Packet Arrivals Research Articles

A Packet Delay Assessment Model in the Data Link Layer of the LTE

The issues of modeling and evaluating the characteristics of the LTE data link layer functioning are considered. Transmitting packets in the data link layer are represented by a probabilistic-temporal graph consisting of two subgraphs. The first subgraph describes the operation of the HARQ protocol, and the second subgraph describes the operation of the ARQ protocol. The first subgraph is nested within the second subgraph. The probabilities of correct reception, non-error detection, and retransmission of packets in the MAC and RLC layers and generating functions of the packet service time based on the HARQ and ARQ protocols are determined. With the help of generating functions, the average value, variance, and coefficient of variation of the packet service time are determined. To calculate the average packet delay time in the LTE data link layer, the type of queuing system is selected, taking into account the coefficient of variation of the packet service time. The analysis of packets' delay time in the network's data link layer is carried out for different values of the intensity of packet arrival and the probabilities of a bit error in the physical layer of the network. For the sustainable functioning of the data link layer of the network, the limit values of the intensity of the arrival of packets are determined for a given probability of a bit error in the physical layer of the network.

Transmission scheduling for multi-process multi-sensor remote estimation via approximate dynamic programming

In this paper, we consider a remote estimation problem where multiple dynamical systems are observed by smart sensors, which transmit their local estimates to a remote estimator over channels prone to packet losses. Unlike previous works, we allow multiple sensors to transmit simultaneously even though they can cause interference, thanks to the multi-packet reception capability at the remote estimator. In this setting, the remote estimator can decode multiple sensor transmissions (successful packet arrivals) as long as their signal-to-interference-and-noise ratios (SINR) are above a certain threshold. In this setting, we address the problem of optimal sensor transmission scheduling by minimizing a finite horizon discounted expected estimation error covariance cost across all systems at the remote estimator, subject to an average transmission cost. While this problem can be posed as a stochastic control problem, the optimal solution requires solving a Bellman equation for a dynamic programming (DP) problem, the complexity of which scales exponentially with the number of systems being measured and their state dimensions. In this paper, we resort to a novel Least Squares Temporal Difference (LSTD) Approximate Dynamic Programming (ADP) based approach to approximating the value function. More specifically, an off-policy based LSTD approach, named in short Enhanced-Exploration Greedy LSTD (EG-LSTD), is proposed. We discuss the convergence analysis of the EG-LSTD algorithm and its implementation. A Python based program is developed to implement and analyse the different aspects of the proposed method. Simulation examples are presented to support the results of the proposed approach both for the exact DP and ADP cases.

Performance evaluation of a distributed energy model with compound Poisson arrivals on an improvised forked network: A detailed analysis

Performance evaluation of a distributed energy model with compound poisson arrivals on an improvised forks network discusses the usage and operations on communication networks, the utilization of these network models had changed the scenario of communication around the world. Establishment and working with these networks lead to work efficiently and effectively for various modes of communications like voice transmission and data transmission. In the current network model, we had considered and developed the network model for the arrival of packets to the network model following non-homogeneous compound poisson arrivals with a uniform distribution pattern. The current model compares with the other homogeneous arrival of packets which follow the Duane process of arrival of packets to the network. When considering energy preservation, it is possible to enhance end-user performance while simultaneously decreasing energy consumption for the mean number of packets, throughput, network node utilization, and dynamic bandwidth utilization. After Implementation of the model, it is observed that energy reservation of the network nodes had increased a lot. During high traffic, our model demonstrates that its emptiness with the comparison of the homogenous model like poisson process and duan process was more efficient with a percentage increase of 16 % and 19 % respectively for a homogeneous network, mean number of packets with a rise of 10 % and 37 %, utilization of packets with 11 % and 18 %, mean delay of packets with 16 % and 5% and throughput of the network with 47 % and 38 %. The results obtained from the equation models were derived and developed with the help of the software MATLAB and MATHCAD. Comparing our heterogeneous model with the results of the homogenous network model had enormously decreased the computational time of the network. Thus, we can clearly state that our proposed model outperformed than remaining other homogenous models.

