Data Routing Research Articles

Routing Protocols in Wireless Body Area Networks: Architecture, Challenges, and Classification

The wireless body area network (WBAN) is a branch of the wireless sensor network (WSN) intended for tracking essential patients’ physiological signals and transferring this knowledge to the coordinator. During the routing of data, WBANs encounter critical routing problems like WSNs. Moreover, the particular constraints of WBANs make it more challenging that they need to be addressed. This survey article categorizes and briefly analyzes a range of current routing methods utilized in WBANs. The routing protocol is essential to the creation of any efficient and reliable wireless body area network due to a limited size of battery in the body sensor nodes. A comparison study of numerous routing protocols has been made in this paper, which is helpful in selecting the optimal routing protocol for the application being targeted. The article describes the WBAN architecture and addresses numerous challenges in the context of successful WBAN routing. In this paper, several existing WBAN routing methods are presented which are influenced by network structure, energy, quality of service (QoS), node temperature, human position, node transmission range, and other factors. The protocols, including cross-layered, cluster-based, QoS-aware, postural movement-based, temperature-aware, postural movement-based, and routing protocols, are given in an expressive taxonomy. Different routing protocols that have already been developed for WBANs are compared with more advanced protocols. The pros and cons of each protocol are looked at based on factors like delay, packet delivery ratio, and energy use. The researchers can utilize this survey paper as a reference for studying various routing protocols particularly in the medical and healthcare systems.

An Enhanced Virtual Cord Protocol Based Multi-Casting Strategy for the Effective and Efficient Management of Mobile Ad Hoc Networks

To solve problems with limited resources such as power, storage, bandwidth, and connectivity, efficient and effective data management solutions are needed. It is believed that the most successful algorithms for circumventing these constraints are those that self-organise and collaborate. To make the best use of available bandwidth, mobile ad hoc networks (MANETs) employ the strategy of multi-casting. The communication cost of any network can be significantly reduced by multi-casting, and the network can save resources by transmitting only one set of data to numerous receivers at a time. In this study, we implemented multi-casting in the virtual cord protocol (VCP), which uses virtual coordinates (VC) to improve effective routing and control wireless data transmission. We have improved the classic VCP protocol by making it so that intermediate nodes can also forward or re-transmit the dataset to interested nodes. This improves data transmission from the sender to multiple receivers. Simulation results proved efficacy of our proposed enhanced virtual cord protocol-based multi-casting strategy over traditional VCP protocol and helped in reduction of number of MAC transmissions, minimization of end-to-end delay, and maximization of packet delivery ratio.

Techno-economic-environmental evaluation of aircraft propulsion electrification: Surrogate-based multi-mission optimal design approach

Driven by the sustainability initiatives in the aviation sector, the emerging technologies of aircraft propulsion electrification have been identified as the promising approach to realize sustainable and decarbonized aviation. This study proposes a surrogate-based multi-mission optimal design approach for aircraft propulsion electrification, which innovatively incorporates realistic aviation operations into the electric aircraft design, with the aim of improving the overall aircraft fuel economy over multiple flight missions and conditions in practical scenarios. The proposed optimal design approach starts with the flight route data analysis to cluster the flight operational data using gaussian mixture model, so that a concise representation of flight mission profiles can be achieved. Then, an optimal orthogonal array-based Latin hypercubes are employed to generate sampling points of design variables for electrified aircraft propulsion. The mission analysis is performed with coupled propulsion-airframe integration in order to propose energy management strategy for mission-dependent aircraft performance. Consequently, fuel economy surrogate model is established via support vector machines to obtain the optimal design points of electrified aircraft propulsion. For assessing the viability of novel propulsion technologies, techno-economic evaluation is conducted using sensitivity analysis and breakeven electricity prices under a series of environmental regulatory policy scenarios.

