Packet Transmission Probability Research Articles

Constructing the mathematical model of a recommender system for decentralized peer-to-peer computer networks

Recommender systems make it easier to search with a large amount of content, supplementing or replacing the classic search output with recommendations. In P2P networks, their use can have additional benefits. Because of indexing and search problems, previously added files may not be available to P2P network users. If the user cannot find the file he is looking for, one can provide him with a list of recommendations based on his preferences and search query. The object of research is the process of creating recommendations for users of decentralized P2P networks to facilitate data search. The urgent task of increasing the accuracy of mathematical modeling of recommender systems by taking into account the requirements for reliability and data security during changes in the structure of a decentralized P2P network is solved. An analytical model of the recommender system of a decentralized P2P network has been developed, the main feature of which is taking into account the requirements of reliability and security of recommendation messages. This was done by introducing the following indicators into the general model of the decentralized recommender system – the probability of reliable packet transmission and the probability of safe packet transmission. The developed analytical model makes it possible to conduct a comparative analysis of different methods of operation of recommender systems and to set acceptable parameters under which the degree of relevance does not fall below a certain threshold. The developed mathematical model of the system based on the GERT scheme differs from the known ones by taking into account the reliability and security requirements during changes in the structure of the decentralized P2P network. This has made it possible to improve the accuracy of simulation results up to 5 %. The proposed mathematical model could be used for prototyping recommender systems in various fields of activity

Wave-packet propagation in a graphene geometric diode

Dynamics of electron wave-packets is studied using the continuum Dirac model in a graphene geometric diode where the propagation of the wave packet is favored in certain direction due to the presence of geometric constraints. Clear rectification is obtained in the THz frequency range with the maximum rectification level of 3.25, which is in good agreement with recent experiments on graphene ballistic diodes. The rectification levels are considerably higher for systems with narrower channels. In this case, the wave packet transmission probabilities and rectification rate also strongly depend on the energy of the incident wave packet, as a result of the quantum nature of energy levels along such channels. These findings can be useful for fundamental understanding of the charge carrier dynamics in graphene geometry diodes.

A Formula for the Probability of Successful Packet Transmission in Cognitive Radio Networks

In this article, we derive a closed-form expression for the probability of successful packet transmission (PSPT) over Rayleigh fading channels in cognitive radio networks (CRNs). The derived expression is novel, as it does not require prior channel state information. This is achieved by employing a probabilistic model for the packet transmission time, avoiding the less realistic deterministic model that is widely used in the literature. Furthermore, the derived expression allows trading accuracy with computational complexity. Our expression is computationally efficient with linear computational complexity. Simulation results demonstrate high accuracy in evaluating the PSPT, with controllable computational complexity.

A Semi-Markov Process Model for Performance Evaluation of DSRC Vehicular Safety Communication

In this study, a new semi-Markov process (SMP)-based model is devised to evaluate the IEEE 802.11p enhanced distributed channel access (EDCA) broadcast performance for vehicular safety communication. Differing from the existing SMP analytical models, the proposed model takes the virtual collision among various prioritized access categories (ACs) inside each vehicle into consideration. Moreover, in contrast to the Markov chain-based models, our model is simpler but with approximate accuracy. Concretely, we first capture the behavior of each AC’s backoff entity using SMP. Then, the parameters of interest in the vehicular ad hoc network (VANET) such as packet transmission probability, conditional collision probability, and saturation throughput are derived. Finally, via MATLAB simulations, we demonstrate that the newly developed model achieves comparable accuracy in calculating these output parameters while its complexity and computation time is around one-tenth of that of the Markov chain-based models. Therefore, the proposed model is more suitable for real-time performance analysis of IEEE 802.11p EDCA safety communication in a freeway scenario.

