Minimum Transmission Delay Research Articles

LEO Satellite Downlink Distributed Jamming Optimization Method Using a Non-Dominated Sorting Genetic Algorithm

Due to their low orbit, low-Earth-orbit (LEO) satellites possess advantages such as minimal transmission delay, low link loss, flexible deployment, diverse application scenarios, and low manufacturing costs. Moreover, by increasing the number of satellites, the system capacity can be enhanced, making them the core of future communication systems. However, there have been instances where malicious actors used LEO satellite communication equipment to illegally broadcast events in large sports stadiums or engage in unauthorized leakage of military secrets in sensitive military areas. This has become an urgent issue in the field of communication security. To combat and prevent abnormal and illegal communication activities using LEO satellites, this study proposes a LEO satellite downlink distributed jamming optimization method using a non-dominated sorting genetic algorithm. Firstly, a distributed jamming system model for the LEO satellite downlink is established. Then, using a non-dominated sorting genetic algorithm, the jamming parameters are optimized in the power, time, and frequency domains. Field jamming experiments were conducted in the southwest outskirts of Xi’an, China, targeting the LEO constellation of the China Satellite Network. The results indicate that under the condition that the jamming coverage rate is no less than 90%, the proposed method maximizes jamming power, minimizes time delay, and minimizes frequency compensation compared to existing jamming optimization methods, effectively improving the real-time jamming performance and success rate.

Joint Power Control and Caching for Transmission Delay Minimization in Wireless HetNets

Joint Power Control and Caching for Transmission Delay Minimization in Wireless HetNets

Intelligent UAV planning for task-offloading with limited buffer and multiple computing servers

Dynamically moving Unmanned Aerial Vehicles (UAVs) have emerged as an effective means to significantly enhance the flexibility and transmission performance of mobile edge computing (MEC). However, in practical scenarios, UAVs often face limitations in terms of data storage capacity and computational power. In this paper, a UAV-enabled MEC network with multiple users and multiple edge computing servers is proposed, where the UAV is equipped with limited-size buffers. An optimization problem is formulated to jointly optimize UAV flight trajectories, offload server pairings, task offload ratios, and UAV transmit power to minimize transmission delay, computation delay, and system energy consumption. To tackle the intractable non-convex optimization issue, an intelligent optimization algorithm based on Deep Dueling Double Q-Network (D3QN)-Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed, which is able to efficiently determine the optimal solution. Simulation results demonstrate that our proposed intelligent algorithm exhibits good convergence and achieves a favorable balance between delay and energy consumption.

Robust Beamforming Design for Cache-Enabled C-RAN With Fronthaul Multicast

In this article, we study a robust beamforming design problem in the cached-enable cloud radio access network, where the imperfect channel state information of the access links are considered. Two transmission schemes, nonsimultaneous transmission (NST) scheme and simultaneous transmission (ST) scheme, are developed. To improve the system performance, the cooperative transmission technique is applied. In NST scheme, the base stations (BSs) obtain the uncached required files from the central processor (CP) and then transmit all required files to users. Based on this, we design an effective transmission model based on cache files and the qualities of the fronthaul link channels, and formulate a transmission delay minimization problem. Then, an alternative optimization algorithm is developed to solve it. In ST scheme, the BSs obtain the uncached files from the CP and transmit the cached files to users simultaneously. We divide two cases according to the access link transmission rate and cached files. For each case, we formulate an optimization problem by jointly designing the fronthaul multicast beamforming and access link transmission beamforming. Next, an iterative algorithm is developed via the L0 norm approximation, successive convex approximation and semidefinite relaxation techniques. Finally, simulation results show the proposed schemes can reduce the transmission delay better than the conventional multicast schemes.

