Packet Arrivals Research Articles

Optimizing QoS in Wireless Sensor Networks: A Review of Advanced Traffic Control Protocols and Techniques

In wireless sensor network technology (WSNs) the proper congestion processing is the essential key to prevent the parameters of QoS from facing intense loss such as Likelihood of occurrence output reduction and also it may lead to maximize the consumed energy and it also reflect on the increase of the end-to-end time delivery side by side with a noticeable decrease in the percentage of transmitted packet arrival at the destination end. Thus, evolving more superior technology is consider to be one of the most significant challenge in (WSNs) that help to deal with issues like avidness, detection and control in WSNs. This paper review most updated protocols to mitigate the dilemmas of congestion in the WSNs based on the mechanism of congestion’s innate behavior. The protocol categories can be divided into three divisions which are traffic based, resource based and hybrid. The first protocol category (traffic-based) is also witnessed more subdivide based on either the techniques of end-to-end transmission or hop-by-hop. Both the establishment procedure of route and the exploit of active bandwidth techniques. It is also important to indicate that inner operational process of the attenuation linked to congestion protocols are discuss in this paper. Lastly. An intensive study is exploited in this paper to analyze congestion protocols in connection with different performance standards in order to determine which a specific protocol category case is worth to develop. The performance survey shows that the difficulties of accurate detection to the congestion for a particular network if it is only considering the protocols class attitude when it alters with applicable platform and a metric on its own.

An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology

Wireless sensor networks (WSNs) based on the IEEE 802.15.4 standard have important applications in many fields, such as the Internet of Things and smart cities because of their low energy consumption. Hybrid carrier sense multiple access/time division multiple access (CSMA/TDMA) is the key technique to reduce energy consumption in the standard, but it also increases packet delay and reduces network throughput. Although the cluster tree topology is a typical topology defined by the IEEE 802.15.4 standard, there are few efficient medium access control (MAC) protocols specifically for this type of topology. To this end, we present an improved hybrid CSMA/TDMA MAC protocol based on a sharable slot algorithm for WSNs with cluster tree topology, called sharable slot-based MAC (SS-MAC). By designing the operating mechanism and frame structure, improving the hybrid CSMA/TDMA and channel-hopping techniques of IEEE 802.15.4 MAC, and introducing a sharable slot algorithm to wake up tree nodes asynchronously as well as a short address strategy to identify member nodes, the proposed protocol improves packet delay and throughput under the premise of low collision and low node energy consumption. Moreover, we derive mathematical expressions of the parameters of the sharable slot algorithm and evaluate the energy consumption, throughput and packet delay of the SS-MAC based on the queue modeling of packet arrivals. Numerical simulations verify that the proposed MAC protocol outperforms the other three existing MAC protocols, namely, IEEE 802.15.4 MAC, SSMA and LELLMAC, in terms of energy consumption, throughput and packet delay.

Near-Optimal Packet Scheduling in Multihop Networks with End-to-End Deadline Constraints

Scheduling packets with end-to-end deadline constraints in multihop networks is an important problem that has been notoriously difficult to tackle. Recently, there has been progress on this problem in the worst-case traffic setting, with the objective of maximizing the number of packets delivered within their deadlines. Specifically, the proposed algorithms were shown to achieve Ω(1/log(L)) fraction of the optimal objective value if the minimum link capacity in the network is C min = Ω(log (L)), where L is the maximum length of a packet's route in the network (which is bounded by the packet's maximum deadline). However, such guarantees can be quite pessimistic due to the strict worst-case traffic assumption and may not accurately reflect real-world settings. In this work, we aim to address this limitation by exploring whether it is possible to design algorithms that achieve a constant fraction of the optimal value while relaxing the worst-case traffic assumption. We provide a positive answer by demonstrating that in stochastic traffic settings, such as i.i.d. packet arrivals, near-optimal, (1-ε)-approximation algorithms can be designed if C min = Ω((over (logL/ε) Ω 2 ). To the best of our knowledge, this is the first result that shows this problem can be solved near-optimally under nontrivial assumptions on traffic and link capacity.