QoS ROUTING IN TELECOMMUNICATIONS NETWORKS

With the increasing demand for quality of service (QoS) in today's multiservice networks, more and more attention is paid to the routing facilities. With the transition to new generation networks, the issues of improving routing algorithms and protocols seem to be especially relevant. The existing routing algorithms have been developed quite fully and, on the whole, function effectively if the optimization of the route selection is carried out according to one parameter. In the case of multi-criteria routing, specialists are faced with the problem of reducing many criteria to a single one by means of additive convolution. Less commonly, multiplicative convolution is used, since it is not always possible to apply multiplicative transformation to real physical processes. The problem of applying multiplicative convolution to various QoS requirements is to choose the weight coefficients for each of the parameters. In this paper, we analyze the choice of the optimal path for multicriteria routing, taking into account the weight coefficients and cost parameters on the basis of the above twolevel approach. The results of numerical modeling of the search for the optimal path for various values of weight coefficients and cost coefficients are presented. It is shown that when choosing a path for multi-criteria optimization, it is necessary to choose the coefficients of the additive convolution as the product of the weight coefficients and the cost coefficients directly. It has been found that the value of the probability of packet transmission affects to a greater extent the choice of the optimal path than the values of the coefficients for the parameters of delay, delay variation, and bandwidth.

Highly Reliable Transmission and Channel Assignment for CR-IoT Networks

In this article, we propose a highly reliable transmission strategy based on exploiting channel diversity for Internet of Things (IoT) networks equipped with cognitive radio (CR) capabilities (referred to as CR-IoT networks). Specifically, we investigate the channel-assignment problem with the objective of minimizing the number of assigned channels for each CR transmission subject to link reliability, data rate, and success probability constraints. We use the theory of continuous-time Markov chains to derive mathematical expressions for channel availability and reliability. These expressions are employed in a binary linear program model to formulate the channel assignment optimization problem, which is suboptimally solved using a polynomial-time sequential fixing procedure. Theoretical performance analysis of the proposed channel assignment scheme is derived in terms of a lower bound on the achievable probability of successful packet transmission. Through simulation experiments, we demonstrate significant performance enhancement compared to existing reliability-unaware CR channel assignment algorithms.

Energy-Efficient Routing in Wireless Sensor Networks

Efficient data collection is the core concept of implementing Industry4.0 on IoT platforms. This requires energy aware communication protocols for Wireless Sensor Networks (WSNs) where different functions, like sensing and processing on the IoT nodes must be supported by local battery power. Thus, energy aware network protocols, such as routing, became one of fundamental challenges in IoT data collection schemes.In our research, we have developed novel routing algorithms which guarantee minimum energy consumption data transfer which is achieved subject to pre-defined reliability constraints. We assume that data is transmitted in the form of packets and the routing algorithm identifies the paths over which the packets can reach the Base Station (BS) with minimum transmission energy, while the probability of successful packet transmission still exceeds a pre-defined reliability parameter. In this way, the longevity and the information throughput of the network is maximized and the low energy transmissions will considerably extend the lifetime of the IoT nodes. In this paper we propose a solution that maximizes the lifetime of the nodes.

An MPTCP-Based Transmission Scheme for Improving the Control Stability of Unmanned Aerial Vehicles.

Recently, unmanned aerial vehicles (UAVs) have been applied to various applications. In order to perform repetitive and accurate tasks with a UAV, it is more efficient for the operator to perform the tasks through an integrated management program rather than controlling the UAVs one by one through a controller. In this environment, control packets must be reliably delivered to the UAV to perform missions stably. However, wireless communication is at risk of packet loss or packet delay. Typical network communications can respond to situations in which packets are lost by retransmitting lost packets. However, in the case of UAV control, delay due to retransmission is fatal, so control packet loss and delay should not occur. As UAVs move quickly, there is a high risk of accidents if control packets are lost or delayed. In order to stably control a UAV by transmitting control messages, we propose a control packet transmission scheme, ConClone. ConClone replicates control packets and then transmits them over multiple network connections to increase the probability of successful control packet transmission. We implemented ConClone using real equipment, and we verified its performance through experiments and theoretical analysis.

Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study.

