Sink Path Research Articles

ExAq-MSPP: An Energy-Efficient Mobile Sink Path Planning Using Extended Aquila Optimization Algorithm

AbstractWireless sensor networks play a crucial role in gathering data from remote or hard-to-reach locations, enabling real-time monitoring and decision-making in a wide range of industries and applications. The mobile sink path planning (MSPP) enables mobile sinks (e.g., drones or rovers) to navigate through the environment, collecting data from different sensor nodes, ensuring comprehensive coverage, and adaptively addressing changing conditions. Still, the energy-efficient routing with minimal delay is the challenging aspect. This research focuses on improving data gathering in wireless sensor networks by introducing an efficient routing protocol. In this proposed protocol, sensor nodes are initially deployed using Voronoi diagrams to ensure uniform network coverage. The network is then divided into clusters using the low-energy adaptive clustering hierarchy (LEACH) algorithm for energy-efficient routing. To optimize the path planning of a mobile sink for data collection, we introduce the extended Aquila (ExAq) optimization algorithm, which uses a multi-objective fitness function considering factors such as delay, residual energy, link quality, priority, and distance. Simulation results demonstrate the effectiveness of the proposed ExAq-MSPP protocol in terms of reduced delay, improved network lifetime, higher packet delivery ratio, enhanced residual energy, and increased throughput compared to existing protocols with the values of 1.169, 99.857, 99.920, 0.997, and 255.306, respectively. Thus, the energy-efficient routing and optimizing path planning for mobile sinks, the proposed ExAq-MSPP protocol can extend network lifetime, increase data accuracy, and provide more robust performance under changing environmental conditions.

Path optimization algorithm for mobile sink in wireless sensor network

In order to alleviate the limitation of mobile sink in Wireless Sensor Network (WSN) by environmental and power problems, this paper proposes a new signal collection method for network communication in wireless sensor network with the goal of shortening the path of the mobile sink, and then proposes the ACO-TU (Ant Colony Optimization-Team Up) algorithm based on ant colony algorithm by adding concepts such as node pheromone. The algorithm groups the mobile units of the ant colony algorithm and further optimizes the sink path in the wireless sensor network by specific mobile strategies. Experimental results show that the method of sink moving path optimization proposed has a significant improvement in reducing power consumption and distance shortening, which can further increase the service life of the mobile sink and improve the quality of signal transmission. The results of comparison with the most recent relevant algorithms show that the ACO-TU algorithm has the best optimization performance, which proves the effectiveness of the algorithm.

Lesser Antilles slab reconstruction reveals lateral slab transport under the Caribbean since 50 Ma

The link between surface tectonic plates and mantle slabs is fundamental for paleo-tectonic reconstructions and for our understanding of mantle dynamics. Many seismic tomography-based studies have assumed vertical slab sinking and projected mantle features to the surface to reconstruct paleo-trench locations or explain tectonic features. Here, we used a slab-unfolding approach that does not require assumptions about sinking paths or rates to re-interpret the seismic structure of the Lesser Antilles slab underneath the Caribbean. A recent study invoked mainly vertical slab sinking and a highly folded and deformed slab to explain seismic Caribbean mantle structures. However, our results show that the upper-mantle Lesser Antilles slab structure can be better explained by limited intra-slab deformation and up to ∼900 km lateral slab transport towards the northwest after subduction. Our results indicate that such lateral slab transport can occur even with probable weaknesses in the slab that originate from a subducted fossil ridge-transform system. We ascribe the lateral slab transport in the mantle to a kinematic connection with the North American plate, which has migrated northwestward since the Eocene.

Dynamic Mobile Sink Path Planning for Unsynchronized Data Collection in Heterogeneous Wireless Sensor Networks

Dynamic Mobile Sink Path Planning for Unsynchronized Data Collection in Heterogeneous Wireless Sensor Networks

Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization

The main objective of this study is to optimize the performance of a wireless sensor network (WSN) in terms of energy consumption and data routing delay using evolutionary metaheuristics. A WSN mobile sink-based routing method in which the sink moves to certain selected rendezvous nodes for data collection is used to address the hot-spot problem. However, there are two challenges, namely, clustering and mobile sink shortest trajectory traversal, which significantly affect the network energy consumption, lifetime, and delay. To achieve the goal of this study, first, a formal model is presented to solve the optimization problem of determining the optimal number of clusters and corresponding cluster heads, which are taken as rendezvous nodes accessed by the mobile sink considering the network energy consumption, intracluster communication, and transmission delay. Second, a discrete differential evolution algorithm is proposed to solve the formulated optimization problem. Third, an ant colony optimization-based algorithm is proposed to construct the shortest path for the mobile sink to traverse the selected rendezvous nodes. The experimental evaluations show that the proposed strategy significantly improves cluster head selection, balances the cluster members, increases the network lifetime by 54%, decreases the transmission delay by 63%, and reduces energy consumption by 47% compared to several routing strategies in the literature.

Using Minimum Connected Dominating Set for Mobile sink path planning in Wireless Sensor Networks

Wireless sensor networks are a motivating area of research and have a variety of applications. Given that these networks are anticipated to function without supervision for extended periods, there is a need to propose techniques to enhance the performance of these networks without consuming the essential resource sensor nodes have, which is their battery energy. In this paper, we propose a new sink node mobility model based on calculating the minimum connected dominating set of a network. As a result, instead of visiting all of the static sensor nodes in the network, the mobile sink will visit a small number or fraction of static sensor nodes to gather data and report it to the base station. The proposed model's performance was examined through simulation using the NS-2 simulator with various network sizes and mobile sink speeds. Finally, the proposed model's performance was evaluated using a variety of performance metrics, including End-To-End delay, packet delivery ratio, throughput, and overall energy consumption as a percentage.

Mobile Multiple Sink Path Planning for Large-Scale Sensor Networks Based on Hyper-Heuristic Artificial Bee Colony Algorithm

Large-scale wireless sensor networks consists a terrific amount of nodes, a wide range of deployment, extended data transmission time, and large network energy consumption. To solve the above problems, a mobile multiple Sink path planning based on hyper-heuristic artificial bee colony algorithm is proposed. The artificial bee colony algorithm is used as the high-level strategy. In view of the changes in the network operation process, namely the number of nodes and energy changes, the design of three stages of the artificial bee colony algorithm is used to choose and manipulate the low-level heuristic operator. The selected lower layer operator set plans the path of each mobile sink nodes. Compared with other famous meta-heuristic algorithms, it is proved that the proposed hyper-heuristic algorithm is an effective, efficient and robust algorithm, which can effectively solve the path planning problem in view of multiple sink nodes, reduce network delay and reduce network energy consumption.

MOBILE SINK PATH FORMATION TO EXTEND THE NETWORK LIFETIME IN WSN

Wireless Sensor Network (WSN) is a group of sensor nodes that is utilized to observe and record the several physical, environmental, and significant real time data.Data traffic obtained through the Base Station (BS) in WSN minimizes the energy of close sensor nodes as equated to other sensor nodes. This issue is called as hot spot issue in the WSN. But, the Mobile Sink (MS) is proposed for solving this hot spot issue in the WSN. MS is used to gather the modified information from the Cluster Head (CH) and it forward to the BS. This approach presents a Mobile Sink Path Estimation (MSPE) to extend the network lifetime in WSN. The major objective of this article is to designing a MS route to minimizing the energy utilization and delay. In this approach each CH has a threshold level of the load ahead of that if there is an inflow of data, then the node routinely forwards an Mobile Sink Route Request (MS_REQ) for assist from the MS. Depend on the requests received from all CH nodes; the MS evaluates the most appropriate region to move other position. We examine the function of MSPE approach by applying Network simulator. Simulation results demonstrate that the introduced MSPE approach has enhanced the residual energy and minimized the network delay.   &nbsp

A Novel Clustering Strategy-Based Sink Path Optimization for Wireless Sensor Network

To optimize the path of mobile sinks in the wireless sensor network (WSN) and use of 5G signals to transmit information, a new clustering strategy is proposed, which not only balances the number of cluster nodes but also shortens the path of mobile sinks. By moving rendezvous points (RPs) to change the cluster position, we first propose a metaheuristics clustering algorithm named the dynamic clustering-based rectangular evolutionary algorithm (DCREA). The algorithm adjusts the clustering structure of the network by limiting the cluster movement range; then, it is optimized in combination with the greedy algorithm. Extensive experimental results show that this method can significantly shorten the path of the mobile sink, increase the efficiency of the mobile sink, and improve the quality of signal transmission while maintaining the balance of the cluster structure. The experimental results show that the DCREA is the most effective and indicate the effectiveness of the algorithm by comparing it with related recent algorithms.

