Mobile Sink Node Research Articles

Intelligent energy efficient routing in wireless body area network with mobile sink nodes using horse electric fish optimization

Intelligent energy efficient routing in wireless body area network with mobile sink nodes using horse electric fish optimization

Energy‐efficient adaptive clustering (EEAC) with rendezvous nodes and mobile sink

SummaryWireless 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.

Mobile Sink Node with Discerning Motility Approach for Energy Efficient Delay Sensitive Data Communication over Wireless Sensor Body Area Networks

The sensors nearby the static sink drains their energy resources rapidly, since they continuously involve to build routes in Wireless sensor networks, which are between data sources and static sink. Hence, the sensors nearby the sink having limited lifespan, which axing the network lifetime.The mobile-sink strategy that allows the sink to move around the network area to distribute the transmission overhead to multiple sensor nodes. However, the mobile-sink strategy is often tall ordered practice due to the continuous need of establishing routes between source nodes and the mobile sink (MS) at new position occurred due to its random mobility. In regard to above stated argument, this manuscript proposed a novel energy data transmission strategy which is effective for WSN with mobile sink. Unlike the traditional contributions, which relies on mobile sink with random mobility strategies, the proposal defines a discerning path for mobile sink routing between sectioned clusters of the WSN. The proposal of the manuscript titled “Mobile Sink Node with Discerning Motility Approach (MSDMA) for Energy Efficient Data Communication over WBAN”. The method defined in proposed model sections the target network in to multiple geographical clusters and prioritize these clusters by the delay sensitivity of the data transmitted by the sensor nodes of the corresponding clusters. Further, discriminating these clusters by their delay sensitive priority to define mobile sink route. For estimation of the delay sensitive priority of the clusters, set of metrics are proposed. The experimental study carried on simulation to assess the significance of the suggested method. The performance improvement of the suggested method is ascended through comparative analysis performed against benchmark model under divergent metrics.

ECKN: An Integrated Approach for Position Estimation, Packet Routing, and Sleep Scheduling in Wireless Sensor Networks.

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.

Remote Data Acquisition Using UAVs and Custom Sensor Node Technology

Precision agriculture technology has advanced rapidly in the 21st century. Despite this, the vast majority of US farmers do not employ any form of precision agriculture. Reasons for this include the high initial cost, lack of internet connectivity in rural areas, and complex setup and operation. The basis of this project was to create a low-cost, energy-efficient data collection system using an unmanned aerial vehicle (UAV) as a mobile sink node in a local wireless system. This was accomplished through the design and manufacture of custom sensor nodes and a custom drone-mounted wireless receiver node. The sensor node and drone node enclosures were 3D printed and assembled using low-cost materials and internal components. The system was successfully tested in a field where it collected soil data, including soil moisture, soil temperature, and electrical conductivity. The cost and scalability of the system are discussed, as well as potential improvements and comparisons with existing technologies. The system was concluded to have many potential applications in its current state but with room to expand and improve its operation and features.

Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs

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.

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.

Modified Ring Routing Protocol for Mobile Sinks in a Dynamic Sensor Network in Smart Monitoring Applications

The stationary hierarchical network faces considerable challenges from hotspots and faster network breakdowns, especially in smart monitoring applications. As a solution to this issue, mobile sinks were recommended since they are associated with huge and balanced ways to transfer data and energy across the network. Again, due to the mobile sink node advertisement around the network latency and the energy utilization overheads introduced across the network, ring routing reduces the control overhead while preserving the benefits of the mobile sink, thereby optimizing the energy and improving the network life span. Consequently, we suggested a novel, distributed advanced ring routing strategy, in this work, for the mobile wireless sensor network. Extensive simulations and performance evaluation, in comparison to previous distributed mobile approaches, reveal a 37% and 40% boost in the network throughput and end-to end delay, respectively. Additionally, the lifespan of a network is determined by the control overhead and energy demand.

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.

