Internet Routing Research Articles

Efficient load balancer for local internet service providers based on operation research

Load balancing in internet services acts as a reverse proxy to distribute network bandwidth or application traffic across multiple servers. To decrease the internet route cost and to make the network intermediate devices more intelligent distance, number of hops, bandwidth capacity, equipment maintenance, power consumption ...etc must be considered. .. The aim is for the devices to have self-decision: acting upon data found in the network and transport layer protocols (Internet Protocol IP, Transmission Control Protocol TCP, File Transfer Protocol FTP, User Datagram Protocol UDP), and delivering the services to the secondary internet (wireless or optical fibers) ISPs. To achieve this target, the use of an operation research algorithm, such as linear programming, has been proposed to solve the problem of minimizing transport and distribution costs by developing and overcoming the transmission load cost of the path selection. The proposed EWRRLB (Efficient Weighted Round Robin Load Balancer) will assign different costs to each internet connection based not only on its capacity or priority but also on the cost of transmission paths. This allows load balancers to allocate the best economic path and the share of the bandwidth to certain connections.

Chaos Reptile Search Algorithm Based Clustering and Routing for Internet of Things assisted Wireless Sensor Network

Chaos Reptile Search Algorithm Based Clustering and Routing for Internet of Things assisted Wireless Sensor Network

The application and challenge of energy router in energy internet

AbstractThe energy internet has emerged as a promising area of research in power systems with distributed generation. Similar to an internet router to connect and switch networks, the energy router within the energy internet plays a crucial role to integrate and distribute the energy flow. This paper provides an overview of the application and challenges associated with energy routers in the energy internet. Firstly, the ability of energy routers to bring together multiple energy sources and information is highlighted. Secondly, the discrepancies in power structure when energy routers are utilised in different voltage‐level power grids are discussed. Besides, the roles, structures, and design requirements of energy routers and how they interconnect with the power system, as well as some practical examples of energy router are delved. Finally, the main challenges faced by energy routers are summarised.

Analyzing Remote Peering Deployment and Its Implications for Internet Routing

Analyzing Remote Peering Deployment and Its Implications for Internet Routing

Secure location-aware geocast routing in Internet of Vehicles

Secure location-aware geocast routing in Internet of Vehicles

Algorithmic Content Recommendations on a Video-Sharing Platform Used by Children

Free video-sharing platforms (VSPs) make up a high proportion of children's daily screen use. Many VSPs make algorithmic recommendations, appearing as thumbnail images from the video, which content creators use to advertise their video content. To explore how VSP thumbnails use attention-capture designs to encourage engagement with content and to test whether VSP algorithmic recommendations offer more problematic thumbnail features over time. In this cross-sectional study conducted in January 2022, researchers mimicked the search behavior of children on a popular VSP by randomly clicking on recommended videos in order to test whether thumbnail designs changed over 20 sequential video engagements. A digital, footprint-free data collection setting was created by using a new computer and wireless internet router. Data were collected from YouTube via an internet browser not logged into a user account. Data analysis occurred from April to December 2022. Manual searches using 12 top-searched terms popular with school-aged children were conducted. Researchers captured the video thumbnails recommended at the end of each video and randomly clicked subsequent videos for 20 sequential engagements. Thumbnail content codes were developed through iterative review of screenshots by a multidisciplinary research team and applied by trained coders (reliability, κ >.70). The prevalence of problematic thumbnail content and change in prevalence over 20 engagements was calculated using the Cochran-Armitage trend test. A total of 2880 video thumbnails were analyzed and 6 features were coded, including visual loudness; drama and intrigue; lavish excess and wish fulfillment; creepy, bizarre, and disturbing; violence, peril, and pranks; and gender stereotypes. A high proportion contained problematic features including the creepy, bizarre, and disturbing feature (1283 thumbnails [44.6%]), violence, peril, and pranks feature (1170 thumbnails [40.6%]), and gender stereotypes feature (525 thumbnails [18.2%]). Other features included attention-capture designs such as the visual loudness feature (2278 thumbnails [79.1%]), drama and intrigue feature (2636 thumbnails [91.5%]) and lavish excess and wish fulfillment feature (1286 thumbnails [44.7%]). Contrary to the hypotheses, problematic feature prevalence did not increase over time, but the gender stereotypes feature increased with more engagement in the recommendations feed (P for trend < .001). In this study of video recommendations for search terms popular with children, thumbnails contained problematic and attention-capturing designs including violent, stereotyped, and frightening themes. Research is needed to understand how children respond to thumbnail designs and whether such designs influence the quality of content children consume.

