Mobile Sink Research Articles

Federated Learning-Based Lightweight Two-Factor Authentication Framework with Privacy Preservation for Mobile Sink in the Social IoMT

The social Internet of Medical Things (S-IoMT) highly demands dependable and non-invasive device identification and authentication and makes data services more prevalent in a reliable learning system. In real time, healthcare systems consistently acquire, analyze, and transform a few operational intelligence into actionable forms through digitization to capture the sensitive information of the patient. Since the S-IoMT tries to distribute health-related services using IoT devices and wireless technologies, protecting the privacy of data and security of the device is so crucial in any eHealth system. To fulfill the design objectives of eHealth, smart sensing technologies use built-in features of social networking services. Despite being more convenient in its potential use, a significant concern is a security preventing potential threats and infringement. Thus, this paper presents a lightweight two-factor authentication framework (L2FAK) with privacy-preserving functionality, which uses a mobile sink for smart eHealth. Formal and informal analyses prove that the proposed L2FAK can resist cyberattacks such as session stealing, message modification, and denial of service, guaranteeing device protection and data integrity. The learning analysis verifies the features of the physical layer using federated learning layered authentication (FLLA) to learn the data characteristics by exploring the learning framework of neural networks. In the evaluation, the core scenario is implemented on the TensorFlow Federated framework to examine FLLA and other relevant mechanisms on two correlated datasets, namely, MNIST and FashionMNIST. The analytical results show that the proposed FLLA can analyze the protection of privacy features effectively in order to guarantee an accuracy ≈89.83% to 93.41% better than other mechanisms. Lastly, a real-time testbed demonstrates the significance of the proposed L2FAK in achieving better quality metrics, such as transmission efficiency and overhead ratio than other state-of-the-art approaches.

A Novel Multi-Objective Optimization-Based Path Formulation for Mobile Sink in Wireless Sensor Networks

A Novel Multi-Objective Optimization-Based Path Formulation for Mobile Sink in Wireless Sensor Networks

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.

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

SummaryThis paper proposes a novel distributed stochastic routing strategy using mobile sink based on double Q‐learning algorithm to improve the network performance in wireless sensor network with uncertain communication links. Furthermore, in order to extend network lifetime, a modified leach‐based clustering technique is proposed. To balance the energy dissipation between nodes, the selected cluster head nodes are then rotated based on the newly suggested threshold energy value. The simulation results demonstrate that the proposed algorithms outperform the QWRP, QLMS, ESRP and HACDC in terms of network lifetime by 18.33%, 35.1%, 39.7% and 44.7%, respectively. Moreover, the proposed algorithms considerably enhances the learning rate and hence reduces the data collection latency.

Enhancing Network Lifetime of Duty Cycle-Based WSN With Mobile Sink Using Ambient Energy Harvesting

In order to guarantee a successful data collection process in wireless sensor networks with mobile sinks (WSN-MS), two primary objectives must be reached: 1) enabling the mobile sink to retrieve the maximum amount of data and 2) making sure that the network operates as long as possible. The first problem has been solved previously by proposing an innovative solution HXMAC. To address the second problem, on which this paper focuses, ambient energy harvesting is used to continuously supply power to each sensor node. Thus, this paper's main contribution is to propose EH-HXMAC (HXMAC with energy harvesting), which is based on all these improvements: seamless handover, duty cycling optimization, and mainly energy harvesting capability. EH-HXMAC has been evaluated using Cooja Contiki simulator. Obtained results based on the evaluation of the proposal EH-HXMAC clearly show its suitability as a good solution to promote unlimited lifetime for WSN-MS.