Cooperative content caching and delivery in vehicular networks: A deep neural network approach

The growing demand for low delay vehicular content has put tremendous strain on the backbone network. As a promising alternative, cooperative content caching among different cache nodes can reduce content access delay. However, heterogeneous cache nodes have different communication modes and limited caching capacities. In addition, the high mobility of vehicles renders the more complicated caching environment. Therefore, performing efficient cooperative caching becomes a key issue. In this paper, we propose a cross-tier cooperative caching architecture for all contents, which allows the distributed cache nodes to cooperate. Then, we devise the communication link and content caching model to facilitate timely content delivery. Aiming at minimizing transmission delay and cache cost, an optimization problem is formulated. Furthermore, we use a multi-agent deep reinforcement learning (MADRL) approach to model the decision-making process for caching among heterogeneous cache nodes, where each agent interacts with the environment collectively, receives observations yet a common reward, and learns its own optimal policy. Extensive simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

An efficient congestion control in multimedia streaming using adaptive BRR and fuzzy butterfly optimization

AbstractRecently, various video streaming platforms such as YouTube, Netflix and so forth have achieved great growth in the media industry. However, uncertain dynamic activities such as low bandwidth, packet loss and rapid fluctuations, quality, and interrupted flow are still creating several complications in multimedia streaming applications and it is necessary to provide enhanced throughput and minimum frame delivery delay. Therefore, the significant objective of this article is to attain enhanced bandwidth allocation as well as a high packet scheduling rate. In this article, an adaptive BBR technique is proposed for transmitting real‐time video with minimum transmission delay and packet loss. The adaptive BBR mainly uses the pacing gain update mechanism to maintain different flow rates and minimal data in the bottleneck queue. Also, a fuzzy butterfly optimization (FBO) with a ranking strategy is proposed to attain optimal bandwidth allocation. The FBO algorithm with ranking strategy computes the minimal bandwidth needed by the model to satisfy the service requirement of every queue in a particular round. Then, the packet scheduling is done by employing an intelligent time slice‐based round‐robin (ITSBRR) technique. The ITSBRR algorithm is employed to enhance the throughput and end‐to‐end delay by implementing the time quantum‐based scheduling mechanism. Finally, the experimental analysis is carried out for various parameters namely single path transmission delay, packet loss; frame delay is computed for various techniques to compute the efficiency of the proposed system.

Enhanced Route Discovery Mechanism Using Improved CH Selection Using Q-Learning to Minimize Delay

With the technological advancements, practical challenges of establishing long-distance communication should be addressed using hop-oriented routing networks. However, long-distance data transmissions usually deteriorate the quality of service (QoS) especially in terms of considerable communication delay. Therefore, in the presented work, a reward-based routing mechanism is proposed that aims at minimizing the overall delay which is evaluated under various scenarios. The routing process involved a refined CH selection mechanism based on a mathematical model until a threshold simulation is not attained. The illustrations for the coverage calculations of CH in the route discovery are also provided for possible routes between the source and the destination to deliver quality service. Based on this information, the data gathered from the past simulations is passed to the learning mechanism using the Q-learning model. The work is evaluated in terms of throughput, PDR, and first dead node in order to achieve minimal transmission delay. Furthermore, area variation is also involved to investigate the effect of an increase in the deployment area and number of nodes on a Q-learning-based mechanism aimed to minimize the delay. The comparative analysis against four existing studies justifies the success of the proposed mechanism in terms of throughput, first dead node, and delay analysis.

Coded Caching for Broadcast Networks with User Cooperation.

Caching technique is a promising approach to reduce the heavy traffic load and improve user latency experience for the Internet of Things (IoT). In this paper, by exploiting edge cache resources and communication opportunities in device-to-device (D2D) networks and broadcast networks, two novel coded caching schemes are proposed that greatly reduce transmission latency for the centralized and decentralized caching settings, respectively. In addition to the multicast gain, both schemes obtain an additional cooperation gain offered by user cooperation and an additional parallel gain offered by the parallel transmission among the server and users. With a newly established lower bound on the transmission delay, we prove that the centralized coded caching scheme is order-optimal, i.e., achieving a constant multiplicative gap within the minimum transmission delay. The decentralized coded caching scheme is also order-optimal if each user’s cache size is larger than a threshold which approaches zero as the total number of users tends to infinity. Moreover, theoretical analysis shows that to reduce the transmission delay, the number of users sending signals simultaneously should be appropriately chosen according to the user’s cache size, and always letting more users send information in parallel could cause high transmission delay.

