Network Resource Consumption Research Articles

Efficiently Enlarging RDMA-Attached Memory with SSD

RDMA-based in-memory storage systems offer high performance but are restricted by the capacity of physical memory. In this paper, we propose TeRM to extend RDMA-attached memory with SSD. TeRM achieves fast remote access on the SSD-extended memory by eliminating page faults of RDMA NIC and CPU from the critical path. We also introduce a set of techniques to reduce the consumption of CPU and network resources. Evaluation shows that TeRM performs close to the performance of the ideal upper bound where all pages are pinned in the physical memory. Compared with existing approaches TeRM significantly improves the performance of unmodified RDMA-based storage systems, including a file system and a key-value system.

Communication-Efficient Wireless Traffic Prediction with Federated Learning

Wireless traffic prediction is essential to developing intelligent communication networks that facilitate efficient resource allocation. Along this line, decentralized wireless traffic prediction under the paradigm of federated learning is becoming increasingly significant. Compared to traditional centralized learning, federated learning satisfies network operators’ requirements for sensitive data protection and reduces the consumption of network resources. In this paper, we propose a novel communication-efficient federated learning framework, named FedCE, by developing a gradient compression scheme and an adaptive aggregation strategy for wireless traffic prediction. FedCE achieves gradient compression through top-K sparsification and can largely relieve the communication burdens between local clients and the central server, making it communication-efficient. An adaptive aggregation strategy is designed by quantifying the different contributions of local models to the global model, making FedCE aware of spatial dependencies among various local clients. We validate the effectiveness of FedCE on two real-world datasets. The results demonstrate that FedCE can improve prediction accuracy by approximately 27% with only 20% of communications in the baseline method.

PSO-based lightweight neural architecture search for object detection

In recent years, Neural Architecture Search (NAS) has received widespread attention for its remarkable ability to design neural networks. However, existing NAS works have mainly focused on network search, with limited emphasis on downstream applications after discovering efficient neural networks. In this paper, we propose a lightweight search strategy based on the particle swarm optimization algorithm and apply the searched network as backbone for object detection tasks. Specifically, we design a lightweight search space based on Ghostconv modules and improved Mobileblocks, achieving comprehensive exploration within the search space using variable-length encoding strategy. During the search process, to balance network performance and resource consumption, we propose a multi-objective fitness function and incorporated the classification accuracy, parameter size, and FLOPs of candidate individuals into optimization. For particle performance evaluation, we propose a new strategy based on weight sharing and dynamic early stopping, significantly accelerating the search process. Finally, we fine-tune the globally optimal particle decoded as the backbone, adding Ghost PAN feature fusion modules and detection heads to build an object detection model, and we achieve a 17.01% mAP on the VisDrone2019 dataset. Experimental results demonstrate the competitiveness of our algorithm in terms of search time and the balance between accuracy and efficiency, and also confirm the effectiveness of object detection models designed through NAS methods.

Water and Energy Conservation across Provinces and Sectors in China: Based on a Complex Network Perspective

Previous studies on the water–energy nexus mainly focused on the calculation and comparison of resource consumption at the national or regional level, lacking interprovincial sector-sector transfer analysis. In this study, the intensity of water and energy consumption of various sectors in China was calculated, the key nodes and paths of resource networks were identified, and countermeasures for resource conservation were proposed from the new perspective of the “dual saving” and “bidirectional saving” of water and energy. The results showed that the metallurgical industry (Me) in Jiangsu and the chemical industry (Ch) in Hebei and Jiangsu had high node strength in the water and energy network and were key sectors in China with “dual saving” effects of water and energy. The construction industry, Ch, Me in Jiangsu, electricity and hot water production and supply industry in Beijing, and Me in Hebei had high node strength in the water-related energy network and energy-related water network, significantly supporting the “bidirectional saving” effect of water and energy in China. The electrical equipment industry (El) in Jiangsu → El in Zhejiang, El in Zhejiang → El in Shanghai frequently appeared in key paths, which could effectively reduce the resource consumption of the entire network.