Data Collection in WSNs using a Probability-Based Rendezvous Points Selection Algorithm

Wireless Sensor Networks are becoming more prevalent in various industries, including military operations and distant environmental monitoring. This is important because sensors are getting smarter, smaller, and less expensive. The energy hole problem in the WSN has been a major focus of recent research. The mobile sink is an efficient solution for the energy hole problem in a wireless sensor network. A mobile sink gets data from sensors by moving around the network often to avoid problems with hotspots or energy holes. It gets data from network nodes by traveling regularly and visiting a group of nodes known as rendezvous points (RPs). This research will present a probability-based RP selection (PRPS) technique for data collection in wireless sensor networks. To begin, a directed spanning tree is used to construct a tree that eliminates duplication in the data forwarding path. The proposed method is employed to compute the likelihood of RPs. Finally, using the shortest path technique, a mobile sink is constructed between these locations. The path provided is the best path that connects all of the RPs. The proposed approach improves the previous solutions by choosing the nodes with the most data packets as RPs. As a result, it extends network lifetime by lowering energy consumption and addressing the energy hole problem.

Optimized UAV-based data collection from MWSNs

In this paper, we formulate an Energy Efficient (EE) deployment for Mobile Wireless Sensor Networks (MWSNs) that consists of multiple clusters of sensor nodes, one mobile sink in each of them, and an Unmanned Aerial Vehicle (UAV) for the whole MWSN. The UAV receives the collected sensed data from the mobile sinks wirelessly in order to deliver them to various IoT-based users’ devices. Experimental results show that the proposed model can achieve optimization in terms of energy consumption minimization and network lifetime enhancement.

RQAR: Location‐free reliable and QoS‐aware routing protocol for mobile sink underwater wireless sensor networks

SummaryUnderwater Wireless Sensor Networks (UWSNs) have attracted attention from academics and industries due to many applications such as pollution monitoring, military, tsunami warning, and underwater exploration. One of the effective factors in these applications is efficient communication between underwater sensors. But this process is very challenging in UWSNs due to special conditions and underwater harsh environments. Therefore, designing routing protocols for efficient communication between sensors and sink is an important issue in UWSNs. In this context, this paper proposed a location‐free Reliable and QoS‐Aware Routing (RQAR) protocol for mobile sink UWSNs. RQAR designed using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and does not require the position information of the sensor nodes, which is cumbersome and difficult to obtain. Proposed protocol considers multiple parameters including link quality, hop count, congestion, and residual energy for qos support, and reliable data delivery RQAR also minimizes the effects of hole occurrence by increasing the transmission range as much as possible and ensures communication stability. The simulation results using NS2 showed the superiority of RQAR in improving end‐to‐end delay, packet delivery ratio, and network throughput compared to the previous methods.

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.

Improved Network Validity Using Various Soft Computing Techniques

Nowadays, when a life span of sensor nodes are threatened by the shortage of energy available for communication, sink mobility is an excellent technique for increasing its lifespan. When communicating via a WSN, the use of nodes as a transmission method eliminates the need for a physical medium. Sink mobility in a dynamic network topology presents a problem for sensor nodes that have reserved resources. Unless the route is revised and changed to reflect the location of the mobile sink location, it will be inefficient for delivering data effectively. In the clustering strategy, nodes are grouped together to improve communication, and the cluster head receives data from compactable nodes. The sink receives the aggregated data from the head. The cluster head is the central node in the conventional technique. A single node uses more energy than a node that is routed to a dead node. Increasing the number of people using a route shortens its lifespan. The proposed work demonstrates the effectiveness with which sensor node paths can be modified at a lower cost by utilising the virtual grid. The best routes are maintained mostly by sink node communication on routes based on dynamic route adjustment (VGDRA). Only specific nodes are acquired to re-align data supply to the mobile sink in accordance with new paradigms of route reconstruction. According to the results, VGDRA schemes have a longer life span because of the reduced number of loops.

Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

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.

Genetics Based Compact Fuzzy System for Visual Sensor Network

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 algorithm-based 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 LEACH-Expected Residual Energy (LEACH-ERE).

