Multiple Mobile Sinks Research Articles

SMS2DC: Synchronous mobile sinks scheduling for data collection in internet of things‐enabled wireless sensor networks

SummaryEnergy‐efficient data collection in wireless sensor networks (WSNs) is crucial due to the limited battery capacity of sensor nodes (SNs). Using a mobile sink (MS) for data collection can lower the energy consumption of SNs to avoid relaying in WSNs. However, a single MS is not a feasible solution for large‐scale WSNs, so it was necessary to use multiple MSs to collect data. A synchronous MS scheduling strategy for data collection (SMS2DC) is proposed in this paper, which uses two types of MS, a local MS to collect data from SN and a global MS to collect data from local MS. In this process, we begin by partitioning the network based on chemical reaction optimization. For each partition, a MS is assigned and scheduled using a path construction strategy according to a geometric path construction approach. In addition, a global MS is scheduled based on a local MS trajectory by identifying the most appropriate collision point to collect data. As a result, the algorithm increases data collection accuracy while minimizing network data loss. The asymptotic time complexity of the proposed SMS2DC algorithm needed . The comparison results show the superiority of the proposed SMS2DC strategy under multiple scenarios under various deployment conditions.

A novel Asanoha-inspired method for optimal path planning of multiple mobile sinks

A novel Asanoha-inspired method for optimal path planning of multiple mobile sinks

Computational intelligence techniques for energy efficient routing protocols in wireless sensor networks: A critique

AbstractWireless Sensor Networks (WSNs) play a pivotal role in modern data acquisition systems, but their energy‐efficient operation remains a significant challenge. This work provides a concise overview of the key challenges and proposed solutions discussed in the context of energy‐efficient routing protocols for WSNs based on a systematic literature review. The need for fault tolerance to ensure network resilience is vital, given the limited resources and environmental challenges faced by sensor nodes. This work emphasizes the importance of addressing several open research questions, including the integration of diverse topologies, cross‐layer routing, and the use of multiple mobile sinks. Additionally, it highlights the need for QoS‐aware routing for real‐time applications, mathematical modeling for protocol evaluation, and the challenges of testing routing protocols on real hardware. Furthermore, this article suggests leveraging the RPL routing protocol with the Node‐based Metric RNE for enhanced energy efficiency and explores the promising field of hierarchical routing protocols, exemplified by the LEACH protocol and its variants. This work serves as a roadmap for researchers and practitioners, identifying critical challenges and proposing directions for future research to achieve energy‐efficient routing protocols in WSNs, ultimately enabling effective communication in resource‐constrained environments.

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks.

The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.

MMSCM: A multiple mobile sinks coverage maximization based hierarchical routing protocol for mobile wireless sensor networks

AbstractMobile 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.

Reinforcement learning movement path for multiple mobile sinks in wireless sensor networks

SummaryMobile sink nodes play a very active role in wireless sensor network (WSN) routing. Because hiring these nodes can decrease the energy consumption of each node, end‐to‐end delay, and network latency significantly. Therefore, mobile sinks can soar the network lifetime dramatically. Generally, there are three movement paths for a mobile sink, which are as follows: (1) Random/stochastic, (2) controlled, and (3) fixed/ predictable/predefined paths. In this paper, a novel movement path is introduced as a fourth category of movement paths for mobile sinks. This path is based on deep learning, so a mobile sink node can go to the appropriate region that has more data at a suitable time. Thereupon, WSN routing can improve very much in terms of end‐to‐end delay, network latency, network lifetime, delivery ratio, and energy efficiency. The new proposed routing suggests a reinforcement learning movement path (RLMP) for multiple mobile sinks. The network in the proposed work consists of a couple of regions; each region can be employed for a special purpose, so this method is hired for any application and any size of the network. All simulations in this paper are done by network simulator 3 (NS‐3). The experimental results clearly show that the RLMP overcomes other approaches by at least 32.48% in the network lifetime benchmark.

Multiobjective Multiple Mobile Sink Scheduling via Evolutionary Fuzzy Rough Neural Network for Wireless Sensor Networks

The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this article studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length, and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed. This framework can balance multiple objectives and complete complex scheduling tasks. Compared with static sinks, random-moving sinks, sinks with manually designed strategy, gene expression programming-based sinks, as well as the other state-of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results.

