Reduce Transmission Delay Research Articles

The method of forming ensembles of complex signals based on multi-scale decomposition of time intervals at different levels of detail

This article addresses the challenges of forming signal ensembles in dynamic cognitive radio environments, focusing on the limitations of traditional methods. The study proposes a new approach based on multiscale time interval decomposition, which allows for the creation of signal ensembles at varying levels of temporal detail. The key innovation of this method is its ability to improve signal reproduction accuracy, reduce inter-symbol and inter-channel interference, and enhance the overall efficiency of data processing. The method's adaptability to changing environmental conditions is also a core advantage, enabling better use of bandwidth and reduced transmission delay. Through the decomposition of time intervals into coarse, intermediate, and fine levels, this method allows for the detailed analysis of short-term, medium-term, and long-term signal components. Experimental results demonstrate the superiority of this approach in terms of signal recovery accuracy, noise resilience, and processing speed. These findings highlight the potential for optimizing signal processing in cognitive radio networks, particularly in environments with high levels of noise and interference. Future research aims to integrate machine learning algorithms to further enhance adaptability in real-time scenarios.

Optimized Resource Management and Dynamic Routing Protocol for Wireless Sensor Networks Through Load Balancing, Packet Scheduling, and Intelligent Clustering

Heterogeneous Wireless Sensor Networks (HWSNs) are pivotal for providing weather-related event data, enabling universal location access, and facilitating remote monitoring through multi-hop transmission. Efficient energy utilization is critical in ensuring the optimal functioning of HWSNs. Previously, Compressive Sensing (CS) technology was established to enhance communication efficiency within HWSNs. While previous methods were effective in managing energy consumption and reducing transmission delays across network devices, the increased number of devices has impacted their efficacy. Consequently, energy becomes a vital limitation in constructing HWSNs. In order to address these challenges, this study introduces Load Balancing and Packet Scheduling with Intelligent Clustering based Improved Routing Protocol (LPICR). This integrates load balancing, packet scheduling, intelligent clustering, and enhanced routing techniques. The protocol is structured into three main categories: intelligent route selection, load balancing-based Cluster Head (CH) selection, and path scheduling. Initially, an efficient opportunistic routing is conducted by the intelligent route selection process. This routing method minimizes data forwarding during communication and significantly decreases energy consumption in the HWSN. Furthermore, by using a load balancing-oriented procedure for selecting cluster heads, the system achieves efficient determination of cluster heads and construction of clusters, resulting in the most efficient use of energy in communication. Path scheduling reduces the probability of delays by facilitating effective data flow between the source and destination in the HWSN. The NS2 platform is used to implement the proposed LPICR-HWSN protocol. The calculation of the result and comparison analysis is considered for the parameters are Data loss rate, communication time, packet success rate, malicious detection ratio, throughput, Routing overhead and energy efficiency. The results are thoroughly investigated by accounting for factors like the quantity of nodes and the varying speed of the network. To assess the efficacy of this proposed protocol, we conduct a comparative analysis using established methodologies such as CDAS-WSN, EEPC-WSN, TCCS-WSN, and MTODS-HWSN. The results suggest that the proffered LPICR-HWSN model demonstrates superior performance compared to previous methods.

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

Optimizing Opportunistic Routing between Communities Using Decision Trees and Apriori Algorithm

This study proposes a novel data transmission framework aimed at optimizing node data transmission between different communities. We have designed an intelligent routing strategy based on node characteristics for situations where the source node and destination node have different communities. Through the decision tree algorithm, we trained a model to identify "messenger nodes" that are specifically responsible for cross community data transmission. In addition, the association rule model constructed using the apriori algorithm can calculate the probability of establishing a connection between the "messenger node" and the destination community node, thereby selecting the optimal path for data transmission. This method effectively reduces transmission delay and improves the efficiency and reliability of data transmission.

