Probabilistic Prediction based Scheduling for Delay Sensitive Traffic in Internet of Things (original) (raw)
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Markov Chain Based Priority Queueing Model for Packet Scheduling and Bandwidth Allocation
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2017
This paper considers classification of diverse traffic types in Internet of Things (IoT) based on importance of data rate, packet size and proposes a priority-based probabilistic packet scheduling strategy for efficient packet transmission. Reduction of peak resource usage, dynamic control of service rate corresponding to arrival rate and QoS buffer management are few main factors considered to develop this strategy. By calculating percentage of link bandwidth required for prioritized traffic in each cycle, we provide quality of service (QoS) to real time traffic in IoT and non-IoT applications. Different experiments including MPEG traffic traces and Poisson traffic are conducted to verify the proposed scheduler. Also, performance of scheduler for both IoT and Non-IoT applications is compared for different data rates. We observe that the proposed packet scheduler satisfies QoS requirements for both IoT and non-IoT traffic.
Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach
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Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet holdup , high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.
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In the given paper a general analytical model for a queuing system with limited capacity buffer intended to control packets’ traffic in Internet of Things applications is proposed. This model is based on the following assumptions. There is a fixed number of packets’ classes. For each pair of these classes either preemptive or non-preemptive priority is set. Packets of each class arrive according to the Poisson process with the given arrival rate, and are transmitted without errors with the given transmission rate. The criterion on the structure of the set of priorities between the classes of packets avoiding unnecessary push-out of packets being in the transmission is proved. Continuous-Time Markov Chain associated with the proposed model has been defined and analyzed. Basic characteristics including blocking probability, push-out probability, delay, and utilization have been estimated for each class of packets. Basic measures for the proposed model, such as Grade of Service, cost f...
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Zenodo (CERN European Organization for Nuclear Research), 2022
IoT gateways aim to meet the deadlines and QoS needs of packets from as many IoT devices as possible, though this can lead to a form of congestion known as the Massive Access Problem (MAP). While much work was conducted on predictive or reactive scheduling schemes to match the arrival process of packets to the service capabilities of IoT gateways, such schemes may use substantial computation and communication between gateways and IoT devices. This paper proves that the recently proposed "Quasi-Deterministic-Transmission-Policy (QDTP)" traffic shaping approach which delays packets at IoT devices, substantially alleviates the MAP: QDTP does not increase overall end-to-end delay and reduces gateway queue length. We then introduce the Adaptive Non-Deterministic Transmission Policy (ANTP) that requires only one packet buffer at the gateway, offering substantial QoS improvement over FIFO scheduling.
IoT Networks QoS Guarantee, 2023
Buffer space management is critical to ensuring full utilization of network resources in present IoT networks. It allows the multiplexing of services with different quality of service requirements. Current IoT technologies supports a wide range of services including medical treatment, preventive equipment maintenance, remote operation of machinery, environmental monitoring, video surveillance and real-time alerts, connected transport as well as multiplexed services consisting of combinations of these. Traffic prioritization plays an important role in quality of service management in all mobile networks. From a network perspective, the aim is to ensure a minimum waiting time for packets of higher priority classes in the buffers of these networks. In order to use the resources correctly, it is necessary for each type of traffic or service to share the traffic capacity depending on the quality of service requirements they have.
Improving QoS for Real-time Traffic using Multiple Low Latency Queueing Scheduling Mechanisms
University of Khartoum Engineering Journal
In this work, a Multiple Low Latency Queuing scheduling mechanism model is developed to improve the QoS performance for real time and critical mission data traffic in LTE mobile networks. The main objective of this model is to achieve minimum delay and improve the QoS for real time applications (like Live Video and Voice over LTE). In addition, issues likestarvation of lower priority queues and bandwidth allocation are addressed. The model is composed of four components, first, classifier to classify the incoming traffic in router interface. Second, four Class Based Weighted Fair Queues (CBWFQ) scheduling mechanisms, with activation of strict priority feature in the first two queues. Third, two separate rate limiters (policers), one for each strict priority queue. Two scenarios are designed and simulated using Optimized Network Engineering Tool (OPNET). The results show that, in the case of Multiple Low Latency Queuing scheduling mechanism model, the real time traffic suffers less d...