Application of the NARX neural network for predicting a one-dimensional time series

Time series data analysis and forecasting tool for studying the data on the use of network traffic is very important to provide acceptable and good quality network services, including network monitoring, resource management, and threat detection. More and more, the behavior of network traffic is described by the theory of deterministic chaos. The traffic of a modern network has a complex structure, an uneven rate of packet arrival for service by network devices. Predicting network traffic is still an important task, as forecast data provide the necessary information to solve the problem of managing network flows. Numerous studies of actually measured data confirm that they are nonstationary and their structure is multicomponent. This paper presents modeling using Nonlinear Autoregression Exogenous (NARX) algorithm for predicting network traffic datasets. NARX is one of the models that can be used to demonstrate non-linear systems, especially in modeling time series datasets. In other words, they called the categories of dynamic feedback networks covering several layers of the network. An artificial neural network (ANN) was developed, trained and tested using the LM learning algorithm (Levenberg-Macwardt). The initial data for the prediction is the actual measured network traffic of the packet rate. As a result of the study of the initial data, the best value of the smallest mean-square error MSE (Mean Squared Error) was obtained with the epoch value equal to 18. As for the regression R, its output ANN values in relation to the target for training, validation and testing were 0.97743. 0.9638 and 0.94907, respectively, with an overall regression value of 0.97134, which ensures that all datasets match exactly. Experimental results (MSE, R) have proven the method's ability to accurately estimate and predict network traffic

On the Rate and Age of Information for non-preemptive systems with prioritized arrivals and deterministic packet deadlines in IoT networks

In this paper, we consider two different classes of IoT sensors with high and low priorities in which both classes send their data to a Monitoring Center (MC) equipped with one server and a buffer to store the arrival packets waiting in the queue, representing an M/M/1/2 system. An error-free channel predominates the link between the sensors and MC. We assume a non-preemptive scheme applying priorities on the buffer in MC so that the packets with lower priority in the queue can be replaced by more urgent higher priority packets but not in the service, as well. The arrival packets are specified by deadline characteristic due to the great demand for fresh data. The deadline is provided to free the buffer and let the monitoring center be updated by the latest data. As significant parameters in calculating the throughput and freshness of data, Rate of Information (RoI) and system’s Age of Information (AoI) are investigated in the MC. Our goal is to study the effect of priority and packet deadlines on the rate and status update of the system. Thus, a Markov model is applied to derive the closed form expressions considering deterministic deadlines and the impact of high and low priority packets. Also, a new metric named the Age to Rate Ratio of Information (ARRoI) is introduced and discussed for better realization of the relation between effective arrival rate and AoI. Simulation results for various parameters confirm the analytical results and show that the newly introduced metric, ARRoI, could be employed in finding proper transmission rates.

Modeling and Online Adaptation of ALOHA for Low-Power Wide-Area Networks (LPWANs)

Unslotted ALOHA protocol has been adopted as a channel access mechanism in commercial low-power wide-area networks (LPWANs), such as Sigfox and long-range (LoRa) alliance. This work examines the throughput and random access (RA) delay distribution of unslotted ALOHA systems by considering exponential random backoff (ERB) or uniform random backoff (URB) algorithm. We further characterize the operating region of the systems as unsaturated stable, bistable, and saturated regions in terms of the new packet arrival and retransmission rates. To run the system stably with the maximum throughput, we propose a Bayesian online backoff algorithm that estimates the number of backlogged devices. Its performance is compared with other algorithms, such as particle filter (PF)-based algorithm, binary exponential backoff (BEB) algorithm, and the algorithm of exploiting exact backlog size information. Through extensive simulations, it is demonstrated that the performance of the proposed algorithm is very close to the upper bound and robust to time-varying traffic condition.

Feedback-Based Network Coding for Broadcast: Queueing Analysis

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">randomized drop when seen</i> (rDWS) scheme is an online, feedback-based network coding technique proven to be an efficient, lightweight variant of the deterministic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">drop when seen</i> (DWS) scheme. We have proposed a non-generation-based, generalized version of rDWS with stochastic packet arrival at the sender queue (SQ) in this letter. Next, a queueing analysis is performed to find the expected SQ length with respect to the load factor. Analysis shows that asymptotic SQ length in rDWS approaches the same of DWS as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">innovativeness factor</i> approaches unity, which establishes the effectiveness of rDWS in maintaining shorter expected SQ length.