Age of Information in NOMA-IoT Networks: A Temporal Queuing Model Perspective

The Internet of Things (IoT) with non-orthogonal multiple access (NOMA) has been anticipated to offer diverse real-time applications, wherein the crux is to guarantee the age of information (AoI) for dynamic traffic. However, the traffic temporal variation provokes the interdependence between queue status and interference, in which context the AoI performance remains to be further explored. In this paper, an analytical framework is established to characterize the AoI performance in NOMA-IoT networks with random Bernoulli and deterministic periodic arrivals. Particularly, a numerical algorithm is devised to obtain the queue service rate, and tractable expressions for AoI violation probability and average AoI under both the first-come first-served (FCFS) and the preemptive last-come first-served (LCFS-PR) service disciplines are derived. Simulations are conducted to validate the proposed analysis. The results unveil that LCFS-PR conduces to better AoI performance than FCFS, and yet the gain diverges for each device with different traffic arrival configurations. In addition, the result shows that with sporadic traffic arrival, the periodic pattern outperforms the Bernoulli pattern, whereas this advantage gradually diminishes with more frequent packet arrival.

Dynamic load balancing in energy packet networks

Energy Packet Networks (EPNs) model the interaction between renewable sources generating energy following a random process and communication devices that consume energy. This network is formed by cells and, in each cell, there is a queue that handles energy packets and another queue that handles data packets. We assume Poisson arrivals of energy packets and of data packets to all the cells and exponential service times. We consider an EPN model with a dynamic load balancing where a cell without data packets can poll other cells to migrate jobs. This migration can only take place when there is enough energy in both interacting cells, in which case a batch of data packets is transferred and the required energy is consumed (i.e. it disappears). We consider that data packet also consume energy to be routed to the next station. Our main result shows that the steady-state distribution of jobs in the queues admits a product form solution provided that a stable solution of a fixed point equation exists. We prove sufficient conditions for irreducibility. Under these conditions and when the fixed point equation has a solution, the Markov chain is ergodic. We also provide sufficient conditions for the existence of a solution of the fixed point equation. We then focus on layered networks and we study the polling rates that must be set to achieve a fair load balancing, i.e., such that, in the same layer, the load of the queues handling data packets is the same. Our numerical experiments illustrate that dynamic load balancing satisfies several interesting properties such as performance improvement or fair load balancing.

Downlink Scheduler for Delay Guaranteed Services Using Deep Reinforcement Learning

In this paper, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.

COMPARISON OF QUALITY OF SERVICE PARAMETERS FOR FOUR CELLULAR NETWORK PROVIDERS IN PUNGGUR KECIL VILLAGE

This research aims to analyze Quality of Service (QoS) parameters on cellular networks using four providers by measuring throughput, packet loss, delay and jitter parameters when conducting Video Streaming and Online Games using the Wireshark application. The research method involves direct measurement of QoS parameters in several strategic locations in Punggur Kecil village, data was taken at three different location points with a distance comparison of ±500 meters. From all the recapitulations of QoS parameter measurement results, it can be concluded that the value of each QoS parameter can vary for each package which includes the categories poor, medium, good and very good because the bandwidth received by each provider is different. The cause of high and low values of different QoS parameters, Throughput is caused by the number of packet arrivals measured during the observation process, Packet Loss is caused by the number of queues exceeding capacity, Delay is caused by low Throughput values and the number of packets sent, while the Jitter value is influenced by the Delay value. The results obtained are that the Indosat provider is the recommended provider when carrying out Video Streaming and Online Games, because the Quality of Service value produced is superior to other providers. The average values of the Throughput, Packet Loss, Delay and Jitter parameters at each location for the Indosat provider are respectively 796kbps, 2.63%, 22.6ms and 22.6ms. Provider XL is 708kbps, 1.67%, 12.6ms, and 12.6ms. Provider Telkomsel 667kbps, 0.33%. 55.9ms, and 56ms. As well as the Smartfren provider 50kbps, 0.38%, 102.6ms, and 102.7ms.

