Data Routing Research Articles

Optimized Group-Centric Data Routing in Heterogeneous Wireless Sensor Networks for Enhanced Energy Efficiency

Wireless Sensor Networks (WSNs) are increasingly being utilized in environments where human presence is limited or dangerous. The main goal is to enhance the data processing capabilities of these components to extend the overall lifespan of the design. Researchers have explored conventional energy-saving methods to address the energy constraints of sensor nodes. However, it became clear that traditional routing methods, specifically those based on packet grouping, were inadequate. The proposed system, known as Optimized Group-Centric Data Routing (OGC-DR), introduces an efficient method of data routing by utilizing the concept of grouping nodal points. This approach enhances data routing management by differentiating between routing within a nodal group and routing between adjacent nodal groups. Group Heading Nodes (GHN) are assigned to each group of sensory nodes according to fitness criteria. The implementation of a tree-based routing structure improves data routing by creating a meeting-zone and strategically selecting intermediary nodes between the source and destination node. To improve data privacy, a sender and receiver engage in an asymmetric secret-key exchange at nodal points. Data is then directed to its ultimate destination via predetermined intermediary nodes and Group Heading Nodes. Simulations of the proposed method indicate several advantages, such as lower end-to-end delays, reduced energy consumption, higher active node count, and enhanced packet delivery rates. Furthermore, it improves data privacy for all communication within the sensory architecture.

Underwater Wireless Sensor Networks (Review)

Underwater wireless sensor networks (UWSNs) are becoming increasingly popular among researchers due to their potential for real-world applications such as marine surveillance, sea monitoring, deep sea archaeology, oil monitoring, and more. With almost 70% of the earth’s surface covered in water, it is challenging for humans to gather valuable information from the seabed without advanced technology. In UWSNs, sensor nodes are placed to sense the underwater environment, and the data collected is sent to a sink node, which then transfers the data to a base station for processing. The deployment of sensor nodes in UWSNs is difficult due to the harsh underwater environment, and the routing of data is complicated by the nodes’ limited communication range and high energy consumption. This study provides a comprehensive overview of UWSNs, including their applications, deployment methods, and routing algorithms. A comparative analysis of deployment techniques and routing algorithms is presented to help researchers identify research gaps in these areas. The study also reviews some UWSN applications, which offer valuable insights into the approach. The study covers the conventional technologies used in UWSNs and highlights significant research approaches towards UWSNs’ applications, deployment techniques, and routing processes. The insights provided in this study will assist researchers in understanding the present state of UWSNs and identifying future research directions in this exciting field.

HDRA: A Haybird Data Reduction and Routing Algorithm

Presently, wireless sensor networks (WSNs) are emerging as a vibrant field of research due to various challenging aspects such as energy consumption, routing strategies, effectiveness, among others. Despite unresolved issues within WSNs, a substantial array of applications has already been developed. For any application design, a primary objective is to optimize the WSN in terms of its lifecycle and functionality. Recent studies on data reduction methods have shown that sensor nodes often transmit data directly (single hop) to the base station (BS). However, a significant concern is that most existing multi-hop routing protocols do not address data reduction before forwarding data to the BS. Consequently, this study introduces a Hybrid Data Reduction and Routing Algorithm (HDRA). The principal aim of HDRA is to prolong the lifespan of cluster-based WSNs. It strives to decrease the packet transmission by sensor nodes, especially when there's minimal change in sensor readings. The findings indicate that HDRA outperforms the LEACH protocol in terms of energy efficiency in sensor networks, irrespective of network type (T, H, or TH) or deployment scenarios (200x200m or 400x400m). Overall, the proposed algorithm enhances network performance by conserving energy and extending network lifespan.

Evaluation of mixtures of flavor chemicals in a 90-day nose-only exposures in sprague-dawley rats.