Performance analysis of congestion-aware secure broadcast channels

Congestion-aware scheduling in case of downlink cellular communication has ignored the distribution of diverse content to different clients with heterogeneous secrecy requirements. Other possible application areas that encounter the preceding issue are secure offloading in mobile-edge computing, and vehicular communication. In this paper, we extend the work in Arvanitaki et al. (SN Comput Sci 1(1):53, 2019) by taking into consideration congestion and random access. Specifically, we study a two-user congestion-aware broadcast channel with heterogeneous traffic and different security requirements. We consider two randomized policies for selecting which packets to transmit, one is congestion-aware by taking into consideration the queue size, whereas the other one is congestion-agnostic. We analyse the throughput and the delay performance under two decoding schemes at the receivers, and provide insights into their relative security performance and into how congestion control at the queue holding confidential information can help decrease the average delay per packet. We show that the congestion-aware policy provides better delay, throughput, and secrecy performance for large arrival packet probabilities at the queue holding the confidential information. The derived results also take account of the self-interference caused at the receiver for whom confidential data is intended due to its full-duplex operation while jamming the communication at the other user. Finally, for two decoding schemes, we formulate our problems in terms of multi-objective optimization, which allows for finding a trade-off between the average packet delay for packets intended for the legitimate user and the throughput for the other user under congestion-aware policy.

Влияние клиентских приложений на передачу мобильного трафика в сетях связи

The efficiency of traffic transmission through wireless communication networks is currently of considerable interest from a research point of view. This is due to the rapid growth of Internet resources, the convergence of technologies for transmitting heterogeneous traffic, the increased mobility of subscribers of communication networks, as well as the growth in the number of different client applications using mobile data transmission. In this paper, we investigate the impact of the growth of client applications on the transmission of mobile traffic. The study shows that the mobile terminal traffic is stable. It is more variable than a random series, because it consists of frequent reversals, but does not have long-term memory, since the Hurst index ranges from 0.3 to 0.5. The distribution of intervals between the arrival of TCP packets is described by the exponent. The distribution of packet lengths for TCP traffic shows that the bulk of the traffic is carried in long packets (1300–2500 bytes). The immutability of the statistical characteristics of traffic when installing several client applications in comparison with the characteristics obtained in previous studies (with a small number of client applications) indicates the immutability of the quality of service. Therefore, the growth of client applications does not affect the transmission of mobile traffic.

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.

Signal processing for a reverse‐GPS wildlife tracking system: CPU and GPU implementation experiences

AbstractWe present robust high‐performance implementations of signal‐processing tasks performed by a high‐throughput wildlife tracking system called ATLAS. The system tracks radio transmitters attached to wild animals by estimating the time of arrival of radio packets to multiple receivers (base stations). Time‐of‐arrival estimation of wideband radio signals is computationally expensive, especially in acquisition mode (when the time of transmission is not known, not even approximately). These computations are a bottleneck that limits the throughput of the system. We developed a sequential high‐performance CPU implementation of the computations a few years back, and more recently a GPU implementation. Both strive to balance performance with simplicity, maintainability, and development effort, as most real‐world codes do. The article reports on the two implementations and carefully evaluates their performance. The evaluations indicates that the GPU implementation dramatically improves performance and power‐performance relative to the sequential CPU implementation running on a desktop CPU typical of the computers in current base stations. Performance improves by more than 50X on a high‐end GPU and more than 4X with a GPU platform that consumes almost 5 times less power than the CPU platform. Performance‐per‐Watt ratios also improve (by more than 16X), and so do the price‐performance ratios.

Routing Algorithm in Networks on the Globe

Packet switching of data in networks is done by either the distance-vector or the link-state routing protocols. These protocols use the Bellman-Ford and the Dijkstra's algorithms re- spectively for the least cost path from a source base station to a destination station. For inter-network transmission, the path-vector routing protocol is in use. With progress of time, the network topologies are becoming huge in size, requiring large demand on book keeping of routing tables and transmission of the data packets dynamically to several other stations of the network by broadcast, increasing the load on the network. Here, assuming the router stations to be terrestrially located with links along the ground, a large network is assumed to lie on a spherical surface, and so the shortest geodesic path from source to destination becomes a great circular arc. For fast transmission, the cost of a link to a node is multicast to its neighboring nodes only for selection of the path lying as close as possible to the geodesic line between the source and the destination. As the arrival and dispatch of data packets at a nodal station occurs randomly, the cost of a link is estimated in this paper by the waiting time of a queueing process. This process at a router station is thus modeled by the Markovian M=M=c model, where c is the number of servers at the router station. If other commercial xed charge is involved for the use of a link, then that can be included in the total cost of a link. Finally, a method of search of a mobile destination is also presented using sphericity of the network. Algorithms for the near geodesic path, costs of links as waiting times and destination search in mobile environment are clearly presented.

