Data Routing Research Articles

Considerations for Applying Route-to-Route Extrapolation to Assess the Safety of Oral Exposure to Substances

The safety evaluation of oral exposure to substances, such as food ingredients, additives, and their constituents, relies primarily on a careful evaluation and analysis of data from oral toxicity studies. When relevant oral toxicity studies are unavailable or may have significant data gaps that make them inadequate for use in safety evaluations, data from non-oral toxicity studies in animals, such as studies on inhalation, dermal exposure, etc., might be used in support of or in place of oral toxicity studies through route-to-route (R-t-R) extrapolation. R-t-R extrapolation is applied on a case-by-case basis as it requires attention to and comparison of substance-specific toxicokinetic (TK) and toxicodynamic (TD) data for oral and non-oral exposure routes. This article provides a commentary on the utility of R-t-R extrapolation to assess the safety of oral exposure to substances, with an emphasis on the relevance of TK and systemic toxicity data.

Digital routes and borders in the Middle East: the geopolitical underpinnings of Internet connectivity

ABSTRACT In the second half of the 2010s, the Arabian Peninsula experienced major foreign policy changes that suggested a reshuffling of the region as a security complex. This paper offers an analysis of the geography of Internet data routing in the Middle East in order to assess whether foreign policy shifts apply to cyberspace and the potential for meaningful cybersecurity cooperation. We specifically focus on the Qatar blockade and the Abraham agreements. Our main hypothesis is that the architecture of connectivity can reveal the level of existing cooperation between data-routing operators of different countries and can therefore serve as one among many indicators for the level of trust (or lack thereof) between these countries and, as a result, for the potential success of cyber cooperation. We argue that the architecture of connectivity only partially reflects foreign policy shifts in the Gulf Region and despite the political announcements, digital borders and rivalries in the region remain deep and could potentially create obstacles for meaningful cybersecurity cooperation.

A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions

Long-haul heavy-duty vehicles, including trucks and coaches, contribute to a substantial portion of the modern-day European carbon footprint and pose a major challenge in emissions reduction due to their energy-intensive usage. Depending on the hydrogen fuel source, the use of fuel cell electric vehicles (FCEV) for long-haul applications has shown significant potential in reducing road freight CO2 emissions until the possible maturity of future long-distance battery-electric mobility. Fuel cell heavy-duty (HD) propulsion presents some specific characteristics, advantages and operating constraints, along with the notable possibility of gains in powertrain efficiency and usability through improved system design and intelligent onboard energy and thermal management. This paper provides an overview of the FCEV powertrain topology suited for long-haul HD applications, their operating limitations, cooling requirements, waste heat recovery techniques, state-of-the-art in powertrain control, energy and thermal management strategies and over-the-air route data based predictive powertrain management including V2X connectivity. A case study simulation analysis of an HD 40-tonne FCEV truck is also presented, focusing on the comparison of powertrain losses and energy expenditures in different subsystems while running on VECTO Regional delivery and Longhaul cycles. The importance of hydrogen fuel production pathways, onboard storage approaches, refuelling and safety standards, and fleet management is also discussed. Through a comprehensive review of the H2 fuel cell powertrain technology, intelligent energy management, thermal management requirements and strategies, and challenges in hydrogen production, storage and refuelling, this article aims at helping stakeholders in the promotion and integration of H2 FCEV technology towards road freight decarbonisation.

Interest Flooding Attacks in Named Data Networking: Survey of Existing Solutions, Open Issues, Requirements, and Future Directions

Named Data Networking (NDN) is a prominent realization of the vision of Information-Centric Networking. The NDN architecture adopts name-based routing and location-independent data retrieval. Among other important features, NDN integrates security mechanisms and focuses on protecting the content rather than the communications channels. Along with a new architecture come new threats, and NDN is no exception. NDN is a potential target for new network attacks such as Interest Flooding Attacks (IFAs). Attackers take advantage of IFA to launch (D)DoS attacks in NDN. Many IFA detection and mitigation solutions have been proposed in the literature. However, there is no comprehensive review study of these solutions that has been proposed so far. Therefore, in this article, we propose a survey of the various IFAs with a detailed comparative study of all the relevant proposed solutions as counter-measures against IFAs. We also review the requirements for a complete and efficient IFA solution and pinpoint the various issues encountered by IFA detection and mitigation mechanisms through a series of attack scenarios. Finally, in this survey, we offer an analysis of the open issues and future research directions regarding IFAs.