Efficient routing method for IoT networks using bee colony and hierarchical chain clustering algorithm

Hierarchical chain communication is an efficient technique for saving sensor energy while maintaining load balance and scalability. However, the long delay caused by the formation of long chains is a significant research gap in this paradigm. In this article, a two-level hierarchical chain routing scheme based on the bee colony algorithm called PEG_ABC is proposed to reduce transmission delay and take advantage of the benefits of chain communication. Initially, the remaining sensor energy, Euclidean distance between sensors, and Euclidean distance between sensors and base station are simultaneously adjusted to the fitness function of the bee colony algorithm to select the most suitable chain leader at the first level. Also, in our proposed method at the second level, the collected data in the leader nodes of each level is transferred to the BS through end-to-end path formation between the leaders based on the bee colony algorithm. At this level, the data transmission delay and remaining energy in the leader nodes adjust the fitness function of the bee colony algorithm. Simulation results show the efficiency of the proposed PEG_ABC scheme compared to literature research in terms of energy consumption, network lifetime, number of packets sent to BS, and transmission delay. Also, this article presents a relationship based on extensive simulation results to determine the optimal number of clusters in hierarchical chain communication.

An improved deep reinforcement learning routing technique for collision-free VANET

Vehicular Adhoc Networks (VANETs) is an emerging field that employs a wireless local area network (WLAN) characterized by an ad-hoc topology. Vehicular Ad Hoc Networks (VANETs) comprise diverse entities that are integrated to establish effective communication among themselves and with other associated services. Vehicular Ad Hoc Networks (VANETs) commonly encounter a range of obstacles, such as routing complexities and excessive control overhead. Nevertheless, the majority of these attempts were unsuccessful in delivering an integrated approach to address the challenges related to both routing and minimizing control overheads. The present study introduces an Improved Deep Reinforcement Learning (IDRL) approach for routing, with the aim of reducing the augmented control overhead. The IDRL routing technique that has been proposed aims to optimize the routing path while simultaneously reducing the convergence time in the context of dynamic vehicle density. The IDRL effectively monitors, analyzes, and predicts routing behavior by leveraging transmission capacity and vehicle data. As a result, the reduction of transmission delay is achieved by utilizing adjacent vehicles for the transportation of packets through Vehicle-to-Infrastructure (V2I) communication. The simulation outcomes were executed to assess the resilience and scalability of the model in delivering efficient routing and mitigating the amplified overheads concurrently. The method under consideration demonstrates a high level of efficacy in transmitting messages that are safeguarded through the utilization of vehicle-to-infrastructure (V2I) communication. The simulation results indicate that the IDRL routing approach, as proposed, presents a decrease in latency, an increase in packet delivery ratio, and an improvement in data reliability in comparison to other routing techniques currently available.

A Service-Driven Routing Algorithm for Ad Hoc Networks in Urban Rail Transit

Due to increased traffic pressure, traditional urban rail vehicle–ground communication systems are no longer able to meet the increasing communication requirements. In this paper, ad hoc networks are applied to urban rail transit vehicle–ground communication systems to improve link reliability and reduce transmission delay. In the proposed network, a service-driven routing algorithm is proposed, which considers the distance factor for cluster head selection and optimizes the routing transmission delay by service priority and congestion level. An auxiliary node-based routing maintenance mechanism is also proposed to avoid the problem of frequent breakage of communication links due to the high-speed movement of trains. Through the simulation, the proposed algorithm can effectively reduce the packet loss rate, end-to-end delay, and routing overhead of vehicle–ground communication compared with the traditional routing algorithm, which is more conducive to meeting the next generation of urban rail transit vehicle–ground communication requirements.

P4SQA: A P4 Switch-Based QoS Assurance Mechanism for SDN

Data center networks include a wide variety of service categories, which leads to a complex network topology. As a result, the QoS of different service flows cannot be guaranteed. In this paper, we propose a QoS assurance mechanism based on P4 switches (P4SQA) to make full use of network resources. P4SQA includes two functions: in-network traffic classification and in-network queue management. To reduce the communication overhead and computational pressure on the Software Defined Network (SDN) controller, we offload part of the neural computation from the control plane to the data plane. A fully connected neural network-based traffic classification method is deployed in the programmable data plane to classify different traffic types. In addition, the optimized active queue management algorithm CoDeL is integrated into the P4 switch (PCoDeL) to prevent network congestion while guaranteeing QoS more effectively. The evaluation results show that traffic classification accuracy has improved, especially in terms of the F1_score, which reached 0.97, significantly better than other state-of-the-art methods. Moreover, compared to traditional queue management algorithms, PCoDeL can improve network throughput and reduce transmission delay, which verifies the effectiveness and superiority of P4SQA in ensuring QoS.