Integrated Evacuation and Rescue Management System in Response to Fire Incidents

Indoor evacuation and rescue systems are necessary for responding to unexpected events. In the event of fire incidents, finding a proper evacuation route planning for impacted people as an immediate response and a real-time rescue planning for the trapped people is challenging. Several approaches are developed to provide an evacuation or rescue system by integrating building data and BIM models. However, none of them took the issues of both evacuation and rescue into account, and the provided solutions are not properly aware of dynamic environmental change variables derived from remotely sensed data. In this research, an evacuation and rescue management system is designed based on the integration of dynamic data of fire progress, people status, and routing data. This system consists of two stages: dynamic evacuation and dynamic rescue with priority assessment. First, the environmental data is gathered and integrated with the static building data. Then, leveraging a risk assessment method, the level of rout safety is evaluated and a dynamic risk-aware rout planning is generated for each person to evacuate safely and fast. Finally, in the rescue stage, rescuers are assigned to the trapped people using a priority assessment method so that the success rate of rescue operation increases. The same rout risk assessment is used to develop route planning for rescue team to ensure their safety. A system framework and architecture is proposed as a reference for emergency response systems and the system is evaluated over two state of art baselines under several scenarios in AnyLogic. The results demonstrates that dynamic evacuation and rescue with priority assessment approach helps to save more people, reduce total time, lower the risk of human injury and efficiently assigns relief resources to the trapped users.

Distributed and Distribution-Robust Meta Reinforcement Learning (D$^{2}$-RMRL) for Data Pre-Storage and Routing in Cube Satellite Networks

In this paper, the problem of data pre-storage and routing in dynamic, resource-constrained cube satellite networks is studied. In such a network, each cube satellite delivers requested data to user clusters under its coverage. A group of ground gateways will route and pre-store certain data to the satellites, such that the ground users can be directly served with the pre-stored data. This pre-storage and routing design problem is formulated as a decentralized Markov decision process (Dec-MDP) in which we seek to find the optimal strategy that maximizes the pre-store hit rate, i.e., the fraction of users being directly served with the pre-stored data. To obtain the optimal strategy, a distributed distribution-robust meta reinforcement learning (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RMRL) algorithm is proposed that consists of three key ingredients: value-decomposition for achieving the global optimum in distributed setting with minimum communication overhead, meta learning to obtain the optimal initial to reduce the training time under dynamic conditions, and pre-training to further speed up the meta training procedure. Simulation results show that, using the proposed value decomposition and meta training techniques, the satellite networks can achieve a 31.8% improvement of the pre-store hits and a 40.7% improvement of the convergence speed, compared to a baseline reinforcement learning algorithm. Moreover, the use of the proposed pre-training mechanism helps to shorten the meta-learning procedure by up to 43.7%.

A novel traffic controlling model for enhancing routing in mobile Ad-Hoc network

The advancement in the communication and services demand for better QoS, but in parallel the increase in network traffic demands for an effective and significant traffic controlling model to manage high network traffic and also reduce congestion. In case of MANET circumstances it is further difficult to control and manage due to high mobilizing nodes and limited resource restrictions. Even MANET’s dynamic mobility creates high maintenance overhead for effective communication. In the past most of the designed routing protocols utilize reactive routing mechanism, which are primary source driven. In case of a link failure and data failure it has to update the source node and source node only have to decide next course of action. This will generates an additional overhead in the network and may reduce throughput of the protocols. This paper aims to propose a novel Traffic Controlling Model (TCM) to efficiently manage the routing overhead in MANET. The TCM will handle heavy traffic, which is the main cause of network congestion and overhead. It first determines the network traffic and evaluates the probable Node Link Life (NLL) to recommend the TCM process. The analysis of the results generated through the experiment evaluation shows that the data routing has improved and substantiates the effectiveness of the proposal in compared to the existing routing approaches.

Effective Communication between IoT Devices using Machine Learning

The Internet of Things (IoT) consisting of several interconnected sensors capable of transferring the data over the network without human involvement. All these interconnected sensors can be managed through internet remotely. The Sensors connected in the networks are deployed randomly and are mobile in nature. The loss of sensor’s energy occurs due to the mobility nature, communication between the sensors and operations performed. The perfectly suitable sensor nodes as relay nodes and Cluster Heads (CH), and energy efficient path to the Base Station (BS) should be selected at each and every stage in the IoT network for effective communication. This paper presenting the method for effective communication between IoT Devices using ML comprising of Sensor Deployment, Clustering, Data Aggregation, Data Routing, and Reconstruction. In this paper for maximum coverage across the BS, relay sensors are deployed in hexagonal fashion. The sensors in the network transmit the diverse data and data aggregation is performed at CH level by machine learning algorithm that is Particle Swarm Optimization (PSO). The Ant Colony Optimization (ACO) algorithm is used to effectively routing the data over the network and dimensionality of the data is reduced with the Principle Component Analysis (PCA). The effective communication is established here by reducing the residual nodes. The total of 600 mobile sensor nodes and 12 relay sensor nodes are considered in the simulation of this work by the Network Simulation-3 (NS-3), only 42 joules energy is consuming over 200 milliseconds with network life time of 3000 milliseconds.

