optimized clustering and energy-aware routing techniques are employed to evenly distribute network traffic and enable energy-efficient data transmission in SDN-enabled WSNs. However, the issue of hotspots or energy holes consistently persists in cluster-based routing protocols. This research aims to develop an energy-aware routing protocol incorporating a mobile sink, aiming to achieve energy consumption equilibrium and extend the network's lifespan. To address these concerns and ensure the longer sustainability of SDN-enabled WSNs, a novel approach called mobile sink-based energy-aware routing is proposed for energy-efficient data delivery. It utilizes optimized sink mobility to resolve the hotspot issue based on a proposed fitness function and centroid point approach. The fitness function considers essential parameters such as energy, control node density, and distances from control nodes. The flow rules are also generated based on the rank-based tree topology for multi-hop data transmission. The proposed approach is executed with an ONOS controller to implement SDN policies, and the performance of the heterogeneous network is evaluated through simulation using the ns-3 simulator. Furthermore, the proposed MS-EAR demonstrates significant improvements in the network lifespan compared to existing techniques such as GM-WOA, GMPSO, and FJAPSO, with increases of 18.0 % , 47.5 % and 94.0 % , respectively. It also outperforms the current state-of-the-art by considering various performance metrics, including stability period, number of alive nodes, network residual energy, packets transmitted to the control server, and average delay. [ABSTRACT FROM AUTHOR]

In recent advancements within wireless sensor networks (WSN), the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality. This research introduces a sophisticated framework, driven by computational intelligence, that merges clustering techniques with UAV mobility to refine routing strategies in WSNs. The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads (CHs). This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination. Employing a greedy algorithm for inter-cluster dialogue, our framework orchestrates CHs into an efficient communication chain. Through comparative analysis, the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing (CCMAR) and the energy-efficient cluster-based dynamic algorithms (ECCRA). Specifically, it showcases an impressive 15% increase in energy conservation and a 20% reduction in data transmission time, highlighting its advanced performance. Furthermore, this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN, emphasizing the vital role of sophisticated computational strategies in optimizing network operations. [ABSTRACT FROM AUTHOR]

Summary: The wireless sensor network‐assisted Internet of Things convergence has diverse applications. In most applications, the sensors are battery‐powered, and it is necessary to use the energy judiciously to extend their functional duration effectively. Mobile sinks‐based data collection is used to extend the lifespan of these networks. However, providing a scalable and effective solution with consideration for multicriteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network with long‐term evolution‐assisted architecture. The issue of maximizing lifetime and providing multifactor quality of service is solved as a two‐stage optimization problem involving clustering and data collection path scheduling. Hybrid meta‐heuristics is used to solve the clustering optimization problem. Minimal Steiner tree‐based graph theory is applied to schedule the data collection path for sinks. Unlike existing works, the lifetime maximization without QoS degradation is addressed by hybridizing multiple approaches of multicriteria optimal clustering, optimal path scheduling, and network adaptive traffic class‐based data scheduling. This hybridization extends the network's lifespan and improves the QoS regarding packet transmission within the proposed solution. Through simulation analysis, the introduced approach yields a noteworthy increase of at least 6% and reduces packet delivery delay by 26% compared with existing methodologies. [ABSTRACT FROM AUTHOR]

In a society where better, cleaner power generation and management are needed, IoT devices and battery technologies have gained prominence. The Internet of Things (IoT) paradigm has revolutionized various industries by enabling seamless connectivity and data exchange among heterogeneous devices. However, the energy constraints of IoT devices, particularly in dynamic environments with mobile sinks, pose significant challenges to network performance and longevity. This article proposes a novel approach to enhance energy efficiency in IoT networks with dynamic sinks using reinforcement learning (RL). Better and more effective wireless communication methods could increase the lifespan of battery-powered devices and networks as these technologies advance. Compared to more conventional methods, the application of reinforcement learning can encourage even more advancements in these protocols. In order to elect cluster leaders and create clusters in a network of one hundred randomly generated nodes, a revised version of a previously suggested methodology will be presented in this study. Predictively moving the sink, or base station, from its fixed location in the middle of the plot will be the adjustment. [ABSTRACT FROM AUTHOR]

