Routing Delay Research Articles

Social-Aware Link Reliability Prediction Model Based Minimum Delay Routing for CR-VANETs

Cognitive radio vehicle ad hoc networks (CR-VANETs) can utilize spectrum resources flexibly and efficiently and mitigate the conflict between limited spectrum resources and the ever-increasing demand for vehicular communication services. However, in CR-VANETs, the mobility characteristics of vehicles as well as the dynamic topology changes and frequent disruptions of links can lead to large end-to-end delays. To address this issue, we propose the social-based minimum end-to-end delay routing (SMED) algorithm, which leverages the social attributes of both primary and secondary users to reduce end-to-end delay and packet loss. We analyze the influencing factors of vehicle communication in urban road segments and at intersections, formulate the end-to-end delay minimization problem as a nonlinear integer programming problem, and utilize two sub-algorithms to solve this problem. Simulation results show that, compared to the intersection delay-aware routing algorithm (IDRA) and the expected path duration maximization routing algorithm (EPDMR), our method demonstrates significant improvements in both end-to-end delay and packet loss rate. Specifically, the SMED routing algorithm achieved an average reduction of 11.7% in end-to-end delay compared to EPDMR and 25.0% compared to IDRA. Additionally, it lowered the packet loss rate by 24.9% on average compared to EPDMR and 32.5% compared to IDRA.

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Design of Routing Algorithm for Communication of Power Wireless Sensor Networks Based on Improved Harmony Search

In this paper, a routing algorithm for the communication of power wireless sensor networks based on improved harmony search is proposed to reduce the communication routing delay and energy consumption of power wireless sensor networks. Firstly, according to the composition of communication routing delay and energy consumption of power wireless sensor networks, the delay model and energy consumption model are constructed respectively, and the objective function is constructed by assigning different weights. Then, artificial bee colony algorithm and Levy flight mechanism improved harmony search algorithm are adopted for solving the objective function. Experiment results reveal that the algorithm can meet different users’ different demands on delay and energy consumption. If the user needs low delay, set the low delay weight to 1 and the power consumption weight to 0, then the average delay is 7s, and the average power consumption is 3203J. When user demand is low energy consumption, set the delay weight to 0 and the energy consumption weight to 1, so that the average energy consumption is 2667J and the average delay is 15s, which can meet the needs of users.

Unveiling risk factors influencing the selection of the Northern Sea Route: A conjoint analysis approach for Japanese shippers

This paper explores how Japanese corporations assess risks associated with using the Northern Sea Route (NSR) as a new maritime transport route in Asia and identifies measures to promote its usage. We address two key questions: (1) What risks influence shippers' route choices in maritime transport? (2) Does risk evaluation vary by cargo type and company size? Using choice-based conjoint analysis and a conditional logit model with online survey data, we identified five main risk factors influencing shippers: route unavailability rate, delay probability, piracy probability, transport costs, and war conditions, ranked in importance from highest to lowest as war conditions, delay probability, transport costs, route unavailability rate, and piracy probability. Additionally, the risk factors significantly influencing route selection were found to be robust, showing minimal variation across company size, type of trade (import/export), and the nature of the goods transported. Our findings suggest several policy implications. Diplomatic efforts are crucial for safe vessel navigation on the NSR. Government initiatives should focus on reducing transport delays through technology investments and implementing strategies to decrease NSR delay rates without increasing shippers' costs. Additionally, emphasizing the lower piracy risks on the NSR compared to Suez Canal Route could boost its attractiveness.

ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.

EDCS: Efficient data collection systems by using bundling technology for effective communications

The energy of Wireless Sensor Network (WSN) in edge network is limited. In order to save energy, data bundling is proposed to reduce the energy consumed in setting up connection links by joining several packets into a single unit. However, although current data bundling schemes can reduce energy consumption, they will increase the delay of data routing, so it makes the multi-hop end to end delay a lot in WSN. Also, there is no appropriate solution to tackle the delayed growing problem caused by data bundling. And the energy consumption of the Acknowledgment (ACK) transmission is not comprehensively considered either. Regarding to those issues of previous researches, in this paper, a Data and ACK Bundling Scheme (DAB) is proposed to aggregate several data or ACK packets into a single unit to reduce the energy consumption as well as the end to end delay of the WSN. In the proposed DAB scheme, not only multiple packets but also ACK bundling into a single unit can reduce energy consumption. More importantly, the parameters of bundling technology are optimized from a global perspective in DAB scheme, so that WSN can not only improve the network lifetime, but also reduce the end to end delay which is impossible in previous studies. The theoretical analysis and experimental results illustrate that, while ensuring the reliability of data collection, our DAB scheme improves the energy efficiency by 29.03 % and reduces the delay by 22.29 % when compared with our traditional data collection scheme.

