Orion: A L2 Solution for Scalable Blockchains Under Dynamic Network Conditions

This paper addresses the problem of scheduling real-time wireless flows under dynamic network conditions and general traffic patterns. The objective is to maximize the fraction of packets of each flow to be delivered within their deadlines, referred to as timely-throughput. The scheduling problem under restrictive frame-based traffic models or greedy maximal scheduling schemes like LDF has been extensively studied so far, but scheduling algorithms to provide deadline guarantees on packet delivery for general traffic under dynamic network conditions are very limited. We propose two scheduling algorithms using deep reinforcement learning approach to optimize timely-throughput for general traffic in dynamic wireless networks: RL-Centralized scheduling algorithm and RL-Decentralized scheduling algo-rithm. Specifically, we formulate the centralized scheduling problem as a Markov Decision Process (MDP) and a multi-environments double deep Q-network (ME-DDQN) structure is proposed to adapt to the dynamic network conditions. The decentralized scheduling problem is formulated as a Partially Observable Markov Decision Process (POMDP) and an expert-apprentice centralized training and decentralized execution (EA-CTDE) structure is designed to accelerate the training speed and achieve the optimal timely-throughput. The extensive results show that the proposed scheduling algorithms converge fast and adapt well to network dynamics with superior performance compared to baseline policies. Finally, experimental tests confirm simulation results and also show that the proposed algorithms are feasible in practice on resource limited platforms.

The advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically designed for intelligent agricultural systems, with a focus on combine tractors equipped with advanced nutrient and crop health sensors. Unlike conventional FL applications, our architecture uniquely addresses the challenges of communication efficiency, dynamic network conditions, and resource allocation in rural farming environments. By adopting a decentralized approach, we ensure that sensitive data remain localized, thereby enhancing security while facilitating effective collaboration among devices. The architecture promotes the formation of adaptive clusters based on operational capabilities and geographical proximity, optimizing communication between edge devices and a global server. Furthermore, we implement a robust checkpointing mechanism and a dynamic data transmission strategy, ensuring efficient model updates in the face of fluctuating network conditions. Through a comprehensive assessment of computational power, energy efficiency, and latency, our system intelligently classifies devices, significantly enhancing the overall efficiency of federated learning processes. This paper details the architecture, operational procedures, and evaluation methodologies, demonstrating how our approach has the potential to transform agricultural practices through data-driven decision-making and promote sustainable farming practices tailored to the unique challenges of the agricultural sector.

The increasingly complicated and diverse applications have distinct network performance demands, e.g., some desire high throughput while others require low latency. Traditional congestion controls (CC) have no perception of these demands. Consequently, literatures have explored the objective-specific algorithms, which are based on either offline training or online learning, to adapt to certain application demands. However, once generated, such algorithms are tailored to a specific performance objective function. Newly emerged performance demands in a changeable network environment require either expensive retraining (in the case of offline training), or manually redesigning a new objective function (in the case of online learning). To address this problem, we propose a novel architecture, DeepCC. It generates a CC agent that is generically applicable to a wide range of application requirements and network conditions. The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training towards a specific objective function, DeepCC trains its deep neural network model using multi-objective optimization. With the trained model, DeepCC offers near Pareto optimal policies w.r.t different user-specified trade-offs between throughput, delay, and loss rate without any redesigning or retraining. In addition, a quick online fine-tuning phase further helps DeepCC achieve the application-specific demands under dynamic network conditions. The simulation and real-world experiments show that DeepCC outperforms state-of-the-art schemes in a wide range of settings. DeepCC gains a higher target completion ratio of application requirements up to 67.4% than that of other schemes, even in an untrained environment.

Internet-based social and interactive video applications have become major constituents of the envisaged applications for next-generation multimedia networks. However, inherently dynamic network conditions, together with varying user expectations, pose many challenges for resource allocation mechanisms for such applications. Yet, in addition to addressing these challenges, service providers must also consider how to mitigate their operational costs (e.g., energy costs, equipment costs) while satisfying the end-user quality of service (QoS) expectations. This paper proposes a heuristic solution to the problem, where the energy incurred by the applications, and the monetary costs associated with the service infrastructure, are minimized while simultaneously maximizing the average end-user QoS. We evaluate the performance of the proposed solution in terms of serving probability, i.e., the likelihood of being able to allocate resources to groups of users, the computation time of the resource allocation process, and the adaptability and sensitivity to dynamic network conditions. The proposed method demonstrates improvements in serving probability of up to 27%, in comparison with greedy resource allocation schemes, and a several-orders-of-magnitude reduction in computation time, compared to the linear programming approach, which significantly reduces the service-interrupted user percentage when operating under variable network conditions.