A wireless sensor network is a wireless communication network, and each sensor node has several sensors to collect environmental information. Wireless sensor network nodes have limited energy resources and need optimal routing protocols to reduce energy consumption. Failure to reduce energy consumption by sensor nodes reduces network life and efficiency. The main problem in routing is finding optimal paths for sending packets by reducing energy consumption in sensor nodes. This paper proposes an optimal routing method to reduce energy consumption in wireless sensor networks. In the first step, wireless sensor nodes are clustered with the Water strider algorithms (WSA), and cluster heads are selected for routing. In the second step, a mobile sink collects the packets from the cluster heads and sends them to the base station. The mobile sink uses the Ant colony optimization (ACO) algorithms to travel a shorter path between the cluster heads. The authors contribute to presenting a discrete version of the WSA algorithm for cluster head selection to reduce energy consumption. The authors contribute by providing a more comprehensive objective function for clustering network nodes considering error rate, energy consumption, PDR rate, and Euclidean distance. Cluster head traversal with a version of the ACO algorithm to reduce energy consumption and cluster head traversal coding like the TSP problem is the contribution of other authors. The paper aims to reduce energy consumption, reduce the error rate of sending packets and increase the lifetime of the wireless sensor network. Experiments are simulated on several simulated scenarios in Matlab. Criteria such as energy consumption, Packet delivery ratio (PDR), package loss rates, and the number of alive nodes to evaluate the proposed method are used. Experiments show that the proposed algorithm reduces the energy consumption and loss rates of packages of the wireless sensor network by optimally selecting cluster heads and increasing the PDR and number of alive nodes. Comparisons show In terms of energy consumption, Packet delivery ratio (PDR), Loss rates of packages, and the number of alive nodes, the proposed method is more efficient than Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO), Information-centric wireless sensor networks, and Cluster based routing (CBR) routing methods. The PDR index in the proposed method is equal to 97.3% and is higher than PSO, GWO, and CS algorithms. The delay of the proposed method in routing is 25.97%, 5.78%, and 17.98% less than HHO, WOA, and GWO algorithms, respectively. [ABSTRACT FROM AUTHOR]

Simultaneous use of an efficient routing protocol and mobile sink not only prevents rapid sensor energy depletion but also effectively improves the energy balance in wireless sensor networks. In this paper, a new data aggregation method is proposed called "Ant colony-based Routing and Petal-shaped Data Aggregation (ARPDA)", which includes clustering, Cluster Head (CH) selection, intra-cluster routing, determining polling points, and designing the sink node trajectory. In the proposed method, the network is divided by several virtual concentric circles that are equidistant from each other. The intersection point of these circles with the virtual lines passing through the origin of the network determines the probable polling points for the sink node. First, by efficiently clustering the sensor nodes and forming an intra-cluster routing tree based on the ant colony optimization algorithm, the sensor data is aggregated in CHs; then, by selecting the appropriate polling points and designing a petal-shaped path, the sink node periodically collects the aggregated data in CHs based on two linear and circular movements. Considering the sink movement in the CH selection process is another advantage of the proposed algorithm. Numerical results confirm the better performance of the proposed algorithm compared to the EDT algorithm. [ABSTRACT FROM AUTHOR]

Effective research has been aimed at increasing the distributed compute dependent Software Define Network (SDN) with high-level Intelligent - Internet of Things (I-IoT). Wireless sensor networks come with a set of resource restrictions. Still, only a few functions are often configured such as energy restraint and the concerted demands that are vital for IoT application routing performance. A major technique for solving the expansion of network scalability by applying Mobile Sink (MS). The construction of data transmission optimal path, the detection of an optimal set data-gathering points $O_{DG} $ and MS scheduled with dynamic networks for energy-efficient techniques, that the network’s lifetime in enormous complications, principally in large-scale IoT networks. The research work proposes an Research Objective: i) Develop an energy-efficient routing technique for large-scale I-IoT networks within a cloud-based SDN system. ii) Optimize network scalability, lower-level routing, and load balancing using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The prime aim of cloud-based SDN with AI is to determine: a lower level routing in the perception layer, a load-balanced Cluster Table (CT), an optimal $O_{DG} $ points, and MS optimal paths $O_{MSpath} $ . The main contribution of proposed routing is i) Energy Minimization (EM): The proposed routing minimizes energy dissemination by the Cluster Head (CH) in critical conditions (EM-CH). ii) Enhanced Energy Balance (EEB): The EC-based SDN, considering both Optimal Data-Gathering ( $O_{DG}$ ) and Mobile Sink (MS) advancements, achieves enhanced energy balance during network routing (EEB-SDN). Research results validate the proposed model stability that improves the network lifetime up to 63%, the energy usage in the network is reduced up to 78%, the high volume data loaded to the MS up to 95%, and the delay of the $O_{MSpath} $ by 69% when compared with various model.