Bi-Level Minimal Resource Protected Key Generation Framework For Fog Computing Applications

Fog computing is a viewpoint that expands on the Cloud stage concept by placing processing assets at the organization’s edges. It might be described as a cloud-like platform with comparable data, computation, storage, and applications. They are unique in that they are decentralized in nature. Data protection and route analysis of time-sensitive data are made easier using fog computing. This minimizes the volume and distance of data sent to the cloud, lowering the risk of security and privacy breaches in IoT applications. When it comes to security and privacy, fog computing confronts several issues. The constraints of fog computing resources are the root of these difficulties. The fog system, in fact, may raise new security and privacy concerns. To address these challenges, cryptography is used in conjunction with key management techniques to provide safe data transfer. A Minimal Resource Viterbi based Bi-level Secured Key Generation (MRV-BSKG) technique for a secured fog-based system is proposed to compromise the security level and computational complexity. The BSKG technique, which combines Lagrange’s Key Generation (LKG) and the Location-Based Key (LBK) generation approaches, can safeguard secrecy and integrity. In comparison to the previous techniques, the new MRV, BSKG, delivers security with improved outcomes.

Towards developing a machine learning-metaheuristic-enhanced energy-sensitive routing framework for the internet of things

The heterogeneous nature, communication constraints, multi-hop data transmissions, and inherent challenges of wireless connections have caused routing and data transmission processes to play a vital role in the Internet of Things (IoT) infrastructures efficiency and stability. Energy efficiency and optimal load equilibrium between sensing devices are fundamental challenges due to the limitation of energy resources in IoT devices. Considering the cruciality of prolonging IoT networks and the NP-hard nature of energy optimization in heterogeneous and distributed IoT infrastructures, this research represents a hybrid energy-aware protocol for data routing in IoT. It is deployed based on Machine Learning (ML) and metaheuristic HTOA (Heat Transfer Optimizer). The proposed protocol adopts a four-phase approach for fuzzy clustering, predicting energy consumption based on Support Vector Regression (SVR) and time series techniques, selecting optimal cluster heads (CH) considering energy and centralization factors, and routing and data transmission from each sensor to and from CH to the sink using Ant Colony Optimization (ACO) algorithm. The simulation outputs revealed that our scheme performs more efficiently in energy consumption, end-to-end delay (EtED), load balancing, overhead, and network lifetime in all designed scenarios compared to other methods.

LSSDNF: A Lightweight Secure Software Defined Network Framework for Future Internet in 5G–6G

As information technology advances quickly, so does the 5G–6G network management system, which is moving toward greater integration, decentralization, diversity, and intelligence. As flexibility is a crucial criterion for 5G–6G network architecture, we use the Software Defined Network (SDN) paradigm to make the programmability more flexible. Due to their ability to replace the current TCP/IP architecture with one that separates the control plane and data plane, software-defined networks have gained much popularity. However, they are susceptible to routing attacks. Therefore, this work proposes Lightweight Security Framework that combines blockchain technology with Software-Defined Networking (LSSDNF) to address this problem. The proposed framework adds the routing data that the controller withheld to the multichain blockchain. Here, a mininet network simulator is used to model the proposed framework. The data transfer rate or network throughput, bandwidth variation, and jitter have all been used to assess the performance of single-controller-SDN networks and multi-controller-SDN networks. The results demonstrate that the proposed framework performs better than the conventional single-controller-SDN architecture in terms of throughput, bandwidth fluctuation, and jitter.

Retraction Note: Maximum data collection rate routing for data gather trees with data aggregation in rechargeable wireless sensor networks

Retraction Note: Maximum data collection rate routing for data gather trees with data aggregation in rechargeable wireless sensor networks

End-to-End Service Auction: A General Double Auction Mechanism for Edge Computing Services

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.

Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control for Intelligent Transportation Systems in Smart Cities

Abstract In the last few years, some progress had been made in smart cities, and reduction in traffic congestion was the topmost concern in the development of smart cities. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation Systems (ITS). The rapid improvement in automobiles occurs which increased the number of road accidents and traffic congestion. Machine Learning (ML) was an advanced technique to find hidden insights into ITSs without being explicitly programmed by learning from datasets. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. To accomplish this, the IAOADL-TCC model employs hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, IAOA based hyperparameter tuning strategy is derived to optimally modify the hyperparameter values of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental result study of the IAOADL-TCC method can be tested by making use of road traffic dataset from Kaggle repository. The experimental outcome stated the better performance of the IAOADL-TCC model over other DL methods.

SISTEM MONITORING TRUK KELAPA SAWIT MENGGUNAKAN GPS TRACKING BERBASIS WEBSITE

Transport vehicles in the form of trucks are one of the important components in supporting the logistics system when collecting crops in a plantation company. Fresh fruit bunches (FFB) produced by oil palm plantations must be sent to the location of the crude palm oil (CPO) processing plant at a predetermined time so that fruit quality is maintained. Therefore, scheduling and arranging the route of truck trips plays an important role. Constraints faced in plantations are the absence of a cellular internet network in plantations and limited road infrastructure so that location, time and route data cannot be accurately monitored. Scheduling can be done optimally and can reduce waiting times and queues of trucks that want to enter the shelter location. To overcome this problem, a monitoring system for oil palm trucks was created using a website-based GPS tracking. The components used are GPS Neo-6M, NodeMCU ESP8266, GSM SIM800L. It can be concluded that the tool can display data and information as expected, and the tool works well and can control the coordinates of the oil palm truck remotely.

Data Allocation in Distributed Database based on CSO

Distributed databases (DDBs) provide smart processing of large databases, the problems of fragmentation and allocation are vital design problems in addition to the centralized design. The majority of performance degradation in DDBs is due to the communication cost by query remote access and retrieval of data. This can be optimized through an efficient data allocation approach that will provide flexible retrieval of a query by low cost accessible sites. In this paper, a novel high performance data allocation approach is designed using Chicken Swarm Optimization (CSO) algorithm. Data allocation problem (DAP) is a NP-Hard problem modelled as optimization problem. The proposed data allocation approach initially characterizes the DAP into optimal problem of choosing the appropriate and minimal communication cost provoking sites for the data fragments. Then the CSO algorithm optimally chooses the sites for each of the data fragments without creating much overhead and data route diversions. This enhances the overall distributed database design and subsequently ensures quality replication. The experimental results illustrate that the proposed CSO based intelligent data fragment allocation approach has better performance than most existing approaches and thus signifies the impact of efficient data allocation in DDBs.

CVFP: Energy and trust aware data routing protocol based on Competitive Verse Flower Pollination algorithm in IoT

Internet of Things (IoT) is a network of things that are interconnected to provide a variety of services. These things can include smartphones, actuators, sensors, computers, and wearables. The IoT enables the collection and exchange of data between these objects. The expansion of smart networks is what has given the Internet of Things its global influence. Routing is an important strategy that involves the creation of routes and the transmission of data packets from the source to the intended recipient in the network. Energy and trust are considered to be the two most important parts that provide secure data transmission in a network. However, secure routing in the highly distributed and dynamic environment of IoT still results in a great challenge due to the heterogeneity of devices. Hence, an efficient secure data routing mechanism is designed with the proposed Competitive Verse Flower Pollination (CVFP) algorithm that considers trust, energy, delay, distance, and link lifetime (LLT) parameters to find an optimal routing path. Moreover, the selection of optimal path is made by involving CH nodes such that nodes have higher energy are chosen in the routing strategy. The quality of the link is measured using the LLT constraint in order to decide whether data can be routed or to perform a route maintenance mechanism to ensure reliability in data routing. The performance of the proposed algorithm is analyzed using the simulated data. The proposed algorithm attains a lower delay of 0.204 s, higher residual energy of 0.093 J, a maximum throughput of 94.59%, and a higher trust of 0.713.