The IoT gateway with active queue management
International Journal of Applied Mathematics and Computer Science, 2021
As the traffic volume from various Internet of things (IoT) networks increases significantly, the need for adapting the quality of service (QoS) mechanisms to the new Internet conditions becomes essential. We propose a QoS mechanism for the IoT gateway based on packet classification and active queue management (AQM). End devices label packets with a special packet field (type of service (ToS) for IPv4 or traffic class (TC) for IPv6) and thus classify them as priority for real-time IoT traffic and non-priority for standard IP traffic. Our AQM mechanism drops only non-priority packets and thus ensures that real-time traffic packets for critical IoT systems are not removed if the priority traffic does not exceed the maximum queue capacity. This AQM mechanism is based on the PIα controller with non-integer integration order. We use fluid flow approximation and discrete event simulation to determine the influence of the AQM policy on the packet loss probability, queue length and its vari...
IoT Traffic Management and Integration in the QoS Supported Network
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This paper proposes: 1) a traffic flow management policy, which allocates and organizes machine type communication (MTC) traffic flows network resources sharing within evolved packet system (EPS); 2) an access element as a wireless sensor network gateway for providing an overlaying access channel between the machine type devices and EPS; and 3) it addresses the effect and interaction in the heterogeneity of applications, services and terminal devices, and the related quality of service (QoS) issues among them. This paper overcomes the problems of network resource starvation by preventing deterioration of network performance. The scheme is validated through simulation, which indicates the proposed traffic flow management policy outperforms the current traffic management policy. Specifically, simulation results show that the proposed model achieves an enhancement in QoS performance for the MTC traffic flows, including a decrease of 99.45% in packet loss rate (PLR), a decrease of 99.89% in packet end to end (E2E) delay, a decrease of 99.21% in packet delay variation (PDV). Furthermore, it retains the perceived quality of experience of the real time application users within high satisfaction levels, such as the voice over long term evolution service possessing a mean opinion score (MOS) of 4.349 and enhancing the QoS of a video conference service within the standardized values of a 3GPP body, with a decrease of 85.28% in PLR, a decrease of 85% in packet E2E delay and a decrease of 88.5% in PDV.
International Journal of Advanced Computer Science and Applications
Previous studies have considered scheduling schemes for Internet of Things (IoT)-based healthcare systems like First Come First Served (FCFS), and Shortest Job First (SJF). However, these scheduling schemes have limitations that range from large requests starving short requests, process starvation that results in long time to complete if short processes are continuously added, and performing poorly under overloaded conditions. To address the mentioned challenges, this paper proposes an analytical model of a prioritized scheme that provides service differentiation in terms of delay sensitive packets receiving service before delay tolerant packets and also in terms of packet size with the short packets being serviced before large packets. The numerical results obtained from the derived models show that the prioritized scheme offers better performance than FCFS and SJF scheduling schemes for both short and large packets, except the shortest short packets that perform better under SJF than the prioritized scheme in terms of mean slowdown metric. It is also observed that the prioritized scheme performs better than FCFS and SJF for all considered large packets and the difference in performance is more pronounced for the shortest large packets. It is further observed that reduction in packet thresholds leads to decrease in mean slowdown and the decrease is more pronounced for the short packets with larger sizes and large packets with shorter sizes.
Optimizing multimedia communication in internet of thing network for improving quality of service
The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023
The internet of things (IoT) has revolutionized the way we interact with technology, with the proliferation of interconnected devices leading to an increase in the volume of data transmitted over the network. One of the key applications of IoT is in multimedia communication, where real-time audio and video data is transmitted over the network. However, the diverse nature of applications and the sheer volume of data in IoT networks can lead to network congestion, latency, and variable quality of service (QoS) at the internet side, resulting in a degradation of the overall QoS for multimedia traffic. In this paper, we propose a cross-layer multimedia optimization solution for multi-point to point IoT networks that incorporates service differentiation and bandwidth reservation techniques to improve the QoS of multimedia traffic. We evaluate the performance of our proposed solution using simulations and compare it with existing solutions. Our results show that our proposed solution can significantly improve the QoS of multimedia traffic in IoT networks, even during periods of high network congestion or variable QoS at the internet side.