One of the challenges to using some flavor chemicals in aerosol products is the lack of route of administration specific toxicology data. Flavor chemicals (88) were divided into four different flavor mixtures based upon chemical compatibility and evaluated in 2-week dose-range-finding and subsequent 90-day nose-only rodent inhalation studies (OECD 413 and GLP compliant). Sprague-Dawley rats were exposed to vehicle control or one of three increasing concentrations of each flavor mixture. In the dose-range-range-finding studies, exposure to flavor mixture four resulted in adverse nasal histopathology in female rats at the high dose, resulting in this flavor mixture not being evaluated in a 90-day study. In the 90-day studies daily exposures to the three flavor mixtures did not induce biologically meaningful adverse effects (food consumption, body weights, respiratory physiology, serum chemistry, hematology, coagulation, urinalysis, bronchoalveolar lavage fluid analysis and terminal organ weights). All histopathology findings were observed in both vehicle control and flavor mixture exposed animals, with similar incidences and/or severities, and therefore were not considered flavor mixture related. Based on the absence of adverse effects, the no-observed-adverse-effect concentration for each 90-day inhalation study was the highest dose tested, 2.5mg/L of the aerosolized high dose of the three flavor mixtures.

Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks

Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival, or angle of arrival from adjacent SNs. The accuracy of sink node localization directly influences the efficiency of data aggregation, routing procedures, and complete performance of the network in tasks like environmental monitoring, target tracking, and event recognition. As WSNs are frequently used in remote environments where physical involvement is unusable, an effective and accurate sink node localization model plays a vital part in certifying the network's longevity and reliability. This study develops an Efficient Sink Node Position Estimation using the Harris Hawks Optimization (SNPE-HHO) Algorithm in WSN. The main intention of the SNPE-HHO technique is to recognize the optimal position of the sink node in the network. To achieve this, the SNPE-HHO technique employs the HHO system which gets inspiration from the hunting tactics of Harris Hawk. Moreover, the SNPE-HHO technique computes a fitness function that can drive the searching direction of the HHO algorithm and enhance the node estimation performance. The performance analysis of the SNPE-HHO method is verified by utilizing distinct metrics. The experimentation values confirmed the improved estimation performance of the SNPE-HHO technique over other existing methods

Exact and heuristic approaches for maximizing flows in UAV-enabled wireless cellular networks with multi-hop backhauls

This paper investigates the problem of data routing in backhaul networks using Unmanned Aerial Vehicles (UAVs) to relay data from Small Cells (SCs) to the core network. The objective is to maximize the total fulfilled demand of data to be routed, while ensuring technical requirements such as hop constraints and edge capacity. The problem is formulated using a compact mixed-integer programming model, which can solve small- and medium-sized topologies. In addition, a fast constructive heuristic based on a maximal tree is developed to solve large-scale topologies, resulting in a significant reduction in CPU time. The quality of the heuristic is evaluated by using column generation for solving the linear programming relaxation of an exponential formulation. The computational study shows the effectiveness and value of the proposed compact model and constructive heuristic for various topology sizes. Furthermore, experiments demonstrate that by keeping the network setup constant and updating the demand vector only, the computational time of the compact model can be drastically reduced for all topology sizes.

