Search

We focus on dual-user operation, where two users control a single remote robot equipped with a force sensor using haptic interface devices. We employ a cooperative work in which the two users control the remote robot to collaborate with remote robot systems with force feedback to carry an object. By measuring the force acting upon the object, we aim to better understand the underlying mechanisms by which the user with lower network latency can help the other user, as observed in our previous work. We notice that with increasing network delays, the force exerted on the object tends to intensify, indicating that it becomes more challenging for users to operate the remote robot effectively as network delays increase. We also measure the force applied to the object by changing the network delays between the remote robot and the two users to clarify why the user with the lower network delay can assist the other user. We find that when the total network delay is the same, the average force magnitude and the average maximum force magnitude remain nearly identical. This is because, despite the challenges faced by the user with the larger network delay, the user with the smaller delay can operate the remote robot more easily and assist the other user. In order to reduce the force acting upon the object, we propose an enhancement method for the robot position control, which determines the position of the remote robot arm while accounting for network delay, and investigate the effects by experiment. Experimental results demonstrate that our proposed method is effective and can reduce the applied force. This is because the proposed method adjusts the ratio between the user with the lower delay and the user with the higher delay. The user with the lower delay can operate the remote robot more easily and respond to it more quickly. Our findings and proposed method can be useful in improving work accuracy and operability when designing a remote robot system with force feedback for applications. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate and compare how well different routing protocols perform in terms of load sharing, link failover, and overall network performance. Wireshark was used for packet-level analysis, VMWare was used for virtualization, GNS3 was used for network simulation, and Iperf3 was used to measure network performance parameters. Convergence time, packet losses, network jitter, and network delay are the parameters that were selected for assessment. To examine the behaviors of Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) routing protocols in a variety of network settings, a simulated network environment incorporating both protocols along with Border Gateway Protocol (BGP) is created for the research. The setup for the experiment entails simulating different network conditions, such as fluctuating traffic loads and connection failures, to track how the protocols function in dynamic situations. The efficiency metrics for OSPF and EIGRP with BGP are measured and evaluated using the data generated by Wireshark and Iperf3. The results of this research show that EIGRP has a better failover convergence time and packet loss percentage as compared to the OSPF. [ABSTRACT FROM AUTHOR]

A fault detection method based on a combination of event-triggered mechanism and Delta operator is used for the fault problems caused by time delay and packet loss in the network due to bandwidth limitation of the communication network in the case of high-speed sampling of the network control system. In this study, while co-designing the fault detection filter and the controller, the construction vector is added to the Delta operator filter model to avoid the generation of bilinear terms in the parameter design. Constructing Lyapunov-Krasovskii generalized connotations of the δ-domain for the stability and H∞ performance of the residual system. The numerical simulation example verifies the effectiveness of the proposed event triggering mechanism and demonstrates its high sensitivity to faults by the residual evaluation function. [ABSTRACT FROM AUTHOR]

Studies on the delay characteristics under the Internet macro-topology provides a reference for resolving the real-time performance issue of the data transmission of Internet devices. With the overall advancement of IPv6 Internet deployment, the changes in network structure and paths will generate different degrees of delay. In this context, a comparative analysis of the behavioral characteristics of IPv4 and IPv6 network delay was performed in this study. We selected the sampled data of valid paths located at four monitors on different continents under the CAIDA_Ark project to obtain statistics for the network delay and communication diameter on the IPv4 and IPv6 Internet and found that their correlation was extremely weak. Furthermore, the communication diameter on IPv6 Internet was slightly shorter than that on IPv4 Internet. The network delay exhibited a bimodal or multimodal heavy-tailed distribution. The network delay and maximum link delay for IPv4 and IPv6 Internet were strongly correlated, indicating that the bottleneck delay affects the relationship between the network delay and communication diameter. Next, we analyzed the relationship between network delay and bottleneck delay for IPv4 and IPv6 Internet and found that bottleneck delay has a more significant impact on the network delay on the valid paths for IPv4 Internet than for IPv6 Internet. After mapping the IP addresses at both ends of the bottleneck delay to the Autonomous Systems (ASes), we found that the bottleneck delay on the valid paths for the IPv4 Internet was mostly distributed in the intra-AS, whereas it was in the inter-AS for the IPv6 Internet. Finally, we analyzed the factors affecting bottleneck delay and found that propagation delay in the long-distance range is an important factor (L > 4000 km on IPv4 Internet and L > 7000 km on IPv6 Internet). In addition, for IPv4 Internet, queuing delay is an important factor affecting bottleneck delay, whereas in the process of data communication on the IPv6 Internet, the impact of propagation and queuing delays on the bottleneck delay is weakened. [ABSTRACT FROM AUTHOR]