SAOA: Multi-Objective Fault-Tolerance Based Optimized RPL Routing Protocol in Internet of Things

Several technologies are implemented for serving industrial information and controlling the process in which the increased complexity and allied fault failed to achieve a reliable transmission to activate the timely network. These data must be transferred from one node to another node without any fault or delay in their nodes. To resolve this problem, an algorithm named SAOA is proposed to communicate the messages from various services without any delay in their nodes. IoT with mobile sink node is simulated in the network based on the MoO4RPL objective and the phases involved in this strategy are topology, route discovery, communication, and route maintenance phase. The topology network is generated for network topology and the rank is computed based on the energy, trust, delay, fault tolerance, and link quality in the multi-objective route discovery phase. In the communication phase, the optimal route is selected using proposed SAOA with fitness function factors. At last, the route maintenance phase is performed, and the proposed SAOA has achieved a minimum distance of 53.807 m, maximum energy of 0.415 J, maximum link quality of 2.163, and maximum trust of 0.701, respectively. In the future, another algorithm is implemented in IoT applications with minimum modifications to evaluate the various metric values.

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.

Collecting large volume data from wireless sensor network by drone

Data collection is the most important task in wireless sensor networks (WSN). Each sensor node has to send the sensed data to a special node called sink, which is the user interface. The sensor nodes far from the sink send data to intermediate nodes that forward it by multi-hop data paths. This characteristic leads to higher energy consumption in the sensor nodes close to the sink since they have to relay data from all other sensor nodes. The literature presents several studies that use mobile sinks for data collection to reduce the number of hops in the data paths and distributes the energy consumption, considering that the nodes close to the mobile sink change. However, the majority of these studies consider only the network limitation, such as energy. Furthermore, they also consider sensor nodes sending only one data packet to the mobile sink. This work assumes a quad-copter drone as a mobile sink and sensor nodes having several data packets to send to the sink. We propose two GRASP-based heuristics to define drone tours for data collection. Since this vehicle has limited flight time, the primary metric analyzed here is the overall data collection time. Furthermore, they guarantee that the mobile sink will stay a minimal time inside the radio range of each sensor node to ensure that all of them will have enough time to send all data. The heuristics achieve this guarantee by looking for a subset of locations, among the infinite points inside the monitored area, where the drone will hover for data gathering. Hence, the proposed heuristics have to search for good locations to reduce the data gathering time and define the shortest path to reduce the trip time. Simulated experiments showed that the proposed GRASP-based heuristics outperformed the greed algorithm found as state of the art for this type of scenario, mainly when the volume of data stored in each sensor node is high.

Cooperative Recharge Scheme Based on a Hamiltonian Path in Mobile Wireless Rechargeable Sensor Networks

The energy problem and limited capacity of batteries have been fundamental constraints in many wireless sensor network (WSN) applications. For WSN, the wireless energy transmission technology based on magnetic resonance coupling is a promising energy transmission technology. To reduce the cost and energy consumption during charging in mobile wireless rechargeable sensor networks (MWRSNs), a cooperative mobile charging mechanism based on the Hamiltonian path is proposed in this paper. To improve the charging task interval, we study the use of a mobile charger (MC) as a mobile sink node to collect the data in this paper. Then, we used the sink and the charging sensors selected by the MC to construct the undirected complete graph. Finally, the Euclidean distance between nodes is used as the edge weight and a Hamiltonian loop is found by using the improved Clark–Wright (C-W) saving algorithm to solve the problem of charging a rechargeable sensor network. In addition to the energy usage efficiency (EUE) and the network lifetime, the average energy loss per unit time is considered as the evaluation index according to the impact of the MC on the energy consumption during charging. The simulation results show that the proposed scheme increases the average network lifetime, decreases the average energy loss per unit time, and improves the EUE.