Analytical study of Restricted Access Window with short slots for fast and reliable data delivery from energy-harvesting sensors

To address the peculiarities of various Internet of Things applications and improve the performance of Wi-Fi-based sensor stations, a recent Wi-Fi HaLow standard introduces a mechanism called Periodic Restricted Access Window (PRAW). PRAW allows mapping groups of stations to periodic time intervals during which only these stations can transmit while the others cannot. This approach reduces contention and saves energy. However, the standard does not provide any recommendations on how to select the PRAW parameters according to scenario-specific requirements, leaving room for the research. A typical IoT scenario considers low-intensity traffic and requires fast and reliable data delivery from energy-harvesting devices while using the channel efficiently: there shall be still time for transmissions of other stations in the network. This paper develops an analytical model of PRAW that evaluates the probability of delivering data in time from every sensor station that harvests energy, accumulates it in small capacitors, and, thus, may not have enough energy for multiple retries. The model allows selecting such parameters of PRAW that minimize the channel resource consumption of a Wi-Fi HaLow network, provided that the delay and energy requirements are satisfied.

Co-design of adaptive event-triggered mechanism and controller for networked control systems with uncertainty and time-varying delay

ABSTRACT This paper investigates the co-design problem of event-triggered mechanisms and state feedback controllers for networked control systems with parameter uncertainty and network-induced delay. Firstly, to overcome the network bandwidth limitation, an adaptive event-triggered mechanism is used to filter the required sampling signals to reduce the network resource occupancy. Second, establish a unified model subject to event triggering mechanisms, parameter uncertainty, and network delay constraints, and derive the stability conditions of the system for the model by combining Lyapunov stability theory and the linear matrix inequality method. Then, the state feedback controller is designed to satisfy the event-triggering condition and certain robust performance requirements, and an optimisation method is proposed to compromise network resource consumption and control performance. Finally, the effectiveness of the proposed method is verified by a numerical example.

High-Level Defence Model against Routing Attacks on the Internet-of-Things


 
 
 
 This paper is part of the doctoral work that aims to answer the following research question: “To what extent can an intelligent security model effectively defend against routing attacks in RPL-based Internet of Things (IoT) with a demonstration of less network resource consumption, high detection rate, and minimal false negatives?” To answer this question, this paper proposes a high-level conceptual framework to defend the IoT against routing attacks. In recent works, mitigation techniques have been proposed to act against routing attacks, however conceptual defence or mitigation framework is not presented as a set of steps to follow to develop an effective and robust intelligent security model. This paper aims to present a high-level conceptual defence framework against routing attacks; specifically, sinkhole, rank, DIS-Flooding, and worst parent. The four mentioned routing attacks are capable of disturbing IoT network functions and operations, and consuming network resources such as memory and power.
 
 
 
 

Dynamic triggered‐based tracking approach for connected vehicle system via extended observer

SummaryThis paper investigates the tracking control problem of connected vehicle system suffer from disturbance and communication load. A new dynamic triggered‐based tracking approach was presented. First, an extended state observer is designed to estimate the equivalent disturbance, and the convergence of observed error is proved. Then, a tracking approach based on backstepping control is proposed. Furthermore, in order to reduce the consumption of network resources, a novel tracking method on dynamic event‐triggering strategy is developed, and all signals in connected vehicle system are ultimately uniformly bounded without Zeno behavior is verified. Finally, the effectiveness of the proposed control strategy is testified by simulation results.

Efficient and choreographed quality-of- service management in dense 6G verticals with high-speed mobility requirements

Future 6G networks are envisioned to support very heterogeneous and extreme applications (known as verticals). Some examples are further-enhanced mobile broadband communications, where bitrates could go above one terabit per second, or extremely reliable and low-latency communications, whose end-to-end delay must be below one hundred microseconds. To achieve that ultra-high Quality-of-Service, 6G networks are commonly provided with redundant resources and intelligent management mechanisms to ensure that all devices get the expected performance. But this approach is not feasible or scalable for all verticals. Specifically, in 6G scenarios, mobile devices are expected to have speeds greater than 500 kilometers per hour, and device density will exceed ten million devices per square kilometer. In those verticals, resources cannot be redundant as, because of such a huge number of devices, Quality-of-Service requirements are pushing the effective performance of technologies at physical level. And, on the other hand, high-speed mobility prevents intelligent mechanisms to be useful, as devices move around and evolve faster than the usual convergence time of those intelligent solutions. New technologies are needed to fill this unexplored gap. Therefore, in this paper we propose a choreographed Quality-of-Service management solution, where 6G base stations predict the evolution of verticals at real-time, and run a lightweight distributed optimization algorithm in advance, so they can manage the resource consumption and ensure all devices get the required Quality-of-Service. Prediction mechanism includes mobility models (Markov, Bayesian, etc.) and models for time-variant communication channels. Besides, a traffic prediction solution is also considered to explore the achieved Quality-of-Service in advance. The optimization algorithm calculates an efficient resource distribution according to the predicted future vertical situation, so devices achieve the expected Quality-of-Service according to the proposed traffic models. An experimental validation based on simulation tools is also provided. Results show that the proposed approach reduces up to 12% of the network resource consumption for a given Quality-of-Service.