In this paper, a clustering-based energy-efficient routing protocol is designed to overcome the hotspot problem in Wireless Sensor networks (WSNs). We have divided the sensor nodes into several clusters, each cluster having a Cluster Head (CH) or a leader node and also implemented a centroid-based mobile sink. The sink moves to a location called "Energy Dependent Centroid Point" where the CHs need minimal energy to transmit data to the sink. We have defined this "Centroid Point" by an equation where the residual energies of CHs are used as a "weight factor" on a normal centroid equation. We have simulated the proposed algorithm and analyzed the results by varying the values of the energy weight factor in the centroid equation up to certain limits (α = 0, 1, 2, and 3). The simulation results are compared with WSN's existing routing protocols having a mobile sink and a static sink. The comparison results show that our proposed centroid model with α = 0, where the mobile sink moves independent of the residual energies of CHs gives better performances in terms of network lifetime, energy loss, and total data packet transmission. [ABSTRACT FROM AUTHOR]

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

Summary: Wireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively. [ABSTRACT FROM AUTHOR]

Unattended Wireless Sensor Networks (U-WSNs) use a non-stationary objects so-called sink node (SN) or mobile sink (MS) to perform a few data computation processes including collection and analysis. This network has a wide range of medical applications to exploit the key features of primary sensing units associated with their computing devices such as blood pressure, pulse oximeter and insulin pump to play a crucial role in stimulating the monitoring systems remotely (hostile environment). In traditional WSNs, the sensing units report the observed data to SN/MS via dedicated network, whereas in U-WSNs, the sensing units gather and keep the observed data in their local cache. At a later time, SN/MS accesses the network throughout the time interval to recover the observed data from the local cache. To fulfill the potential features of SN/MS, the emerging technologies practiced in healthcare applications use fundamental features of the Internet of Things (IoT). Despite the fact of being more accessible and potentially in service, security is highly concerned in restraining the unsatisfied threats in 5G infrastructure. Thus, in this paper, a lightweight two-factor authentication with anonymous data access framework is proposed (L2FA-ADA) using SN/MS. To examine the key properties of cryptographic protocols, the appropriate key agreement schemes were extensively researched. Moreover, the proposed L2FA-ADA utilizes a strategy of two-factor authentication with anonymous data access to ensure perfect forward secrecy with a significant degree of security efficiency. The security analysis including formal and informal shows that the proposed L2FA-ADA is provably protected in securing data privacy, integrity, and confidentiality. In conclusion, using a practical testbed, the key metrics such as transmission efficiency and overhead ratio were evaluated to signify the performance efficiencies of the proposed L2FA-ADA compared to other relevant state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The convergence of wireless sensor network-assisted Internet of Things has diverse applications. In most applications, the sensors are battery-powered, and it is necessary to use the energy judiciously to extend their functional duration effectively. Mobile sinks-based data collection is used to extend the lifespan of these networks. But providing a scalable and effective solution with consideration for multi-criteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network-Long term evolution assisted architecture. The problem of maximizing lifetime and providing multi-factor quality of service is solved as a two-stage optimization problem involving clustering and data collection path scheduling. Hybrid meta-heuristics is used to solve the clustering optimization problem. Minimal Steiner tree-based graph theory is applied to schedule the data collection path for sinks. Unlike existing works, the lifetime maximization without QoS degradation is addressed by hybridizing multiple approaches of multi-criteria optimal clustering, optimal path scheduling, and network adaptive traffic class-based data scheduling. This hybridization helps to extend the lifetime and enhance the QoS regarding packet delivery within the proposed solution. Through simulation analysis, the introduced approach yields a noteworthy increase of at least 6% and reduces packet delivery delay by 26% compared to existing methodologies.

