Analysis of sliding frame R-ALOHA protocol for real-time distributed wireless networks

As a component of the intelligent transportation system (ITS) and one of the concrete applications of mobile ad-hoc networks, inter-vehicle communication (IVC) has attracted research attention from both the academia and industry. A key issue in the wireless network is the design of medium access control (MAC) layer protocols for distributed channel assignment. A promising class of protocols for IVC networks is R-ALOHA-based protocols. In this paper, we document our investigation of near-far effects and capture effects on R-ALOHA for a real-time distributed packet wireless network or IVC in the presence of multipath, and shadowing. The research recorded in this paper also studied the stability of R-ALOHA protocol for IVC analytically and by simulation. In order to analyze how fast R-ALOHA makes the network stable once communication among terminals is initiated, we construct a discrete Markov chain and derive new recursive formulas based on solutions to Markov chain to evaluate distribution and mean value of system stabilization time in the fading environment.

Cyber-physical systems are realized as those systems where the computation, communication and physical processes interact with each other (e.g., robotic systems, automotives, etc). A large class of cyber-physical systems are associated with strict timeliness requirements. They are known as ‘real-time systems’ (e.g., smart grids, avionics, etc). Industrial control systems (ICS) are real-time systems that consist of several closed-loop controls. The main components of a control loop are — a physical plant, sensors, actuators and a controller. The sensors transmit the measurement data to the controller that generates appropriate control signals to actuate the physical plant. Large-scale wireless sensor actuator networks form the main communication framework among the network devices in ICS. Most of the communications between each of these devices are periodic real-time flows with hard deadlines. To ensure reliability, the communication in these systems are time-division multiple access (TDMA) based. Dedicated network resources (time-slots and frequencies) are allocated to these devices in advance and the communication schedule is pre-computed to satisfy the hard deadlines of the real-time flows. The same schedule is repeated over time which makes the schedule predictable in nature. However, a malicious attacker can exploit this predictable time-slots in the schedules to launch timing attacks. Since these applications are time-critical, timing attacks can completely undermine the system performance leading the system to an unsafe state. Schedule randomization is a popular defense mechanism to mitigate timing attacks. Although a few works exist in the literature that use schedule randomization as a countermeasure against timing attacks in real-time uniprocessor systems, none of them are applicable for real-time wireless networks. Schedule randomization as a countermeasure against timing attacks in real-time wireless networks remained largely unexplored. With this objective in focus, this thesis addresses some schedule randomization techniques to mitigate timing attacks in real-time wireless networks. Among the existing wireless network protocols that are in use, the WirelessHART is the most suitable and widely adopted protocol in ICS. A WirelessHART protocol supports centralized architecture, TDMA based communication, multiple channels, etc. All of these characteristics of a WirelessHART network facilitate reliable and predictable communication with real-time flow guarantees in ICS. However, the predictable communication exposes the system to timing attacks, such as selective jamming attacks. Selective jamming attacks are stealthy attacks, however, it can lead the system to an unstable state and can disrupt the safety of the system. As a countermeasure against such attack in WirelessHART network, we propose a centralized schedule randomization technique that randomizes the time-slots and channels in the schedule over every hyperperiod (least common multiple of the periods of the real-time flows) without violating the hard deadlines of the real-time flows, while still satisfying the feasibility constraints of a schedule in a WirelessHART network. The centralized technique generates the schedules offline and distributes the schedules online, hence, cannot support any change in topology in the network. Further, this technique has energy overheads in distributing the schedules to all the network devices at runtime. Hence, we propose a distributed online schedule randomization technique that can generate random feasible schedules at runtime in each network device without affecting the closed-loop control stability. To increase the extent of randomization of time-slots in the schedules, this online distributed technique adopts a period adaptation strategy that can adjust the transmission periods of the real-time flows at runtime depending on the stability of the closed-loop controls. To support an ever-growing number of devices and dense connectivity, the fifth generation (5G) cellular networks is expected to serve as the main communication standard in the future wireless sensor networks. Among the different service categories supported by 5G, the ultra-reliable low-latency communication (URLLC) is mostly suitable for time-critical applications such as the ICS. The communication in 5G is organized into slots over multiple frequencies. To satisfy the hard deadlines of the URLLC flows in ICS, a part of the resources (slots and frequencies) in 5G are reserved for URLLC traffic and the same schedule is repeated over time. The repetition of the same schedule over time makes the slots in the schedules predictable. This, in turn, makes the 5G networks vulnerable to timing attacks. However, the proposed schedule randomization techniques for WirelessHART networks are not applicable for dynamic networks like 5G where the number of flows and the amount of available network resources for URLLC flows vary at runtime. Moreover, the feasibility of the real-time flows need to be guaranteed at runtime. Hence, we propose an online schedule randomization technique that randomizes the slots and the frequencies of the periodic URLLC flows while guaranteeing the feasibility of the flows at runtime.