Attenuation characteristics of concrete using smart aggregate transducers: Experiments and numerical simulations of P-wave propagation

Concrete is a highly heterogeneous construction material. Waves that propagate through concrete face significant reflection, scattering, and attenuation issues. Understanding the behavior of waves as they propagate through concrete and arrive at a sensor has generated much attention, especially for developing real-world field applications. In this study, a predictive model of attenuated P-wave propagation using Rayleigh damping is presented. The method used frequency excitations ranging from 20 to 200 kHz and smart aggregates (SAs) were embedded in a concrete specimen to excite and receive P-waves. Moreover, 10 distances were marked opposite the exciter at two propagation paths. In the simulations and experiments, signal processing methods were utilized to extract the first arrival packet for calculating amplitude attenuation. The P-wave damping coefficient was modeled using the multi-physical finite element method, and the results of the predictive model were compared with the experimental results. A discussion on the utilization of frequency-dependent attenuation coefficients was conducted to explore potential P-wave attenuation factors and their respective contributions to the overall attenuation. Numerical studies have demonstrated a strong correlation with the experiments when an appropriate level of material damping coefficient was considered. By enhancing the overall comprehension of the P-wave damping coefficient and attenuation characteristics within concrete, damage detection techniques based on P-waves can be improved.

ПРИМЕНЕНИЕ ТРАНСПОРТНОЙ ЗАДАЧИ ДЛЯ БАЛАНСИРОВКИ НАГРУЗКИ В УСЛОВИЯХ НЕЧЕТКОСТИ ИСХОДНЫХ ДАННЫХ

The structure of the ISO/OSI model does not allow choosing the optimal packet transmission route at the channel level and preventing the formation of ring routes. These functions are performed by the network layer. In general, the problem of packet routing is solved by the switch based on the routing algorithm, which contains a hidden "flooding" mechanism. A packet from the sender's switch is sent to all ports, except for the port where the packet was received. When a packet arrives, the switch analyzes the header and if the address in the header matches the address to which the switch belongs, then the packet is accepted. This situation, combined with the unevenness of sending a message, creates an increased load on switching devices at random times and determines the problem of distributing the input data flow in conditions of pulsating traffic. Pulsating traffic can be considered as a fuzziness lying within certain boundaries. To smooth traffic, it is possible to use clusters of switching devices, which in turn are considered as outgoing devices for the next level of clusters. Thus, to distribute the load, it is possible to apply the flow distribution algorithm used in solving the transport problem.

A four-state Markov model for modelling bursty traffic and benchmarking of random early detection

Active Queue Management (AQM) techniques are crucial for managing packet transmission efficiently, maintaining network performance, and preventing congestion in routers. However, achieving these objectives demands precise traffic modeling and simulations in extreme and unstable conditions. The internet traffic has distinct characteristics, such as aggregation, burstiness, and correlation. This paper presents an innovative approach for modeling internet traffic, addressing the limitations of conventional modeling and conventional AQM methods' development, which are primarily designed to stabilize the network traffic. The proposed model leverages the power of multiple Markov Modulated Bernoulli Processes (MMBPs) to tackle the challenges of traffic modeling and AQM development. Multiple states with varying probabilities are used to model packet arrivals, thus capturing the burstiness inherent in internet traffic. Yet, the overall probability is maintained identical, irrespective of the number of states (one, two, or four), by solving linear equations with multiple variables. Random Early Detection (RED) was used as a case study method with different packet arrival probabilities based on MMBPs with one, two, and four states. The results showed that the proposed model influences the outcomes of AQM methods. Furthermore, it was found that RED might not effectively address network burstiness due to its relatively slow reaction time. As a result, it can be concluded that RED performs optimally only with a single-state model.

Near-Optimal Packet Scheduling in Multihop Networks with End-to-End Deadline Constraints

Scheduling packets with end-to-end deadline constraints in multihop networks is an important problem that has been notoriously difficult to tackle. Recently, there has been progress on this problem in the worst-case traffic setting, with the objective of maximizing the number of packets delivered within their deadlines. Specifically, the proposed algorithms were shown to achieve Ω(1/log(L)) fraction of the optimal objective value if the minimum link capacity in the network is Cmin =Ω(log (L)), where L is the maximum length of a packet's route in the network (which is bounded by the packet's maximum deadline). However, such guarantees can be quite pessimistic due to the strict worst-case traffic assumption and may not accurately reflect real-world settings. In this work, we aim to address this limitation by exploring whether it is possible to design algorithms that achieve a constant fraction of the optimal value while relaxing the worst-case traffic assumption. We provide a positive answer by demonstrating that in stochastic traffic settings, such as i.i.d. packet arrivals, near-optimal, (1-ε)-approximation algorithms can be designed if Cmin = Ω(log (L/ε)/ε2). To the best of our knowledge, this is the first result that shows this problem can be solved near-optimally under nontrivial assumptions on traffic and link capacity. We further present extended simulations using real network traces with non-stationary traffic, which demonstrate that our algorithms outperform worst-case-based algorithms in practical settings.