Secure data dissemination and routing in Internet of Things

Secure data dissemination and routing in Internet of Things

Some operations on intuitionistic fuzzy graphs via novel versions of the Sombor index for internet routing

Topological indices (TIs) are numerical structures that are associated with a graph to identify its topology. TIs are highly popular in the literature with a wide range of applications from chemistry to economics. However, TIs have limitations in representating complex relations within the graphs creating some uncertainities. Fuzzy graph (FG) and intuitionistic fuzzy graph (IFG) are introduced to overcome these uncertainities. While a FG a describes degree of membership of an object in a graph, IFG delineate information on membership or nonmembership under uncertainity. This study aims to introduce novel TIs such as the general second Zagreb index, the Sombor index of the third version, and the Sombor index of the fourth version in the IFG framework in order to improve practicality of FG and IFG applications. Some properties of the proposed indices and their upper bounds are provided as well. Proposed TIs are applied to an internet routing network as a case study. Results of the study show that adding more internet routers in the network can increase internet speed and the strength of the entire system. Finally, comparative studies for the Sombor index of the third version and the Sombor index of the fourth version are also revealed.

Experiences, Lessons, and Challenges With Adapting REDCap for COVID-19 Laboratory Data Management in a Resource-Limited Country: Descriptive Study.

The COVID-19 pandemic brought challenges requiring timely health data sharing to inform accurate decision-making at national levels. In Botswana, we adapted and integrated the Research Electronic Data Capture (REDCap) and the District Health Information System version 2 (DHIS2) platforms to support timely collection and reporting of COVID-19 cases. We focused on establishing an effective COVID-19 data flow at the national public health laboratory, being guided by the needs of health care professionals at the National Health Laboratory (NHL). This integration contributed to automated centralized reporting of COVID-19 results at the Ministry of Health (MOH). This paper reports the experiences, challenges, and lessons learned while designing, adapting, and implementing the REDCap and DHIS2 platforms to support COVID-19 data management at the NHL in Botswana. A participatory design approach was adopted to guide the design, customization, and implementation of the REDCap platform in support of COVID-19 data management at the NHL. Study participants included 29 NHL and 4 MOH personnel, and the study was conducted from March 2, 2020, to June 30, 2020. Participants' requirements for an ideal COVID-19 data management system were established. NVivo 11 software supported thematic analysis of the challenges and resolutions identified during this study. These were categorized according to the 4 themes of infrastructure, capacity development, platform constraints, and interoperability. Overall, REDCap supported the majority of perceived technical and nontechnical requirements for an ideal COVID-19 data management system at the NHL. Although some implementation challenges were identified, each had mitigation strategies such as procurement of mobile Internet routers, engagement of senior management to resolve conflicting policies, continuous REDCap training, and the development of a third-party web application to enhance REDCap's capabilities. Lessons learned informed next steps and further refinement of the REDCap platform. Implementation of REDCap at the NHL to streamline COVID-19 data collection and integration with the DHIS2 platform was feasible despite the urgency of implementation during the pandemic. By implementing the REDCap platform at the NHL, we demonstrated the possibility of achieving a centralized reporting system of COVID-19 cases, hence enabling timely and informed decision-making at a national level. Challenges faced presented lessons learned to inform sustainable implementation of digital health innovations in Botswana and similar resource-limited countries.

Identification of hazardous organic compounds in e-waste plastic using non-target and suspect screening approaches

End-of-life electric and electronic devices stand as one of the fastest growing wastes in the world and, therefore, a rapidly escalating global concern. A relevant fraction of these wastes corresponds to polymeric materials containing a plethora of chemical additives. Some of those additives fall within the category of hazardous organic compounds (HOCs). Despite the significant advances in the capabilities of analytical methods, the comprehensive characterization of WEEE plastic remains as a challenge. This research strives to identify the primary additives within WEEE polymers by implementing a non-target and suspect screening approach. Gas chromatography coupled to time-of-flight mass spectrometry (GC-QTOF-MS), using electron ionization (EI), was applied for the detection and identification of more than 300 substances in this matrix. A preliminary comparison was carried out with nominal resolution EI-MS spectra contained in the NIST17 library. BPA, flame retardants, UV-filters, PAHs, and preservatives were among the compounds detected. Fifty-one out of 300 compounds were confirmed by comparison with authentic standards. The study establishes a comprehensive database containing m/z ratios and accurate mass spectra of characteristic compounds, encompassing HOCs. Semi-quantification of the predominant additives was conducted across 48 WEEE samples collected from handling and dismantling facilities in Galicia. ABS plastic demonstrated the highest median concentrations, ranging from 0.154 to 4456 μg g−1, being brominated flame retardants and UV filters, the families presenting the highest concentrations. Internet router devices revealed the highest concentrations, containing a myriad of HOCs, such as tetrabromobisphenol A (TBBPA), tribromophenol (TBrP), triphenylphosphate (TPhP), tinuvin P and bisphenol A (BPA), most of which are restricted in Europe.

A source location privacy protocol‐based energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in social Internet of Things

SummarySource location privacy is a developing research topic in the social Internet of Things. Source location privacy holds paramount importance in security critical wireless sensor network applications like tracking and monitoring. Several methods have been proposed for source location privacy in the social Internet of Things, but the existing methods have some issues such as improper path selection, the transmission of duplicate messages, and low network lifetime. To overcome these issues, a source location privacy protocol based on energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in the social Internet of Things is proposed in this manuscript. At first, the optimal route for the source is selected with the help of energy‐efficient and link‐reliable routing, this method helps to avoid improper path selection. To estimate the quality of the selected optimal path, the multi‐scale bifurcated deep Capsnet is applied. The introduced method is executed in MATLAB. The introduced method's performance is estimated with the aid of several performances evaluating metrics like sensitivity, energy consumption, network lifetime, safety period, and delay.