An effective adjustment approach of event reliability (EAAER) with mobile sink using shortest path routing for WSN

A crucial requirement for Wireless Sensor Networks (WSNs) is reliable data transfer. To maintain the overall reliability of the network, it is essential that the data delivered from a source node reach its intended destination without any loss or errors. In order to collect data from the sensor nodes that are deploy randomly around the sensor region, wireless sensor networks (WSNs) typically use static sinks. In the static sink-based technique, the data packets are transmitted through the network to reach the base station. The static sink is ineffective at utilizing energy as a result. This paper presents an effective adjustment approach for event reliability (EAAER) to find duplicate events using a mobile sink for homogenous wsn’s. The EAAER approach improves the routing against the base model by using shortest path routing. Simulation results show that the performance of EEAER increases battery utilization and the number of received packets while minimizing duplicate packets and packets lost during transmission compared to the base model.

A SURVEY ON EFFICIENT AUDITING SCHEME FOR SECURE DATA STORAGE IN FOG-TO-CLOUD COMPUTING

With the rise of the Internet of Things, fog-to-cloud computing has emerged as a new cutting-edge approach (IoT). Except for the cloud service provider, fog-to-cloud computing necessitates the participation of additional entities, such as mobile sinks and fog nodes (CSP). As a result, fog-to-cloud storage integrity audits will differ from standard cloud storage auditing. Tian et al. has completed the first stage in developing a public auditing system for fog-to-cloud computing. However, because they employ complex public key cryptography techniques like bilinear mapping and proof of knowledge, their system becomes inefficient. We offer a more broad and efficient auditing system based on MAC and HMAC, two prominent private key cryptography algorithms in this study. By implementing MAC and HMAC, we give a real instantiation of our auditing system. Finally, theoretical analysis and actual findings show that our suggested system has lower communication and computing costs.

Wireless Network Security Using Load Balanced Mobile Sink Technique

Real-time applications based on Wireless Sensor Network (WSN) technologies are quickly increasing due to intelligent surroundings. Among the most significant resources in the WSN are battery power and security. Clustering strategies improve the power factor and secure the WSN environment. It takes more electricity to forward data in a WSN. Though numerous clustering methods have been developed to provide energy consumption, there is indeed a risk of unequal load balancing, resulting in a decrease in the network’s lifetime due to network inequalities and less security. These possibilities arise due to the cluster head’s limited life span. These cluster heads (CH) are in charge of all activities and control intra-cluster and inter-cluster interactions. The proposed method uses Lifetime centric load balancing mechanisms (LCLBM) and Cluster-based energy optimization using a mobile sink algorithm (CEOMS). LCLBM emphasizes the selection of CH, system architectures, and optimal distribution of CH. In addition, the LCLBM was added with an assistant cluster head (ACH) for load balancing. Power consumption, communications latency, the frequency of failing nodes, high security, and one-way delay are essential variables to consider while evaluating LCLBM. CEOMS will choose a cluster leader based on the influence of the following parameters on the energy balance of WSNs. According to simulated findings, the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability, improves the network’s lifetime, decreases data latency, and balances network capacity.

Designing dynamic adjustment approach of event reliability in wireless sensor network

Many industries, such as environmental monitoring, industrial automation, healthcare, agriculture, and more, use wireless sensor networks. A critical requirement for wireless sensor networks (WSNs) is reliable transmission from source to destination. The main challenges for reliable data transmission are network connectivity, coverage optimization, data aggregation, shortest path, congestion, bandwidth, and quality of service. In a static environment, all sensor units transmit maximum data to the sink, dramatically raising their energy utilization and resulting in their early death. A dynamic environment can be utilized to collect data to reduce the amount of energy required to transport lengthy data packets over a wide area while still maintaining sensor node energy usage. The dynamic environment and duplicate packet simulation reflect real-world challenges like changing node positions and the potential for data duplication due to wireless communication issues. This paper introduces a dynamic event reliability protocol (DERP) in which the new positions of the sensor nodes are generated randomly in each event and a mobile sink collects data from every sensor node. The proposed technique consists of four parts, including a network model, a radio and energy model, a proposed approach, and a performance analysis. Our results show that DERP significantly improves energy consumption and unique packet delivery while reducing packet loss and the number of duplicate packets against LEACH and the average k-means clustering approach.