Recent research emphasized the utilization of rechargeable wireless sensor networks (RWSNs) in a variety of cutting-edge fields like drones, unmanned aerial vehicle (UAV), healthcare, and defense. Previous studies have shown mobile data collection and mobile charging should be separately. In our paper, we created an novel algorithm for mobile data collection and mobile charging (MDCMC) that can collect data as well as achieves higher charging efficiency rate based upon reinforcement learning in RWSN. In first phase of algorithm, reinforcement learning technique used to create clusters among sensor nodes, whereas, in second phase of algorithm, mobile van is used to visit cluster heads to collect data along with mobile charging. The path of mobile van is based upon the request received from cluster heads. Lastly, we made the comparison of our proposed new MDCMC algorithm with the well-known existing algorithms RLLO [32] & RL-CRC [33]. Finally, we found that, the proposed algorithm (MDCMC) is effectively better collecting data as well as charging cluster heads. [ABSTRACT FROM AUTHOR]

Energy efficient routing of data from sensor to base station (BS) is attained utilizing the clustering of sensor nodes, thus minimizing the number of hops and circling the task of the cluster head (CH) sporadically. In addition, the clusters near to the BS take a substantial load over multi-hop communication. The hot spot problem affects wireless sensor networks (WSNs) with BS nodes, which is caused by sensor nodes close to the BS allowing for increased traffic load. So, the entire network lifespan is minimized owing to the element some nodes drain their energy resources much faster equated to the break. To solve these issues fuzzy based clustering and form the optimal route (FCOR) by mobile sink (MS) approach for efficient data collection is proposed. The fuzzy logic method is used for elected the CH by node remaining energy, node connectivity and node distance parameters. Discovering an optimal movable trajectory for the MS is serious so as to attain energy efficiency. Improved ant colony optimization (IACO) method is a better solution to discovering an optimal traversal route. Simulation results proves that FCOR increases the energy efficiency, throughput and minimized the network delay in the WSN. [ABSTRACT FROM AUTHOR]

Abstract Mobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios.

During the past decades, Wireless Sensor Networks (WSNs) have become extensively used due to their prominent number of applications. The use of WSNs is a domineering need for future radical areas commencing from smart home to military surveillance in which hundreds or thousands of sensor nodes are positioned. The usage of mobile sink in those large scale WSNs, for data aggregation amends the functioning of the network by bringing down the energy conservation, amending the network lifetime and data transmission time lag between the nodes. In this paper Center of Energy -Reinforcement Learning based On-Demand Transition State Update algorithm (CERL-ODTST) is proposed to dynamically update mobile sink traversal path. Initially cluster formation and cluster head election for large scale WSNs are done by novel center of energy method. Clustering and data aggregation techniques are applied to reduce the amount of data transmission hence decreasing the energy consumption in the network. In this context cluster heads aggregates data from cluster members which are collected by mobile sinks. The amount of data transmission can be significantly reduced by using Machine Learning algorithms like neural networks and swarm intelligence and also using the distributive features of the network. It offers a reasonable study of the functioning of diverse methods to support the engineers for projecting suitable machine learning based results for grouping the nodes and data aggregation applications. Compared to traditional methods, in the proposed CERL-ODTST, reinforcement learning is used for intra cluster data aggregation to improve aggregation efficiency in the whole network. The implementation results show that proposed CERL-ODTST performs well in terms of overall tour length, energy efficiency and reduces the transmission delay hence increases network lifetime. [ABSTRACT FROM AUTHOR]