Optimizing Residual Energy and Delay in WSN Routing using Particle Swarm Optimization

Optimizing Residual Energy and Delay in WSN Routing using Particle Swarm Optimization

Analysis of Mobile ADHOC Network's Unipath and Multipath Routing Protocols

A mobile area network, also known as a MANET, is a structure that is constructed by wireless connections that connect mobile devices in order to establish a dynamic architecture. The use of routing protocols is an essential component in the process of data transmission across a network. Multipath and unipath are the two kind of routing protocols that are considered to be the most fundamental. Within the scope of this study, we investigated the effectiveness of two prevalent on-demand routing strategies, namely AOMDV and MDART, as well as AODV, which is a unipath routing protocol. These protocols were chosen because of their superior performance in comparison to their counterparts in a variety of domains, particularly in terms of reducing routing load and delay.

MixMatch: Flow Matching for Mixnet Traffic

Mixnets provide communication anonymity against network adversaries by routing packets independently via multiple hops, delaying them artificially at each hop, and introducing cover traffic. We show that these features (particularly the use of cover traffic) significantly diminish the effectiveness of state-of-the-art flow correlation techniques developed to link the two ends of a Tor connection. In this work, we propose novel methods to determine whether a set of endpoints exchanges packets via a mixnet and demonstrate their effectiveness by applying them to the Nym mixnet. We consider Nym in both an idealized lab setup and the official live network, and propose and compare three classifiers to conduct flow matching on it. Our statistical classifier tests whether egress packet timestamps are consistent with ingress timestamps and the (known) routing delay characteristic of the mixnet. In contrast, our two deep learning (DL) classifiers learn to distinguish matched from unmatched flow pairs from collected datasets directly, rather than relying on priors that describe the delay distribution. All three classifiers use our flow merging technique, which enables testing a match for sets of communicating endpoints of any cardinality. Considering a use case where two observed endpoints communicate exclusively to exchange a file through Nym, we find that flow matching is fast and accurate in the idealized lab setup. If flow pairs are aligned using all network observations in a download, we achieve a TPR of circa 0.6 (DL) and 0.47 (statistical) at an FPR of 10^-2 after only processing 100 observations. We evaluate classifier performance under key variations of this setup: the absence of loop cover traffic, an increased or decreased average per-mix delay, larger communicating sets (three endpoints) with faster responders, and the presence of realistic network effects (live network). The classifiers' matching performance diminishes on the live network where packet losses and variable propagation delays exist, reducing DL TPR to circa 0.26 and statistical TPR to circa 0.28 at an FPR of 10^-2. Informed by the insights of our analyses, we outline countermeasures that can be deployed in mixnets such as Nym to mitigate flow matching threats.

An optimal cooperative relay selection strategy for delay aware image transmission in large scale multi-radio multi-channel multimedia wireless sensor network

High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs’ dynamic nature, which reflects in the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method’s performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%.

Low-latency equal optical path difference sampling for multi-field VLWIR interference signals

The spaceborne Fourier Transform Spectrometer has been utilized for vertical atmospheric soundings with a HgCdTe infrared focal plane arrays (FPAs) detector. However, the VLWIR FPA Fourier spectrometer system faces the difficulty of poor sensitivity due to the major contribution of the system sampling error. In this work, low-latency data acquisition of multi-field VLWIR interference signals based on VLWIR FPA Fourier spectrometer system is realized with an optimized field programmable gate array (FPGA) timing driver scheme and layout algorithm. This scheme can effectively reduce routing delay and constrain critical timing paths, and thus leads to a significant decrease in the deviation of equal optical path difference sampling time from 10.27μs to 0.10μs. Interferograms and spectrograms are efficiently obtained. Furthermore, the average value of noise equivalent spectral radiance (NESR) in VLWIR FPA Fourier spectrometer system, is reduced by 1.75% by timing optimization. In particular, the decay of the NESR can be as high as 15% for some of the pixels. These results illustrate that the proposed scheme will acquire hyperspectral data accurately with VLWIR FPA and meet the needs of rapid soundings development of the Geostationary Interferometric Infrared Sounder (GIIRS) availing a rapid prediction of the appearance of an extreme weather event.