Energy aware Load Balanced Clustering using Golden Eagle Optimization in MS based LR-WPAN

Escalation in the real world applications using Internet of Things (IoT) now demands low cost and minimum power consuming devices having sensing, communicating, and computing capabilities. Low-rate Wireless Personal Area Network (LR-WPAN) is an emerging solution which can provide these capabilities with low data rate and less complexity. The longevity and connectivity of the network is a critical issue during the data collection in LR-WPAN. An Energy aware Load Balanced Clustering (ELBC) approach is proposed in this paper that enhances the network life time by minimizing the energy consumption. Clustering is done using the Golden Eagle Optimization (GEO) and by taking connectivity, Average energy of cluster, balancing factor and cohesion as parameters for optimal cluster head selection. Mobile Sink based data collection strategy using Gravitational Search Algorithm (GSA) is used to collect data from clusters. The simulation is done in network simulator-3 and the result shows that, the performance of ELBC-GEO is better in terms of network lifetime, average energy of nodes, and data collection delay.

Hybrid Rendezvous Clustering Model for Efficient Data Collection in Multi Sink Based Wireless Sensor Networks

Mobile sink based data collection has comparatively lower energy consumption compared to multi hop forwarding in wireless sensor networks. Energy efficiency is achieved by minimizing the hop count to sink in mobile sink based approaches. These approaches are of two types: Mobile sink collect stored data at rendezvous nodes or cluster heads forwarding data to mobile sink without storage by using its maximum transmission range. First approach provides higher energy gain at cost of delay. Second approach provides lower delay at cost of additional energy consumption. This work combines both the approaches with rendezvous node deciding to store or forward based on prediction of trajectory of multiple mobile sinks and packet latency deadlines.

Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) are emerging technology for disclosing multiple applications, such as oil, earth quick, and marine environments. All sensor nodes deployed in UWSNs operate through limited power batteries. Prolonging the network’s lifetime in such environments is an essential task and a hot topic among researchers. Multiple routing protocols have been designed to overcome the limited power issue and reduce energy consumption. Each routing protocol evaluated different parameters, but the issue is still unclear as to which one is better. In this study, we evaluated multiple routing protocols to investigate which is better in terms of parameters, such as packet delivery ratio (PDR), energy consumption, end-to-end delay, and the number of alive nodes. The simulation results indicate that Reliability and Adaptive Cooperation for Efficient UWSNs Using Sink Mobility (RACE-SM) performs better in all performance metrics than other routing schemes.

Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization.

Energy conservation is one of the main problems in a wireless sensor network (WSN). Compared with a single cluster head (CH), a dual CH optimization was proposed for less energy consumption by the WSN and an acquisition delay by the mobile sink (MS). Firstly, a fuzzy c-means clustering algorithm and a multi-objective particle swarm optimization were utilized for the determinations of the first and second CHs. Following that, the ideal trajectory of MS was assessed using the improved ant colony algorithm. Finally, the lifetimes, the death rounds of the first node and the 50% node, and the number of packets received at the base station were compared among the proposed approach. Moreover, five algorithms were compared to validate the optimization, and the improved trajectory was compared with the original one as well. It was found that, for 100 nodes, the number of dead rounds from the proposal increased by 7.9%, 22.9%, 25.1%, 61%, and 74.4% for the first node, and that of the 50% nodes increased by 27.8%, 34.2%, 98.3%, 213.1%, and 211.2%, respectively. The base station packet reception increased by about 19.3%, 53.5%, 27%, 86.8%, and 181.2%, respectively. The trajectory of MS could also decrease by about 10%.