Age of Information Cost Minimization with No Buffers, Random Arrivals and Unreliable Channels: A PCL-Indexability Analysis

Over the last decade, the Age of Information has emerged as a key concept and metric for applications where the freshness of sensor-provided data is critical. Limited transmission capacity has motivated research on the design of tractable policies for scheduling information updates to minimize Age of Information cost based on Markov decision models, in particular on the restless multi-armed bandit problem (RMABP). This allows the use of Whittle’s popular index policy, which is often nearly optimal, provided indexability (index existence) is proven, which has been recently accomplished in some models. We aim to extend the application scope of Whittle’s index policy in a broader AoI scheduling model. We address a model with no buffers incorporating random packet arrivals, unreliable channels, and nondecreasing AoI costs. We use sufficient indexability conditions based on partial conservation laws previously introduced by the author to establish the model’s indexability and evaluate its Whittle index in closed form under discounted and average cost criteria. We further use the index formulae to draw insights on how scheduling priority depends on model parameters.

Randomized Scheduling of Real-Time Traffic in Wireless Networks Over Fading Channels

Despite the rich literature on scheduling algorithms for wireless networks, algorithms that can provide deadline guarantees on packet delivery for general traffic and interference models are very limited. In this paper, we study the problem of scheduling real-time traffic under a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conflict-graph interference model</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable links</i> due to channel fading. Packets that are not successfully delivered within their deadlines are of no value. We consider traffic (packet arrival and deadline) and fading (link reliability) processes that evolve as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unknown</i> finite-state Markov chain. The performance metric is efficiency ratio which is the fraction of packets of each link which are delivered within their deadlines compared to that under the optimal (unknown) policy. We first show a conversion result that shows classical non-real-time scheduling algorithms can be ported to the real-time setting and yield a constant efficiency ratio. In particular, Max-Weight Scheduling () yields an efficiency ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/2$</tex-math> </inline-formula> . We then propose randomized algorithms that achieve efficiency ratios strictly higher than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/2$</tex-math> </inline-formula> , by carefully randomizing over the maximal schedules. Further, we propose low-complexity and myopic distributed randomized algorithms, and characterize their efficiency ratio. Simulation results are presented that verify that the randomized algorithms outperform classical ones such as and for scheduling real-time traffic over fading channels.

Irregular repetition slotted Aloha with multiuser detection: A density evolution analysis

Irregular repetition slotted Aloha (IRSA) has shown significant advantages as a modern technique for uncoordinated random access with massive number of users due to its capability of achieving theoretically a throughput of 1 packet per slot. When the receiver has also the multi-packet reception of multi-user (MUD) detection property, by applying successive interference cancellation, IRSA also obtains very low packet loss probabilities at low traffic loads, but is unable in general to achieve a normalized throughput close to 1. In this paper, we reconsider the case of IRSA with k-MUD receivers and derive the general density evolution equations for the non-asymptotic analysis of the packet loss rate, for arbitrary frame lengths and two operational variants: frame-structured and frameless transmissions. The first one defers transmission attempts until the beginning of the next frame of slots, while the second allows transmission immediately after a packet arrival. Next, using the potential function, we give new capacity bounds on the capacity of the system, showing the threshold arrival rate for zero decoding error probability. Our numerical results illustrate performance in terms of throughput and average delay for k-MUD IRSA with finite memory at the receiver, and also with bounded maximum delay.

Single Packet, Single Channel, Switched Antenna Array for RF Localization

Cost-effective and accurate means of localizing radio transmitters has the potential to enable a wide range of applications in the consumer electronics, IoT, and healthcare domains. However, existing multi-antenna localization methods require high-cost synchronized receivers, long integration times, and/or specialized packet structures. This paper proposes using a high-speed RF mux that sequentially connects antennas to a single 2MHz radio receiver and sub-packet switching to determine the Angle of Arrival of individual packets. Importantly, this approach does not need synchronization between the mux and the receiver, reducing cost and system complexity. Our signal processing pipeline recovers both switch timing and the antenna number from the received RF signal. The sub-packet waveforms are used to generate a synthetic reference packet, and our customized Multi-Resolution Beaming and MUSIC algorithms are used to determine the Angle of Arrival. Results show that our real-time system is highly accurate even when the target is moving, with a mean AoA accuracy of 3.4 degrees and a 2D localization accuracy of 36.4 cm. Furthermore, the system is capable of tracking multiple users carrying smartphones in either their hands or pockets. Ultimately this approach enables a single low-cost, low bandwidth commodity RF receiver to be used to create an N-element phased array receiver.