PaCHNOC: Packet and Circuit Hybrid Switching NoC for Real-Time Parallel Stream Signal Processing.

Real-time heterogeneous parallel embedded digital signal processor (DSP) systems process multiple data streams in parallel in a stringent time interval. This type of system on chip (SoC) requires the network on chip (NoC) to establish multiple symbiotic parallel data transmission paths with ultra-low transmission latency in real time. Our early NoC research PCCNOC meets this need. The PCCNOC uses packet routing to establish and lock a transmission circuit, so that PCCNOC is perfectly suitable for ultra-low latency and high-bandwidth transmission of long data packets. However, a parallel multi-data stream DSP system also needs to transmit roughly the same number of short data packets for job configuration and job execution status reports. While transferring short data packets, the link establishment routing delay of short data packets becomes relatively obvious. Our further research, thus, introduced PaCHNOC, a hybrid NoC in which long data packets are transmitted through a circuit established and locked by routing, and short data packets are attached to the routing packet and the transmission is completed during the routing process, thus avoiding the PCCNOC setup delay. Simulation shows that PaCHNOC performs well in supporting real-time heterogeneous parallel embedded DSP systems and achieves overall latency reduction 65% compared with related works. Finally, we used PaCHNOC in the baseband subsystem of a real 5G base station, which proved that our research is the best NoC for baseband subsystem of 5G base stations, which reduce 31% comprehensive latency in comparison to related works.

TUBER: Time-aware UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network.

This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. Comparative network performance of TUBER was investigated and evaluated with respect to RUBER and UAV-based energy-efficient reconfigurable routing (UBER) schemes. The metrics adopted for this comparative performance analysis are Cluster Survival Ratio (CSR), Network Stability (NST), Energy Dissipation Ratio (EDR), Network Coverage (COV), Packet Delivery Ratio (PDR), Fault Tolerance Index (FTI), Load Balancing Ratio (LBR), Routing Overhead (ROH), Average Routing Delay (ARD), Failure Detection Ratio (FDR), and Failure Recovery Ratio (FRR). With reference to best-obtained values, TUBER demonstrated improvements of 36.25%, 24.81%, 34.53%, 15.65%, 38.32%, 61.07%, 31.66%, 63.20%, 68.96%, 66.19%, and 78.63% over RUBER and UBER in terms of CSR, NST, EDR, COV, PDR, FTI, LBR, ROH, ARD, FDR, and FRR, respectively. These experimental results confirmed the relative effectiveness of TUBER against the compared routing schemes.

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.

IMPROVEMENT OF THE PROCESS OF CARGO TRANSPORTATION BY ROUTES IN THE DIRECTION TO THE WESTERN BORDERS OF UKRAINE

The paper examines the process of movement of carloads with export goods in the modern conditions of railway transport of Ukraine. In the conditions of a full-scale war, after blocking the ports, the export transportation of goods takes place largely through the western border crossings. The volumes of cargo transportation passing through the western border stations, which have been increasing recently, were studied. Bulk cargo, such as grain, iron ore, ferrous metals, and others, is a significant part of cargo transported in the direction of the western borders to the EU countries or in transit through their territory. The transportation of such cargoes is usually organized as part of route trains, since the large volume of the load allows full trains to be accumulated. The technology of planning the transportation of goods by routes is proposed, which makes it possible to rationally manage the transportation process, provided that trains are dispatched according to a fixed schedule that is consistent with the schedule of the foreign carrier. A model of stochastic programming with optimization of operational costs arising in the process of formation and routing of a route train in the direction of the western borders has been developed. The developed model takes into account the probabilistic nature of the formation time of the route train at the loading station, which is subject to the normal distribution law. To ensure the practical implementation of the model, the technological and regulatory conditions reflected in the system of restrictions are taken into account. The use of the proposed technology will make it possible to avoid the processing of wagons in the process of transportation, increase the productivity of rolling stock by reducing the turnover of wagons, optimize the technology of planning the transportation process, and also increase the predictability of cargo shipments for shippers. In addition, the technology takes into account multiple cases of route delays on a fixed schedule. Determining the amount of train delay is solved using the theory of fuzzy sets. Taking into account the received data on the places of delays and their causes, it is necessary to lay time reserves on the route of freight trains moving according to a fixed schedule.