Optimizing Information Freshness Through Computation–Transmission Tradeoff and Queue Management in Edge Computing

Edge computing applications typically require generated data to be preprocessed at the source and then transmitted to an edge server. In such cases, transmission time and preprocessing time are coupled, yielding a tradeoff between them to achieve the targeted objective. This paper presents analysis of such a system with the objective of optimizing freshness of received data at the edge server. We model this system as two queues in tandem whose service times are independent but the transmission service time is monotonically dependent on the computation service time in mean value. This dependence captures the natural decrease in transmission time due to lower offloaded computation. We analyze various queue management schemes in this tandem queue where the compute queue has a single server, Poisson packet arrivals, general independent service and no extra buffer to save incoming packets. The transmit queue has a single server receiving packets from the compute queue with memoryless service time. We consider the transmit queue in two forms: (i) No data buffer and (ii) One unit data buffer and last come first serve with discarding. We analyze various non-preemptive as well as preemptive cases. We perform stationary distribution analysis and obtain closed form expressions for average age of information (AoI) and average peak AoI. Our numerical results illustrate analytical findings on how computation and transmission times could be traded off to optimize AoI and reveal a consequent tradeoff between average AoI and average peak AoI.

Increasing transmission control protocol speed by reducing the acknowledgement collision probability

Transmission control protocol (TCP) employs acknowledgement (TCP-ACK) for every transmitted packets to ensure reliable transmission. As a result, it sends the next window packets after receiving the TCP-ACK packet of previous window. This means that the earlier the TCP-ACK packet arrives, the faster the TCP next window transmission and the better the TCP performances. To do so, there should be a special treatment to the transmitted TCP-ACK to fasten next window transmission. This paper proposes a collision probability reduction for the transmitted TCP-ACK packets so that the overall TCP delay reduces. Collision probability reduction can be implemented in many ways. Initially, mathematical analysis is provided to prove that method can work as expected. The mathematic analysis shows that when TCP-ACK collision probability is reduced, the overall TCP delay is also reduced. The proposed method is then implemented in 802.11, 802.16 and complex networks. The NS-2 simulations evaluations for the aforementioned networks and the proposed method proved that collision reductions on TCP-ACK exert average TCP delay reductions about 11.86%, 28.04% and 9.46% subsequently&lt;em&gt;.&lt;/em&gt;The proposed method is also applicable for other TCP types.

Distributed Channel Ranking Scheduling Function for Dense Industrial 6TiSCH Networks.

The Industrial Internet of Things (IIoT) is considered a key enabler for Industry 4.0. Modern wireless industrial protocols such as the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) deliver high reliability to fulfill the requirements in IIoT by following strict schedules computed in a Scheduling Function (SF) to avoid collisions and to provide determinism. The standard does not define how such schedules are built. The SF plays an essential role in 6TiSCH networks since it dictates when and where the nodes are communicating according to the application requirements, thus directly influencing the reliability of the network. Moreover, typical industrial environments consist of heavy machinery and complementary wireless communication systems that can create interference. Hence, we propose a distributed SF, namely the Channel Ranking Scheduling Function (CRSF), for IIoT networks supporting IPv6 over the IEEE 802.15.4e TSCH mode. CRSF computes the number of cells required for each node using a buffer-based bandwidth allocation mechanism with a Kalman filtering technique to avoid sudden allocation/deallocation of cells. CRSF also ranks channel quality using Exponential Weighted Moving Averages (EWMAs) based on the Received Signal Strength Indicator (RSSI), Background Noise (BN) level measurements, and the Packet Delivery Rate (PDR) metrics to select the best available channel to communicate. We compare the performance of CRSF with Orchestra and the Minimal Scheduling Function (MSF), in scenarios resembling industrial environmental characteristics. Performance is evaluated in terms of PDR, end-to-end latency, Radio Duty Cycle (RDC), and the elapsed time of first packet arrival. Results show that CRSF achieves high PDR and low RDC across all scenarios with periodic and burst traffic patterns at the cost of increased end-to-end latency. Moreover, CRSF delivers the first packet earlier than Orchestra and MSF in all scenarios. We conclude that CRSF is a viable option for IIoT networks with a large number of nodes and interference. The main contributions of our paper are threefold: (i) a bandwidth allocation mechanism that uses Kalman filtering techniques to effectively calculate the number of cells required for a given time, (ii) a channel ranking mechanism that combines metrics such as the PDR, RSSI, and BN to select channels with the best performance, and (iii) a new Key Performance Indicator (KPI) that measures the elapsed time from network formation until the first packet reception at the root.