Cloud computing has transformed digital service delivery by providing scalable, flexible access to computing resources, including servers, storage, and applications, under a pay-per-use model. This model utilizes geographically distributed data centers to enhance service delivery and dynamically adjust Service Level Agreement (SLA) pricing based on demand. However, effective resource allocation strategies remain challenging, especially for ensuring low latency and fast execution in real-time applications and interactive services. Increased data center load can degrade performance, impacting cost and productivity. To address these challenges, we developed the Intelligent Validation Cloud Broker System (IVCBS). Uses an algorithm to classify virtual machine (VM) resources and match them with users’ request sizes, relying on a mathematical model aligned with the trapezoidal membership function in fuzzy logic. This reduces fuzzy rules and improves decision-making accuracy. We tested 11 types of AWS General Purpose EC2 specifications across 31 data centers in various regions. Implementing and comparing IVCBS with a traditional method through two policies—optimize response time and dynamically reconfigure load—showed that the IVCBS with optimized response time policy outperformed in terms of overall response time, data center processing, and total VM cost. IVCBS addresses scalability and performance challenges by efficiently assigning VMs, managing workload distribution, and preventing data center overload. Improving the average data center request servicing time maintains a high quality of service (QoS) and energy optimization.

Security and reliability are the major requirements for the successful implementation of Internet of Things (IoT) applications in smart cities such as healthcare. IoT networks are subject to various types of network attacks that can put critical IoT data at risk. To mitigate these security issues, robust security algorithms based on Elliptic Curve Cryptography (ECC) may be utilized. However, using such cryptographic techniques requires additional delay for signing/encrypting and verification/decryption of the messages. Hence, there is a tradeoff between the security strength of the algorithm and packet delay. In this paper, we present a novel technique to adaptively select the key size of ECC-based algorithms for multi-hop IoT networks. The proposed technique uses k-means clustering to classify the secrecy rates of the potential relay nodes into k clusters. We allocate lower key sizes to the relay nodes that have the highest secrecy rates and thus have better physical layer security. The key sizes and security strengths are selected as per the recommendation of the National Institute of Standards and Technology (NIST). The second part of the work utilizes the Hungarian algorithm to select the best relay nodes for each source-destination transmission to minimize the total delay. The techniques are implemented in MATLAB and results show that the total delay of the proposed technique is improved by 57% as compared to other techniques in the literature.

In 2022, the digital population reached 5.4 billion, leading to network congestion, primarily attributed to the widespread use of social media, robotic programs, and prime-time television during peak hours. This congestion posed a significant challenge, as sensitive services struggled to operate effectively during peak hours, while network resources remained largely underutilized during off-peak hours. To tackle this issue, we propose the implementation of Regional Servers. These servers are designed to initially store less critical data during peak hours and subsequently transfer it to the cloud during off-peak hours, effectively optimizing network resources. The results demonstrate that the deployment of regional servers proved highly efficient in mitigating delays and reducing jitters that users typically experience during peak hours, successfully addressing the challenges posed by network congestion.

We focus on dual-user operation, where two users control a single remote robot equipped with a force sensor using haptic interface devices. We employ a cooperative work in which the two users control the remote robot to collaborate with remote robot systems with force feedback to carry an object. By measuring the force acting upon the object, we aim to better understand the underlying mechanisms by which the user with lower network latency can help the other user, as observed in our previous work. We notice that with increasing network delays, the force exerted on the object tends to intensify, indicating that it becomes more challenging for users to operate the remote robot effectively as network delays increase. We also measure the force applied to the object by changing the network delays between the remote robot and the two users to clarify why the user with the lower network delay can assist the other user. We find that when the total network delay is the same, the average force magnitude and the average maximum force magnitude remain nearly identical. This is because, despite the challenges faced by the user with the larger network delay, the user with the smaller delay can operate the remote robot more easily and assist the other user. In order to reduce the force acting upon the object, we propose an enhancement method for the robot position control, which determines the position of the remote robot arm while accounting for network delay, and investigate the effects by experiment. Experimental results demonstrate that our proposed method is effective and can reduce the applied force. This is because the proposed method adjusts the ratio between the user with the lower delay and the user with the higher delay. The user with the lower delay can operate the remote robot more easily and respond to it more quickly. Our findings and proposed method can be useful in improving work accuracy and operability when designing a remote robot system with force feedback for applications.