Leveraging look-ahead information for optimal battery thermal management

The efficiency and range of electric vehicles (EVs) is an actual object of concern among manufacturers. The fast market share growth together with issues such as range anxiety demand evermore robust battery thermal management system (BTMS) controllers to maximize its electrical output capability. Current rule-based controllers often cannot cope with the high variability of energy demand from EVs, leading to oscillations where derating occurs and increasing the EV overall energy consumption. This study proposes a prediction horizon estimation of the future energy demand based on driven cycles. Together with a look-ahead algorithm, it is possible to keep track of an optimal battery temperature which avoids battery derating during the warm-up phase of the vehicle. A battery temperature estimation using a probability matrix based on a Markov chain is proposed in which the controller improves its estimations by repeating the same route over several trips. Results show that the method can minimize the use of the electric battery heater by predicting the necessary battery temperature over a prediction horizon. Therefore, up to 4% of overall energy consumption is saved when the EV performs a daily commute driving cycle, when compared to the original controller. Also, a learning method is implemented, improving the future estimations by storing route data as more cycles are performed.

Rl4dtn: Q-Learning for Opportunistic Networks

Opportunistic networks are highly stochastic networks supported by sporadic encounters between mobile devices. To route data efficiently, opportunistic-routing algorithms must capitalize on devices’ movement and data transmission patterns. This work proposes a routing method based on reinforcement learning, specifically Q-learning. As usual in routing algorithms, the objective is to select the best candidate devices to put forward once an encounter occurs. However, there is also the possibility of not forwarding if we know that a better candidate might be encountered in the future. This decision is not usually considered in learning schemes because there is no obvious way to represent the temporal evolution of the network. We propose a novel, distributed, and online method that allows learning both the network’s connectivity and its temporal evolution with the help of a temporal graph. This algorithm allows learning to skip forwarding opportunities to capitalize on future encounters. We show that explicitly representing the action for deferring forwarding increases the algorithm’s performance. The algorithm’s scalability is discussed and shown to perform well in a network of considerable size.

Maximum Concurrent Flow Solutions for Improved Routing in IoT Future Networks.

Due to the advent of IoT and the increasing interest of billions of Internet users towards video contents, a huge multimedia flows has been generated, and as a consequence, a massive load is applied on the underlying core network. This change can affect the network stability and lead to potential performance degradation (such as congestion and delays). This is because multimedia flows are hungry in bandwidth, and also the classical routing protocols currently used in IoT core network (like OSPF) are not adapted yet to support the heavy and the large scale of multimedia traffics with a good quality of service (QoS). In this paper, we introduce the multicommodity-based routing by proposing two contributions, Maximum Concurrent Flow Protocol (MCFPr) and Cache-based Maximum Concurrent Flow (C-MCF). They are conceived based on the Maximum Concurrent Flow approach in order to optimize the routing of multimedia data in the backbone of IoT networks. Both contributions are studied and compared with the state-of-art approaches under different scenarios showing good results, especially in the number of data packets sent (improvement by 50%), and in the transmission time (50% faster compared to the majority), which makes them promising solutions for a rapid and efficient routing in IoT core networks.

Node Centrality Comparison between Bus Line and Passenger Flow Networks in Beijing

In recent decades, complex network theory has become one of the most important approaches for exploring the structure and dynamics of traffic networks. Most studies mainly focus on the static topology features of the traffic networks, and there are also increasing literature focusing on passenger flow networks. However, not much work has been completed on comparing the static networks with dynamic flow networks from the perspective of supply and demand. Therefore, this study aimed to apply the complex network approach to explore the spatial relationship between bus line organization and bus flows in Beijing. Based on the bus route data and the passenger flow data obtained from the Beijing smart bus card, this study investigated the spatial characteristics of the bus line network and the temporal bus flow networks, and presented a comparison analysis on the spatial relationship between them by using the node centrality indices, namely degree centrality, betweenness centrality and closeness centrality. The results show that the overall spatial patterns of node centralities between the bus line network and the bus flow network were similar, while there were also some differences. For weekdays, the correlation between them is higher, as calculated by the degree of centrality. For weekends, the two networks have a greater correlation measured by degree centrality and betweenness centrality. The highest coefficients of correlation between the line network and traffic network appear in the morning peak, which implies that the congestion issues during the morning peak hours might receive the highest priority in Beijing’s bus-line network planning. Our study can provide implications for policymakers to improve the public urban transport network, and thus enhance residents’ happiness.