In the context of smart cities, there is a general benefit from monitoring close encounters among pedestrians. For instance, for the access control to office buildings, subway, commercial malls, etc., where a high amount of users may be present simultaneously, and keeping a strict record on each individual may be challenging. GPS tracking may not be available in many indoor cases; video surveillance may require expensive deployment (mainly due to the high-quality cameras and face recognition algorithms) and can be restrictive in case of low budget applications; RFID systems can be cumbersome and limited in the detection range. This information can later be used in many different scenarios. For instance, in case of earthquakes, fires, and accidents in general, the administration of the buildings can have a clear record of the people inside for victim searching activities. However, in the pandemic derived from the COVID-19 outbreak, a tracking that allows detecting of pedestrians in close range (a few meters) can be particularly useful to control the virus propagation. Hence, we propose a mobile clustering scheme where only a selected number of pedestrians (Cluster Heads) collect the information of the people around them (Cluster Members) in their trajectory inside the area of interest. Hence, a small number of transmissions are made to a control post, effectively limiting the collision probability and increasing the successful registration of people in close contact. Our proposal shows an increased success packet transmission probability and a reduced collision and idle slot probability, effectively improving the performance of the system compared to the case of direct transmissions from each node.

Analysis and Implementation for Traffic-Aware Channel Assignment and Contention Scheme in LoRa-Based IoT Networks

Internet of Things (IoT) is a promising technology attracting huge attentions in recent years, allowing an excessive number of connections between sensors and devices. Different from conventional human-oriented applications, long range (LoRa) developed in IoT facilitates massive simultaneous sensor data transmissions for the low-power wide-area network (LPWAN), where massive LoRa devices perform packet contention and backoff mechanisms to access opportunity for uplink data transfer. Therefore, it is compellingly imperative to take into account the fluctuation of different channel qualities and various traffic-buffer types for the optimum contention policy, which are not considered in open literatures. In this article, we propose a traffic-aware channel and backoff window size allocation (TCBA) scheme to improve network capacity and latency. Moreover, a statistical latency-aware network model is designed to derive the closed forms of the optimum packet transmission probability and maximum number of LoRa devices supported. The performance results validate that the theoretical analysis approaches the simulated one. Moreover, in both simulated and experimental results, our proposed TCBA scheme is capable of supporting massive LoRa connections achieving the highest throughput and the lowest end-to-end latency compared to other schemes in existing literatures.

RE-ORA: Residual Energy-Aware Online Random Access for Improving the Lifetime of Slotted ALOHA-Based Swarming Drone Networks

In this paper, to maximize the lifetime of drone swarms by resolving the battery exhaustion problems caused by undesired retransmissions, we propose a residual energy-aware online random access scheme (RE-ORA) by adjusting the packet transmission opportunities based on the residual energy of a drone in S-ALOHA-based swarming drone networks. We aim to improve the battery lifetime of the drone with the smallest energy among the drone swarms. In addition, we analyze the success, collision, and idle probabilities of the swarming drone networks. In particular, we analyze the intradrone swarm collision and interdrone swarm collision probabilities. Through intensive simulations, we show that the proposed RE-ORA scheme outperforms the conventional algorithm with respect to the average lifetime of drone swarms and successful packet transmission probability according to the number of drones and the residual energy.

MODIFIED PASSIVE AVAILABLE BANDWIDTH ESTIMATION IN IEEE 802.11 WLAN

Quality of Service provisioning for real-time multimedia applications is largely determined by a network’s available bandwidth. Until now, there is no standard method for estimating bandwidth on wireless networks. Therefore, in this study, a mathematical model called Modified Passive Available Bandwidth Estimation (MPABE) was developed to estimate the available bandwidth passively on a Distributed Coordination Function (DCF) wireless network on the IEEE 802.11 protocol. The mathematical model developed was a modification of three existing mathematical models, namely Available Bandwidth Estimation (ABE), Cognitive Passive Estimation of Available Bandwidth V2 (cPEAB-V2), and Passive Available Bandwidth Estimation (PABE). The proposed mathematical model gave emphasis on what will be faced to estimate available bandwidth and will help in building strategies to estimate available bandwidth on IEEE 802.11. The developed mathematical model consisted of idle period synchronisation between sender and receiver, the overhead probability occurring in the Medium Access Control (MAC) layer, as well as the successful packet transmission probability. Successful packet transmission was influenced by three variables, namely the packet collision probability caused by a number of neighbouring nodes, the packet collision probability caused by traffic from hidden nodes, and the packet error probability. The proposed mathematical model was tested by comparing it with other relevant mathematical models. The performance of the four mathematical models was compared with the actual bandwidth. Using a series of experiments that have been performed, it was found that the proposed mathematical model is approximately 26% more accurate than ABE, 36% more accurate than cPEABV2, and 32% more accurate than PABE.