Using the ns simulator, we perform a detailed study of the fairness and the smoothness properties of SimdNR, an enhanced version of SIMD. Our study includes TFRC and TCP congestion control protocols in both steady-state and highly dynamic network conditions through RED and DropTail routers. Our results show that SimdNR is fair to TCP through RED routers in steady-state scenario. But, SimdNR is not fair to TCP over drop tail routers. Also our results show that SimdNR demonstrates less smoothness than TFRC in steady-state scenario and superior smoothness than TFRC in dynamic network conditions

In network multimedia applications such as multiparty teleconferencing, users often need to send the same information to several (but not necessarily all) other users. To manage such one-to-many or many-to-many communication efficiently in wide-area internetworks, it is imperative to support and perform multicast routing. Multicast routing sends a single copy of a message from a source to multiple receivers over a communication link that is shared by the paths to the receivers. Loop-freedom is an especially important consideration in multicasting because applications using multicasting tend to be multimedia and bandwidth intensive, and loops in multicast routing duplicate looping packets. We present and verify a new multicast routing protocol, called multicast Internet protocol (MIP), which offers a simple and flexible approach to constructing both group-shared and shortest-paths multicast trees. MIP can be sender-initiated or receiver-initiated or both; therefore, it can be tailored to the particular nature of an application's group dynamics and size. MIP is independent of the underlying unicast routing algorithms used. MIP is robust and adapts under dynamic network conditions (topology or link cost changes) to maintain loop-free multicast routing. Under stable network conditions, MIP has no maintenance or control message overhead. We prove that MIP is loop-free at every instant, and that it is deadlock-free and obtains multicast routing trees within a finite time after the occurrence of an arbitrary sequence of topology or unicast changes.

The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly.

Growing use of vehicles in the city has increased the number of accidents and reduced the safety level to the passengers. Alerting drivers about the condition of road, traffic and related aspect is crucial to safety and for the regulation of vehicle flow. To achieve this in timely manner, an accurate information is needed. So an efficient, robust and scalable VANET (Vehicular Ad-hoc Network) routing protocol will play a key role to achieve this. This protocol have a potential to protect the generation by incorporating the new generation wireless network into vehicle. This paper represent a fitness based VANET routing protocol, which is operable in dynamic network conditions and compatible with both communication modes like V-I (vehicle-Infrastructure) and V-V (vehicle-vehicle) communication. It also focuses on both multi-path and single-path in the multi hop routing based on delay sensitive and delay non-sensitive data in a dense and sparse environment. It also provide a cost-efficient, intelligent routing strategy by sending a safety packet delivery assurance by optimally measuring the fitness in terms of distance, moving direction and link quality metric in V-V and V-I communication. The performance of our proposed protocol is evaluated by Ns2 simulator. The proposed protocol is compared to existing protocols like AODV (Ad-hoc On demand Distance Vector Routing), GPSR (Greedy Perimeter Stateless Routing) and prove the safety and efficiency of our protocol in term of Road Safety Assurance, and Packet Delivery Ratio.

Traffic Engineering (TE) is an important network operation that routes/reroutes flows based on network topology and traffic demands to optimize network performance. Recently, new emerging applications pose challenges to TE with dynamic network conditions, where frequent routing updates are required to maintain good network performance with Software-Defined Networking (SDN). However, flow rerouting operations could lead to considerable Quality of Service (QoS) degradation and service disruption, which is often neglected by existing TE solutions. In this paper, we apply a new QoS metric named network disturbance to measure the negative impact of flow rerouting operations performed by TE. To achieve near-optimal load balancing performance and mitigate network disturbance together in dynamic network scenarios, we propose a flexible and disturbance-aware TE solution called FlexDATE that combines Reinforcement Learning (RL) and Linear Programming (LP). Specifically, FlexDATE leverages RL to intelligently identify flexible numbers of critical flows for each traffic matrix and reroutes these critical flows based on LP optimization to improve network performance with low disturbance. Empowered by a customized actor-critic architecture coupled with Graph Neural Networks (GNNs), FlexDATE can generalize well to unseen traffic scenarios and remain resilient to single link failures. Extensive simulations are conducted on five real-world network topologies to evaluate FlexDATE with real and synthetic traffic traces. The results show that FlexDATE can achieve the performance target (i.e., 90% of optimal performance) in 99% of network scenarios and effectively mitigate the average and maximum network disturbance by up to 9.1% and 38.6%, respectively, compared to state-of-the-art TE solutions.