A security-aware service function chain deployment method for load balance and delay optimization

Network function virtualization (NFV) decouples network functions from hardware devices. However, it introduces security challenges due to its reliance on software, which facilitates attacks. This security problem has a significant negative impact on the interests of users. Existing deployment methods are not suitable for SFC requests with a security demand, causing the use of substrate resources unreasonable and lower acceptance ratio. Moreover, a strict delay requirement is another challenge for NFV. To make the use of the substrate resources more reasonable and reduce the transmission delay, this paper proposes a security-constraint and function-mutex-constraint consolidation (SFMC) method for virtual network function (VNF) to reduce resource consumption and transmission delay. In addition, a security-aware service function chain (SASFC) deployment method for load balance and delay optimization is presented, which deploys service function chains according to the consolidated results of the SFMC method. The SASFC method first obtains a candidate server node set using resource, hosting capacity, security and node load constraints. It then obtains candidate paths according to the metric of the minimum transmission delay and link load constraint using the Viterbi algorithm. Finally, the path with the highest VNF security level match degree among the candidate paths is adopted to deploy virtual links, and the corresponding server nodes are employed to deploy VNFs. As a result, the SASFC method makes the use of substrate resources more reasonable. It improves the acceptance ratio and long-term average revenue to cost ratio, reduces transmission delay, and achieves load balancing. Experiment results show that when the number of VNFs is five, the acceptance ratio and long-term average revenue to cost ratio of the SASFC method are close to 0.75 and 0.88, which are higher than those of the compared methods. Its transmission delay and proportion of bottleneck nodes are 7.71 and 0.024, which are lower than those of the compared methods. The simulations demonstrate the effectiveness of the SASFC method.

Relay based thermal aware and mobility support routing protocol for wireless body sensor networks

The evolvement of wireless technologies has enabled revolutionizing the health-care industry by monitor patient health condition requiring early diagnosis and interfering when a chronic situation is taking place. In this regard, miniaturized biosensors have been manufactured to cover various medical applications forming therefore a Wireless Body Sensor Network (WBSN). A WBSN is comprised of several small and low power devices capable of sensing vital signs such as heart rate, blood glucose, body temperature etc.. Although WBSN main purpose is to provide the most convenient wireless setting for the networking of human body sensors, there are still a great number of technical challenges to resolve such as: power source miniaturization, low power transceivers, biocompatibility, secure data transfer, minimum transmission delay and high quality of service. These challenges have to be taken into consideration when creating a new routing protocol for WBSNs. This paper proposes a new Relay based Thermal aware and Mobile Routing Protocol (RTM-RP) for Wireless Body Sensor Networks tackling the problem of high energy consumption and high temperature increase where the mobility is a crucial constraint to handle.

Mobility-Aware Proactive Edge Caching Optimization Scheme in Information-Centric IoV Networks.

Edge caching is a promising approach to alleviate the burden on the backhaul of network links. It has a significant role in the Internet of Vehicle (IoV) networks performance by providing cached data at the edge and reduce the burden of the core network caused by the number of participating vehicles and data volume. However, due to the limited computing and storage capabilities of edge devices, it is hard to guarantee that all contents are cached and every requirement of the device are satisfied for all users. In this paper, we design an Information-Centric Network (ICN) with mobility-aware proactive caching scheme to provide delay-sensitive services on IoV networks. The real-time status and interaction of vehicles with other vehicles and Roadside Units (RSU) is modeled using a Markov process. Mobility aware proactive edge caching decision that maximize network performance while minimizing transmission delay is applied. Our numerical simulation results show that the proposed scheme outperforms related caching schemes in terms of latency by 20–25% in terms of latency and by 15–23% in cache hits.

A novel scalable network architecture for the evolution of robot swarm networks with dual connectivity application in control-data plane

The swarm robot network is the widely used wireless communication technology, and significantly, it has higher uplink and downlink throughput than the conventional communication networks. Apart from the appreciable advantages, there are various challenges like minimized data retention rate, low data transmission rate, countless handovers over a short time, and a short transmission range due to higher data transmission usage. To resolve these challenges, Scalable Network Architecture with Dual Connectivity Application (SN-DCA) in control and data plane is proposed to improve data retention rates and minimize network usage by controlling unwanted access in the distributive clouds during transmission. Here, Two-stage control data plane splitting (TSCDP) is introduced to enhance network usage by optimizing unwanted access. It further improves the data retention rate during transmission. The analytical performance of the proposed techniques has been validated computationally and examined through simulation studies. The Simulation results show that these techniques achieve a higher performance ratio in the network with a minimum transmission delay and a high data retention rate.