6G Goal-Oriented Communications: How to Coexist with Legacy Systems?

6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on (i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; (ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper, we investigate how to effectively share communication spectrum resources between a legacy communication system (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted goal effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as goal cost (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization and the superiority of the goal-oriented strategy against baseline 5G-compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments.

Sampled-based adaptive event-triggered resilient control for multiagent systems with hybrid cyber-attacks

The multiagent systems have shared broad application in many practical systems including unmanned aircraft clusters, intelligent robots, and intelligent transportation. However, many unexpected cyber-attacks may disturb or disrupt the normal communication of the agents, thus reducing the interacting efficiency of multiagent systems. Ever since the cyber-attacks have been proposed, the resilient control problem for multiagent systems has been intensively explored in light of the communication network growth. However, most of the consequences only focused on denial-of-service (DoS) attacks or deception attacks independently. Distinguished from the existing resilient control mechanisms, the current investigation represents the first attempt at designing an adaptive resilient controller for multiagent systems according to the sampled-based adaptive event-triggered manner, where denial-of-service (DoS) attacks and deception attacks are both considered. First, the hybrid cyber-attacks model and its impact on the closed-loop system are addressed. And then, an adaptive event-triggered strategy is proposed to reduce network resource consumption and ease the communication burden, where the designed adaptive law can automatically adjust the triggering threshold. Finally, the consensus state of multiagent systems is capable of achieving via a series of reasonable control rules formulated through Lyapunov functional approach despite suffering hybrid cyber-attacks. And a simulation example is given to substantiate the feasibility of the proposed method.

Toward Cooperative Federated Learning Over Heterogeneous Edge/Fog Networks

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has been vulnerable to several dimensions of network heterogeneity, such as varying computation capabilities, communication resources, data qualities, and privacy demands. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-toserver (D2S) interactions. Through D2D and D2S cooperation, CFL counteracts network heterogeneity in edge/fog networks through enabling a model/data/resource pooling mechanism, which will yield substantial improvements in ML model training quality and network resource consumption. We propose a set of core methodologies that form the foundation of D2D and D2S cooperation and present preliminary experiments that demonstrate their benefits. We also discuss new FL functionalities enabled by this cooperative framework such as the integration of unlabeled data and heterogeneous device privacy into ML model training. Finally, we describe some open research directions at the intersection of cooperative edge/fog and FL.

Event-Triggered Resilient Filtering With the Interval Type Uncertainty for Markov Jump Systems.

The problem of event-triggered resilient filtering for Markov jump systems is investigated in this article. The hidden Markov model is used to characterize asynchronous constraints between the filters and the systems. Gain uncertainties of the resilient filter are the interval type in this article, which is more accurate than the norm-bounded type to model the uncertain phenomenon. The number of linear matrix inequalities constraints can be decreased significantly by separating the vertices of the uncertain interval, so that the difficulty of calculation and calculation time can be reduced. Moreover, the event-triggered scheme is applied to depress the consumption of network resources. In order to find a balance between reducing bandwidth consumed and improving system performance, the threshold parameter is designed as a diagonal matrix in the event-triggered scheme. Utilizing the convex optimization method, the sufficient conditions are derived to guarantee that the filtering error systems are stochastically stable and satisfy the extended dissipation performance. Finally, a single-link robot arm system is delivered to certify the effectiveness and advantages of the proposed method.