Artificial Intelligence Based A Optimization Routing in Mobile Ad Hoc Networks

A Mobile Ad Hoc Network (MANET) is a hotchpotch of nodes with mobility feature, the established network utilization is dynamically outlined based on temporary architecture. In MANETs, the challenging and vital role is played by the routing protocols performance factors under different condition and environments. The routing protocols are liable to handle many nodes with limited resources. There exits many routing protocols in MANETs, one of the main key note that has to be considered in designing a routing protocol is to observe that the designed routing protocol is having an proportionate effect on network performance. The existence of obstacles may lead to many geographical routing problems like excess consumption of power and congestion of data. The aim of this paper is to take the assistance of A* algorithm that finds the walk-able path avoiding the concave obstacle in the path relaying on the gaming-theory model[29]. This algorithm decreases the delays in packet transmission and in turn increases the success rate of transmission. We take into consideration path length, penalty for node availability as probability of forwarding criteria and processes effective packet transmission. The simulated results analyse the performance of our protocol over other conventional algorithms based on congestion cost, path length, node availability penalty, delay, packet loss, throughput.

Cooperative packet-forwarding strategies in mobile ad hoc networks with unreliable channels: An evolutionary game approach

In mobile ad hoc networks, network nodes accomplish a target task usually by cooperative packet forwarding from the source to the destination. It is a challenge to enforce their mutual cooperation for a node’s self-interest. In this article, we focus on cooperative packet forwarding in a one-hop unreliable channel, which leads to packet loss and retransmission. We model the process of packet forwarding with the nodes’ remaining energy and reputation value. We propose a packet-forwarding non-cooperative game model reflecting the utilities of different packet-forwarding strategies, in which an incentive mechanism is introduced to enforce cooperation of packet forwarding. Furthermore, we analyze the packet-forwarding game with replicator dynamics and derive and prove three theorems. If the conditions of the theorems are met, the evolutionarily stable strategies can be attained. Three inferences also reveal how convergence speed to evolutionarily stable states is affected by the cooperative incentive, the probability of successful packet transmissions, and the upper limit of the retransmission number. The simulation results support the proposed theorems and inferences. In addition, we show that our game model with a reputation value and the mechanism of incentive cooperation can improve the probability of successful packet transmissions, and reduce the network overhead.

Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System With Online Reinforcement Learning

Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this article, we propose an optimal and low-complexity dynamic spectrum access framework for RF-powered ambient backscatter system. In this system, the secondary transmitter not only harvests energy from ambient signals (from incumbent users), but also backscatters these signals to its receiver for data transmission. Under the dynamics of the ambient signals, we first adopt the Markov decision process (MDP) framework to obtain the optimal policy for the secondary transmitter, aiming to maximize the system throughput. However, the MDP-based optimization requires complete knowledge of environment parameters, e.g., the probability of a channel to be idle and the probability of a successful packet transmission, that may not be practical to obtain. To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy. Simulation results show that the proposed learning algorithm not only efficiently deals with the dynamics of the environment, but also improves the average throughput up to 50% and reduces the blocking probability and delay up to 80% compared with conventional methods.

Robust H∞ Fault Detection for Networked Control Systems with Markov Time-Delays and Data Packet Loss in Both S/C and C/A Channels

This paper investigates the robust H∞ fault detection problem for networked control systems with Markov time-delays and data packet loss in both S/C and C/A channels. First, the time-delay from sensor to controller (S/C) and the time-delay from sensor to actuator (C/A) are described by two different Markov chains. Two random variables obeying the Bernoulli distribution are used to describe the packet loss between the sensor and the controller together between the controller and the actuator. Based on this, a fault detection filter is constructed and the closed-loop system mathematical model is established. Then, the solution method of the fault detection filter and controller gain matrix is given. The relationship between the probability of successful packet transmission and the ability to suppress external disturbance is obtained. Finally, simulation verifies the effectiveness of the proposed method.