VANET: Framework, Challenges and Applications

Abstract: Vehicular Ad-Hoc Network (VANET) is one of the essential research field because of large increase in usage of vehicles on road. Many VANET applications are employed to improve road safety conditions, vehicle traffic, an emergency warning to vehicle drivers, collision avoidance and other non-safety applications for comfort. The main aim of these applications are to modernize the various processes associated with road traffic, vehicles, drivers, passengers and pedestrians by implementing smart transport systems. This study is to implement an intelligent vehicular transport design to improve the road safety, navigation and comfort. VANET communication technology is a combination of mobile vehicles with ad hoc networks. The purpose of this research is to predict and prevent road accidents by transmitting emergency messages using vehicular network technology. While transmitting emergency messages, the VANET has various challenges. They are High mobility of the vehicles, Dynamic topology, Wireless communication, Minimum transmission delay, Connectivity of the network, optimal usage of transmission power. These challenges leave the vehicular networking disconnected and make an exchange of information very difficult. Even though many researchers have done significant work during the last decade on vehicular networking, some problems have not yet been solved.

VANET: Framework, Challenges and Applications

Vehicular Ad-Hoc Network (VANET) is one of the essential research field because of large increase in usage of vehicles on road. Many VANET applications are employed to improve road safety conditions, vehicle traffic, an emergency warning to vehicle drivers, collision avoidance and other non-safety applications for comfort. The main aim of these applications are to modernize the various processes associated with road traffic, vehicles, drivers, passengers and pedestrians by implementing smart transport systems. This study is to implement an intelligent vehicular transport design to improve the road safety, navigation and comfort. VANET communication technology is a combination of mobile vehicles with ad hoc networks. The purpose of this research is to predict and prevent road accidents by transmitting emergency messages using vehicular network technology. While transmitting emergency messages, the VANET has various challenges. They are High mobility of the vehicles, Dynamic topology, Wireless communication, Minimum transmission delay, Connectivity of the network, optimal usage of transmission power. These challenges leave the vehicular networking disconnected and make an exchange of information very difficult. Even though many researchers have done significant work during the last decade on vehicular networking, some problems have not yet been solved.

Minimum transmission delay algorithm for multi-hop nodes in Internet of Things based on symmetric algorithm

In view of the increase of network information transmission times, the transmission delay of multi hop nodes in the Internet of Things increases, resulting in the decline of network information transmission performance. In order to improve the quality and performance of information transmission, a minimum transmission delay algorithm of multi-hop nodes in the Internet of Things is designed based on symmetric algorithm. In the perception layer of the Internet of Things, based on the revocable symmetric encryption algorithm, the multi-hop nodes in the perception layer are encrypted; The link information transmission delay under the direct transmission, cooperative transmission and multi-hop transmission modes is compared, and the gradient projection algorithm is used to solve the problem of minimizing the delay of multi-hop nodes’ information transmission in the Internet of Things. After calculating the projection matrix and the gradient, the iteration terminates when the steady link traffic and the delay value are obtained. The source node refers to the network node that acts as the source to send the original data packet; the source node uses this algorithm to select the transmission mode with minimum delay, and sends information to the destination node step by step. The simulation test shows that the algorithm can guarantee the security of data transmission of multi-hop nodes in the Internet of Things, and the transmission delay is the minimum.

Bagging Ensembled Perceptron Classifier for Seamless Mobility System in Heterogeneous Network

Wireless mobile devices require a handover decision system to get a seamless connection in a heterogeneous wireless networking environment. The handover process is one of the most significant processes in a cellular network. Few research works have been developed for providing seamless connectivity using different handover techniques. But, controlling data traffic during the process of seamless mobile data connectivity was not solved. So, there is a necessity to introduce a new model to control the traffic and improving the seamless mobility management in heterogeneous network. A new model called Bagging Ensembled Perceptron Classification based Seamless Mobility (BEPC-SM) introduced to achieve higher data delivery rate with minimum packet loss rate and data transmission delay by means of classifying the mobile nodes in heterogeneous network. In BEPC-SM model, randomly considers a number of mobile nodes in the heterogeneous network as input. Then, BEPC-SM model determines signal strength for each mobile node in a heterogeneous network. Bagging Ensembled Perceptron Classification algorithm is used in BEPC-SM model with the aim of accurately classifying all mobile nodes as strong or weak strength node with a lower amount of time consumption. After that, the distance between the weak strength node and the access point in the network is measured. Lastly, BEPC-SM Model selects the nearby access point with maximum bandwidth availability for each weak strength node in the network to perform the handover process. Thus, the performance of seamless data communication in a heterogeneous network is improved in BEPC-SM model. The BEPC-SM model is used in traffic-aware seamless data communication in a heterogeneous network. Simulation evaluation of the BEPC-SM Model is carried out on factors such as data delivery rate, packet loss rate, data transmission delay with respect to a number of data packets. The simulation result depicts that the BEPC-SM Model is able to increases the data delivery rate and also reduces delay when compared to state-of-the-art works.