An intelligent base link recovery management and path stability in clustered randomly distributed mobile ad hoc networks

MANETs are networks of mobile nodes that are randomly distributed and play a major role in data transmission, route discovery, and route maintenance. Connection recovery and path stability are significant issues in the MANET, making data transfer problematic. When mobile nodes move out of range or when the node lacks adequate energy to maintain connection or path stability, data packet loss happens. We suggest a Clustered Intermediate System-Clustered Intermediate System Local Connection Failure Recovery Algorithm (Clustered Intermediate System-Clustered Intermediate System LLFRA) routing migration technique that builds the node while simultaneously repairing the broken link. Also utilised to look at node energy, energy- drained nodes, and route stability is Dolphin Partner Optimisation (DPO). The suggested approach's primary goal is to guarantee route stability while reducing packet loss. The suggested method is put to the test using the NS-2 simulator. The proposed recovery protocol beats the current technique in charge of PDR, end to end delay, throughput and node failures, according to experimental outputs.

UAV data delivery and routing optimization in Piggyback Network

UAV data delivery and routing optimization in Piggyback Network

Development of a unified digital transport and logistics intelligent platform based on the National Operator

This article is devoted to the research and development of a single digital transport and logistics intelligent platform based on the infrastructure of the National Operator. In the context of a rapidly developing transport and logistics industry, the creation of such a platform becomes a key strategic initiative. The abstract analyzes the main components and benefits of the platform, such as data integration, route and load optimization, demand forecasting, warehouse management, cargo tracking, improved planning and coordination, and analytics and reporting.

Big Data for Studying Population Migration: Analytical Capabilities

The article discusses the possibilities and features of usage the “big data” processing methods for studying population migration. Particular attention is paid to the main problems of obtaining primary information from digital sources and the limitations of their use. The article assesses the possibilities of using “digital traces” of mobile operators, application routing and open data from social networks to analyze migration flows. It also provides specific examples of such research and draws conclusions about the prospects for using digital “big data” services to enrich the information of classical migration registration tools.

Performance analysis of multi-layered clustering network using fault tolerance multipath routing protocol (MRP-FT) in a wireless sensor network (WSN)

Wireless sensor networks (WSNs) are ad hoc and self-configuring networks having the possibility that any sensor node can connect or leave the network. With no central controller in WSN, wireless sensor nodes are considered responsible for data routing in the networks. The wireless sensor nodes are very small in size and have limited resources, therefore, it becomes difficult to recharge or replace the battery of the sensor nodes at far places. The present study focused on reducing the battery consumption of the sensor nodes by the deployment of the newly proposed Fault Tolerance Multipath Routing Protocol (MRP-FT) as compared with the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol under particle swarm optimisation based fault tolerant routing (PSO-FT) technique. The proposed algorithm of MRP-FT-based on the dynamic clustering technique using Boltzmann learning of the neural network and the weights were adjusted according to the area of networks, number of nodes and rounds, the initial energy of nodes (E0), transmission energy of nodes (d &lt; d0), data reviving energy (ERX), data aggregation energy (EDA), energy dissipation on free space (εfs), energy dissipation of multi-path delay (ε mp) and the packet size. The results of the present study revealed that the packet heads remains constant during the initial time period (up to 2500 seconds), and exhibited a sharp increase thereafter. The network energy consumption remains constant up to 2300 s and exhibited a sharp increase thereafter. High energy use after 2300 seconds describes the faulty occurrence in the network and leads to decreased reliability (%) of the existing protocol. The energy consumption was substantially reduced by 15 J (38.5%) due to the implementation of the newly proposed MRP-FT, compared with the existing PSO-FT protocol. The reduced delay of 22 packets was achieved with MRP-FT protocol, compared with the existing PSO-FT technique-based LEACH protocol. Nonetheless, the MRP-FT enhanced the packet overhead of 10.8% over the current protocol due to deploying more uniform clustering. Additionally, a 12% increase in reliability was achieved with the implementation of MRP-FT protocol emphasizing that network lifespan was prolonged efficiently with the proposed algorithm.