Data acquisition using mobile sink in wireless sensor networks (WSNs) has shown significant advantages, especially in large-scale networks. Unlike the acquisition of sensor data by static sink through multi-hop forwarding, mobile sink travels across the sensing field to acquire data, which significantly reduces the energy consumption of sensor nodes. However, deciding sojourning locations for mobile sink in sensing field and designing a delay efficient trajectory path for its movement are very challenging. This paper takes up this issue and proposes an energy and delay efficient data acquisition technique, named as EDEDA. It divides the sensor field into virtual grids and identifies a certain number of grid cells, termed as visiting points (VPs), in such a way that mobile sink sojourns in them and acquires data from nine adjacent grid cell heads in single-hop. Furthermore, the mobility pattern of mobile sink is modelled as Hamiltonian cycle starting and ending at the base station (BS) after visiting all the VPs. Mobile sink offloads collected data at BS after each cycle. Simulations are performed on NS-2 to evaluate the performance of EDEDA at varying number of sensor nodes and found that it outperforms existing routing protocols in terms of energy consumption and throughput. Moreover, EDEDA results in 25.72%, 25.72%, 19.54%, 14.57% improvement in data acquisition latency for varying number of sensor nodes when compared with TCBDGA, PSOBS, RkM, VGRSS, respectively. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), balancing energy consumption is always a critical problem. Multi-hop data transmission may cause the nodes around the static sink to exhaust energy prematurely. It can be avoided by introducing the mobile sink (MS) in WSNs. The MS only needs to visit some specific nodes, such as cluster heads (CHs) in the clustering network, to collect data. In this paper, we propose an energy-adaptive clustering method based on Taguchi-based-GWO optimizer (EACM-TGWO). Different from the existing clustering protocols, we develop the optimal number of CHs according to the characteristic of energy consumption in MS-based WSNs. Afterwards, the fitness function is established by combining the residual energy and the average distance within clusters to select CHs. Meanwhile, an adaptive weight factor is introduced to dynamically tune the influence degree of the two factors on the clustering results. Furthermore, a novel meta-heuristic algorithm Taguchi-based grey wolf optimizer (TGWO) is used to search for the optimal CHs set. Compared with grey wolf optimizer (GWO) and some of its improved versions, TGWO is more excellent tested in CEC2017. We also make an elaborate comparison of EACM-TGWO with LEACH-C, EACM-GWO, MSECA, and GAOC. The simulation results indicate that EACM-TGWO is more powerful in terms of balancing energy consumption and saving network energy. [ABSTRACT FROM AUTHOR]

The Mobile Wireless Sensor Network (MWSN) is a new technology that has a variety of applications, such as traffic monitoring, wildlife monitoring, military surveillance, and so on. It enables sensor nodes to move freely and communicate with one another without the need for a fixed infrastructure. Energy conservation is one of the major concerns in mobile WSN because of the high packet delivery ratio, high computing power, short battery life, low throughput, high packet dropping ratio, high routing packet overhead, and high energy consumption. Congestion is also a serious issue for communication networks. It faces difficulties due to node buffer overflow, packet collision, transmission channel contention, a transmission channel with dynamic time variation, and transmission rate. Therefore, this review analyzes energy conservation and congestion control techniques in mobile WSN based on various categories. Initially, the energy conservation-based techniques are classified based on their network structure, clustering, and routing techniques, whereas the congestion control techniques are classified as queue-based, supervised learning-based, resource-based, and priority-based techniques. The nature of these techniques and their advantages and disadvantages are discussed. Furthermore, the software tools and evaluation metrics used in the existing techniques are also identified. Finally, the open challenges that need to be considered in future work are discussed. [ABSTRACT FROM AUTHOR]

As a consequence of the limited battery life of sensor nodes ( S N s) in wireless sensor networks (WSN s), the S N s nearby the sink exhaust rapidly and exhibit the hot spot problem. The mobile sink ( M S ) is proposed to be the solution as it helps in decreasing the energy consumption of S N s. This further results in an increased network lifetime. Moreover, some sensor applications require a precise data collection time and hence need to optimize the path length of the M S . This ensures that M S will collect data from all the S N s in a certain threshold time. In the proposed approach, we use the concept of the Voronoi diagram and consider their vertices as the probable set of rendezvous points ( R P s) for the mobile sink to collect data from S N s. These rendezvous points are further optimized using a cost function which is generated by using several parameters that affect the performance of each rendezvous point. The final set of rendezvous points resulting in the longest path within permissible delay is then finalized. The proposed method is simulated and compared with the existing approaches. The comparison is performed under different parameters like network lifetime, number of hop count, number of alive S N s, and so on. [ABSTRACT FROM AUTHOR]