F-EVM: improving routing in Internet of Things using fuzzy MAIRCA approach and fuzzy eigenvector method

F-EVM: improving routing in Internet of Things using fuzzy MAIRCA approach and fuzzy eigenvector method

Secure and Efficient AI-SDN-based Routing for Healthcare-Consumer Internet of Things

Secure and Efficient AI-SDN-based Routing for Healthcare-Consumer Internet of Things

Energy and trust-aware hybrid optimisation for RPL routing in mobile internet of things

Energy and trust-aware hybrid optimisation for RPL routing in mobile internet of things

Energy efficient random forest classifier-based secure routing for opportunistic internet of things

Energy efficient random forest classifier-based secure routing for opportunistic internet of things

Secure multi-path routing for Internet of Things based on trust evaluation.

In the realm of the Internet of Things (IoT), ensuring the security of communication links and evaluating the safety of nodes within these links remains a significant challenge. The continuous threat of anomalous links, harboring malicious switch nodes, poses risks to data transmission between edge nodes and between edge nodes and cloud data centers. To address this critical issue, we propose a novel trust evaluation based secure multi-path routing (TESM) approach for IoT. Leveraging the software-defined networking (SDN) architecture in the data transmission process between edge nodes, TESM incorporates a controller comprising a security verification module, a multi-path routing module, and an anomaly handling module. The security verification module ensures the ongoing security validation of data packets, deriving trust scores for nodes. Subsequently, the multi-path routing module employs multi-objective reinforcement learning to dynamically generate secure multiple paths based on node trust scores. The anomaly handling module is tasked with handling malicious switch nodes and anomalous paths. Our proposed solution is validated through simulation using the Ryu controller and P4 switches in an SDN environment constructed with Mininet. The results affirm that TESM excels in achieving secure data forwarding, malicious node localization, and the secure selection and updating of transmission paths. Notably, TESM introduces a minimal 12.4% additional forwarding delay and a 5.46% throughput loss compared to traditional networks, establishing itself as a lightweight yet robust IoT security defense solution.

A path forward: Improving Internet routing security by enabling zones of trust

A path forward: Improving Internet routing security by enabling zones of trust

Optimal congestion management in network routers subject to constraints, disturbances, and noise using the model predictive control approach

AbstractA predictive queue management method is proposed for constrained congestion control in internet routers in the face of communication delays. The proposed method uses the queue and router models, input traffic rate, and queue length to precisely characterise the entire process. The model that has been built is then used to construct an optimal constrained active queue management (CAQM) strategy using the model predictive control method. Important factors, such as link capacity, Transmission Control Protocol (TCP) sessions, round‐trip time, and a few others, have been selected and used to linearise the interconnection of TCP. Then, an efficient MPC‐based structure to manage the CAQM in the face of unknown disturbances is designed. Simulations are used to validate the proposed method's effectiveness and robustness.

Secure Message Dissemination with QoS Guaranteed Routing in Internet of Vehicles

Internet of Vehicles (IoV) is a variant of vehicular adhoc network (VANET) where vehicles can communicate with other vehicles, infrastructure devices, parking lots and even pedestrians. Communication to other entities is facilitates through various services like DSRC, C2C-CC. Fake messages can be propagated by attackers for various selfish needs. Complex authentication procedures can affect the propagation of emergency messages. Thus a light weight mechanism to ensure the trust of messages without affecting the delivery deadlines for emergency messages. Addressing this problem, this work proposes a clustering based network topology for IoV where routing is optimized for message dissemination of various classes using hybrid meta-heuristics. In addition, two stage message authentication technique combining collaborative authentication with Bayesian filtering is proposed to verify the authenticity of message. Through simulation analysis, the proposed solution is found to detect fake messages with an accuracy of 96% with 10% lower processing delay compared to existing works.

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges.

Internet of Things (IoT) devices are increasingly popular due to their wide array of application domains. In IoT networks, sensor nodes are often connected in the form of a mesh topology and deployed in large numbers. Managing these resource-constrained small devices is complex and can lead to high system costs. A number of standardized protocols have been developed to handle the operation of these devices. For example, in the network layer, these small devices cannot run traditional routing mechanisms that require large computing powers and overheads. Instead, routing protocols specifically designed for IoT devices, such as the routing protocol for low-power and lossy networks, provide a more suitable and simple routing mechanism. However, they incur high overheads as the network expands. Meanwhile, reinforcement learning (RL) has proven to be one of the most effective solutions for decision making. RL holds significant potential for its application in IoT device's communication-related decision making, with the goal of improving performance. In this paper, we explore RL's potential in IoT devices and discuss a theoretical framework in the context of network layers to stimulate further research. The open issues and challenges are analyzed and discussed in the context of RL and IoT networks for further study.