An enhanced ACO-based mobile sink path determination for data gathering in wireless sensor networks

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.

MSSPP: modified sparrow search algorithm based mobile sink path planning for WSNs

MSSPP: modified sparrow search algorithm based mobile sink path planning for WSNs

Data Acquisition through Mobile Sink for WSNs with Obstacles Using Support Vector Machine

Mobile sink-based data collection in wireless sensor networks has become an attractive research area to mitigate hotspot issues. It further increases the efficiency of the WSN, such as throughput, lifetime, and energy efficiency, while decreasing delay and packet losses. Mobile sink algorithms developed by many researchers in recent years have only contributed to obtain efficient path planning, and only a few researchers have focused on solving the problem of network environment with obstacles. Here, constructing an obstacle-aware path for the mobile sink to collect data in WSN is a challenging issue. In this context, we present the data acquisition through mobile sink for WSNs with obstacles using support vector machine (DAOSVM). The DAOSVM algorithm works in two phases: visiting point selection and path construction. The visiting point selection uses spanning tree approach, and the path selection uses SVM. The computational complexity of the proposed DAOSVM is estimated and compared using the existing techniques, and it is lower. The DAOSVM also outperforms traditional methods concerning multiple performance metrics under various scenarios.

Analysis rendezvous-based techniques relate to power conservation

Power conservation is a vibrant area of research on wireless sensor networks. Several research papers have shown that using mobile sync in the sensor field, which collects data from sensor nodes via single or multi-hop communication, can save significant energy. However, sinks move slowly, which increases sensor network delays, especially in delay-sensitive applications. To address this issue, various rendezvous-based techniques are described in which a subset of sensor nodes are selected from the field as rendezvous points (RPs). The remaining nodes transfer the captured data to the next RP, where the data is buffered. The RP is then used to build a path for the mobile sink to tour and collect the buffered data. This article describes various rendezvous-based techniques and analyzes their strengths and weaknesses in terms of energy savings.

ANALYSIS RENDEZVOUS-BASED TECHNIQUES RELATE TO POWER CONSERVATION

Power conservation is a vibrant area of research on wireless sensor networks. Several research papers have shown that using mobile sync in the sensor field, which collects data from sensor nodes via single or multi-hop communication, can save significant energy. However, sinks move slowly, which increases sensor network delays, especially in delay-sensitive applications. To address this issue, various rendezvous-based techniques are described in which a subset of sensor nodes are selected from the field as rendezvous points (RPs). The remaining nodes transfer the captured data to the next RP, where the data is buffered. The RP is then used to build a path for the mobile sink to tour and collect the buffered data. This article describes various rendezvous-based techniques and analyzes their strengths and weaknesses in terms of energy savings.

Analysis rendezvous-based techniques relate to power conservation

Power conservation is a vibrant area of research on wireless sensor networks. Several research papers have shown that using mobile sync in the sensor field, which collects data from sensor nodes via single or multi-hop communication, can save significant energy. However, sinks move slowly, which increases sensor network delays, especially in delay-sensitive applications. To address this issue, various rendezvous-based techniques are described in which a subset of sensor nodes are selected from the field as rendezvous points (RPs). The remaining nodes transfer the captured data to the next RP, where the data is buffered. The RP is then used to build a path for the mobile sink to tour and collect the buffered data. This article describes various rendezvous-based techniques and analyzes their strengths and weaknesses in terms of energy savings.