Optimization of sink sojourn time for enhancing the lifetime of wireless sensor network

Reducing the energy holes by using multiple mobile sinks and reducing the one hop sensor energy consumption is one of the important research issues in wireless sensor network. The proposed work focus on deploying multiple mobile sinks to move across a predetermined trajectory for data collection. The network area is divided into triangular partitions and the mobile sinks are allowed to move along triangular partitions. The work proposes a clustering protocol to form data collection trees. The contribution is extended further by formulation an optimization model for improvising the sensor network lifetime. The mobile sinks are allowed to travel along the predetermined trajectory and stop at each sojourn location for an optimal time location. The optimization model considers the one hop residual energy and energy consumption of the individual sensor. Simulation experiments showcase better performance of the proposed work in comparison with existing model.

A secure energy-efficient routing protocol for disease data transmission using IoMT

The outlook of the World toward health infrastructure has drastically changed due to COVID-19 which created the need for the development of emerging technologies where interactions between the patients and the health workers can be minimized. Consequently, a secure and energy-efficient internet of medical things (IoMT) enabled wireless sensor network (WSN) is proposed for communicable infectious diseases that utilizes genetic algorithm. The proposed system makes use of movable sinks in IoT-enabled WSNs for healthcare called OptiGeA. The OptiGeA protocol is depicted for cluster heads (CHs) election by joining the factor of energy, density, distance, and heterogeneous node's capacity for fitness function. Additionally, a novel deployment technique and multiple mobile sink approaches are proposed to reduce transmission distance between sink and CH during system operation which mitigates hotspot issues. It is evident from the simulations that the OptiGeA protocol outflanks state-of-the-art protocols in terms of different performance measurements.

Underwater Wireless Sensor Networks with Energy Efficient Routing Protocol Using Load Balancing Technique

Underwater Wireless Sensor Networks is effective and intelligent utilization of energy for routing protocol in longer network lifetime. Energy Consumption and load balancing are the vital roles for a network lifetime. The use of load balancing in WSN is granted as the best resource of sink mobility which protects energy sources to organize. The aim of this paper is to evaluate various deployed strategies involving sink mobility. Multiple mobile sinks are capable of performing computational operations like collecting information from electric joints instantly, storage and also communication capability. It evaluates the results and the effect of sink mobility by comparing with another routing protocol GEDAR.

GCCP - NS: Grid based Congestion Control protocol with N-Sinks in a Wireless Sensor Network

Wireless Sensor Networks (WSN) have been a current trend in the research field and has many issues when there are multiple mobile sinks. Data dissemination gets critical as their locations have to be repeatedly updated and results in huge consumption of the restricted battery supply in sensor nodes. In this paper, we propose GCCP – NS, a grid based congestion control protocol with N –sinks that solves the data dissemination problem leading to congestion. We construct a dual level grid structure to trail the locations of all the source nodes that reports the information to the mobile sinks by monitoring the network in a hierarchical manner. As an added advantage, it aids in data dissemination based on query flooding from the mobile sinks using quorum based method within each cell in the grid and avoids congestion in an effective manner. Simulation results show that our proposed protocol outperforms the other schemes in terms of packet delivery ratio, energy expenditure and throughput.

Energy- and Delay-Efficient Algorithm for Large-Scale Data Collection in Mobile-Sink WSNs

This paper deals with the problem of large-scale data collection in wireless sensor networks equipped with multiple multi-interface mobile sinks. Employing mobile sinks for data collection leads to a trade-off between energy consumption and delay. We propose a new algorithm called “Data Gathering with Multi-interface Mobile Sink (DGM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> S)” to reduce both energy consumption and delay in the network. DGM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> S considers delays due to sink mobility and sink stopping for receiving the aggregated data from Cluster Heads (CHs). The proposed scheme partitions the network into smaller sub-areas, where in each sub-area, an appropriate trajectory is determined for the corresponding sink. In each partition, DGM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> S uses an Energy- and Delay-efficient Polling point Selection (EDPS) strategy to determine data of which CHs should be collected at different points of the corresponding sink tour. EDPS considers path loss and shadowing phenomena, as well as the considerations for delay and energy consumption. As another advantage, DGM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> S employs a scheduling mechanism to make the best use of the multi-interface technology in collecting data of CHs at each polling point. Extensive simulations are conducted to illustrate the advantages of the proposed DGM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> S algorithm in terms of energy consumption, delay, network’s lifetime, and time complexity.

Fuzzy-Based Multi-Layered Clustering and ACO-Based Multiple Mobile Sinks Path Planning for Optimal Coverage in WSNs

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.