A clustering approach to improve VANETs performance

Vehicular ad-hoc network (VANET) is a technique that uses cars moved in cities or highways as nodes in wireless networks. Each car in these networks works as a router and allows cars in the range to communicate with each other. As a result of this movement, some cars will become out of range, but these networks can connect to the internet and the cars in these networks can connect to each other. This research proposes a unique clustering strategy to improve the performance of these networks by making their clusters more stable. One of the biggest problems these networks face is traffic data, which consumes network resources. Agent based modeling (ABM) evaluates better networks. The evaluation showed that the proposed strategy surpasses earlier techniques in reachability and throughput, but ad hoc on-demand distance vector (AODV) (on-demand/reactive) outperforms it in total traffic received since our hybrid approach needs more traffic than AODV.

Delay/Disruption-Tolerant Networking-based the Integrated Deep-Space Relay Network: State-of-the-Art

Communication with deep-space elements poses significant challenges due to vast distances, orbital motions, and harsh environmental conditions that restrict point-to-point or end-to-end communication. Disruption/Delay-Tolerant Networking (DTNs) is a special kind of computer network architecture, that overcomes the shortcomings of the TCP/IP suite in meeting deep-space networking requirements with intermittent end-to-end connectivity. In DTN, data transmission follows a store-carry-forward approach, where bundles (data) are transferred as custody between relay nodes. This necessitates more complex routing strategies involving additional route computations and network resource consumption but provides greater flexibility for unstable connectivity. This study is an updated literature review in the context of DTN-based deep-space communication. It covers the recent studies that address the architecture of current and future deep-space communication systems, the environmental challenges of deep-space communication, various implementations and demonstrations of DTN's architecture, and the validation of its related routing strategies over the Deep Space Relay network. Additionally, this review explores the integration of machine learning techniques into DTN routing strategies, along with ongoing research directions and open issues in DTN-based DSN. Examining the state-of-the-art literature reveals that developing an intelligent unified n-copy-based forwarding routing scheme holds promise for effectively integrating into deep space communications and achieving optimal networking among the diverse segments of the future unified Interplanetary Internet.

Task Offloading Decision-Making Algorithm for Vehicular Edge Computing: A Deep-Reinforcement-Learning-Based Approach.

Efficient task offloading decision is a crucial technology in vehicular edge computing, which aims to fulfill the computational performance demands of complex vehicular tasks with respect to delay and energy consumption while minimizing network resource competition and consumption. Conventional distributed task offloading decisions rely solely on the local state of the vehicle, failing to optimize the utilization of the server's resources to its fullest potential. In addition, the mobility aspect of vehicles is often neglected in these decisions. In this paper, a cloud-edge-vehicle three-tier vehicular edge computing (VEC) system is proposed, where vehicles partially offload their computing tasks to edge or cloud servers while keeping the remaining tasks local to the vehicle terminals. Under the restrictions of vehicle mobility and discrete variables, task scheduling and task offloading proportion are jointly optimized with the objective of minimizing the total system cost. Considering the non-convexity, high-dimensional complex state and continuous action space requirements of the optimization problem, we propose a task offloading decision-making algorithm based on deep deterministic policy gradient (TODM_DDPG). TODM_DDPG algorithm adopts the actor-critic framework in which the actor network outputs floating point numbers to represent deterministic policy, while the critic network evaluates the action output by the actor network, and adjusts the network evaluation policy according to the rewards with the environment to maximize the long-term reward. To explore the algorithm performance, this conduct parameter setting experiments to correct the algorithm core hyper-parameters and select the optimal combination of parameters. In addition, in order to verify algorithm performance, we also carry out a series of comparative experiments with baseline algorithms. The results demonstrate that in terms of reducing system costs, the proposed algorithm outperforms the compared baseline algorithm, such as the deep Q network (DQN) and the actor-critic (AC), and the performance is improved by about 13% on average.