Reasonable allocation of resources is an important guarantee for efficient support of power business in edge IoT agents. Facing the above problems of the current power Internet of Things, this paper proposes a resource optimization allocation method based on deep Q-learning. This method first comprehensively considers the communication performance and network security. Involving indicators such as latency and service satisfaction, a complete and reliable mathematical model of the edge Internet of Things proxy network is constructed to achieve efficient and reliable modeling of the power Internet of Things (pIoT), aiming to better fit the practical interaction needs for efficient and secure communication. The Q-learning network model is optimized, and the method combining Reinforcement learning and deep learning is used to solve the model. Used by this network, the optimization and improvement of the deep network model is realized, so that the status, action and other parameters of the network model can be solved in a timely manner, so as to better support the reliable and efficient information interaction of the communication network. The test results prove that the delay of the proposed method can be maintained within 12 ms in more complex scenarios, and the interaction success rate reaches 0.975, confirming that the proposed method can provide good information interaction guarantee services. [ABSTRACT FROM AUTHOR]

Background: Network latency is the most important factor affecting the performance of telemedicine. The aim of the study is to assess the feasibility and efficacy of a novel network latency management system in 5G telesurgery. Methods: We conducted 20 telesurgery simulation trials (hitching rings to columns) and 15 remote adrenalectomy procedures in the 5G network environment. Telemedicine Network Latency Management System and the traditional "Ping command" method (gold standard) were used to monitor network latency during preoperative simulated telesurgery and formal telesurgery. We observed the working status of the Telemedicine Network Latency Management System and calculated the difference between the network latency data and packet loss rate detected by the two methods. In addition, due to the lower latency of the 5G network, we tested the alert function of the system using the 4G network with relatively high network latency. Results: The Telemedicine Network Latency Management System showed no instability during telesurgery simulation trials and formal telesurgery. After 20 telesurgery simulation trials and 15 remote adrenalectomy procedures, the p-value for the difference between the network latency data monitored by the Telemedicine Network Latency Management System and the "Ping command" method was greater than 0.05 in each case. Meanwhile, the surgeons reported that the Telemedicine Network Latency Management System had a friendly interface and was easy to operate. Besides, when the network latency exceeded a set threshold, a rapid alarm sounded in the system. Conclusion: The Telemedicine Network Latency Management System was simple and easy to operate, and it was feasible and effective to use it to monitor network latency in telesurgery. The system had an intuitive and concise interface, and its alarm function increased the safety of telesurgery. The system's own multidimensional working ability and information storage capacity will be more suitable for telemedicine work. [ABSTRACT FROM AUTHOR]

In this study, we investigate the edge offloading of sensory data in the internet of things. Multiple edge servers cooperatively offloading all or part of the sensory data initially sent to a cloud center, which improves users' experience. In the process of cooperative offloading, the transmission of sensory data and information exchange among edge servers will consume system resources, resulting in a cost of cooperation. How to maximize the offloading ratio of sensing data while maintaining a low collaboration cost is a challenge. This study first formulates a joint optimization problem of the sensing data offloading ratio and cooperative ratio satisfying the constraints of network delay and system energy consumption. Subsequently, a distributed alternating direction method of multipliers (ADMM) via constraint projection and variable splitting is proposed to solve the problem. The numerical results show that the proposed method greatly improves network delay and energy consumption compared to the fairness cooperation algorithm. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) have gained significant attention in the past decade for their potential to support intelligent transportation systems and entertainment facilities in smart cities. However, VANETs face significant challenges, such as intermittent connectivity and poor signal strength caused by abstraction from buildings and poles. The excessive packet drop rate results in re-transmissions which negatively affect the end-to-end delay. Diverse ranges of vehicle mobility increase the routing overhead and make the VANET an unscalable network. The severity of these challenges becomes even more intensified when deployed in an urban area and limits the widespread adoption of VANETs. The core aim of this study is to investigate the potential of integrating micro-UAVs with VANETs that can be used for bandwidth hungry as well as delay sensitive applications of intelligent transportation systems. The proposed model leverages the line-of-sight communication capabilities of UAVs to facilitate communication between vehicles and infrastructure. We demonstrated the effectiveness of our approach in high-density urban areas, where traditional VANET struggles to perform. Experiment results show that the proposed model achieves the probability of line-of-sight approaches to 1. This helps to provide a reliable link and has better receive signal strength which results in higher throughput for entertainment applications and short message delay for safety and interactive applications. [ABSTRACT FROM AUTHOR]