EARP: An Enhanced ACO-Based Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

EARP: An Enhanced ACO-Based Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

Priority based data gathering using multiple mobile sinks in cluster based UWSNs for oil pipeline leakage detection

Priority based data gathering using multiple mobile sinks in cluster based UWSNs for oil pipeline leakage detection

CORL 2: Exploring Cooperative Opportunities, Reducing Latency and Prolonging Network Lifetime for Data Collection Using Mobile Sinks in Wireless Sensor Networks

Data collection is one of the most important issues in Wireless Sensor Networks (WSNs). Many data collection algorithms have been proposed for collecting data using multiple mobile sinks. However, the anchor selection strategy, which significantly impacts the network lifetime, still can be improved. In addition, the collaboration between multiple mobile sinks is still rare, resulting in the long latency of each round of data collection. This paper proposes an Exploring Cooperative Opportunities, Reducing Latency and Prolong Network Lifetime algorithm for Data Collection, called CORL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , aiming to improve the network lifetime and reduce the latency of data collection. Initially, the proposed CORL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> partitions the monitoring region into several equal-sized subregions and assigns each mobile sink to each subregion. Then CORL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> further partitions each subregion into several equal-sized grids and constructs a path passing through all grids in that subregion. To reduce the energy consumption for packet forwarding, the proposed CORL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> further partitions each grid into several small grids and adjusts the path such that it can pass through high-density small grids, aiming to maximize the number of anchors and prolong the network lifetime. Then the cooperative strategy is applied to cooperatively reduce the maximal latency of data collection in all subregions and balance the data collection workload between neighboring mobile sinks. Performance evaluations reveal that the proposed CORL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> outperforms existing data collection algorithms in terms of latency, energy consumptions, the workload balancing improving index(WBII), and network lifetime for data collection in the given WSNs.

Energy efficient rendezvous points based routing technique using multiple mobile sink in heterogeneous wireless sensor networks

Energy efficient rendezvous points based routing technique using multiple mobile sink in heterogeneous wireless sensor networks

Routing Protocol Based On Energy Efficient For Under Water Wireless Network

Underwater Wireless Sensor Networks is effective and intelligent utilization of energy for routing protocol in longer network lifetime. Energy Consumption and load balancing are the vital roles for a network lifetime. The use of load balancing in WSN is granted as the best resource of sink mobility which protects energy sources to organize. The aim of this paper is to evaluate various deployed strategies involving sink mobility. Multiple mobile sinks are capable of performing computational operations like collecting information from electric joints instantly, storage, and also communication capability. It evaluates the results and the effect of sink mobility by comparing with another routing protocol GEDAR.

IARP: An Intelligent ACO-Based Routing Protocol with Multiple Mobile Sinks Support for Wireless Sensor Networks

Using a single fixed sink in wireless sensor networks (WSNs) creates the hotspot problem. Recently, the mobile sink technique is considered as a good solution to the hotspot problem. Mobile sinks travel physically in the network to collect data directly from special nodes, such as cluster heads (CHs). Find the optimal path for the mobile sink is of utmost importance in order to achieve energy efficiency. The ant colony optimization (ACO) algorithm has been founded to be a good solution to meet this objective. Whereas the traditional ACO algorithm and its versions do not restrict the spread of ants which consumes a lot of energy and makes the convergence speed slow. In this paper, we propose an Intelligent ACO-based Routing Protocol with multiple mobile sinks support for wireless sensor networks (named IARP). This proposed protocol divides the WSN into clusters where each one is managed by a CH. The CH selection is conceived in such a way to use the minimum control overhead and also avoid the greedy re-clustering process. It considers not only pheromone but also remaining energy and hop count heuristic parameters. As well, the scope of ants search is restricted to save a lot of energy. Additionally, we have conceived a fault-tolerant algorithm to resume the normal functionality of the network after the failure of critical nodes i.e. CHs. The proposed protocol is tested by using simulations. The results show that the proposed IARP can significantly improve the performance of WSNs compared to other benchmark protocols.

A New Preventive Routing Method Based on Clustering and Location Prediction in the Mobile Internet of Things

In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) are an impressive technology. These networks are extremely resource constrained and require the design of energy-efficient routing techniques. The clustering and location prediction routing method based on multiple mobile sinks (CLRP-MMSs) for the Mobile Internet of Things (MIoT) is presented in this article. Recently, mobile sinks are used in routing more durability and energy saving in WSN. In this work, first, the entire nodes are divided into clusters, and then each cluster selects a cluster head (CH) by calculating the CH choosing function (CHCF). When clustering runs on networks with moving nodes, the possibility of disconnecting the nodes from CH nodes will cause a lot of data loss. It will change the amount of energy and rate of data received, but the amount of wasted energy is reduced by predicting the location and reducing the sink and CH nodes' distance. The simulation results using NS-2 clearly showed that the proposed method improves energy consumption at least 28.12% and increases throughput at least 26.74% compared to energy efficient routing algorithm with mobile sink support and high-available and location-predictive data gathering scheme using mobile sink methods.