In the context of query-driven wireless sensor networks (WSNs), a unique scenario arises where sensor nodes are solicited by a base station, also known as a sink, based on specific areas of interest (AoIs). Upon receiving a query, designated sensor nodes are tasked with transmitting their data to the sink. However, the routing of these queries from the sink to the sensor nodes becomes intricate when the sink is mobile. The sink's movement after issuing a query can potentially disrupt the performance of data delivery. To address these challenges, we have proposed an innovative approach called Query-driven Virtual Grid-based Routing Protocol (VGRQ), aiming to enhance energy efficiency and reduce data delivery delays. In VGRQ, we construct a grid consisting of square-shaped virtual cells, with the number of cells matching the count of sensor nodes. Each cell designates a specific node as the cell header (CH), and these CHs establish connections with each other to form a chain-like structure. This chain serves two primary purposes: sharing the mobile sink's location information and facilitating the transmission of queries to the AoI as well as data to the sink. By employing the VGRQ approach, we seek to optimize the performance of query-driven WSNs. It enhances energy utilization and reduces data delivery delays. Additionally, VGRQ results in ≈10% and ≈27% improvement in energy consumption when compared with QRRP and QDVGDD, respectively. [ABSTRACT FROM AUTHOR]

Network lifetime and localization are critical design factors for a number of wireless sensor network (WSN) applications. These networks may be randomly deployed and left unattended for prolonged periods of time. This means that node localization is performed after network deployment, and there is a need to develop mechanisms to extend the network lifetime since sensor nodes are usually constrained battery-powered devices, and replacing them can be costly or sometimes impossible, e.g., in hostile environments. To this end, this work proposes the energy-aware connected k-neighborhood (ECKN): a joint position estimation, packet routing, and sleep scheduling mechanism. To the best of our knowledge, there is a lack of such integrated solutions to WSNs. The proposed localization algorithm performs trilateration using the positions of a mobile sink and already-localized neighbor nodes in order to estimate the positions of sensor nodes. A routing protocol is also introduced, and it is based on the well-known greedy geographic forwarding (GGF). Similarly to GGF, the proposed protocol takes into consideration the positions of neighbors to decide the best forwarding node. However, it also considers node residual energy in order to guarantee the forwarding node will deliver the packet. A sleep scheduler is also introduced in order to extend the network lifetime. It is based on the connected k-neighborhood (CKN), which aids in the decision of which nodes switch to sleep mode while keeping the network connected. An extensive set of performance evaluation experiments was conducted and results show that ECKN not only extends the network lifetime and localizes nodes, but it does so while sustaining the acceptable packet delivery ratio and reducing network overhead. [ABSTRACT FROM AUTHOR]

The mobile sink is usually applied to improving the performance of wireless sensor networks (WSNs). This improvement is subject to the itinerary plan, through which the sojourn times and settlement locations are determined. This paper simultaneously considers multiple criteria like data merit, latency, deadline, used memory, and travel time, then presents a multi-objective Integer Quadratically Constrained Quadratic Programming (IQCQP) for planning itinerary of mobile sink in WSNs. In practice, this programming should be solved in a centralized node and it is computationally NP-hard, but the mathematical formulation can help estimate the problem characteristic and figure out the optimal path for the mobile sink. In addition, this paper proposes a multi-attribute algorithm where the sink gathers attributes of possible sites and compares them by applying fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Therefore, the neighboring adjacent sites are ranked by TOPSIS, then the best site is selected and the mobile sink moves there. The proposed algorithm has lower complexity and utilizes local information to make a decision, therefore it is can handle dynamic features of WSNs. The proposed methods are assessed through rigorous simulations, the results reveal that the proposed distributed algorithm outperforms counterparts in terms of performance metrics (total merit, latency, gathered, expired, and lost data) in a heterogeneous WSN. [ABSTRACT FROM AUTHOR]

Abstract By facilitating the data delivery in wireless sensor networks, the movement of mobile sink can enhance the network connectivity and sensory coverage. However, the optimal path determination of mobile sink is a NP-hard optimization problem. By jointly considering the cluster-based routing and sink mobility, this paper proposes an enhanced ACO-based movement scheduling of mobile sink for data gathering in wireless sensor networks. To meet the delay requirements and balance the energy consumption of the sensor nodes, the optimal cluster heads selection is introduced. Then, an enhanced ACO-based movement scheduling algorithm is proposed to obtain the shortest path of mobile sink by traversing the network. The simulation results show that our proposed method can offer a promising performance in terms of reducing data delivery latency and extending the lifetime of the network.

Mobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we extended a hierarchical clustering technique, which is the most researched in the sensor network field, and studied a dynamic differential clustering technique to minimize energy consumption and ensure equal lifespan of all sensor nodes while considering the mobility of sinks. In a sensor network environment with mobile sinks, clusters close to the sinks tend to consume more forwarding energy. Therefore, clustering that considers forwarding energy consumption is desired. Since all clusters form a hierarchical tree, the number of levels of the tree must be considered based on the size of the cluster so that the cluster size is not growing abnormally, and the energy consumption is not concentrated within specific clusters. To verify that the proposed DDC protocol satisfies these requirements, a simulation using Matlab was performed. The FND (First Node Dead), LND (Last Node Dead), and residual energy characteristics of the proposed DDC protocol were compared with the popular clustering protocols such as LEACH and EEUC. As a result, it was shown that FND appears the latest and the point at which the dead node count increases is delayed in the DDC protocol. The proposed DDC protocol presents 66.3% improvement in FND and 13.8% improvement in LND compared to LEACH protocol. Furthermore, FND improved 79.9%, but LND declined 33.2% when compared to the EEUC. This verifies that the proposed DDC protocol can last for longer time with more number of surviving nodes. [ABSTRACT FROM AUTHOR]

This paper aims to formulate an enhanced ant colony optimization algorithm that helps find a suitable data route that improves energy efficiency and reduces the time consumption for the delivery of packets. Energy equations have been framed to analyze energy used at the transmitting, receiving, and relay nodes. The network is segmented into smaller virtual segments for easier analysis of the proposed algorithm. Each segment is assumed to have nodes with a cluster head. The sink gathers information from different cluster heads as it moves from one segment to another. The nodes not in close connection with the network used the overhearing mechanism to share the information with the sink. The simulation has been done using Cisco Packet Tracer software. The proposed algorithm has been applied to different types of wireless networks to determine their efficiency. The wireless networks considered for this purpose are Bluetooth, Wi-Max, and Wi-Fi. Packet delivery ratio, end-to-end delay, collision, and lifetime are evaluated for the different types of wireless networks. The obtained results are analyzed, and graphs are plotted. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (W.S.N.s) are a critical research area with numerous practical applications. W.S.N.s are utilized in real-life scenarios, including environmental monitoring, healthcare, industrial automation, smart homes, and agriculture. As W.S.N.s advance and become more sophisticated, they offer limitless opportunities for innovative solutions in various fields. However, due to their unattended nature, it is essential to develop strategies to improve their performance without draining the battery power of the sensor nodes, which is their most valuable resource. This paper proposes a novel sink mobility model based on constructing a bipartite graph from a deployed wireless sensor network. The proposed model uses bipartite graph properties to derive a controlled mobility model for the mobile sink. As a result, stationary nodes will be visited and planned to reduce routing overhead and enhance the network's performance. Using the bipartite graph's properties, the mobile sink node can visit stationary sensor nodes in an optimal way to collect data and transmit it to the base station. We evaluated the proposed approach through simulations using the NS-2 simulator to investigate the performance of wireless sensor networks when adopting this mobility model. Our results show that using the proposed approach can significantly enhance the performance of wireless sensor networks while conserving the energy of the sensor nodes. [ABSTRACT FROM AUTHOR]