Quadratic ensemble weighted emphasis boosting based energy and bandwidth efficient routing in Underwater Sensor Network

Underwater Sensor Network (UWSN) is a network that comprises a large number of independent underwater sensor nodes to perform monitoring tasks over a given area. UWSN minimized propagation delay, bandwidth, and packet loss. However, the implementation of efficient communication is a significant problem at UWSN. Therefore, Energy and Bandwidth-aware Quadratic Ensemble Weighted Emphasis Boosting Classification (EB-QEWEBC) method for performing energy-efficient routing in UWSN is proposed. Initially, different numbers of underwater sensor nodes are considered as input. Next, the bandwidth and energy consumption of every underwater sensor node is measured. After that, classification between underwater sensor nodes is made by considering energy and bandwidth as factors using Regularized Quadratic Classifier (i.e., weak classifier) for performing routing with minimum delay. Followed by, Weighted Emphasis Boosting is utilized to ensemble weak learners to form strong learners for improving data routing performance results with the biconvex combination. Finally, after classifying the node, data packets are sent to higher energy and bandwidth-efficient underwater sensor nodes. The classification process is carried out at every underwater sensor node for transmitting data packets to the sink node with minimum delay. This method determines the energy-efficient data communication through classification and boosting to reduce the misclassification rate. Experimental results EB-QEWEBC shows a minimization of 14%, 21%, 26%, and 54% in terms of bandwidth, energy consumption, end-to-end delay, and misclassification rate as compared to state-of-art-methods respectively.

Performance Analysis of Mobile Ad Hoc Routing Protocols in Vehicular Ad Hoc Networks using NS3

Vehicular Ad hoc Networks (VANET) is an emerging technology that brings tremendous technological advancement in the method of communication among smart vehicles. Due to its complex infrastructure and vehicle speed, data routing remains difficult at VANET. A VANET is also considered similar to Mobile Ad Hoc Network (MANET), and MANET routing protocols can be adapted for VANETs for performance evaluation. In this work, we have selected a real world scenario from our locality with high traffic issues, Nanthoor Mangalore City Karnataka, India and a Map is generated using Open Street Map using SUMO and the MANET routing protocols AODV, OLSR, DSDV and OLSR are evaluated using NS3.

A reliable routing mechanism with energy-efficient node selection for data transmission using a genetic algorithm in wireless sensor network

Energy-efficient and reliable data routing is critical in Wireless Sensor Networks (WSNs) application scenarios. Due to oscillations in wireless links in adverse environmental conditions, sensed data may not be sent to a sink node. As a result of wireless connectivity fluctuations, packet loss may occur. However, retransmission-based approaches are used to improve reliable data delivery. These approaches need a high quantity of data transfers for reliable data collection. Energy usage and packet delivery delays increase as a result of an increase in data transmissions. An energy-efficient data collection approach based on a genetic algorithm has been suggested in this paper to determine the most energy-efficient and reliable data routing in wireless sensor networks. The proposed algorithm reduced the number of data transmissions, energy consumption, and delay in network packet delivery. However, increased network lifetime. Furthermore, simulation results demonstrated the efficacy of the proposed method, considering the parameters energy consumption, network lifetime, number of data transmissions, and average delivery delay.

Dijkstra's algorithm to find the nearest vaccine location

Since the start of Covid-19 pandemic has made many people look for vaccine locations. In general, Dijkstra algorithm is used to find the shortest path. The shortest path problem concentrates on finding the path with the minimum distance. The Dijkstra algorithm was chosen because it has several advantages other than advantageous in terms of running time to find the shortest path. This research focuses on calculation and implementation of Dijkstra algorithm that will result the shortest path with visual by Google Maps API. The project is tested with manual calculation first by using graph explanation. The graph is obtained based on route data obtained with the help of google maps then it is tested by implementing the Dijkstra algorithm using HTML, JavaScript and also Google Maps API to visualize the nearest vaccine location. From the research founded that there were shortcomings problem where program had to enter each road turn as a vertex and an edge, If not including the entire path that was formed, it would pass through buildings or structures that should not be passed. The results that will be displayed on the website based on the purpose of the research.