Secured Routing Protocol for Improving the Energy Efficiency in WSN Applications

A staggering number of applications rely on the network architecture to carry out their tasks, which has led to a fast growth in wireless sensor networks (WSN). The possibility of harmful activity and data theft is growing as a result of the growth in devices and data. Thus, the network’s regular users have an impact on legitimate data delivery, which lowers customer happiness and worsens network standards. The data have been saved using a variety of security procedures that have been developed in past research studies. However, harmful activity continues to engage in its illegal operations despite their efforts to safeguard data transmission in the network. As a result, a number of recent research projects have concentrated on predicting innovative techniques and processes to offer security in WSN. In comparison to existing methods, this work attempted to offer an effective tighter security for WSN and suggested an ML‐Based Secured Routing Protocol (MLSRP) for WSN with improved energy efficiency and overall performance. Energy efficiency is the main requirement of WSNs, hence a clustered network is proposed where the data are routed through the cluster head nodes. In this paper, a multicriteria based decision‐making (MCDM) model is used by the MLSRP to perform data routing, clustering, and cluster head election while also analyzing a number of network characteristics that might affect the quality of a node, route, and data. In NS2 software, the suggested framework is put into practice and simulated. The results are then validated to gauge performance. The observed quantitative results reveal that the proposed MLSRP method attains an improved network lifetime by 5% and network throughput of 6%. It reduces energy consumption by 40%, curtails overhead to 37%, and minimizes end‐to‐end delay by 30% than the other conventional methods. The suggested framework performs better than others when its total performance is compared to that of older methods.

Novel methods in utilizing wireless sensor networks with blockchain technology: A review

Wireless Sensor Networks (WSNs) play a vital role in achieving quick and efficient results. These networks comprise small, affordable sensors that collect valuable data from their surroundings. The core objective of this project is to optimize the pathways for data transmission, improve communication among sensor nodes, and filter out irrelevant or erroneous data prior to sending it to the central station. This project introduces a new method by implementing blockchain intelligence, which stores and provides optimal data transmission routes. This empowers sensor nodes to identify the most efficient paths for data sharing, eliminating unnecessary routes. Additionally, the blockchain acts as a filter, ensuring that only relevant data is transmitted to the central station. This approach not only saves time by streamlining data routes but also secures data security through the validity of blockchain technology. Furthermore, increases data quality by discarding unnecessary information. This project showcases a smart solution that tackles common WSN challenges, promising to elevate technological capabilities for a variety of applications.

Information-Centric Robotic Ad Hoc Networking Based Continuous Data Routing and Delivery for Disaster Scenes

Information-Centric Robotic Ad Hoc Networking Based Continuous Data Routing and Delivery for Disaster Scenes

Microwave frequency switching delays in phase-locked period-one dynamics of semiconductor lasers.

Modern microwave switches require high switching speeds to rapidly route data over multiple radio channels while minimizing the routing delay. This Letter proposes a novel, to the best of our knowledge, microwave frequency switching system using phase-locked Period-one (P1) dynamics of semiconductor lasers. When a semiconductor laser is optically injected by microwave-modulated optical signals, which carry two-tone input microwaves at 29 and 37 GHz, with proper injection power controlled by dual-voltage control signals, P1 dynamics are excited in the semiconductor laser and subsequently phase-locked by one of the input microwave tones. We have observed positive and negative switching delays in the switching process. For instance, a positive delay is observed when the system requires additional optical power to transition from a phase-locked state at 29 GHz to an unlocked state. Conversely, a negative delay occurs when the unlocked P1 dynamics approach but do not reach a 37-GHz frequency and then rapidly lock to the tone, thereby surpassing the speed of the control signals. These dual delays are instrumental in enhancing the switching speed of our system, enabling it to surpass the voltage switching time of the control signals by a factor of 3.6. In addition, by leveraging these dual delays, the duration of the microwave tones can be further extended in the switching process.

Road network free flow speed estimation using microscopic simulation and point-to-point travel times

Roads in mesoscopic traffic simulation models are often parametrized by a free speed attribute, which describes the speed at which a vehicle can traverse a road in the absence of congestion. Unlike microscopic models, mesoscopic models do not typically simulate traffic lights, individual driver behavior or acceleration and deceleration of vehicles. In consequence, the free speed parameter of each road must account for all the aforementioned effects. Modeling free speed accurately is important, as it determines the travel time, which in turn affects the decision-making of agents in the simulation.This paper introduces an approach combining microscopic simulation with machine learning models to estimate road segment free speeds. Using the microscopic simulator SUMO, we generate training data for a model search employing Bayesian optimization. Subsequently, these models undergo fine-tuning via gradient-based optimization using real-world point-to-point travel times. A significant advantage is the adaptability of this approach to diverse road networks, including those from OpenStreetMap, and the ability to incorporate routing data from various online providers independently.Our evaluation illustrates a notable decrease in prediction error between 30% and 60% compared to baseline models that assume a uniform free speed reduction for all urban roads. The fine-tuned models are able to generalize well to unseen regions and are therefore applicable to case studies where data for new or altered roads is unavailable.