Energy conserving cost selection for fine-grained computational offloading in mobile edge computing networks

In mobile edge computing networks, total or partial computational offloading of delay sensitive and compute intensive applications to the nearby edge servers is a promising solution to reduce transmission delay and energy consumption of mobile nodes (MNs). However, in computational offloading of an application to edge server, considerations of energy consumption and time delay are not sufficient for the decision of minimum cost of application. In this article, a novel battery dependent cost selection strategy for computational offloading of an application capable for sequential execution of dependent sub tasks is proposed. The strategy considers current battery level of MN in the cost function for optimal selection of offloading policy based on the amount of data, computational resources, and radio resources of the server. Our comprehensive cost function takes into account all the said parameters, calculates all the costs for possible offloading policies, and chooses the policy with minimum cost from a huge dataset. The cost selection is energy efficient for low battery level and prioritizes faster execution for full battery level of MNs. A deep neural network is trained on the generated dataset to minimize the overhead of computations. Simulation results reveal that our proposed strategy outperforms the benchmark strategies in terms of deep neural network accuracy for different sizes of dataset, energy consumption, time delay, and cost for different sizes of applications.

Optimization of Delay Using Killer Whale Algorithm (KWA) on NB-IoT

Abstract: NB-IoT is designed to connect IoT devices with low-power, wide-area coverage and efficient costs. Ensuring optimal data transmission delay is a challenge in NB-IoT implementation. Inadequate coverage can hinder IoT adoption. Optimization balances energy saving and delay trade-off. The Killer Whale Algorithm (KWA) optimizes delay by adjusting repetition variables. KWA addresses dimensions, variable limits. Applying KWA in NB-IoT optimizes transmission, enhancing QoS. Optimizing delay involves reducing latency in uplink data transmission using repetition variables. This study applies KWA to optimize NB-IoT delay. Analysis in Table 4 shows non-linear repetition-distance correlation. Interestingly, delay outcomes exhibit a contrasting relationship. Still, delay remains advantageous, remaining under 1 second even at 10 km, specifically 9.2674 ms (0.0092674 seconds). This thesis aims to optimize delay in NB-IoT network transmission using the Killer Whale Algorithm (KWA), crucial for modern communication networks and IoT applications. Leveraging KWA, the research identifies solutions to reduce transmission delay, enhancing efficiency and meeting IoT communication demands for speed and timeliness

A Distributed Caching Approach for Minimizing Average Transmission Delay in Ultra-Dense Networks

Caching at the network edge is a promising approach to alleviate the backhaul congestion pressure and reduce transmission delay in ultra-dense networks (UDNs). However, the existing content caching methods do not fully explore and exploit potential caching and computation capabilities. In this paper, we propose a distributed caching approach as well as the corresponding content addressing method. Furthermore, a problem of caching optimization with the target to minimize the average transmission delay is investigated. We transform this nondifferentiable nonlinear programming problem into a linear programming problem. Then we utilize an Alternating Direction Method of Multipliers (ADMM) algorithm to solve this problem. Simulation results reveal that the proposed algorithm can converge to the global optimum and our proposed distributed caching approach reduce the average transmission delay significantly. In popular caching strategy, the average transmission delay in our proposed method decreases over 30% and 50% when compared with caching maximum distance separable (MDS) packets and caching entire files, respectively.

A Novel Opportunistic Access Algorithm Based on GCN Network in Internet of Mobile Things

The Internet of Things (IoT) will be widely used in all areas of life and transportation as the 5th Generation (5G) communication technology matures and becomes commercially available. Especially in the field of railway transportation, the IoT technology can alleviate the challenge caused by insufficient wireless spectrum resources and improve the railway communication performance. However, the existing IoT is made up of a large heterogeneous network. In such a super-dense heterogeneous network scenario, how to allocate the most appropriate access point (AP) according to the needs of users has become a problem demanding prompt solution, which also brings additional challenges for Intelligent Transportation System (ITS) to develop green and efficient network communication technology. Therefore, focusing on the selection and access of heterogeneous networks in the Railway IoT, this paper studies the spatial characteristics of the intelligent spectrum situation of the internet of mobile things in the railway scenario, and establishes the opportunistic access situation of Railway IoT based on the Graph Convolutional Neural (GCN) network. Furthermore, we utilize the GCN network to mine the spatial correlation between different APs, and propose a railway communication AP decision algorithm based on GCN network combined with the traditional heterogeneous network multi-attribute decision algorithm. Our experimental results prove that the proposed algorithm can effectively reduce transmission delay and improve the throughput of the communication system.