The edge offloading of the internet of things (IoT) sensing data was investigated.Multiple edge servers cooperatively offload all or part of the sensing data initially sent to the cloud center, which protects data privacy and improves user experience.In the process of cooperative offloading, the transmission of the sensing data and the information exchange among edge servers will consume system resources, resulting in the cost of cooperation.How to maximize the offloading ratio of the sensing data while maintaining a low collaboration cost is a challenging problem.A joint optimization problem of sensing data offload ratio and cooperative scale satisfying the constraints of network delay and system energy consumption was formulated.Subsequently, a distributed alternating direction method of multipliers (ADMM) via constraint projection and variable splitting was proposed to solve the problem.Finally, simulation experiments were carried out on MATLAB.Numerical results show that the proposed method improved the network delay and energy consumption compared to the fairness cooperation algorithm (FCA), the distributed optimization algorithm (DOA), and multi-subtasks-to-multi-servers offloading scheme (MTMS) algorithm.

This paper aims to ensure the input-to-state stability (ISS) of a continuous-time linear scalar system which suffers from quantization, unknown noise, and network delay. To deal with the unknown noise, we design a dynamic quantization scheme that switches between "zooming-in" and "zooming-out", and adjusts a "zooming" parameter of finite-level quantizers. Moreover, a two-stage event-triggering scheme is proposed to determine whether to sample the state and whether to transmit the encoded state information. The joint design of the dynamic quantization scheme and the two-stage event-triggering scheme can well counteract the effects of unknown noise and network delay on the desired ISS of the concerned system. Moreover, the proposed schemes can avoid Zeno behavior and do not require any prior bounds on the unknown noise. The effectiveness of the proposed schemes is further verified through simulations. [ABSTRACT FROM AUTHOR]

Because the traditional computing model can no longer meet the particularity of Internet of Vehicles tasks, aiming at its characteristics of high bandwidth, low latency and high reliability, this paper proposes a resource allocation strategy for Internet of Vehicles using reinforcement learning in edge cloud computing environment. First, a multi-layer resource allocation model for Internet of Vehicles is proposed, which uses the cooperation mode of edge cloud computing servers and roadside units to dynamically coordinate edge computing and content caching. Then, based on the construction of communication model, calculation model and cache model, make full use of idle resources in Internet of Vehicles to minimize network delay under the condition of limited energy consumption. Finally, the optimization goal is solved by two-layer deep Q network model, and the best resource allocation plan is obtained. The simulation results based on the Internet of Vehicles model show that the computational energy consumption and system delay of proposed strategy do not exceed 400 J and 600 ms, respectively. Besides, the overall effect of resource allocation is better than other comparison strategies and it has certain application prospects. [ABSTRACT FROM AUTHOR]

Nakamoto consensus is prevailing in the world largest blockchain-based cryptocurrency systems, such as Bitcoin and Ethereum. Since then, various attempts have been studied to attack Nakamoto consensus worldwide. In recent years, network delay has won more attention for making inconsistent ledgers in blockchain-based applications by virtue of attacking Nakamoto consensus. However, so far as we know, most of the existing works mainly focus on constructing inconsistent ledgers for blockchain systems, but not offering fine-grained theoretical analysis for how to optimize the success probability by flexibly dividing computational power and network delay from the viewpoint of adversary. The paper first utilizes network delay and the partition of controlled computation power of honest miners for making forks as long as possible. Then, formally analysis is provided to show the success probability of the proposed attack, and compute the optimal network delay and splitting for adversarial computation power in theory. Finally, simulation experiments validate the correctness of the formal analysis.

An effective transmission method is crucial to the long-term network life of a wireless sensor network. Energy constraints in sensor networks are critical since sensor nodes are often battery-powered and it is either impossible or extremely expensive to replenish them in most implementations. The early mortality of sensor nodes and indeed the network's lifespan are commonly caused by an imbalance in data flow in the communication channel. Proposed solutions address this issue and increase network longevity by altering transmission range based on distances between cluster chiefs and their members. In addition, a firefly optimization algorithm-based mobile data collector is used to extend the network's lifespan. For each case, the new method is compared against existing benchmark algorithms. Simulated findings show that the proposed technique beats current ones in terms of network lifespan. [ABSTRACT FROM AUTHOR]

The transmission of satellite payload data is critical for services provided by aerospace ground networks. To ensure the correctness of data transmission, the TCP data transmission protocol has been used typically. However, the standard TCP congestion control algorithm is incompatible with networks with a long time delay and a large bandwidth, resulting in low throughput and resource waste. This article compares recent studies on TCP-based acceleration algorithms and proposes an acceleration algorithm based on the learning of historical characteristics, such as end-to-end delay and its variation characteristics, the arrival interval of feedback packets (ACK) at the receiving end and its variation characteristics, the degree of data packet reversal and its variation characteristics, delay and jitter caused by the security equipment's deep data inspection, and random packet loss caused by various factors. The proposed algorithm is evaluated and compared with the TCP congestion control algorithms under both laboratory and ground network conditions. Experimental results indicate that the proposed acceleration algorithm is efficient and can significantly increase throughput. Therefore, it has a promising application prospect in high-speed data transmission in aerospace-ground service networks. [ABSTRACT FROM AUTHOR]

Nakamoto consensus is prevailing in the world largest blockchain-based cryptocurrency systems, such as Bitcoin and Ethereum. Since then, various attempts have been studied to attack Nakamoto consensus worldwide. In recent years, network delay has won more attention for making inconsistent ledgers in blockchain-based applications by virtue of attacking Nakamoto consensus. However, so far as we know, most of the existing works mainly focus on constructing inconsistent ledgers for blockchain systems, but not offering fine-grained theoretical analysis for how to optimize the success probability by flexibly dividing computational power and network delay from the viewpoint of adversary. The paper first utilizes network delay and the partition of controlled computation power of honest miners for making forks as long as possible. Then, formally analysis is provided to show the success probability of the proposed attack, and compute the optimal network delay and splitting for adversarial computation power in theory. Finally, simulation experiments validate the correctness of the formal analysis. [ABSTRACT FROM AUTHOR]

This paper focuses on the quantized consensus problem of discrete-time second-order multi-agent systems which suffer from bounded process noise. In such systems, neighboring agents exchange information through a digital network that has only a finite bit rate and is subject to bounded network delay. Network delay may exacerbate the effects of quantization error and degrade the control performance by reducing the information to be extracted from receiving time instants of exchange packets. To handle network delay and save communication bit rates, an event-triggered distributed control protocol based on a dynamic encoding–decoding policy is proposed. Under the proposed event-triggered protocol, the asymptotic consensus of the concerned noise-free multi-agent systems can be achieved at a lower bit rate than that of periodic sampling control. This bit rate superiority results from the fact that our protocol makes use of the extra information being extracted from the event-triggering time instants. Moreover, it is shown that no higher bit rate is required to guarantee the desired consensus in the existence of bounded process noise. Simulations are done to demonstrate the effectiveness of our consensus protocol. As shown by our numerical examples, the occupied bit rate to ensure the desired consensus can be saved by up to 50% compared with periodic sampling strategies. • An event-triggered sampling strategy and a dynamic encoding–decoding policy for MASs. • The proposed event-triggered sampling reduces the bit rate required for consensus. • Effects of bounded network delay and process noise on consensus are considered. [ABSTRACT FROM AUTHOR]

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users' diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users' data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (γ), and 6% performance gain in terms of backhaul saving vs. cache capacity. [ABSTRACT FROM AUTHOR]

Intersatellite links can reduce the dependence of satellite communication systems on ground networks, reduce the number of ground gateways, and reduce the complexity and investment of ground networks, which are important future trends in satellite development. Intersatellite links are dynamic over time, and different intersatellite topologies have a great impact on satellite network performance. To improve the overall performance of satellite networks, a satellite link assignment optimization algorithm based on reinforcement learning is proposed in this article. Different from the swarm intelligence method in principle, this algorithm models the combinatorial optimization problem of links as the optimal sequence decision problem of a series of link selection actions. Realistic constraints such as intersatellite visibility, network connectivity, and number of antenna beams are regarded as fully observable environmental factors. The agent selects the link according to the decision, and the selection action utility affects the next selection decision. After a finite number of iterations, the optimal link assignment scheme with minimum link delay is achieved. The simulation results show that in 8 or 12 satellite network systems, compared with the original topology, the topology calculated by this method has better network delay and smaller delay variance. [ABSTRACT FROM AUTHOR]

This article investigates the input-to-state stability (ISS) of continuous-time networked control systems with model uncertainty and bounded noise based on event triggering. The feedback loop is closed over an unreliable digital communication network. Feedback packets suffer from network delay and may be dropped in an independent and identically distributed (i.i.d.) way, which may harm to the concerned stability. This article focuses on a Lyapunov-based event-triggered control design scheme with the consideration of i.i.d. packet dropouts. By designing a state-dependent event-triggering threshold and updating methods, it can still ensure ISS for the concerned multidimensional system in the presence of i.i.d. packet dropouts and model uncertainty without exhibiting the Zeno behavior. Simulations are done to verify the effectiveness of the achieved results. [ABSTRACT FROM AUTHOR]

Engine oil or lubricating oil has a major effect on the engine life and the proper operation of any engine. Changing the engine oil before it is due increases a customer’s cost. The lubricating oil in every engine performs many functions such as reducing friction, preventing corrosion, protecting the engine against wear, removing all impurities, lubricating the moving parts, and cooling the engine parts. There are several causes for the deterioration of lubricating oil, including the properties of the oil, oil quality, and high engine temperatures. Consequently, the deteriorated oil must be replaced at a specified mileage or at specific time intervals to get the best engine performance. It is very important to know when to change the oil, because changing the oil too late can affect the engine parts and vehicle performance. However, replacing the oil too early influences the economy and environment and is an inefficient use of depleting resources. This study describes the kinematic viscosity, flash point, and fire point of multigrade Society of Automotive Engineers (SAE) 0W-20 Totachi (Totachi Industrial Co. Ltd., Japan) international brand oil, which has a 10,000 km guarantee and is approved by and used in 10 different vehicle brands, to determine the rate of deterioration of the parameters. These parameters are the most important physical behaviors of lubricating engine oils. Having information about these parameters is very important for maintaining an engine’s lifespan. The results of this study showed that after 10,000 km, the Totachi oil parameters such as the kinematic viscosity at cold start, at 40°C and at 100°C, the flash point, and the fire point decreased by 22.03%, 25.98%, 26.75%, 16.94% and 17.34%, respectively, from the base values, and that the oil is suitable to use for 10,000 km.

The transmission of satellite payload data is critical for services provided by aerospace ground networks. To ensure the correctness of data transmission, the TCP data transmission protocol has been used typically. However, the standard TCP congestion control algorithm is incompatible with networks with a long time delay and a large bandwidth, resulting in low throughput and resource waste. This article compares recent studies on TCP-based acceleration algorithms and proposes an acceleration algorithm based on the learning of historical characteristics, such as end-to-end delay and its variation characteristics, the arrival interval of feedback packets (ACK) at the receiving end and its variation characteristics, the degree of data packet reversal and its variation characteristics, delay and jitter caused by the security equipment’s deep data inspection, and random packet loss caused by various factors. The proposed algorithm is evaluated and compared with the TCP congestion control algorithms under both laboratory and ground network conditions. Experimental results indicate that the proposed acceleration algorithm is efficient and can significantly increase throughput. Therefore, it has a promising application prospect in high-speed data transmission in aerospace-ground service networks.

Vehicular ad hoc network is a pretty research vibrant area since last decade. It has been successfully used for intelligent transportation system and entertainment purposes for realization of smart cities. However, intermittent connectivity, high routing overhead, inflexible communication infrastructure, unscalable networks, and high packet collision are the key challenges that put hindrances on the wide applications of vehicular ad hoc network. The severity of these challenges become even more intensified when deployed in urban areas. To overcome these hurdles, integrating micro unmanned aerial vehicles with vehicular ad hoc network provides a viable solution. In this article, we proposed an unmanned aerial vehicle–assisted vehicular ad hoc network communication architecture in which unmanned aerial vehicles fly over the deployed area and provide communication services to underlying coverage area. Unmanned aerial vehicle–assisted vehicular ad hoc network avails the advantages of line-of-sight communication, load balancing, flexible, and cost effective deployment. The performance of the proposed model is evaluated against a case study of vehicle collision on highway. Results show that utilization of unmanned aerial vehicles ensures the guaranteed and timely delivery of emergency messages to nearby vehicles so that a safe action can be taken to avoid further damages. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important part of the new generation of information technology. It utilizes many sensor nodes to collect data and monitor the environment and can be applied in various fields. However, because the energy of sensor nodes is limited and the batteries cannot be replaced, the lifetimes of the sensor nodes are limited by the energy of their batteries. Only a reasonable method for saving energy can reduce the energy loss of the nodes in the communication process. Many studies have found that batteries have the property of battery-friendliness; that is, if they continue working, their energy drops rapidly. However, if they rest after working for a period of time, the batteries' energy can be partially restored. In this paper, an effective relay selection scheme for prolonging the lifetimes of nodes is designed based on the battery-friendly nature of the battery. In the proposed battery-friendly relay selection (BFRS) scheme, the relay node is selected based on the weighted synthesis value of the recovered energy, its distance to the sink and the residual energy. A forwarding node that has recovered more energy and is closer to the sink is more likely to be selected as the relay node because of its larger weight. Hence, the BFRS-based routing scheme can improve the lifetime by recovering more energy. Through extensive experiments, we demonstrate that our BFRS scheme outperforms previously proposed schemes. In a network with 500 nodes, compared with the shortest routing algorithm, the network lifetime is increased by 51.26%, and the amount of transferred data is increased by 42.93%.

This article handles building underwater sensor networks autonomously using multiple surface ships. For building underwater sensor networks in 3D workspace with many obstacles, this article considers surface ships dropping underwater robots into the underwater workspace. We assume that every robot is heterogeneous, such that each robot can have a distinct sensing range while moving with a distinct speed. The proposed strategy works by moving a single robot at a time to spread out the underwater networks until the 3D cluttered workspace is fully covered by sensors of the robots, such that no sensing hole remains. As far as we know, this article is novel in enabling multiple heterogeneous robots to build underwater sensor networks in a 3D cluttered environment, while satisfying the following conditions: (1) Remove all sensing holes. (2) Network connectivity is maintained. (3) Localize all underwater robots. In addition, we address how to handle the case where a robot is broken, and we discuss how to estimate the number of robots required, considering the case where an obstacle inside the workspace is not known a priori. Utilizing MATLAB simulations, we demonstrate the effectiveness of the proposed network construction methods. [ABSTRACT FROM AUTHOR]

Congestion control issues have received consistent attention from several telecommunication researchers and practitioners for many years because network congestion often causes significant packet loss and delay problems. Although many suggested methods for these issues and problems can be available in telecommunication literature, there is room for improvement. This paper's primary objective is to propose a dynamic weighting method in routing to reduce congestion during peak hours by predicting incoming traffics and optimizing weights in the adjacency matrix of the routing table online. While changing weight values in the routing table, this method implicitly changes the routing path of many nodes through the congested node. As a result, the packet losses and delays can be decreased in used nodes. The main steps of this proposed framework are fourfold. First, the candidate weighing list is calculated based on the routing table of the congested node. Second, a proposed auto-adaptive framework (AAF) is used to predict congestion levels and apply the weighing list in the network node. Third, the evaluation of network congestion is then performed by the probability of packet losses due to overflowing buffers. Finally, the case study results demonstrate that the proposed AAF framework can significantly reduce network congestion. [ABSTRACT FROM AUTHOR]

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users’ diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users’ data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (γ), and 6% performance gain in terms of backhaul saving vs. cache capacity.

Machine-to-Machine (M2M) communications have gone forth as the newest technology for succeeding in communication generations. The M2M connections use the sensor nodes to capture an event into data packets and relayed through a network. The sensor nodes consume more energy whenever the increase in data packets transmitted from the sensor nodes in the system. To reduce the energy utilization applying the data aggregation is essential. We proposed a comprehensive model for calculating energy utilization and delay-tolerance by using Multi-Level Data Aggregation Trees (MLDAT). In the proposed scheme, the first stage is about the construction of Multi-Level Data Aggregation Tree, which aggregates the data originated from various wireless sensor nodes in the communication network. In the second stage, a delay-tolerant scheduling algorithm for controlling the delivery delay for user queries presented. Ultimately, the simulation results of the proposed scheme show that the suggested algorithms have better performance than the existing state-of-the-art approaches significantly. [ABSTRACT FROM AUTHOR]