Overview of Apache NiFi

Apache NiFi is an open-source data integration tool designed to automate the flow of data between systems. It provides a user-friendly interface to design data flows and move data between different systems, making it easier to collect, distribute, and manage data in real-time. NiFi supports the concept of data flow, allowing users to define and visualize the movement of data from various sources to various destinations.At its core, Apache NiFi is a data integration tool that facilitates the automated flow of data between systems. It was developed by the National Security Agency (NSA) and later open-sourced as part of the Apache Software Foundation. NiFi is designed to be scalable, flexible, and easy to use, making it suitable for a variety of data integration scenarios.Providing a user-friendly interface for designing, managing, and monitoring data flows, NiFi facilitates the seamless movement of data across different environments. Utilizing a visual design paradigm, users can construct data flows by connecting a variety of processors that perform tasks such as data ingestion, transformation, and routing. NiFi's scalability, provenance tracking, and security features make it well-suited for handling complex data integration scenarios. With a robust community and ongoing development, Apache NiFi continues to play a crucial role in simplifying and optimizing data movement and processing workflows

Optimization-enabled deep learning model for disease detection in IoT platform

ABSTRACT Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspected on an IoT platform. In this paper, a novel algorithm, named artificial flora optimization-based chameleon swarm algorithm (AFO-based CSA), is developed for optimal path finding. Here, data are collected by the sensors and transmitted to the base station (BS) using the proposed AFO-based CSA, which is derived by integrating artificial flora optimization (AFO) in chameleon swarm algorithm (CSA). This integration refers to the AFO-based CSA model enhancing the strengths and features of both AFO and CSA for optimal routing of medical data in IoT. Moreover, the proposed AFO-based CSA algorithm considers factors such as energy, delay, and distance for the effectual routing of data. At BS, prediction is conducted, followed by stages, like pre-processing, feature dimension reduction, adopting Pearson’s correlation, and disease detection, done by recurrent neural network, which is trained by the proposed AFO-based CSA. Experimental result exhibited that the performance of the proposed AFO-based CSA is superior to competitive approaches based on the energy consumption (0.538 J), accuracy (0.950), sensitivity (0.965), and specificity (0.937).

WAN optimization techniques: Exploring BGP attributes across industries

Wide Area Network (WAN) optimization has become a critical focus for industries seeking to enhance network performance, particularly in the era of digital transformation and cloud-based services. This paper explores the role of Border Gateway Protocol (BGP) attributes as a key element in WAN optimization across various industries. BGP, as the protocol responsible for routing data between autonomous systems on the internet, plays a significant role in determining the efficiency and reliability of data transmission across WANs. By leveraging specific BGP attributes, organizations can optimize network paths, reduce latency, and improve overall network performance. The paper begins by providing an overview of WAN optimization techniques and the importance of BGP in modern network architecture. It then delves into the specific BGP attributes that are instrumental in optimizing WAN performance, including Local Preference, AS Path, MED (Multi-Exit Discriminator), and BGP Communities. The discussion includes how these attributes can be manipulated to achieve optimal routing decisions, prioritize traffic, and manage bandwidth more effectively. Case studies from various industries, including financial services, healthcare, manufacturing, and telecommunications, are presented to illustrate the practical applications and benefits of BGP-based WAN optimization. These examples highlight how organizations have successfully implemented BGP attribute manipulation to address industry-specific challenges such as ensuring low-latency communication, maintaining high availability, and supporting large-scale data transfers. The paper also addresses the challenges associated with BGP attribute manipulation, including the complexities of configuration and the potential risks of misconfigurations leading to network instability. It concludes with a discussion on emerging trends in WAN optimization, such as the integration of software-defined WAN (SD-WAN) technologies and the role of machine learning in automating and enhancing BGP-based optimizations. In summary, this paper provides a comprehensive exploration of how BGP attributes can be leveraged for WAN optimization across different industries, offering insights into best practices and future directions in network performance enhancement. Keywords: WAN, Optimization, Techniques, BGP, Attributes.