The k-SIC technology can support at most k parallel transmissions, thus it has the prominent capability of providing fast media access, which is vital for real-time industrial wireless networks. However, it suffers from high power consumption because high interference has to be overcome. In this paper, given the real-time performance requirement of an uplink network supporting k-SIC, we study how to minimize aggregate power consumption of users by power scheduling. We prove that the problem is solvable in polynomial time. A universal algorithm with complexity of O(n 3 ) is proposed for k-SIC, where n is the number of transmitters. For the special case of k = 2, another algorithm with complexity of O(L 4 ) is presented, where L is the frame length. Simulation results reveal that both the aggregate power consumption and the maximal transmit power will be exponentially declined with further relaxation of the real-time performance.

The production of active carbon from bituminous coal

Industrial applications and cyber-physical systems rely on real-time wireless networks to deliver data in a timely and reliable manner. However, existing solutions provide these guarantees only for stationary nodes. In this article, we present REWIMO, a solution for real-time and reliable communications in mobile networks. REWIMO has a two-tier architecture composed of (i) infrastructure nodes and (ii) mobile nodes that associate with infrastructure nodes as they move. REWIMO employs an on-join bandwidth reservation approach and benefits from a set of techniques to efficiently reserve bandwidth for each mobile node at the time of its admission and over its potential data forwarding paths. To ensure association of mobile nodes with infrastructure nodes over high-quality links, REWIMO uses the two-phase scheduling technique to coordinate neighbor discovery with data transmission. To mitigate the overhead of handling network dynamics, REWIMO employs an additive scheduling algorithm, which is capable of additive bandwidth reservation without modifying existing schedules. Compared to the algorithms used by static real-time wireless networks, the techniques and the algorithms employed by REWIMO result in a significant increase in real-time capacity, enhanced reliability, and considerably faster handling of network dynamics.

We assume that a Poisson flow of vehicles arrives at isolated signalized intersection, and each vehicle, independently of others, represents a random number X of passenger car units (PCU’s). We analyze numerically the stationary distribution of the queue process {Zn}, where Zn is the number of PCU’s in a queue at the beginning of the n-th red phase, n → ∞. We approximate the number Yn of PCU’s arriving during one red-green cycle by a two-parameter Negative Binomial Distribution (NBD). The well-known fact is that {Zn} follow an infinite-state Markov chain. We approximate its stationary distribution using a finite-state Markov chain. We show numerically that there is a strong dependence of the mean queue length E[Zn] in equilibrium on the input distribution of Yn and, in particular, on the ”over dispersion” parameter γ= Var[Yn ]/E[Yn ]. For Poisson input, γ = 1. γ > 1 indicates presence of heavy-tailed input. In reality it means that a relatively large ”portion” of PCU’s, considerably exceeding the average, may arrive with high probability during one red-green cycle. Empirical formulas are presented for an accurate estimation of mean queue length as a function of load and g of the input flow. Using the Markov chain technique, we analyze the mean ”virtual” delay time for a car which always arrives at the beginning of the red phase.

Numerical analysis of rational processes beyond Markov chains

This paper presents reliability analysis drawn up for an industrial firm. The main goal of this paper is to estimate the probability of firms failure to satisfy an order to its industrial partners. The second aim is to quantify expected value of amount of manufactured products for specific time period. Discrete Markov chains- well-known method of stochastic modelling describes the issue. The method is suitable for many systems occurring in practice where we can easily distinguish various amount of states. The disadvantage of Markov chains is that the amount of computations usually increases rapidly with the amount of states. The Monte Carlo method was implemented to deal with the problem. Chebyshev’s inequality was applied to estimate sufficient number of simulations.

Stiffness-tolerant methods for transient analysis of stiff Markov chains

The paper analyzes the performance of a DQDB station with a finite buffer capacity. The authors take a per-node approach to the analysis and make a Bernoulli assumption for the bus outcomes. The model, which is based on a discrete-time Markov chain, is solved by means of EPA (equilibrium point analysis). By utilizing this method, one can calculate the steady-state probabilities of the Markov chain; then, one can evaluate throughput, average bus access delay and average MAC delay. The accuracy of the analytical results are examined through simulation. For light to medium loads, the analytical results show little discrepancy when compared to simulation results. A small discrepancy is found for some stations, and it is explained in part by the correlation between outcomes of consecutive slots, which is not captured in the the model. >

Properties of results obtained on Markov processes analysis in medical applications have been illustrated. Calculation was done for measurement of Thoracic Fluid Content by means of impedance cardiograph for chosen medical patients population. Main medical aspect of Markov chain calculation has been underlined to exhibit some constraints in decision making with such math models. Markov chain analysis was done as optimization task. Effective algorithm for irregular data in continuous time has been proposed.