Machine Learning concepts have raised executions in all knowledge domains, including the Internet of Thing (IoT) and several business domains. Quality of Service (QoS) has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors, information and usage. Sensor data gathering is an efficient solution to collect information from spatially disseminated IoT nodes. Reinforcement Learning Mechanism to improve the QoS (RLMQ) and use a Mobile Sink (MS) to minimize the delay in the wireless IoT s proposed in this paper. Here, we use machine learning concepts like Reinforcement Learning (RL) to improve the QoS and energy efficiency in the Wireless Sensor Network (WSN). The MS collects the data from the Cluster Head (CH), and the RL incentive values select CH. The incentives value is computed by the QoS parameters such as minimum energy utilization, minimum bandwidth utilization, minimum hop count, and minimum time delay. The MS is used to collect the data from CH, thus minimizing the network delay. The sleep and awake scheduling is used for minimizing the CH dead in the WSN. This work is simulated, and the results show that the RLMQ scheme performs better than the baseline protocol. Results prove that RLMQ increased the residual energy, throughput and minimized the network delay in the WSN. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is composed of independent sensor nodes (SNs) that undergo random deployment in a particular region to sense the atmosphere effectively. The WSNs are applied in real-time applications like medical sector, home automation, traffic monitoring, and ecological observation. Meanwhile the SNs in WSN are energy constrained, routing process is considered as an effective way in achieving energy efficiency and maximize network lifetime. At the same time, node localization (NL) is also a critical challenge in WSN, which aims to analyze the geographical coordinate of unknown nodes through anchor nodes (ACN). Therefore, NL and routing processes are considered NP hard problems and resolved by the use of metaheuristic optimization algorithm. The study proposes a metaheuristic optimization based NL and multihop routing protocol with mobile sink (MONL-MRPMS) for WSN. The proposed MONL-MRPMS technique aims to achieve energy efficacy with accurate NL performance. The MONL-MRPMS technique involves an efficient Coyote Optimization Algorithm (COA) for NL, (COA-NL) in WSNs, assist in determining the location of the nodes iteratively by taking Euclidian distance as fitness into account. Besides, sea gull optimization based Multihop routing (SGO-MHR) protocol is designed for the optimum selection of routes for intercluster transmission. Eventually, a mobile sink (MS) with route adjustment technique is employed for improved energy efficiency of the WSN, which allows adjusting the routes depending upon the movement of MS. A wide-ranging experiments were performed and the obtained results emphasized the supremacy of MONL-MRPMS algorithm over the recent approaches. [ABSTRACT FROM AUTHOR]

In the realm of Internet of Things (IoT), wireless sensor networks (WSNs) have been the subject of ongoing research into the use of energy harvesting to capture ambient energy, and wireless power transfer (WPT) via a mobile charger to overcome the energy limitations of sensors. Moreover, to mitigate energy imbalance and reduce the number of hops, strategies have been developed to leverage cars or unmanned aerial vehicles (UAVs) as mobile sinks. The primary objective of this work is to increase network lifetime by reducing energy consumption of hotspot nodes and increasing the amount of data acquired from all sensors in an environment that combines the methods mentioned above.To achieve this objective, the proposed method involves developing multiple minimum depth trees (MDTs) for all nodes, considering the energy of the UAV and sensor nodes. Parent nodes prevent their own energy depletion and ensure data transmission without imbalance by adaptively controlling the data sensed at the nodes and their child nodes. Consequently, the energy depletion of nodes in hotspots is prevented, more sensory data is acquired, and balanced data collection from all nodes is achieved. Simulation results demonstrate that the proposed scheme outperforms other state-of-the-art methods in terms of reduced energy depletion, increased network connectivity, and the amount of data collected at the sink node. This scheme will be applied to applications that collect environmental data outdoors, such as climate measurement, to collect data uniformly and increase the lifespan of the network, thereby reducing network maintenance costs while collecting data effectively. [ABSTRACT FROM AUTHOR]

As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks (MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani's fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACHExpected Residual Energy (LEACH-ERE). [ABSTRACT FROM AUTHOR]

Sensor Nodes (SNs) in Wireless sensor networks (WSNs) continuously monitor the environment and accumulate data. But due to the limited memory and processing capabilities of the sensor nodes, the data must be transmitted to the central server for further analysis. To do this, routing via multi-hop channel is used which consumes a lot of particular SN's energy and also utilizes the resources, including the battery and memory of all the SN's involved in routing. This process drains the SN's energy rapidly and further leading to disconnection from the central server. To address this issue, mobile sink concept is introduced. The mobile sink collects the data from the SN's and hands it over to the central server which reduces the SN's involvement in data transmitting, but this process involves computationally complex tasks. In this context, we use linear programming to find the best visiting order to collect the information from a set of prioritized SN's, where other nodes transmit their data to them. We confirm that this process is computationally lightweight. In addition, this approach meat the performance criteria such as the lifespan of WSNs, the usage of buffer, throughput, and delay. We simulate the proposed model using Python, and they show improvement in efficiency when tested with different metrics of WSNs.