Efficient Geographic Routing for High-Speed Data in Wireless Multimedia Sensor Networks

Efficient Geographic Routing for High-Speed Data in Wireless Multimedia Sensor Networks

Feasibility and effects of GIS-based municipal solid waste vehicle routing for Bahrain

<p>In the realm of waste management, efficient route optimisation for municipal solid waste (MSW) collection is becoming increasingly crucial, particularly for developing nations with budgetary considerations. This study leverages the capabilities of the geographic information system (GIS) and integrates the Dijkstra algorithm to enhance route optimisation for MSW vehicles in Bahrain. Utilising comprehensive local vehicle routing data from Urbaser and applying GIS methodologies, three distinct areas in Bahrain were methodically analysed. The results revealed a notable 55% reduction in travel distance, a 17% decrease in time, and a yearly fuel cost saving of 6405 BHD (16,974 USD) in the optimal scenario. Given these findings, the potential applicability of this optimisation algorithm extends beyond Bahrain, suggesting significant benefits for regions with similar challenges. To further refine this approach, the integration of real-time traffic data into the routing algorithm is recommended. Other additions to the optimization process could include additional parameters such as safety.</p>

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. These challenges impact the outcome for extending the lifetime of wireless sensor networks. This study presents a uav assisted reactive zone based EHGR (energy efficient hierarchical gateway routing protocol) that is deployed in a situation where the natural calamity has caused communication and infrastructure damage to a major portion of the sensor network. EHGR is a hybrid multi layer routing protocol for large heterogeneous sensor nodes (smart nodes, basic nodes, user handheld devices etc.) EHGR is tailored to meet two important concerns for a disaster hit wsn ie. optimized deployment and energy efficient routing. The first part of EGHR focuses on maximized coverage during node deployments. Maximized coverage is an important aspect to be considered during the event of disaster since most of the nodes loose coverage and are detached from the wireless sensor network. The first part of EHGR uses state of the art game theory approach to build a model that maximizes the coverage of nodes during the deployment phase from all participating entities i.e. nodes and uavs. Rather than fixing the cluster head as is the case in traditional cluster-based approaches EHGR uses the energy centroid nodes. Energy centroid nodes evolve on the basis of aggregated energy of the zone. This approach is superior to simply electing cluster head nodes on the basis of some probability function. The nodes that fail to achieve any successful outcome from the game theory matching model fail to get any association. These nodes will use multi hop d2d relay approach to reach the energy centroid nodes. Gateway relay nodes used with the game theory approach during the deployment of the uav assisted wsn improves the overall coverage by 25% against traditional leach based hierarchical approaches. Once the optimum deployment phase is completed the routing phase is initiated. Aggregated data is sent by the energy centroid nodes from the ECN nodes to the servicing micro controller enabled un manned aerial vehicles. The routing process places partial burden of zone formation and data transmission to the control center for each phase on the servicing uavs. Energy centroid nodes engage only in the data aggregation process and transmission of data to servicing uav. Servicing-uavs reduce energy dissipated of the entire zone which result in gradual decrease of energy for the zone thus increasing the network lifetime. Node deployment phase and the routing phase of EHGR utilize the computations provide by the mirco controller enabled unmanned aerial vehicles such that the computationally intensive calculations are offloaded to the servicing uav. Experiment results indicate an increase in the first dead node report, half dead node report, and last dead node report. Network lifetime is extended to approximately 1800 rounds which is an increase by ratio of 100% against the traditional leach approach and increase by 50% percent against the latest approaches as highlighted in the literature.