An energy efficient cluster based hybrid optimization algorithm with static sink and mobile sink node for Wireless Sensor Networks

In wireless sensor networks (WSNs), energy efficiency is a significant design challenge that can be resolved by clustering and routing approaches. They are considered as Non-deterministic Polynomial (NP)-hard optimization problems, and the optimal or near-optimal solutions can be determined by using Swarm-Intelligence (SI) based algorithms. With this inspiration, this study focuses on two approaches, viz. the Hybrid Butterfly and Ant Colony optimization algorithm along with Static sink node (HBACS) and HBAC along with Mobile sink node (HBACM), which is a hybridization of Butterfly Optimization (BOA) and Ant Colony Optimization (ACO) algorithm. BOA determines the optimal cluster head, and ACO performs energy-efficient routing, thereby minimizing the energy consumption and maximizing the network’s lifetime. Furthermore, in this study, mobility of the sink node is used to eliminate the multi-hop communication between cluster heads and sink nodes, hence addressing the hot-spot issue and further extending the network lifetime. The proposed HBACS and HBACM approaches are implemented in the NS2 simulator. The simulation findings reveal that the HBACS shows percentage improvement regarding residual energy by 24.23%, 41.98%, and 66.67%; an improved number of alive nodes by 28.19%, 37.81%, and 53.12%; and improved throughput by 8.11%, 14.29%, and 17.65% over CRWO, ERP, and IHSBEER algorithms respectively. Moreover, the HBACM approach performs better in LDN by 18.76%, 63.66%, and 66.28%; HDN by 8.35%, 56.26%, and 58.15%, and FDN by 7.77%, 52.76%, and 74.29% over HGWSFO, SFO, and GWO based approaches, respectively.

Security enhanced optimal trajectory selection for mobile sink using reinforcement learning

Wireless visual sensor networks (WVSNs) have emerged as a strategic inter disciplinary category of WSN with its visual sensor based intelligence that has garnered considerable attention. The growing demand for energy efficient and maximized life time networks in highly critical applications like surveillance, military and medicine has opened up more prospects as well as challenges in the deployment of WVSNs. Multi-hop communication in WVSN results in overloading of intermediate sensor nodes due to its dual function in the network which results in hotspot effect. This can be mitigated with the help of mobile sinks and rendezvous points based route design. But mobile sinks has to visit every cluster head to gather data which results in longer traversal path and higher latency and power consumption related issues if not addressed properly will impact the performance of the network. Our objective is to analyze and determine the optimal trajectory for mobile sink node traversal with the help of a high quality transmission architecture integrated with reinforcement learning and isolation forest based anomaly detection to propose an energy efficient meta-heuristic approach to enhance the performance of network by reducing the latency and securing the network against possible attacks.

SINK MOBILITY MODEL FOR WIRELESS SENSOR NETWORKS USING KOHONEN SELF-ORGANIZING MAP

Wireless sensor networks are expected to operate in an unattended manner for long periods of time. As a result, different mobility models were proposed by many research papers in order to improve the performance and extend the lifetime of the network. In this paper a new sink mobility model that is based on kohonen self-organizing maps is proposed in order to provide a mobile sink node with the ability to collect data from static sensor nodes. Moreover, the performance of the proposed mobility model was studied using NS-2 simulator under different network sizes and movement speeds of the mobile sink. Finally, the performance of the proposed model was evaluated based on different performance metrics namely, end-to-end delay, packet delivery ratio and throughput.

Multi-level energy efficient cooperative scheme for ring based clustering in wireless sensor network

