Performance analysis of the IEEE 802.15.7 CSMA/CA algorithm based on Discrete Time Markov Chain (DTMC)

The prediction of numbers of newborns is an important issue in hospital management. Relying on the inherent non-aftereffect property, discrete-time Markov chain (DTMC) is a candidate for solving the problem. But the classical DTMC is unable to handle the uncertainty of states, especially when the state space is not discrete, which would lead to instable predicted results. In order to overcome the limitation of the existing DTMC model, a belief Markov chain (BMC) model is proposed by synthesizing the classical DTMC and Dempster-Shafer theory effectively. Depending on the advantages of Dempster-Shafer theory in expressing uncertainty, the proposed BMC model is capable of dealing with various uncertainties, which improves and perfects the classical DTMC model. An illustrative example demonstrates the effectiveness of the proposed model. Moreover, a comparison between the proposed BMC model and the classical and fuzzy states modified DTMC models is given to show the superiority of the proposed model against the other two. Finally, the stability of the proposed model has been proven.

A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

The IEEE 802.11 MAC protocol uses Distributed Coordinated Function (DCF) as the main element to determine the efficiency in sharing the limited resources of the wireless channels in wireless local area networks (WLANs). For analyzing 802.11 DCF networks, one of the key assumptions commonly used is that every station always has a packet to transmit (saturated state). However, in practice it may not be valid. In this paper, we assess the accuracy of the non-saturated traffic condition by integrating the traditional M/G/1 queueing model into Discrete Time Markov Chain (DTMC) model. The proposed I-DTMC enables us to analyze the performance of per-station in terms of average MAC system delay observed by each packet for successful transmission over the medium. The comparative results show enhanced performance evaluation of 802.11 DCF against other models.

Goods Allocation by Queuing and the Occurrence of Violence: A Probabilistic Analysis

We propose an analytical model for the performance analysis of the enhanced distributed channel access (EDCA) function of the IEEE 802.11e standard. The proposed discrete-time Markov chain (DTMC) model incorporates the main quality-of-service (QoS) features of 802.11e, namely, contention window (CW), arbitration interframe space (AIFS), and transmit opportunity (TXOP) differentiation. Due to its specific design, the proposed DTMC model jointly considers the state of the medium access control (MAC) layer buffer and the MAC differentiation mechanisms which facilitates a novel performance analysis framework at an arbitrary traffic load. Analytical and simulation results are compared to demonstrate the accuracy of the proposed approach for varying traffic load, EDCA parameters, and MAC layer buffer space.

The rapid development of aviation technology has made the application of unmanned aerial vehicles (UAVs) more popular in recent years. Due to the inadequate capability of a single UAV, flying ad hoc networks (FANETs), which consist of multiple different UAVs, not only break the limits of single UAV by introducing the cooperation between UAVs, but also complete more complex missions by extending the communication range at infrastructure-less areas. One of the most important design issues for FANETs is the communication and cooperation between UAVs, and the number of UAV relays has been proved to be closely related to the improvement of system performance. However, few FANET protocols or algorithms have been proposed from the perspective of scheduling the number of relays. Inspired by the idea, this paper proposes an optimal relay number selection algorithm based on a more realistic network model which includes a novel cooperative ( $n+2$ )-node system model considering the distance metrics between relays and a Nakagami- $m$ short-term static fading channel model more suitable for UAV operation environments. The system outage probability is calculated by introducing the Meijer-G function, and a three-dimensional discrete time Markov chain (DTMC) model is established and analyzed to derive the closed-form binary expressions of throughput, energy efficiency and average transmission delay. In order to balance the three performance to achieve a comprehensive system performance, a trade-off factor named EDT is further proposed and maximized to evaluate the optimal number of relays. Finally, simulation results assess the impact of network parameters on system performance, and verify the superiority of the proposed algorithm over its fixed relay number counterpart.

Introduction.- Discrete-time Markov chain models.- Poisson models and their application to ozone data.- Some counting processes and ozone air pollution.- Comments.- Acknowledgements.- References.

The occurrence of Texas Gulf Coast hurricanes is analyzed using various statistical methods. Simple Poisson, periodic Poisson, and Markov chain models are fitted to the occurrence data for a site offshore of Mustang Island, near Corpus Christi. Use of the periodic Poisson model permits a relatively simple description of cyclical occurrence phenomena. The goodness of fit of the three different models is compared, using interarrival time, hazard function, and comparative maximum likelihood tests. The auto-covariance function for the data is investigated, and the effects of varying-record-Iengths are considered. Results found for other Texas sites are also compared with those found for the Padre Island site. While the simple Poisson and Markov chain models seem to provide a reasonable fit to the occurrence history, the periodic Poisson model is seen to provide a much better fit to the data. Reasons for the acceptance of a periodic model for hurricane occurrences are discussed. The cyclical hurricane occurrence pattern implied by the acceptance of the periodic Poisson model is shown to have a very significant effect on the design of both offshore and onshore structures. An important indication of this study is that the Texas coast is currently nearing the peak of cycle of high hurricane occurrence likelihood. INTRODUCTION The Gulf Coast of the United States is frequently visited by hurricanes, resulting in very large property damages. Since hurricanes are the most severe type of storm generally affecting the region, the high winds and waves associated with such storms produce the critical conditions for which many offshore structures must be designed. Offshore structure designers require some method of predicting hurricane occurrences over time spans of several years. Such prediction models may then be used to develop the suitable design criteria for a particular structure. Study of the Gulf Coast storm records reveals the rather irregular nature of hurricane arrivals at coastal sites. This irregularity suggests the treatment of hurricane occurrences as a stochastic process; that is, a random process in time. Hurricane occurrence probabilities may be obtained from the models of such a random process. This investigation is concerned with the development of probabilistic models for the prediction of hurricanes offshore of Mustang Island,, near Corpus Christi, Texas. (Figure 1) ';. The forecasting methods used in this study are based solely upon the available historical storm occurrence data. The methods utilized are valid only for long term forecasts made in ignorance of future weather conditions. Thus, the probabilistic treatment of long-term hurricane prediction serves as a complement to synoptic weather forecasting procedures, which are suitable only for short-term predictions. Various stochastic models for hurricane predictions with parameters derived from the storm data at the site are investigated and compared. DATA The assembly, categorizing, and interpretation of the available hurricane occurrence data is a very important step in making predictions of future storm likelihoods. The past record of storm occurrences is the information upon which the stochastic models of this study are based.

With the development of the convergence of IT and automotive technology, platoon-based driving systems are getting more attention and how to disseminate messages in the platoon is an important issue. In this paper, to enhance the energy efficiency and traffic throughput (e.g., average velocity) while meeting transmission deadlines, we propose an energy efficient message dissemination scheme (EMDS) in platoon-based driving systems, which also provides proper power control and relay selection. To find out the optimal policy to balance the probability of successful message dissemination and transmission power cost in EMDS, we formulate a Markov decision process (MDP) problem that considers the velocity of the vehicles in the platoon. To evaluate the performance of EMDS, we analyze the outage probability, the average velocity, and the expected power consumption using the discrete-time Markov chain (DTMC) model. Evaluation results demonstrate EMDS with the optimal policy improves the average velocity and the energy efficiency of message dissemination compared with the conventional message dissemination schemes, while reducing the message dissemination failure rate.

Since its emergence, Wake-up radio technology has solved idle listening and overhearing problems of the duty-cycled approach in wireless sensor networks (WSN). Using collision avoidance mechanisms has furthered the success of WuR by improving the overall performance of wireless communications. These latter are characterized by transmission errors in real environments. The effect of non-ideal transmission channel on the performance of WuR has not yet been investigated. In this paper, a discrete-time Markov chain (DTMC) model that takes into account transmission errors is provided. This model is used to evaluate and compare the performance of two WuR based protocols, with and without collision avoidance, referred to as WuR-CA and WuR-NCA, respectively. We demonstrate that WuR-CA provides better performance than WuR-NCA in terms of reliability and throughput, at the cost of higher delay and power consumption, in both ideal and no-ideal channel conditions.