Acoustic Source Localization in Metal Plates Using BP Neural Network

This study introduces a methodology for detecting the location of signal sources within a metal plate using machine learning. In particular, the Back Propagation (BP) neural network is used. This uses the time of arrival of the first wave packets in the signal captured by the sensor to locate their source. Specifically, we divide the aluminum plate into several areas, design eight receiving points for receiving the excitation signal, and determine the location of each sound source. In order to train and test the machine learning network, the aluminum plate model was established using the COMSOL numerical simulation platform and the propagation of five peak waves was simulated. Correspondingly, experimental verification was carried out and a scanning laser Doppler vibrometer (SLDV) was used to build an experimental platform to collect the corresponding wave field information to obtain a data set for machine learning. The results show that the trained BP neural network can classify the sound source region in both environments.

Simple Bounds on Delay-Constrained Capacity and Delay-Violation Probability of Joint Queue and Channel-Aware Wireless Transmissions

The emerging Ultra-Reliable and Low-Latency Communication (URLLC) is expected to meet a hard or probabilistic delay constraint that plays a key role in Time Sensitive Networking (TSN) and Deterministic Networking (DetNet). In this paper, we are interested in simple bounds for delay-constrained wireless communications with deterministic or even random packet arrivals. More specifically, we present a sufficient condition and derive a legitimate data arrival rate, with which the bounded delay can be guaranteed deterministically with an average power constraint. Our derived results will show that the delay-bounded data rate increases with the average power and the tolerated latency normalized to the coherent time. Furthermore, we derive the upper and lower bounds of the delay-violation probability (DVP) when the bounded delay condition cannot be satisfied. It is revealed that in the log coordinate, the DVP as a function of the normalized tolerated latency may enjoy a non-linear decay, with a decay rate that increases with the latency bound. This is in contrast to the large deviation based performance analysis that focuses on the low-latency transmission policies achieving linearly decayed DVP only. Our result demonstrates that the joint channel-aware and queue-aware scheduling may significantly reduce the DVP, compared to the single-layer approaches.

DEVELOPMENT OF A MODIFIED METHOD OF NETWORK TRAFFIC FORMING

Based on the analysis of the statistical characteristics of heterogeneous network traffic of "Quadruple Play" mobile subscribers, it is shown that it cannot be represented by Poisson or Erlang distributions. It is shown that for such similar traffic, the rate of growth of the required buffer volume increases as the Hurst parameter increases. A method of adaptive traffic formation using the control of the length of intervals of the intensity of the arrival of data packets has been developed. Congestion management is carried out by changing the frequency of the marker generator (GM) on the basis of the results of forecasting the required bandwidth of the system and the required buffer size. The shaper adapts to changes in the length and instantaneous intensity of packet input in real-time.

Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals

In this paper, we study via simulation the performance of irregular repetition slotted ALOHA under multi-packet detection and different patterns of the load process. On the one hand, we model the arrival process with a version of the M/G/∞ process able to exhibit a correlation structure decaying slowly in time. Given the independence among frames in frame-synchronous coded-slotted ALOHA (CSA), this variation should only take effect on frame-asynchronous CSA. On the other hand, we vary the marginal distribution of the arrival process using discrete versions of the Lognormal and Pareto distributions, with the objective of investigating the influence of the right tail. In this case, both techniques should be affected by the change, albeit to a different degree. Our results confirm these hypotheses and show that these factors must be taken into account when designing and analyzing these systems. In frameless operations, both the shape of the packet arrivals tail distribution and the existence of short-range and long-range correlations strongly impact the packet loss ratio and the average delay. Nevertheless, these effects emerge only weakly in the case of frame-aligned operations, because this enforces the system to introduce a delay in the newly arrived packets (until the beginning of the next frame), and implies that the backlog of accumulated packets is the key quantity for calculating the performance.