A queuing analytical model is presented to investigate the performances of different sleep and wakeup strategies in a solar-powered wireless sensor/mesh network where a solar cell is used to charge the battery in a sensor/mesh node. While the solar radiation process (and, hence, the energy generation process in a solar cell) is modeled by a stochastic process (i.e., a Markov chain), a linear battery model with relaxation effect is used to model the battery capacity recovery process. Developed based on a multidimensional discrete-time Markov chain, the presented model is used to analyze the performances of different sleep and wakeup strategies in a sensor/mesh node. The packet dropping and packet blocking probabilities at a node are the major performance metrics. The numerical results obtained from the analytical model are validated by extensive simulations. In addition, using the queuing model, based on a game-theoretic formulation, we demonstrate how to obtain the optimal parameters for a particular sleep and wakeup strategy. In this case, we formulate a bargaining game by exploiting the trade-off between packet blocking and packet dropping probabilities due to the sleep and wakeup dynamics in a sensor/mesh node. The Nash solution is obtained for the equilibrium point of sleep and wakeup probabilities. The presented queuing model, along with the game-theoretic formulation, would be useful for the design and optimization of energy-efficient protocols for solar-powered wireless sensor/mesh networks under quality-of-service (QoS) constraints

Irrespective of whether the test for homogeneity is significant or not, most researchers assume time-homogeneity in analysing Markov chains due to scanty literature on the analysis of time-inhomogeneous Markov chains. Based on the assumption that, for each point in time in the future, a stochastic process will be subjected to a randomly selected transition matrix from an ergodic set of transition matrices the process was subjected to in the recent past, a methodology was proposed for analysing the long-run behaviours of time-inhomogeneous Markov chains. The proposed model was implemented to historical data consisting of the exchange rate of cedi-dollar, cedi-pound, and cedi-euro spanning over 6 years (January 2012 to December 2017). The results show that under certain “closeness” conditions, the long-run behaviours of the time-inhomogeneous case are almost identical to those of the time-homogeneous case. The paper asserted that even if the Markov chain exhibit time-inhomogeneity, analysing the Markov chain under the assumption of time-homogeneity is a step in the right direction under certain “closeness” conditions; otherwise, the proposed method is recommended. It was also found that investing in dollars yields better returns than the other currencies in Ghana.

The M/M/k/setup model, where there is a penalty for turning servers on, is common in data centers, call centers and manufacturing systems. Setup costs take the form of a time delay, and sometimes there is additionally a power penalty, as in the case of data centers. While the M/M/1/setup was exactly analyzed in 1964, no exact analysis exists to date for the M/M/k/setup with k>1. In this paper we provide the first exact, closed-form analysis for the M/M/k/setup and some of its important variants including systems in which idle servers delay for a period of time before turning off or can be put to sleep. Our analysis is made possible by our development of a new technique, Recursive Renewal Reward (RRR), for solving Markov chains with a repeating structure. RRR uses ideas from renewal reward theory and busy period analysis to obtain closed-form expressions for metrics of interest such as the transform of time in system and the transform of power consumed by the system. The simplicity, intuitiveness, and versatility of RRR makes it useful for analyzing Markov chains far beyond the M/M/k/setup. In general, RRR should be used to reduce the analysis of any 2-dimensional Markov chain which is infinite in at most one dimension and repeating to the problem of solving a system of polynomial equations. In the case where all transitions in the repeating portion of the Markov chain are skip-free and all up/down arrows are unidirectional, the resulting system of equations will yield a closed-form solution.

Markov chain sampling has recently received considerable attention, in particular in the context of Bayesian computation and maximum likelihood estimation. This article discusses the use of Markov chain splitting, originally developed for the theoretical analysis of general state-space Markov chains, to introduce regeneration into Markov chain samplers. This allows the use of regenerative methods for analyzing the output of these samplers and can provide a useful diagnostic of sampler performance. The approach is applied to several samplers, including certain Metropolis samplers that can be used on their own or in hybrid samplers, and is illustrated in several examples.