AQND: An asymmetric quorum-based neighbor discovery protocol for reducing delay in sensor based systems

Neighbor discovery is basic for Wireless Sensor Networks (WSNs) communication, which is the key to network performance. Since sensor nodes were powered by battery, sleep/active rotation was widely adopted to save energy, which led to the neighbor discovery problem. Many timely neighbor discovery protocols are proposed for wake-up scheduling to expedite the discovery and data routing while saving energy. In this paper, an asymmetric quorum-based neighbor discovery (AQND) protocol is proposed to reduce the delay of neighbor discovery and data routing while improving the energy utilization and maintaining a high lifetime. According to our theoretical analysis, the proposed AQND protocol outperforms previous strategies in main performance indicators such as reducing the average relative error of duty cycle, the maximum neighbor discovery delay and the end-to-end discovery delay while improving the energy utilization ratio. In addition, using the proposed AQND protocol to improve the above network performances doesn’t reduce lifetime. Maintaining the end-to-end delay consistent with the previous strategies, the lifetime of network can be improved.

Accelerating DSP Applications on a 16-Bit Processor: Block RAM Integration and Distributed Arithmetic Approach

Modern processors have improved performance but still face challenges such as power consumption, storage limitations, and the need for faster processing. The 16-bit Digital Signal Processors (DSPs) accelerate DSP applications by significantly enhancing speed and performance for tasks including audio processing, telecommunications, image and video processing, wireless communication, and consumer electronics. This paper presents a novel technique for accelerating DSP applications on a 16-bit processor by combining two methods: Block Random Access Memory (BRAM) and Distributed Arithmetic (DA). Integrating BRAM as a replacement for conventional RAM minimizes timing and critical route delays, improving processor efficiency and performance. Furthermore, the Distributed Arithmetic approach enhances performance and efficiency by utilizing precomputed lookup tables to expedite multiplication operations within the Arithmetic and Logic Unit (ALU). We use the Xilinx Vivado tool, a robust development environment for FPGA-based systems, for the design process and execute the hardware implementation using the Genesys2 Kintex board. The proposed work produces improved efficiency with a cycle per instruction of 2, where the delay is 2.009 ns, the critical path delay is 8.182 ns, and the power consumption is 4 mW.

A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs.

Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node's remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics.

DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with Cell-Free Massive Multiple-Input Multiple-Output.

The network area is extended from ground to air. In order to efficiently manage various kinds of nodes, new network paradigms are needed such as cell-free massive multiple-input multiple-output (CF-mMIMO). Additionally, security is also considered as one of the important quality-of-services (QoS) parameters in future networks. Thus, in this paper, we propose a novel deep learning-based secure multicast routing protocol (DLSMR) in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). We consider the problem of wormhole attacks in the multicast routing process. To tackle this problem, we propose the DLSMR protocol, which utilizes a deep learning (DL) approach to predict the secure and unsecured route based on node ID, distance, destination sequence, hop count, and energy to avoid wormhole attacks. This work also addresses key concerns in FANETs such as security, scalability, and stability. The main contributions of this paper are as follows: (1) We propose a deep learning-based secure multicast routing protocol (DLSMR) to establish a high-stability multicast tree and improve security performance against wormhole attacks. In more detail, the DLSMR protocol predicts whether the route is secure based on network information such as node ID, distance, destination sequence, hop count, and remaining energy or not. (2) To improve the node connectivity and manage multicast members, we propose a top-down particle swarm optimization-based clustering (TD-PSO) protocol to maximize the cost function considering node degree, cosine similarity, cosine distance, and cluster head energy to guarantee convergence to the global optima. Thus, the TD-PSO protocol provides more strong connectivity. (3) Performance evaluations verify the proposed routing protocol establishes a secure route by avoiding wormhole attacks as well as by providing strong connectivity. The TD-PSO clustering supports connectivity to enhance network performance. In addition, we exploit the impact of the mobility model on the network metrics such as packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses.

Federated Learning Assisted Deep Q-Learning for Joint Task Offloading and Fronthaul Segment Routing in Open RAN

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks, which requires routing. Since edge clouds do not have the same available computation resources and tasks’ computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD.