Buffer Occupancy Research Articles

Network Capacity Estimators Predicting QoE in HTTP Adaptive Streaming

The aim of adaptive HTTP streaming technology is preserving the best possible video streaming quality for viewers in heterogeneous network conditions. This can be achieved by making multiple quality versions of the video available. Switching between versions during playback should be imperceptible and fluent. The decision about quality-level switching is typically based on network capacity estimation and buffer occupancy, which predict the risk of stalling. Since quality-level switching and stalling are directly evident to the user, they are often classified as influence factors of quality of experience (QoE). In this paper, we observe different network capacity estimators and buffer behavior in limited network conditions and study how the estimators predict QoE. The challenges of variable bitrate (VBR)-encoded video are considered. We also propose two new estimators to predict QoE. One compares segment fetch time to segment playback time, while the other explores the difference of throughput and average download rate. As segment duration may influence HTTP adaptive streaming (HAS) playback in unstable conditions, the findings are tested with four segment lengths. Moreover, streaming quality is analyzed in a testbed using two popular web players to reveal possible effects of the players’ features.

Spectrum Allocation Techniques for Cognitive Radio Networks

The remarkable growth in the demand for multimedia streaming over wireless networks is challenged by spectrum scarcity. To mitigate the effect of such a challenge, Cognitive Radio Networks (CRNs) were introduced as a promising technology since CRNs offer a great advantage to unlicensed users, also known as secondary users (SUs), by allowing them to opportunistically access the licensed bands when these bands are not used by their primary users (PUs). In this work, an LTE-based CRN is proposed with the objective of guaranteeing continuous video playback at the SUs end at acceptable perceptual quality. To accomplish this objective, different resource allocation schemes are introduced to adaptively assign the available channels to SUs while considering the quality of these channels as well as the buffer occupancies of the different SUs. In addition, a streaming algorithm is proposed to ensure the delivery of the base and enhancement layers of the scalable video frames within the delay constraints with priority given to the base-layers of the video frames to guarantee the continuity of video playback. Furthermore, adaptive modulation is used based on the channel state information (CSI) as fed-back by the SUs. The performance of the proposed schemes is evaluated through extensive Monte-Carlo simulations.

Weighted Online Fountain Codes With Limited Buffer Size and Feedback Transmissions

Online fountain codes (OFC) have attracted much attention for their good intermediate performance, which is important for receivers with low-complexity requirement. However, low-complexity receivers generally have limited buffer size to store coded symbols that have not been fully decoded yet, as well as limited power budget for feedback transmissions. In this paper, we propose improved transmission schemes for online fountain codes to reduce the buffer occupancy and feedback transmissions. Firstly, we analyze the relationship between buffer occupancy and overhead as well as the relationship between recovery rate and overhead for online fountain codes. Motivated by the analysis, we propose the weighted online fountain codes (WOFC) which can adapt to various buffer sizes by adjusting the weight to control the probability that a coded symbol can be fully processed immediately, and analyze its performance. Then we further propose weighted online fountain codes with low feedback (WOFC-LF), which utilize the proposed analysis to estimate the recovery rate, and reduce feedback transmissions. Simulation results verify the effectiveness of the analysis for both OFC and WOFC, and demonstrate the superior performance of WOFC-LF with limited buffer size and feedback transmissions.

Performance evaluation of Focused Beam Routing for IoT applications in underwater environment

Underwater applications are becoming more and more interesting to industry and academy. They include data gathering for human safety and environment monitoring, control of underwater robots for various tasks and so on. Because of the accessibility limitations in underwater environment, applications tend to be automated and delay tolerant. In this paper, we consider IoT applications in underwater environment, while using Delay Tolerant Networking (DTN) carry–store–forwarding paradigm. DTN routing protocols are used to forward data from the monitoring mobile sensors to collecting devices at the water surface and vice-versa. One characteristic of routing protocols for DTN is flooding of messages to increase the delivery probability. For instance, Epidemic Routing (ER) protocol creates a copy of each message for each new node that does not already have the message in its memory. This increases the probability of delivery, but on the other hand, creates overhead in each node’s buffer, and uses a lot of valuable energy from the forwarding and receiving nodes. This work aims to analyze by simulations the performance of Focused Beam Routing (FBR) protocol for different FBR angles and different applications. We use Delivery Probability, Average Number of Hops, Overhead Ratio and Buffer Occupancy to simulate our scenarios by The ONE simulator. Simulation results show that for narrow angles of FBR the performance is better. In case of FBR-45, average hop count and overhead ratio are decreased by 10.9% and 16.6% respectively, compared to FBR-180. However, delivery probability decreases by only 3.9%.

Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles

Data traffic forwarding and network optimization is essential to effective congestion management in software-defined vehicular networks, and it is necessary for software-defined vehicle networks (SDVN). SDVN is needed to optimize connection performance and network controls in dense and sparse networks to govern data flow between nodes as effectively as possible. Intelligent software-defined internet of vehicles (iSDIoVs) has recently emerged as a potential technology for future vehicular networks. It manages the vehicular ad hoc networks systematically. The link connection of moving vehicles from the central SDN controller may fail. It impacts the efficiency and communication performance because of the lack of connection between vehicles and infrastructure (V2I). The researchers have analyzed the network performance and mobility models in a dense and sparse network to maximize network performance by iSDIoVs. By integrating heterogeneous systems such as IEEE 802.11p and cellular networks into vehicular ad-hoc networks, it is possible to reduce buffer occupancy in iSDIoV and control the mobility and delay bound analysis in V2V communication. The SDN will provide flexibility and reliability to the vehicular networks. An SDN controller manages the data flow in the vehicular network and controls the flow matching rules in the control plane. The iSDIoV and queuing models improve the response time and resource utilization and enhance the network complexity analysis for traffic management services.

LECAR: Location Estimation-Based Congestion-Aware Routing Protocol for Sparsely Deployed Energy-Efficient UAVs.

Energy-efficient routing has become a critical issue for advanced energy-hungry unmanned aerial vehicles (UAVs). Routing in a flying ad hoc network is always challenging and becomes even more critical when a small number of UAVs must cover a large area. The routing protocols based on the delay-tolerant network (DTN) are best suited for such scenarios. However, traditional DTN-based routing protocols depend on data dissemination to offer a better packet delivery ratio, leading to congestion and excess transmissions, causing heavy and unnecessary energy consumption. We propose a location estimation-based congestion-aware routing protocol (LECAR) to balance these two issues. Considering outdated location information, LECAR takes advantage of the mobility model to estimate the current location of the destination. In addition, LECAR routes a packet by considering both the distance to destination and buffer occupancy of the neighboring UAVs. Simulation results show that LECAR could ensure both a high packet delivery ratio and low energy consumption. Moreover, LECAR could provide a minimal number of transmissions, while minimizing the number of copies per packet at a time.

Retracted: Fuzzy based rendezvous points selection for mobile data gathering in wireless sensor network

AbstractIn this article, fuzzy based rendezvous points selection (FRPS) technique for mobile data gathering in wireless sensor networks is proposed. At beginning, the importance of detected data is categorized into perilous and non‐perilous based on the perseverance and time limit of data. Then two Sencars are set up: one for gathering perilous data by visiting straight to the corresponding sensors, the other one for gathering non‐perilous data from other sensors. The rendezvous points (RPs) for visiting the Sencars are determined by using fuzzy decision model based on the input parameters energy level, buffer occupancy level, and current load. Based on the output of this model, each sensor is assigned a combined utility value. Then the sensors with high utility values are selected as RPs and the details are broadcast to the respective Sencars.

Updating the theory of buffer sizing

Routers have packet buffers to reduce packet drops during times of congestion. It is important to correctly size the buffer: make it too small, and packets are dropped unnecessarily and the link may be underutilized; make it too big, and packets may wait for a long time, and the router itself may be more expensive to build. Despite its importance, there are few guidelines for picking the buffer size. The two most well-known rules only apply to long-lived TCP Reno flows; either for a network carrying a single TCP Reno flow (the buffer size should equal the bandwidth-delay product, or BDP) or for a network carrying n TCP Reno flows (the buffer size should equal BDP/n). Since these rules were introduced, TCP Reno has been replaced by newer algorithms as the default congestion control algorithm in all major operating systems, yet little has been written about how the rules need to change. This paper revisits both rules. For the single flow case, we generalize the BDP rule to account for changes to TCP, such as Proportional Rate Reduction (PRR), and the introduction of new algorithms including Cubic and BBR. We find that buffers can be made 60%–75% smaller for newer algorithms. For the multiple flow case, we show that the square root of n rule holds under a broader set of assumptions than previously known, including for these new congestion control algorithms. We also demonstrate situations where the square root of n rule does not hold, including for unfair flows and certain settings with ECN. We validate our results by precisely measuring the time series of buffer occupancy in a real network, and comparing it to the per-packet window size.

Research on the Application of UAV Wide-angle Shooting Technology in Multimedia Video Playing

Unmanned aerial vehicles continue to increase the requirements for image resolution in the fields of reconnaissance, surveying, and mapping. The fusion of the video image system and the traditional remote control and telemetry system also needs to be considered in the design. Aiming at the above problems, the article designs and implements an airborne high-definition video image system based on FPGA+DSP. The system can store the video image on the local hard disk, and at the same time, the compressed video image is also convenient for real-time transmission to the ground control station through the wireless link equipment. The system detection found that the buffer occupancy can fluctuate smoothly in a small range, and the subjective image quality has also been improved, which better meets the low-latency application requirements of the airborne reconnaissance system.

Mean-Field Limits for Large-Scale Random-Access Networks

We establish mean-field limits for large-scale random-access networks with buffer dynamics and arbitrary interference graphs. Although saturated buffer scenarios have been widely investigated and yield useful throughput estimates for persistent sessions, they fail to capture the fluctuations in buffer contents over time and provide no insight in the delay performance of flows with intermittent packet arrivals. Motivated by that issue, we explore in the present paper random-access networks with buffer dynamics, where flows with empty buffers refrain from competition for the medium. The occurrence of empty buffers thus results in a complex dynamic interaction between activity states and buffer contents, which severely complicates the performance analysis. Hence, we focus on a many-sources regime where the total number of nodes grows large, which not only offers mathematical tractability but is also highly relevant with the densification of wireless networks as the Internet of Things emerges. We exploit timescale separation properties to prove that the properly scaled buffer occupancy process converges to the solution of a deterministic initial value problem and establish the existence and uniqueness of the associated fixed point. This approach simplifies the performance analysis of networks with huge numbers of nodes to a low-dimensional fixed-point calculation. For the case of a complete interference graph, we demonstrate asymptotic stability, provide a simple closed form expression for the fixed point, and prove interchange of the mean-field and steady-state limits. This yields asymptotically exact approximations for key performance metrics, in particular the stationary buffer content and packet delay distributions.

Buffer occupancy and link state opportunistic routing for wireless mesh networks

Providing a high level of Quality of Service is essential for future wireless networks. This article presents a new multihop wireless routing protocol that opportunistically takes profit from variations of radio conditions in terms of path loss, shadowing and multipath fading to maximize the system capacity. However, guaranteeing high system capacity should not evade the packet delay minimization objective. Consequently, the best path should not only be considered as the path with best throughput but a combination of a good link throughput and, in addition, low router buffer occupancy load. Taking into account the available router buffer occupancy in its path selection, our proposal uses queuing theory information in order to also provide an efficient load balancing solution that adequately distributes the traffic load in the whole network. Exploiting this information, our solution dynamically adapts the selected path across time avoiding overexploited efficient links as well as low throughput link usage. This adaptation is performed considering each link state and the amount of channel information available. This improves the throughput and delay with only small marginal overhead cost. Our proposal applies to all wireless multihop networks, with increased benefit for extending cell coverage. We demonstrate through our simulation study that our solution raises the system capacity by more than 50% in several scenarii as well as reduces packet delays compared to state-of-the-art protocols such as Ad-hoc On-demand Distance Vector, Optimized Link State Routing and Link State Opportunistic Routing.

Instruction Criticality Based Energy-Efficient Hardware Data Prefetching

Hardware data prefetching is a latency hiding technique that mitigates the memory wall problem by fetching data blocks into caches before the processor demands them. For high performing state-of-the-art data prefetchers, this increases dynamic and static energy in memory hierarchy, due to increase in number of requests. A trivial way to improve energy-efficiency of hardware prefetchers is to prefetch instructions on the critical path of execution. As criticality-based data prefetching does not degrade performance significantly; this is an ideal approach to solve the energy-efficiency problem. We discuss limitations of existing critical instruction detection techniques and propose a new technique that uses re-order buffer occupancy as a metric to detect critical instructions and performs prefetcher-specific threshold tuning. With our detector, we achieve maximum memory hierarchy energy savings of 12.3% with 1.4% higher performance, for PPF, and average as follows: (i) SPEC CPU 2017 benchmarks: 2.04% lower energy, 0.3% lower performance, for IPCP at L1D, (ii) client/server benchmarks: 4.7% lower energy, 0.15% lower performance, for PPF, (iii) Cloudsuite benchmarks: 2.99% lower energy, 0.36% higher performance, for IPCP at L1D. IPCP and PPF are state-of-the-art data prefetchers.