A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

Optimized Approach for Localization of Sensor Nodes in 2D Wireless Sensor Networks Using Modified Learning Enthusiasm-Based Teaching–Learning-Based Optimization Algorithm

Wireless Sensor Networks (WSNs) have a wonderful potential to interconnect with the physical world and collect data. Data estimation, long lifespan, deployment, routing, task scheduling, safety, and localization are the primary performance difficulties for WSNs. WSNs are made up of sensor nodes set up with minimal battery power to monitor and reveal the occurrences in the sensor field. Detecting the location is a difficult task, but it is a crucial characteristic in many WSN applications. Locating all of the sensor nodes efficiently to obtain the precise location of an occurrence is a critical challenge. Surveillance, animal monitoring, tracking of moving objects, and forest fire detection are just a few of the applications that demand precise location determination. To cope with localization challenges in WSNs, there is a variety of localization algorithms accessible in the literature. The goal of this research is to use various optimization strategies to solve the localization problem. In this work, a modified learning enthusiasm-based teaching–learning-based optimization (mLebTLBO) algorithm is used to cope with a 2D localization problem applying the notion of an exclusive anchor node and movable target nodes. A modified LebTLBO algorithm seeks to increase overall efficiency by assessing the exploration and exploitation abilities. The computational results reveal that this technique outperforms others with respect to localization errors in a 2D environment of WSN.

Autonomous Underwater Vehicle: 5G Network Design and Simulation Based on Mimetic Technique Control System

The Internet of Underwater Things (IoUT) exhibits promising advancement with underwater acoustic wireless network communication (UWSN). Conventionally, IoUT has been utilized for the offshore monitoring and exploration of the environment within the underwater region. The data exchange between the IoUT has been performed with the 5G enabled-communication to establish the connection with the futuristic underwater monitoring. However, the acoustic waves in underwater communication are subjected to longer propagation delay and higher transmission energy. To overcome those issues autonomous underwater vehicle (AUV) is implemented for the data collection and routing based on cluster formation. This paper developed a memetic algorithm-based AUV monitoring system for the underwater environment. The proposed Autonomous 5G Memetic (A5GMEMETIC) model performs the data collection and transmission to increase the USAN performance. The A5GMEMETIC model data collection through the dynamic unaware clustering model minimizes energy consumption. The A5GMemetic optimizes the location of the nodes in the underwater environment for the optimal data path estimation for the data transmission in the network. Simulation analysis is performed comparatively with the proposed A5Gmemetic with the conventional AEDG, DGS, and HAMA models. The comparative analysis expressed that the proposed A5GMeMEMETIC model exhibits the ~12% increased packet delivery ratio (PDR), ~9% reduced delay and ~8% improved network lifetime.

Smart Hybridized Routing Protocol for Animal Monitoring and Tracking Applications

Wireless sensor networks (WSN) have been exploited for {countless} application domains, most notably the surveillance of environments and habitats, which has already become a critical mission. As a result, WSNs have been implemented to monitor animal care and track their health status. However, excessive energy utilization and communication traffic on packet transmissions lead to system deterioration, especially whenever perceived information captured in the monitoring area is transferred to the access point over multiple dynamic sinks. Further to manage the energy and data transmission issue, the energy consumption and location aware routing protocol has been architected on the wireless Nano sensor nodes. In this article, a novel hybrid energy and location aware routing protocol to cloud enabled IoT based Wireless Sensor Network towards animal health monitoring and tracking has been proposed. However proposed data routing protocol incorporates the trace file for path selection for data transmission to base station using sink node. Trace file has been obtained on processing the cluster heads established in the network. Therefore, clustering of node in the network has to be achieved using LEACH protocol which enhances the network scalability and network lifetime by clustering the nodes with Metaheuristics constraints like location or node density comparability. The objective of the proposed model is to enhance the network scalability and energy consumption by establishing the multiple node clusters with high density cluster head through Metaheuristics Node Clustering optimization techniques. Metaheuristics based node clustering is been obtained using Improved Particle Swarm Optimization. Further it is employed to compute the optimal path for sensed data transmission to base station. Node clustering provides high energy consumption among the sensing nodes and to establish the high energy clusters towards sensed information dissemination to base station on dynamically reforming the nodes clusters with respect to Node density and node location. Simulation analysis of the proposed energy efficient routing protocol provides high performance in energy utilization, packet delivery ratio, packet loss and Average delay compared against the conventional protocols on propagation of the data through sink node to base station