The article is aimed at specifying criteria of synchronization signal inaccuracy when DVB-T2 system local content re-placement cannot be carried out. Some settings of digital information stream are set as the initial data. The first part of the article specifies network delay calculation method. The second part of the article considers the signal cycle instability impact on estimation of local content device delay during its insertion in global information flow. Network delay saw-toothlike dependence on time is revealed, statistical parameters of synchronization signal instability are specified. The third part of the article deals with analysis of method of substitution of two independent information flows by regionali-zation device. A model of T2 superframes comprising baseband frames (BB frames) which belong to different physical layer pipes (PLP) is described. The conditions under which the local information insertion becomes impossible are de-fined. Besides, the article considers other reasons of failure during single frequency networks (SFN) processing of local content insertion dealing with synchronization error at different stages of information flow digital content generation and delivery to DVB-T2.

Background: 5G communication technology has been applied to several fields in telemedicine, but its effectiveness, safety, and stability in remote laparoscopic telesurgery have not been established. Here, we conducted four ultra-remote laparoscopic surgeries on a swine model under the 5G network. The aim of the study was to investigate the effectiveness, safety, and stability of the 5G network in remote laparoscopic telesurgery.Methods: Four ultra-remote laparoscopic surgeries (network communication distance of nearly 3000 km), including left nephrectomy, partial hepatectomy, cholecystectomy, and cystectomy, were performed on a swine model with a 5G wireless network connection using a domestically produced "MicroHand" surgical robot. The average network delay, operative time, blood loss, and intraoperative complications were recorded.Results: Four laparoscopic telesurgeries were safely performed through a 5G network, with an average network delay of 264 ms (including a mean round-trip transporting delay of 114 ms and a 1.20% data packet loss ratio). The total operation time was 2 h. The total blood loss was 25 ml, and no complications occurred during the procedures.Conclusions: Ultra-remote laparoscopic surgery can be performed safely and smoothly with 5G wireless network connection using domestically produced equipment. More importantly, our model can provide insights for promoting the future development of telesurgery, especially in areas where Internet cables are difficult to lay or cannot be laid. [ABSTRACT FROM AUTHOR]

This paper proposed an algorithm for SFC mapping based on deep reinforcement learning which was called DQNSFC, aiming at reducing the influence of SFC mapping on the average time delay and deployment failure ratio in the network. Firstly, it constructed a multi-layer NFV management and scheduling architecture to meet the requirements of resource awareness and equipment configuration of the algorithm. Secondly, based on Markov decision process, it formally described the SFC mapping problem. Finally, it constructed a deep reinforcement learning network, which used the average network delay and the operation expense of the deployment as reward and punishment feedback. After training, the target position of the virtual network function where to be deployed can be determined according to the network status. The simulation experiment verifies correctness and performance of this algorithm. Experiment shows that this algorithm can effectively reduce the average network delay and deployment failure ratio, and has certain advantages in algorithm running time. [ABSTRACT FROM AUTHOR]

The new generation telecommunications is growing at a spectacular pace and Device-to-Device (D2D) communication networks plays major role to meet the growing demands by billions of connected devices over the globe. Generally, indoor based D2D communication networks relays on distributed small cell solutions for better connectivity. This research proposes Signal-to-Interference-plus-Noise Ratio (SINR) based – Device Association Vector Algorithm (S-DAVA) for maximum network connectivity by efficiently discovering the neighbors and its D2D link based on network sum rate and SINR. The algorithm uses Rician model for Line-of-Sight (LoS) component to investigate the D2D network performance. The algorithm when evaluated with other conventional methods showed an improvement of 18.85% in throughput, and 10% of efficiency in energy with reduced network delay of 19%. This paper also checks its performance over traffic load, outage probability and network sum rate. [ABSTRACT FROM AUTHOR]

This paper investigates the input-to-state stability (ISS) of continuous-time networked control systems with model uncertainty and bounded noise based on event-triggering. The information generated at the sensor is quantized and transmitted to the controller through a digital communication channel, which suffers from the network-induced time delay and clock offset. In the concerned event-triggering framework, such clock offset may cause long-term inconsistency between the state estimates of the sensor and the controller, and leads to the failure of ISS. By designing a well-designed state-dependent event-triggering threshold and updating methods, it can still ensure ISS for the concerned system in the presence of bounded clock offset and model uncertainty without exhibiting Zeno behavior. Simulations are done to verify the achieved results. [ABSTRACT FROM AUTHOR]