Markov chain optimization of repair and replacement decisions of medical equipment

As the physical transmission rate develops rapidly in the modern IEEE 802.11 WLANs, the efficiency of traditional stop-and-wait(SAW) automatic repeat request (ARQ) mechanism degrades dramatically. Negative acknowledgement (NAK)-based ARQ mechanism is a proper candidate to improve the link layer performance. In this paper we propose a discrete-time Markov chain (DTMC) model to analyze the performance of the IEEE 802.11 WLAN system with NAK-based ARQ mechanism. Then the analytical performance is compared with that of SAW ARQ. We find that NAK-based ARQ outperforms SAW ARQ in terms of system throughput while performs poorly in terms of transmission delay. Then a hybrid ARQ scheme is proposed, through which the system can leverage between the two mechanisms to achieve better quality of service(QoS) guarantee. The simulation result validates the correctness of the proposed model and scheme.

To improve the efficiency of the identification process in RFID system, anti-collision protocol is crucial. EPC Class-1 Generation-2 standard (Gen2) employs ALOHA-based Q algorithm as anti-collision protocol. However, the same updating rate will cause unnecessary collided and idle slots. In 2010, we proposed a Fast Q algorithm (FQA) that adjusted the value of Q with different steps. Recently, we find that the Fast Q algorithm can be further improved since it did not consider the probabilities of collided/idle slots. Accordingly, this paper proposes an Enhanced Fast Q algorithm (EFQA). In EFQA, Q will be updated after two continuous collided or idle slots with different updating rates, which are calculated by synthesizing the duration time of collided/idle slots and their probabilities. We then quantitatively analyze the performance of EFQA by establishing a Discrete-Time Markov Chain (DTMC) model. Simulation results show that EFQA outperforms other protocols regardless of the number of tags.

We analyze the performance of slotted ALOHA systems with energy harvesting nodes and the retry limit by developing a node-centric two-dimensional discrete-time Markov chain (DTMC) model. We consider not only the number of energy packets consumed when a data packet is transmitted but also a general probability distribution of an energy packet arrival process. We assume that the capacities of data and energy buffer at a node are one packet and $E$ packets, respectively. According to the concept of the equilibrium point analysis, we derive a fixed point equation with respect to the ratio of nodes transmitting a data packet. The accuracy of theoretical results obtained from the fixed point equation is verified by computer simulation. Numerical results under Poisson arriving process of energy packets indicate that throughput, the offered traffic, and the discard probability demonstrate the weak symmetric relationship between the data packet generation probability and the average number of arriving energy packets. However, the average transmission delay rapidly increases when the average number of arriving energy packets becomes small.

This article focuses on data analyses under the scenario of missing at random within discrete-time Markov chain models. The naive method, nonlinear (NL) method, and Expectation-Maximization (EM) algorithm are discussed. We extend the NL method into a Bayesian framework, using an adjusted rejection algorithm to sample the posterior distribution, and estimating the transition probabilities with a Monte Carlo algorithm. We compare the Bayesian nonlinear (BNL) method with the naive method and the EM algorithm with various missing rates, and comprehensively evaluate estimators in terms of biases, variances, mean square errors, and coverage probabilities (CPs). Our simulation results show that the EM algorithm usually offers smallest variances but with poorest CP, while the BNL method has smaller variances and better/similar CP as compared to the naive method. When the missing rate is low (about 9%, MAR), the three methods are comparable. Whereas when the missing rate is high (about 25%, MAR), overall, the BNL method performs slightly but consistently better than the naive method regarding variances and CP. Data from a longitudinal study of stress level among caregivers of individuals with Alzheimer’s disease is used to illustrate these methods.