Collaborative Virtual 3D Object Modeling for Mobile Augmented Reality Streaming Services Over 5G Networks

This paper presents a collaborative virtual 3D object modeling system for mobile augmented reality (AR) streaming services over 5G networks. The objective of the proposed system is to provide AR streaming services with low latency and high virtual object quality by effectively leveraging mobile edge cloud (MEC) and device-to-device (D2D) communication. MEC is utilized to perform computation-intensive 3D object modeling, such as virtual object slicing and mesh simplification, with abundant computing and storage resources. D2D communication is also utilized to reduce transmission delay by sharing virtual objects of interest among adjacent users. To achieve this goal, we propose a part-segment quality selection algorithm that can control the quality of each part segment and the transmission source by considering the users network condition. In the proposed system, all elements in the AR cloud, MEC, and mobile device are technically integrated to work together seamlessly. Finally, the proposed system is fully implemented using well-known open-source frameworks, including WebGL and AR.js. The system was then examined in real wireless network environments. The experimental results demonstrate the performance of the proposed system. The proposed system outperforms conventional systems in terms of service latency and visual 3D object quality.

IMF-PR: An Improved Morton-Filter-Based Pseudonym-Revocation Scheme in VANETs.

Vehicle ad hoc networks (VANETs) are special wireless networks which help vehicles to obtain continuous and stable communication. Pseudonym revocation, as a vital security mechanism, is able to protect legal vehicles in VANETs. However, existing pseudonym-revocation schemes suffer from the issues of low certificate revocation list (CRL) generation and update efficiency, along with high CRL storage and transmission costs. In order to solve the above issues, this paper proposes an improved Morton-filter-based pseudonym-revocation scheme for VANETs (IMF-PR). IMF-PR establishes a new distributed CRL management mechanism to maintain a low CRL distribution transmission delay. In addition, IMF-PR improves the Morton filter to optimize the CRL management mechanism so as to improve CRL generation and update efficiency and reduce the CRL storage overhead. Moreover, CRLs in IMF-PR store illegal vehicle information based on an improved Morton filter data structure to improve the compress ratio and the query efficiency. Performance analysis and simulation experiments showed that IMF-PR can effectively reduce storage by increasing the compression gain and reducing transmission delay. In addition, IMF-PR can also greatly improve the lookup and update throughput on CRLs.

The Integration of WoT and Edge Computing: Issues and Challenges

The Web of Things is an improvement on the Internet of Things (IoT) that incorporates smart objects into both the web architecture (application) and the internet (network). WoT applications are inescapable in residential homes and communities. The intent behind WoT applications is to increase sustainable development for reducing resource consumption. The Web of Things (WoT) aims to create a decentralized Internet of Things. Edge computing addresses IoT computing demands by reducing the escalation in resource congestion situations. In edge computing data is placed closed to the end users which diverts computation load from the centralized data centers. Furthermore, the dispersed structure balances network traffic and minimizes traffic peaks in IoT networks. Therefore, resulting in reducing transmission delays between edge servers and end users which improves response times for real-time WoT applications. Low battery supply to nodes with enough power resources can increase the lifespan of the individual nodes by moving processing and communication overhead from the nodes. This paper integrates WoT and edge computing and compares their functionalities. In addition, it demonstrates how edge computing enhances WoT performance and concentrates on transmission, storage, and computation aspects. Furthermore, for performance evaluation it categorizes edge computing based on different architectures. Moreover, the challenges of Web of Things and edge computing have been discussed in terms of bandwidth, latency, energy, and cost. Finally, advantages of the Web of Things and edge computing have been discussed.

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.