FlowTracker: Improved flow correlation attacks with denoising and contrastive learning

Tor is the most widely used anonymous network which provides anonymity by using proxy nodes to route data. However, many researches have proved that flow correlation attacks can break the anonymity of users’ communication relationships: once attackers have access to the ingress and egress traffic flows at both ends of the network, they will find the specific user who is visiting a monitored Internet service by observing the traffic shape. Existing flow correlation attacks have limited effects because of insufficient traffic shape features, network noise influence and lack of differentiated learning on unrelated flows. In this paper, we propose an improved flow correlation attacking model named FlowTracker which contains four modules: In flow cumulative representation module, raw and normalized cumulative sequences are combined to construct robust traffic shape features. In noise resistant mapping module, stacked auto-encoders are adopted to learn the mapping relationship between related ingress and egress cumulative representations and filter out the network noise. Moreover, we leverage contrast learning in distance optimization module to generate the optimized representation in which difference between unrelated representations is amplified, further reducing the false correlation rate with similar but unrelated flows. Finally, the target flow is reported through a multi-layer decision process in the hierarchical judgment module. We evaluate FlowTracker and other advanced comparison methods on the public and self-built datasets with the more realistic unidirectional setting, experiment results demonstrate that FlowTracker has the highest attacking success rate under general and targeted scenarios, especially when the traffic shape is seriously destroyed by traffic obfuscation.

A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

SUMMARYEstablishment of energy efficient and reliable data routing in wireless sensor network (WSN) is one of the most critical and challenging task in the recent days. Also, the overall performance and lifetime of WSN is highly depends on the energy level of sensor nodes, hence it is most essential to save the energy of network. For this purpose, the different types of clustering and data aggregation mechanisms are developed in the conventional works, which are focusing on improving both the energy conservation and lifetime of network. Yet, it facing the challenges of increased computational complexity, inefficient routing of data, high controlling overhead, and reduced reliability. Thus, the proposed work objects to develop a novel energy efficient mechanism by integrating the functionalities of advanced clustering, path selection, and data aggregation methodologies. Here, the spider monkey optimization based energy efficient routing protocol is developed for optimally selecting the cluster head (CH) based on certain parameters of energy, distance, and weight value. In this framework, the data transmission is performed between the source to destination nodes through the relay nodes and CHs, which helps to minimize the energy consumption of network. Then, the Classy Bellman‐Ford algorithm is deployed for identifying the best paths having shortest distance with the sink nodes. Consequently, an anticipated data aggregation mechanism is utilized for ensuring the security and reliability of data transmission in WSN. For evaluation assessment, various performance metrics have been utilized to validate the results of proposed methodology, and also the obtained values are compared with some other recent state‐of‐the‐art models for proving the betterment of proposed mechanism.

Improved energy-efficient routing architecture for traffic management system using a hybrid meta-heuristic algorithm in Internet of vehicles

Today, Internet of Vehicles (IoV) applications communicate with smart devices and deploy Intelligent Transportation Systems (ITS) to recognize traffic congestion problems. One of the main challenges of smart cities and IoV platforms is data routing methods for smart traffic congestion problems to navigate the individual information of data transmission as a critical issue in the ITS. Providing a way to extend the Quality of Service (QoS) variables and energy efficiency methods for directing data transmission in routing-based traffic management systems within the IoV environment is an important issue since the energy consumption of IoV devices is a critical issue in low-power saving storage. This paper presents a hybrid Genetic Algorithm and Social Spider Optimization (GA-SSO) algorithm for an energy-aware routing schema for optimizing traffic congestion and smart devices in the IoV environment. After conducting some studies and comparisons, the exactness and prevalence of the proposed schema were received. Experimental results confirmed that the GA-SSO algorithm calculation decides optimal and ideal arrangement for energy-aware routing schema demonstrate by essentially making strides the execution of QoS variables can oversee information blockage comes from data transmission between IoV nodes.