A Bio-inspired Hybrid Cross-Layer Routing Protocol for Energy Preservation in WSN-Assisted IoT

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.

An Intelligent Path Evaluation Algorithm for Congestion Control in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), congestion occurs when the traffic load on the node reaches obvious network channel power and is considered as a prominent problem. So it is highly essential to identify, prevent and fix congestion when taking into account the network’s restricted re- sources. If congestion in the network is not managed correctly, it can lead to significant loss of QoS parameters, such as decreasing network throughput, increasing energy usage, and decreasing the distribution ratio of packets at the destination or sink node. In this article, we have formulated an algorithm to manage congestion using a modified ant colony algorithm that considers the residual energy, buffer occupancy and bandwidth of the neighbour node for selecting the best node to route the traffic that needs strict QoS requirements. For routing the traffic, there may be a variety of neighbour nodes exists in the network ,and if congestion occurs at any selected neighbour node the algorithm will rework to check the quality of rest of the neighbor nodes by calculating a path preference probability metric. The neighbour node with maximum path preference probability will be selected for routing the data packets belonging to real-time traffic that have stringent QoS. The proposed algorithm was simulated and tested using NS 2.33 and it was found that as opposed to the standard AntSense algorithm, the proposed algorithm is capable of achieving lower average energy consumption and packet loss ratio respectively with higher throughput.

Performance evaluation of multi service provisioning for multi-hop cooperative data dissemination in SDHVN

A large number of RSUs are deployed to improve vehicular ad-hoc networks (VANETs) in recent years. On the other hand, it leads to an increase in the cost of the network. Multi-hop cooperative data dissemination (MHCDD) protocols can reduce using RSUs. These RSUs must cope with intermittent connectivity for longer distance communication. Minimizing the RSUs increases the data traffic and end-to-end delay. Moreover, mobility management, handling the connection time between vehicles, and data traffic in VANETs are challenging tasks in dense networks. In this context, we proposed a multi-hop cooperative data dissemination technique using a virtual network functioning and software-defined network-enable on the heterogeneous vehicular network (SDHVN). The problem of packet scheduling on the buffer occupancy is solved using an enhanced periodic update (ePU) and enhancing event-driven deadline (eEDD). The results illustrate the proposed algorithm’s significant performance in terms of availability with the limited wireless channels. The queueing models adopted for optimizing the load balancing are by using the mobile edge controller. These queueing model minimizes the latency and maximizes network utility.

FUZZY LOGIC BASED IMPROVED MEDIUM ACCESS CONTROLLER FOR CONGESTION CONTROL AND PREDICTION THE SYSTEMS IN WSNS

in resource-constrained networks, particularly those with limited bandwidth to manage high-volume data transmission, network congestion is a major issue, resulting in poor quality of service, including packet loss and delay throughput. Due to self-contained batteries that limit sensor node lifetime, this issue is important in wireless sensor networks (WSNs) with limitations and restrictions, such as limited processing power, memory, and transmission. By determining a path that avoids congested highways, the network can be extended. As a result, we present a WSN route determination architecture that is congestion-aware. The architecture is divided into three stages: In a top-down hierarchical structure, the first path is created. Energy-aware assisted routing for route derivation. Exponential smoothing is used to forecast congestion, but the final parameters for route determination are not taken into account. We use fuzzy logic systems to evaluate proper weights for a variety of factors, including shop count, remaining energy, buffer occupancy, and forwarding rate, as well as a bat algorithm to optimize the weight over the membership functions. Eventually proposed model shows the high throughput, low packet loss, save energy, and extending network lifetime.

CAFOR: congestion avoidance using fuzzy logic to find an optimal routing path in 6LoWPAN networks

Internet of Things is an increasing technology that has recently seen growth in the field of research. In a 6LoWPAN network, congestion at nodes is caused by huge network traffic, which reduces the overall performance, as well as the quality of routing metrics. Congestion in IoT network affects various performance measures such as energy inefficiency, decrease in throughput and loss of data packets. Therefore, in this paper, congestion avoidance using fuzzy logic algorithm has been proposed to avoid congestion by selecting the best parent in a tree-structured IoT network to find the optimal routing path. Fuzzy weighted sum model is used to model the problem of parent selection into multi-attribute decision making based problem. Routing metrics used are buffer occupancy, expected transmission count and routing metric (RtMetric). The objective function selects, the parent based on the combination of these routing metrics. The proposed algorithm’s dynamic nature can identify the congestion and then selects the non-congested path by selecting the best parent and thus creates a best routing path for the packets. The algorithm has been implemented and simulated on Contiki OS and a comparison of performance is carried out against optimization-based hybrid congestion alleviation (OHCA) and queue utilization-based RPL (QU-RPL) algorithms. Simulation results indicate that the proposed algorithm has 15% more throughput, 10% more goodput, 4.5% less packets loss, 10.1% less energy use, and 19% less one-way delay over OHCA and QU-RPL algorithms.

An energy‐efficient clustering and cross‐layer‐based opportunistic routing protocol (CORP) for wireless sensor network

SummaryEnergy‐efficient data collection is a significant research challenge in wireless sensor networks (WSNs). One of the essential approaches to improve WSN performance is clustering, routing, and denoising technique. Cluster head (CH) selection is an essential issue in WSN. The present work proposes cross‐layer‐based opportunistic routing protocol (CORP) for WSN. The proposed CORP approach has been used to find an optimal traversal path, which reduces computation time and energy consumption as well as improves data delivery reliability. For optimal clustering, K‐medoid with adaptive Harris hawk optimization algorithm (AHHO) has been utilized for clustering the sensor nodes. Quality of service (QoS) impact, energy status criteria, distance, and sensor nodes position have been considered as key factors. These factors can influence the selection of CH in WSN. Moreover, the CH selection method can minimize traffic and energy saving. For efficient data collection, a hybrid Variable Weighted stacked Autoencoder with adaptive sunflower optimization (VWAE‐ASFO) denoising technique has been intended for reducing the data error rate. Initially, in the data collection phase, the variable weighted stacked autoencoder senses the data and collects all the data from the network. Adaptive sunflower optimization algorithm (ASFO) updates the weight of VWAE to minimize the data error and model complexity. The simulation results demonstrate that the proposed CORP approach enhances QoS performance metrics such as energy consumption, packet delivery ratio, packet delay, network lifetime, throughput, jitter, buffer occupancy, and packet loss ratio. When the performance of the proposed approach is compared with the existing algorithms such as HEED, TCBDGA, FRLDG, MOBFO‐EER, and FEEC‐IIR, the proposed approach outperforms them.

Duty Cycle Control Method Considering Buffer Occupancy for IEEE 802.15.4-Compliant Heterogeneous Wireless Sensor Network

This paper proposes a Duty-Cycle (DC) control method in order to improve the Packet Delivery Ratio (PDR) for IEEE 802.15.4-compliant heterogeneous Wireless Sensor Networks (WSNs). The proposed method controls the DC so that the buffer occupancy of sensor nodes is less than 1 and assigns DC to each sub-network (sub-network means a network consisting of a router node and its subordinate nodes). In order to use the appropriate DC of each sub-network to obtain the high PDR, this paper gives analytical expressions of the buffer occupancy. The simulation results show that the proposed method achieves a reasonable delay and energy consumption while maintaining high PDR.

ICMA: An Efficient Integrated Congestion Control Approach

Background: With the growing popularity of various group communication applications, such as file transfer, multimedia events, distance learning, email distribution, multiparty video conferencing, and teleconferencing, multicasting seems to be a useful tool for efficient multipoint data distribution. An efficient communication technique depends on various parameters like processing speed, buffer storage, and amount of data flow between the nodes. If data exceeds beyond the capacity of a link or node, then it introduces congestion in the network. A series of multicast congestion control algorithms have been developed, but due to the heterogeneous network environment, these approaches do not respond nor reduce congestion quickly whenever network behavior changes. Objective: Multicasting is a robust and efficient one-to-many (1: M) group transmission (communication) technique to reduced communication cost, bandwidth consumption, processing time, and delays with similar reliability (dependability) as of regular unicast. This patent presents a novel and comprehensive congestion control method known as an integrated multicast congestion control approach (ICMA) to reduce packet loss. Methods: The proposed mechanism is based on a leave-join flow control mechanism along with a Proportional Integrated and Derivate (PID) controller to reduce packet loss, depending on the congestion status. In the proposed approach, the Proportional integrated and derivate controller computes expected incoming rate at each router and feedback this rate to upstream routers of the multicast network to stabilize their local buffer occupancy. Results: Simulation results using NS-2 exhibit the immense performance of the proposed approach in terms of delay, throughput, bandwidth utilization, and packet loss than other existing methods. Conclusion: The proposed congestion control scheme provides better bandwidth utilization and throughput than other existing approaches. Moreover, we have discussed existing congestion control schemes with their research gaps. In the future, we are planning to explore the fairness and quality of service issues in multicast communication.