Scalable Data Concentrator with Baseline Interconnection Network for Triggerless Data Acquisition Systems

Triggerless data acquisition systems (DAQs) require that the data stream is transmitted from multiple links and into the processing node. The short input data words must be concentrated and packed into the longer bit vectors the output interface (e.g., PCI Express) uses. In that process, the unneeded data must be eliminated, and a dense stream of useful DAQ data must be created. Additionally, the time order of the data should be preserved. This paper presents a new solution using the Baseline Network with Reversed Outputs (BNRO) for high-speed data routing. A thorough analysis of the network’s operation enabled increased scalability compared to the previously published concentrator based on an 8 × 8 network. The solution may be scaled by adding additional layers to the BNRO network while minimizing resource consumption. Simulations were performed for four and five layers (16 and 32 inputs). The FPGA implementation and tests in the actual hardware were successfully performed for 16 inputs. The pipeline registers may be added in each layer independently, shortening the critical path and increasing the maximum acceptable clock frequency.

Fuzzified swarm intelligence framework using FPSOR algorithm for high-speed MANET- Internet of Things (IoT)

Internet of things (IoT) is an inventive technology which permit the association of physical things by means of the digital world throughout the use of heterogeneous networks and communication technologies. Ad-hoc routing protocols may be dispersed and involve every node in route discovery process by making routing data more reliable. Mobile-Adhoc-Networks (MANETs) consist of many portable nodes that can commune directly with each other or through intermediate nodes. Repeatedly, nodes in a MANETs operate with batteries and can roam freely, and thus, a node may exhaust its energy or move away without providing any notice to its cooperative nodes. IoT system using a combination of MANET, a route consists of numerous links in sequence, and so, its lifetime is based on the lifetime of every node and also the wireless links among adjacent nodes. In this research work Fuzzified Particle Swarm Optimization oriented Routing (FPSOR) algorithm is planned to minimize data loss and computational overhead, which in turn maximizes the lifetime of MANETs. Particle Swarm Optimization oriented Routing protocol (PSOR) have taken energy competence as significant criterion for processing routing and driving optimized path for the data accelerating and processing to the source node. The PSOR produces a fresh route for routing by considering fitness value of energy to evaluate diverse path and to choose best-optimized path whose energy consumption is less as compared to ant colony optimization routing paths. This algorithm utilizes a good approach focusing energy levels/status of the nodes through fuzzification and the lengths of the routed ways. NS2 simulator is casted for performance evaluation.

Flip : Data-centric Edge CGRA Accelerator

Coarse-Grained Reconfigurable Arrays (CGRA) are promising edge accelerators due to the outstanding balance in flexibility, performance, and energy efficiency. Classic CGRAs statically map compute operations onto the processing elements (PE) and route the data dependencies among the operations through the Network-on-Chip. However, CGRAs are designed for fine-grained static instruction-level parallelism and struggle to accelerate applications with dynamic and irregular data-level parallelism, such as graph processing. To address this limitation, we present Flip , a novel accelerator that enhances traditional CGRA architectures to boost the performance of graph applications. Flip retains the classic CGRA execution model while introducing a special data-centric mode for efficient graph processing. Specifically, it leverages the inherent data parallelism of graph algorithms by mapping graph vertices onto PEs rather than the operations and supporting dynamic routing of temporary data according to the runtime evolution of the graph frontier. Experimental results demonstrate that Flip achieves up to 36× speedup with merely 19% more area compared to classic CGRAs. Compared to state-of-the-art large-scale graph processors, Flip has similar energy efficiency and 2.2× better area efficiency at a much-reduced power/area budget.