Vehicular digital twin networks (VDTN) is an emerging paradigm integrating physical vehicular networks with their virtual digital twins (DT) mirror, enabling real-time mapping, simulation, and optimization of complex systems. However, constrained resources, high data synchronization costs, and dynamic network conditions in vehicular networks may degrade the performance of DT. We consider the interaction between task performance guarantees and node resource constraints to adaptively determine collaborative task computation transfer optimization in VDTN. In this paper, we design a semantic-aware multi-task vehicular digital twin network model, where vehicles extract semantic representations to achieve lightweight data transmission and efficient DT synchronization. We formulate a problem of maximizing the average utility of DT tasks by jointly considering the synchronization performance of DT tasks and the resource consumption among heterogeneous nodes. To solve the formulated problem, we develop a dynamic collaborative task computation transfer algorithm involving the high mobility of vehicles and heterogeneous resources of nodes. The algorithm is optimized in two phases to maximize average utility. A coarse-grained policy space is first obtained through an adaptive multi-agent deep reinforcement learning approach, aiming to alleviate the policy space explosion caused by dynamic task requirements and heterogeneous node collaboration. Subsequently, a fine-grained policy is derived via a resource-aware refinement mechanism. Numerical results validate the effectiveness and robustness of our proposed algorithm.

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive deep neural network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this article, we propose an edge-enabled multivideo analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov decision process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion-deep-reinforcement-learning-based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes.

Real-time video analytics services are expected to deliver accurate recognition results to users timely. However, existing studies usually fail in the dilemma between reducing delay and improving accuracy. Balancing such conflicting metrics is extremely hard under dynamic network conditions. In this regard, we introduce the age of processed information (AoPI) concept, i.e., the time elapsed since the generation of the latest accurately recognized frame. As a systematic metric, AoPI depicts the integrated impact of recognition accuracy, transmission, and computation efficiency. We derive the closed-form expressions of AoPI under preemptive and non-preemptive computation scheduling policies w.r.t. the transmission/computation rate and recognition accuracy of video frames, which are validated using prototype experiments. Based on the derived results, we study the joint video configuration selection, bandwidth, and computation resource allocation problem to minimize the long-term average AoPI among all cameras. An efficient method is proposed to solve the problem, which utilizes Lyapunov optimization and block coordinate descent to make decisions online without requiring future information about network variations and video content. We prove that our method achieves asymptotically optimal performance. Extensive simulations show that our method reduces the AoPI by up to 4.06X compared with the state-of-the-art baselines.

Significant demands for massive machine-type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communication (URLLC) have been brought about by the introduction of 5G New Radio (NR) and the impending B5G/6G technologies. In high-speed mobile scenarios, ensuring seamless handover (HO) with minimal latency and packet loss remains a persistent challenge. Traditional static or rule-based approaches to mobility management often fail to scale in dense and dynamic network conditions. To address this we propose an advanced framework, termed DeepEdge-LIM<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (Learning-based Intelligent Mobility Management), which leverages edge computing and deep reinforcement learning (DRL) for dynamic handover optimization. Specifically, the proposed system utilizes a dual-stage intelligence: (1) A Kalman filter coupled with fuzzy logic to accurately forecast signal quality fluctuations of the serving and neighboring cells, and (2) A deep Q-learning network (DQN) deployed at the edge nodes to adaptively control the Hysteresis Margin, Time-to-Trigger ensuring real-time decision making. The action selection policy incorporates an improved ε-greedy strategy with decaying exploration and a reward function tailored for latency minimization, signal strength stability, and throughput enhancement. We implement and simulate DeepEdge-LIM<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> using the NS-3 and OpenAI Gym framework. Our suggested approach is appropriate for upcoming intelligent 5G and 6G deployments since evaluation results show that it significantly outperforms traditional and learning-based baselines in lowering handover failures, ping-pong effects, and HO latency, especially in ultra-dense and high-speed mobility scenarios.

This paper develops a cross-layer optimization framework for video streaming from multiple sources to a central proxy station over a wireless network. The proposed framework manages the application rates and transmission opportunities of various video sources based on the dynamic network conditions in such a way that minimizes the overall distortion. The framework utilizes a novel online approach for estimating the effective airtime of the network. We demonstrate the effectiveness of the proposed framework and effective airtime estimation approach through extensive experiments.

Transmission control protocol (TCP) provides reliable end-to-end data communication under unpredictable and dynamic network conditions. Different variants of TCP have been proposed which show varying degree of resilience in best effort IP networks characteristics. Multi-protocol label switching (MPLS) has proven strength in providing quality of service based data services due to its traffic engineering procedures. This paper presents a comprehensive experimental analysis of TCP variants under MPLS with emphasis on Tahoe, Reno and Vegas. Under different traffic load, it has been found that Reno and Tahoe fail to take advantage of MPLS features where as Vegas has shown promising results with almost stable constant end-to-end delay after a transient phase of delay fluctuations in the early stages of data transmission session. The stable end-to-end delay under MPLS makes Vegas an attractive choice for medium to large size real-time traffic.