Fuzzy queue based switch efficiency measures for optical network

In the optical network, researchers face the major challenges and issues like improper packet scheduling, inadequate number of packet switching, less response time of switch and routing efficiency. In this paper a satisfactory model replacing the existing unsatisfactory stream of packet transmission in optical network switching for performance analysis investigation using the fuzzy queuing theory with M/M/2/∞ queuing system based on optical fuzzy queue based switch (OFQS) with a view to improve the performance of the efficiency of the novel OFQS switch design. The investigation uses the uncertainty rate of packet arrival ( $$\lambda$$ ) and rate of service (µ) of the switch and applies the Robust ranking technique to convert the fuzzy values into crisp values for trapezoidal fuzzy analysis. Simulation result shows that the proposed novel switch gives better performance in terms of low conversion switching complexity and packet loss probability as compared to the conventional switch model.

Discrete-Time Queueing Model of Age of Information With Multiple Information Sources

Information freshness in IoT-based status update systems has recently been studied through the Age of Information (AoI) and Peak AoI (PAoI) performance metrics. In this article, we study a discrete-time server arising in multisource IoT systems, which accepts incoming information packets from multiple information sources so as to be forwarded to a remote monitor for status update purposes. Under the assumption of Bernoulli information packet arrivals and a common general discrete phase-type service time distribution across all the sources, we numerically obtain the exact per-source distributions of AoI and PAoI in matrix-geometric form for three different queueing disciplines: 1) nonpreemptive bufferless; 2) preemptive bufferless; and 3) nonpreemptive single buffer with replacement. The proposed numerical algorithm employs the theory of discrete-time Markov chains of quasi-birth-death type and is matrix analytical. Numerical examples are provided to validate the accuracy and effectiveness of the proposed queueing model. We also present a numerical example on the optimum choice of the Bernoulli parameters in a practical IoT system with two sources with diverse AoI requirements.

Moment Generating Function of the AoI in a Two-Source System With Packet Management

We consider a status update system consisting of two independent sources and one server in which packets of each source are generated according to the Poisson process and packets are served according to an exponentially distributed service time. We derive the moment generating function (MGF) of the age of information (AoI) for each source in the system by using the stochastic hybrid systems (SHS) under two existing source-aware packet management policies which we term self-preemptive and non-preemptive policies. In the both policies, the system (i.e., the waiting queue and the server) can contain at most two packets, one packet of each source; when the server is busy and a new packet arrives, the possible packet of the same source in the waiting queue is replaced by the fresh packet. The main difference between the policies is that in the self-preemptive policy, the packet under service is replaced upon the arrival of a new packet from the same source, whereas in the non-preemptive policy, this new arriving packet is blocked and cleared. We use the derived MGF to find the first and second moments of the AoI and show the importance of higher moments.

Applying Buffer to SDN Switches: Benefits Analysis and Mechanism Design

Software-Defined-Networking (SDN) is progressively dominating the dynamic management for timely network trouble shooting and fine grained traffic scheduling in data center networks. One critical issue in SDN is to reduce the communication overhead between the switches and the controller. Such overhead is mainly caused by handling miss-match packets, because for each miss-match packet, a switch will send a request to the controller asking for forwarding rule. Existing approaches to address this problem generally need to deploy intermediate proxy or authority switches to hold rule copies, so as to reduce the number of requests sent to the controller. In this paper, we argue that using the intrinsic buffer in a SDN switch can also greatly reduce the communication overhead without using additional devices. If a switch buffers each miss-match packet, only a few header fields instead of the entire packet are required to be sent to the controller. Experiment results show that this can reduce 78.7 percent control traffic and 37 percent controller overhead at the cost of increasing only 5.6 percent switch overhead on average. If the proposed flow-granularity buffer mechanism is adopted, only one request message needs to be sent to the controller for a new flow with many arrival packets. Thus the control traffic and controller overhead can be further reduced by 64 percent and 35.7 percent respectively on average without increasing the switch overhead.

Approach for Collision Minimization and Enhancement of Power Allocation in WSNs

Wireless sensor networks (WSNs) have attracted much more attention in recent years. Hence, nowadays, WSN is considered one of the most popular technologies in the networking field. The reason behind its increasing rate is only for its adaptability as it works through batteries which are energy efficient, and for these characteristics, it has covered a wide market worldwide. Transmission collision is one of the key reasons for the decrease in performance in WSNs which results in excessive delay and packet loss. The collision range should be minimized in order to mitigate the risk of these packet collisions. The WSNs that contribute to minimize the collision area and the statistics show that the collision area which exceeds equivalents transmission power has been significantly reduced by this technique. This proposed paper optimally reduced the power consumption and data loss through proper routing of packets and the method of congestion detection. WSNs typically require high data reliability to preserve identification and responsiveness capacity while also improving data reliability, transmission, and redundancy. Retransmission is determined by the probability of packet arrival as well as the average energy consumption.