Link survivability rate-based clustering for QoS maximisation in VANET

A clustering technique is used in VANET to manage and stabilise topology information. Data communication in the cluster is performed by Cluster Heads (CH). The major issues involved in the clustering approaches are improper definition of cluster structure and maintenance of cluster structure in dynamic networks. To overcome these issues a link and weight-based clustering approach is developed along with Distributed Dispatching Information Table (DDIT). It is based on relative velocity of two same directional vehicles by forming a cluster with a number of nodes in VANET. The cluster head is selected based on link survival rate of the vehicles to provide the emergency message towards different vehicles in the cluster along with stored data packet information in DDIT table for fault prediction. Finally, an efficient medium access control protocol is used to provide prioritised message for avoiding spectrum shortage of emergency messages in the cluster. Comparative analysis between the proposed method and existing methods proves the effectiveness of our method with minimum transmission delay and reduced routing control overhead.

Mobile Edge Cache Strategy Based on Neural Collaborative Filtering

In order to effectively reduce the network transmission delay and improve the network transmission quality, the concept of Content Delivery Network (CDN) is brought forth to provide necessary technical support. In this paper, the edge cooperative caching (ECC) based on machine learning and greedy algorithm is put forward. To start with, the neural collaborative filtering is used to design the content popularity prediction algorithm to realize more accurate prediction of content popularity. Following that, the greedy algorithm after optimization is used to obtain the content delivery strategy of various servers in the cooperative cache domain. Finally, the ECC is adopted to achieve the optimization goal of minimal average content transmission delay. Meanwhile, the simulation experiment is carried out to verify the performance of the ECC. The experimental results suggest that the ECC can effectively improve the cache hit rate and the content cache space utilization, and shorten the average content transmission delay.

Dueling Deep-Q-Network Based Delay-Aware Cache Update Policy for Mobile Users in Fog Radio Access Networks

Fog radio access networks (F-RANs) can effectively alleviate fronthaul loads and reduce content transmission delay by migrating cloud services to the network edge. This paper addresses a cooperative caching scenario in F-RAN, where each mobile user can acquire the requested contents from any one of its associated fog-computing-based access points (F-APs). However, caching disparate contents in different F-APs will lead to different content delivery delays, since mobile users suffer from diverse channel fadings and interferences when they download contents from different F-APs. Considering limited caching storage in each F-AP, diverse user preferences, unpredictable user mobility and time-varying channel states, an average transmission delay minimization problem is formulated. With the aid of dueling deep-Q-network framework, a delay-aware cache update policy is proposed for mobile users in F-RAN. The proposed cache update policy will decide to replace the stored contents in F-APs with the proper contents at each time slot. Compared with first in first out, least recently used and least frequently used caching policies, simulation experiments are performed to evaluate the performance of the proposed algorithm. Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies.

A local decision making technique for reliable service discovery using D2D communications in disaster recovery networks

Disaster recovery networks (DRN) are prominent is refurbishing communication in disaster prone regions without the support of fixed infrastructures. The network consists of wireless devices that temporarily aid service discovery for effecting communication re-construction. Improving communication rate and non-delay tolerant request processing are challenging tasks in DRN due to the unsynchronized behavior and limited energy of the wireless devices. This manuscript introduces a reliable local decision making (RLDM) technique for improving the efficiency of DRNs. RLDM first classifies the service discovery conditions that are to be satisfied by the forwarding device followed by reliable neighbor selection. Local decision making process refines neighbor selection at the time of neighbor replacement using time-dependent function. The harmonized process of classification and neighbor selection aids to discover optimal neighbors that achieve better request handling. The experimental results prove the stability of the proposed method by achieving higher throughput and device utilization and minimizing transmission delay and overhead.