Abstract Wireless Sensor Network (WSN) is composed of different sensors attached to the same wireless medium. Sensor node data is usually forwarded to the base station, where it has been stored, evaluated, and processed. Ring Routing, a new energy-efficient distributed routing protocol aimed at larger-scale WSN applications with mobile sink and motionless sensor nodes. The structure ring is quickly converted, and they are capable of switching positions with regular nodes in a simple and effective manner, thereby minimizing the hotspot problem. In the network system, the central node is considered, and ring nodes with a certain distance are established while creating a ring structure. Since the sink nodes are powered by batteries, they deplete their batteries quicker than the other nodes, which is attributable to the accumulation of data traffic into the sink. Furthermore, when the ring configuration is disrupted due to ring node expiration, data transfer becomes tedious and causes significant delay. As a ring node starts to exhaust, its neighbor takes over as the ring node, and this selection of the regular node as the ring node is done optimally based on Multiple-Fitness Parameters such as Four-level energy consumption (4En), Distance (distance between the ring node and the regular node Dist ring − reg and distance between network centers to regular node Δ D i s t C − r i n g ${\Delta}Dis{t}_{C-ring}$ ) and delay S. Moreover, this optimal selection of the regular node to act as ring node will be carried out by a new hybrid algorithm referred to as Cuckoo Search Updated Sealion’s Attacking Technique (CSUSAT) that combines the standard crow search and sea lion optimization algorithm. At last, the supremacy of the presented approach is proved over other models.

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.

Optimized fuzzy clustering in wireless sensor networks using improved squirrel search algorithm

Wireless sensor networks (WSNs) are important tools to monitor various events happening in specific environments. In WSNs, regarding the network size and number of sensor nodes, several mobile sink nodes can be used to collect the monitored data. In such cases, having a clustered WSN helps to get the sensed data more efficiently to the sink nodes. Besides, clustering is one of the well-known methods for mitigating the energy consumption of WSNs. This paper presents an efficient distributed multi-level clustering scheme using Sugeno-based Fuzzy Logic Controller (FLC) to organize the WSN into several multi-level clusters. These cluster heads (CHs) collect their members' data and forward them to one of the sinks. Besides, CHs cooperate in multi-hop routing of the data packets to the destination sinks. Also, an improved SSA algorithm is provided to optimize the clustering FLC. By optimizing the FLC its rule-base can be minimized and its effectiveness in the clustering process can be improved. Extensive simulations are conducted in the OMNET++ simulator to evaluate the proposed scheme regarding various metrics such FND (First Node Dead), HND (Half Nodes Dead), LND (Last Node Dead), the average energy of the WSN's nodes, data packet loss, and retransmission ratio. The achieved results exhibit that our presented approach outperforms several well-known clustering and multi-hop routing approaches.

BTA-MM: Burst traffic awareness-based adaptive mobility model with mobile sinks for heterogeneous wireless sensor networks

Using mobile sink increases the coverage time and energy expenditure when there is a burst traffic condition in wireless sensor networks (WSNs). Hence, it is essential to handle burst traffic using mobile sinks in an energy-efficient mobility manner. In most studies, mechanisms of the clustering and routing have not taken into account burst traffic. In fact, the number of studies with integrated mobile sink nodes, burst traffic awareness, multi-criteria cluster head (CH) selection, and mobile sink routing is negligible. For this purpose, a novel burst traffic awareness adaptive mobility scheme is proposed based on heterogeneous clustered WSNs, namely Burst Traffic Awareness-Mobility Model (BTA-MM). In the proposed scheme, the network area is first divided into two cluster groups. The CH selection is performed for each round by the average residual energy an d node load, taking into account the network coverage. An adaptive Gauss–Markov-burst traffic combination model is proposed in the study. In the proposed model, the mobile sinks collect all data in a single-hop communication as soon as they join the coverage intersection points (CIPs) of the CHs. The mobility model utilizes the adaptive minimum-weighted cost of the nodes. Once the burst packets are perceived in a CH, the data packets are backed up on the nearest and highest energy node in the cluster. Then, the mobile sink suddenly updates its trajectory towards the node, including the burst data traffic. Performance analysis of the proposed scheme was performed in NS-2 simulation environment. The most notable of the performance results is that the proposed method increased the network lifetime 42.5% more than any other method and also reduced the control overhead and average mobile path length by 72.5% and 35.9%, respectively. Also, the simulation results showed that the proposed method significantly reduced the average energy consumption 34.2% more than any other method, and increased the packet delivery rate 4.5% more than any other method, even in burst traffic.