Multi-UAV Relay Connectivity Optimization for Heterogeneous Users Based on Load Balancing and Throughput Maximization

By relaying messages between users, unmanned aerial vehicles (UAVs), which are frequently utilized in the communication industry, can be used as airborne communication relays to resolve connectivity problems. In order to effectively connect a large number of heterogeneous users dispersed on the ground, we deploy a large number of UAVs for reliable relay coverage and message transmission between users. We initially put forth a relay coverage algorithm that continuously optimizes the position of UAVs and the cell division of terrestrial users in order to provide users with full coverage. Second, we study several relay message forwarding methods between UAVs while taking into account the features of heterogeneous users. A relay selection method, which takes the relay link throughput as the optimization goal, is proposed to ensure full connectivity between UAVs. Finally, we contrast and analyze how various relay forwarding methods affect the effectiveness of the relay network. The experimental results demonstrate that the proposed relay coverage algorithm can balance the UAV relay load better and reduce transmission delay, while the suggested relay selection strategy can minimize the number of isolated UAVs and maximize the throughput of UAV relay links.

Data Aggregation-based Transmission Method in Ultra-Dense Wireless Networks

As the Internet of Things (IoT) advances, machine-type devices are densely deployed and massive networks such as ultra-dense networks (UDNs) are formed. Various devices attend to the network to transmit data using machine-type communication (MTC), whereby numerous, various are generated. MTC devices generally have resource constraints and use wireless communication. In this kind of network, data aggregation is a key function to provide transmission efficiency. It can reduce the number of transmitted data in the network, and this leads to energy saving and reducing transmission delays. In order to effectively operate data aggregation in UDNs, it is important to select an aggregation point well. The total number of transmitted data may vary, depending on the aggregation point to which the data are delivered. Therefore, in this paper, we propose a novel data aggregation scheme to select the appropriate aggregation point and describe the data transmission method applying the proposed aggregation scheme. In addition, we evaluate the proposed scheme with extensive computer simulations. Better performances in the proposed scheme are achieved compared to the conventional approach.

Workload Allocation Toward Energy Consumption-Delay Trade-Off in Cloud-Fog Computing Using Multi-Objective NPSO Algorithm

The Internet of Things (IoT) generates massive data from smart devices that demand responses from cloud servers. However, sending tasks to the cloud reduces the power consumed by the users’ devices, but increases the transmission delay of the tasks. In contrast, sending tasks to the fog server reduces the transmission delay due to the shorter distance between the user and the server. However, this occurs at the user end’s expense of higher energy consumption. Thus, this study proposes a mathematical framework for workload allocation to model the power consumption and delay functions for both fog and clouds. After that, a Modified Least Laxity First (MLLF) algorithm was proposed to reduce the maximum delay threshold. Furthermore, a new multi-objective approach, namely the Non-dominated Particle Swarm Optimization (NPSO), is proposed to reduce energy consumption and delay compared to the state-of-the-art algorithms. The simulation results show that NPSO outperforms the state-of-the-art algorithm in reducing energy consumption, while NGSA-II proves its effectiveness in reducing transmission delay compared to the other algorithms in the experimental simulation. In addition, the MLLF algorithm reduces the maximum delay threshold by approximately 11% compared with other related algorithms. Moreover, the results prove that metaheuristics are more appropriate for distributed computing.

Accurate and Efficient Monitoring for Virtualized SDN in Clouds

This paper presents V-Sight, a network monitoring framework for programmable virtual networks in clouds. Network virtualization based on software-defined networking (SDN-NV) in clouds makes it possible to realize programmable virtual networks; consequently, this technology offers many benefits to cloud services for tenants. However, to the best of our knowledge, network monitoring, which is a prerequisite for managing and optimizing virtual networks, has not been investigated in the context of SDN-NV systems. As the first framework for network monitoring in SDN-NV, we identify three challenges: non-isolated and inaccurate statistics, high monitoring delay, and excessive control channel consumption for gathering statistics. To address these challenges, V-Sight introduces three key mechanisms: 1) statistics virtualization for isolated statistics, 2) transmission disaggregation for reduced transmission delay, and 3) pCollector aggregation for efficient control channel consumption. The evaluation results reveal that V-Sight successfully provides accurate and isolated statistics while reducing the monitoring delay and control channel consumption in orders of magnitude. We also show that V-Sight can achieve a data plane throughput close to that of non-virtualized SDN.