Optimal Rate Sampling in 802.11 Systems: Theory, Design, and Implementation

Rate Adaptation (RA) is a fundamental mechanism in 802.11 systems. It allows transmitters to adapt the coding and modulation scheme as well as the MIMO transmission mode to the radio channel conditions, to learn and track the (mode, rate) pair providing the highest throughput. The design of RA mechanisms has been mainly driven by heuristics. In contrast, we rigorously formulate RA as an online stochastic optimization problem. We solve this problem and present G-ORS (Graphical Optimal Rate Sampling), a family of provably optimal (mode, rate) pair adaptation algorithms. Our main result is that G-ORS outperforms state-of-the-art algorithms such as MiRA and Minstrel HT, as demonstrated by experiments on a 802.11n network test-bed. The design of G-ORS is supported by a theoretical analysis, where we study its performance in stationary radio environments where the successful packet transmission probabilities at the various (mode, rate) pairs do not vary over time, and in non-stationary environments where these probabilities evolve. We show that under G-ORS, the throughput loss due to the need to explore sub-optimal (mode, rate) pairs does not depend on the number of available pairs. This is a crucial advantage as evolving 802.11 standards offer an increasingly large number of (mode, rate) pairs. We illustrate the superiority of G-ORS over state-of-the-art algorithms, using both trace-driven simulations and test-bed experiments.

Joint Interference Cancellation in Cache- and SIC-Enabled Networks

We consider a cache- and SIC-enabled wireless network, where the locations of the base stations (BSs) and users are modeled by the Poisson point process and Poisson cluster process, respectively. The BS serves two users simultaneously by transmitting a superimposed signal, and each user employs a joint scheme of cache-assisted interference cancellation (CAIC) and successive interference cancellation (SIC). Each user is classified as a caching or non-caching user depending on whether or not it caches data sent to its interferer in the same cell. The receiver operation of each user is classified into three possible cases: CAIC only, both CAIC and SIC, and SIC only. Then, the successful packet transmission probabilities of the caching and non-caching users are derived, based on which the average spectral efficiency (SE) and the average area SE (ASE) are obtained. The average SE and ASE are further optimized. Moreover, extensions to the scenario where the superimposed transmissions are within a finite radius around the BS are provided. Finally, the result for the general scenario of the BS serving multiple users simultaneously is obtained. The optimization problem of the general scenario is solved by converting it to the convex or the difference of convex problems in different subregions. Simulation results verify the analytical accuracy and the advantages of the proposed schemes.

Fragment-based transmission using minimum power consumed routing (FBTMPCR) algorithm of a MIMO integrated MANET

The establishment of an efficient broadcast routing technique in a multiple input multiple output (MIMO)- based mobile ad hoc network is a challenge in wireless communication. MIMO configuration provides enhancement of data throughput even under conditions of interference and multipath fading. Improvement of transmission efficiency by increasing saturation throughput, reducing transmission delay, and minimizing bit error rate is the main motivation of the present work. In this paper a hybrid routing algorithm, fragment-based transmission using minimum power consumed routing, has been developed. It is an integration of the cooperative transmission process and fragmentation of packet payload. The routing algorithm is developed on selecting the best suitable relay node considering effective received signal strength and probability of successful packet transmission. The broadcasting mechanism is done by using multiple fragment of packet transmission through MIMO channel by using a suitable relay node. The performance analysis of this proposed routing algorithm increases the signal to noise interference ratio, resulting in minimization of BER, improvement of saturation throughput, and reduced transmission delay.

Study of Adjustment Delay Scheme on IEEE 802.15.4 Networks at Beacon Enabled Mode

In the contention mechanism used to slotted carrier sense multiple access with collision avoidance (CSMA/CA) on IEEE 802.15.4 standard. Device nodes will perform a backoff process as soon as the clear channel assessment (CCA) detects as busy condition. The challenge of CSMA/CA as following: first, when the device nodes detect the channel in busy condition, the device nodes have to increase the value of backoff exponent (BE) which cause range of blind backoff process also increase. Second, when the device nodes detect the channel in busy condition, the device nodes have to increase number of backoff stage which cause more energy consumptions for entering next backoff stage. This article proposes a scheme to improve IEEE 802.15.4 medium access control, called adjustment delay scheme (ADES). ADES not only reduce probability of collision but also reduce probability of going to next backoff stage. The validity of study is proven by simulation experiments. ADES performs better than IEEE 802.15.4 standard in term of the probability of successful packet transmission, network goodput, bandwidth utilization as well as energy consumption in the networks.