Division of carbon sink functional areas and path to carbon neutrality in coal mines

Remote sensing image data of typical mining areas in the Loess Plateau from 1986 to 2018 were used to analyze the evolution of land use, explore the division of carbon sink functional areas, and propose carbon neutrality paths to provide a reference for the coal industry carbon peak, carbon-neutral action plan. Results show that (1) land use has changed significantly in the Pingshuo mining area over the past 30 years. Damaged land in industrial, opencast, stripping, and dumping areas comprises 4482.5 ha of cultivated land, 1648.13 ha of grassland, and 963.49 ha of forestland. (2) The carbon sink functional areas of the Pingshuo mining land is divided into invariant, enhancement, low carbon optimization, and carbon emission control areas. The proportion of carbon sinks in the invariant area is decreasing, whereas the proportion in enhancement, low carbon optimization, and carbon emission control areas is gradually increasing. (3) The carbon neutrality of the mining area must be reduced from the entire process of stripping–mining–transport–disposal–reclamation, and carbon emissions and carbon sink accounting must start from the life cycle of coal resources. Therefore, carbon neutrality in mining areas must follow the 5R principles of reduction, reuse, recycling, redevelopment, and restoration, and attention must be paid to the potential of carbon sinks in ecological protection and restoration projects in the future.

A Novel Efficient Data Gathering Algorithm for Disconnected Sensor Networks Based on Mobile Edge Computing

Employing mobile elements is an efficient solution to the performance improvement of wireless sensor networks (WSNs). We propose an efficient data gathering mechanism for disconnected WSNs with rendezvous points (DGM-RPs). The mobile sink traverses the entire network and stops only at the rendezvous points (RPs) while gathers the data from sensors in every disconnected segment. In this paper, mobile sinks perform the task of edge computing and alleviate the load of upper cloud. We measure the shape of disconnected segments, layering them by use of the convex hull, and then design the travelling path of the mobile sink to minimize the travel latency to visit all disconnected segments. At least one RPs will be selected in a segment firstly, and then, on this basis, we consider the distribution density of sensor nodes and the location of the RPs already exist to adding new RPs, which make good use of the margin to reducing the energy consumption and prolong the network lifetime.

TEO-MCRP: Thermal exchange optimization-based clustering routing protocol with a mobile sink for wireless sensor networks

Sensor nodes consume their energy quickly due to their continuous perception, routing, and internal circuit mechanisms in Wireless Sensor Networks (WSNs). Especially, the nodes that are close to the sink node consume their energy more quickly, causing the hot spot problem. In this direction, the development of robust clustering and adaptive mobile routing methods is extremely significant to ensure energy efficiency in WSNs. For this purpose, a new mobile clustering routing protocol based on Thermal Exchange Optimization (TEO) inspired by Newton's cooling law, called TEO-MCRP, is presented for heterogeneous WSNs. In the present protocol, two different algorithms are proposed for Cluster Head (CH) selection and Mobile Sink (MS) path detection with objective functions including independent fitness parameters. With the manner of minimizing these objective functions, effective CH selection and MS trajectory determination have been performed for each mobile round using the temperature equations in the TEO algorithm. In parallel, an MS node gathers data from all CHs and transmits all data to the Base Station (BS). The performance results strikingly illustrate that the proposed protocol is better than other algorithms. Due to minimizing packet conflicts and losses, the packet delivery rate was achieved at 99.784% in the network

Delay Aware Efficient Mobile Sink Path with Distributed Fault Detection and Recovery in Wireless Sensor Networks

Delay Aware Efficient Mobile Sink Path with Distributed Fault Detection and Recovery in Wireless Sensor Networks

Learning‐based adaptive feedback control for tracking optimisation in wireless sensor actuator networking systems

In wireless sensor and actuator networks (WSAN), sensors can have much longer lifetime as mobile sink nodes move through the network and collect data from static sensor nodes. Applying mobile sinks in WSAN systems has faced challenges in sink path planning and data gathering scheduling. Many studies have focused on priori-trail planning for mobile sinks; however, they have not considered tracking performance degradation during operation due to system nonlinearities and uncertain WSAN environments. Thus, two learning-based adaptive feedback control methods, adaptive neural-network-based feedback control (ANNFC) and adaptive neural-network-based feedback control with integrator model (ANNFC-IM), are proposed to improve and optimise tracking performance in unknown and uncertain WSAN environments. The proposed algorithms enable a mobile sink to adjust its flying trajectory with online learning by applying a neural-network-based control method. The performance of the proposed algorithm is validated by theoretical analysis and simulation results. The results demonstrate that the proposed methods significantly outperform existing methods in terms of tracking error, system stability, and convergence.