Resilient control for networked control systems with dynamic quantization and DoS attacks

AbstractIn this article, the resilient control for networked control systems in the presence of denial‐of‐service (DoS) attacks is investigated in a sampled‐data and dynamic quantization scheme. A novel dynamic quantization strategy is designed for signal transmissions from encoding systems to decoding systems, in which the quantized states are transmitted through networks with a risk of DoS attacks. An estimator is introduced to the design of control laws. Some sufficient conditions in terms of quantization levels, DoS attack duration and frequencies are given for the asymptotic stability of networked control systems. Furthermore, an event‐triggered communication scheme is designed for signal transmissions in control channels to reduce network resource consumption. The Zeno behavior is excluded in the designed event‐triggered communication scheme. The quantization levels can be adaptively adjusted according to real‐time situations. Finally, the effectiveness of the proposed strategy is illustrated by simulations.

Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City.

The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two phases. The first phase relies on the intelligent process automation (IPA) technique and contains five modules named, specifically, dataset collection and pre-processing module, intelligent automation detection module, analysis module, detection rules and action module, and database module. In the first phase, each module composes an intelligent connecting module to give feedback reports about each module and send information to the next modules. Therefore, any change in each process can be easily detected and labeled as an intrusion. The intelligent connection module (ICM) may reduce the search time, increase the speed, and increase the security level. The second phase is the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. The second phase can learn the new changed behaviors based on reinforced learning to detect zero-day attacks and malicious events in IoT-based smart cities. The experiments are implemented using a UNSW-NB 15 dataset. The proposed model achieves high accuracy rates using RFT, K-NN, and AdaBoost of approximately 98.8%. It is noted that the accuracy rate of the J48 classifier achieves 85.51%, which is lower than the others. Subsequently, the accuracy rates of AdaBoost and Bagging based on J48 are 98.9% and 91.41%, respectively. Additionally, the error rates of RFT, K-NN, and AdaBoost are very low. Similarly, the proposed model achieves high precision, recall, and F1-measure high rates using RFT, K-NN, AdaBoost, and Bagging. The second phase depends on creating an auto-adaptive model through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning using a small number of instances to conserve the memory of any smart device in an IoT network. The proposed auto-adaptive model may reduce false rates of reporting by the intrusion detection system (IDS). It can detect any change in the behaviors of smart devices quickly and easily. The IADM can improve the performance rates for IDS by maintaining the memory consumption, time consumption, and speed of the detection process.

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

During the last decade, Cognitive Radio Network (CRN) technology has been a significant advance in addressing the ever-increasing spectrum demand. As the number of licensed and unlicensed users in a network rises to complete a certain activity, the information exchange between various types of traffic becomes more complex and difficult. Congestion in CRN is also caused by the conflict among several users for channel access (PUs and SUs). In a very crowded network, many applications perform badly owing to packet collisions and, as a result, packet loss before significant buffer queue building. This circumstance is aggravated by an increase in network users. Congestion control is a vital and essential aspect of the present research issue in communication networks. Several recent reviews in the literature indicate that the congestion issue in the CRN has not been thoroughly studied. Thus, effective and efficient congestion control strategies are sought to optimize network resource usage and management. To prevent congestion, it is crucial for CRN to do research on the creation of an efficient congestion management system. This will improve the network's resource consumption and performance. "Performance improvement via efficient spectrum management through optimum resource management and congestion control in the CRN by mitigating different threats" is the primary target of this project. This research also focused on enhancing performance by addressing security issues in an IoT-based CRN environment. This study provides a comprehensive review of several similar studies and their limitations, which may be used to formulate a new research target.

Resource-Constraint Network Selection for IoT Under the Unknown and Dynamic Heterogeneous Wireless Environment

We investigate the problem of network selection for the Internet of Things (IoT) to maximize the quality of experience (QoE) in a heterogeneous wireless environment. Different from the traditional network access approaches with the assumption that the network state information (NSI) is static and known a priori, a scenario where the NSI of networks is unknown and dynamic to IoT devices is considered. Due to hardware limitations in IoT, the device has a limited resource budget and consumes resources, e.g., the energy, during the process of network access. To maximize the cumulative QoE before resource exhausts, the device should learn and estimate the NSI of networks, and make appropriate decisions to balance the network selection and resource consumption. To address this issue, we formulate the problem as a combination of multi-armed bandit and optimization problems and propose two algorithms NCSA and NSA. Moreover, we consider the fact that the device has various traffic types and propose an algorithm MT-NSA. Theoretical analysis shows that the regret of all algorithms has a sub-linear relationship with the resource budget. The effectiveness is validated by simulations.