Semi Markov Research Articles

An Application of a Semi Markov Model in Predicting the Fading Occurrences in Wireless Communication Channels

The most frustrating and troublesome issue in wireless communication is fading. Estimating the fading occurrence for the design of greatly reliable communication link is crucial. In this paper, we present a novel mathematical method for predicting the future signal fading on the basis of current and past data. The application of a Semi Markov Model as a generalization of the Markov Model is discussed for predicting the deep fading occurrence probabilities of the received envelope in wireless communications channels. This flexible model is given for assessing the system performance with the envelope correlation. 142 deep fading data whose amplitudes are lower than a mean of the amplitudes which occurred in a typical wireless system with the Jakes filtering are considered. The transition probability matrix and the holding time mass functions are calculated for the next 1 to 21 unit times. One unit time is regarded as the inverse of sampling frequency; moreover, the core matrix and the cumulative probability distribution of the waiting time are obtained. Calculating the interval transition probabilities for Amplitude to Amplitude transition for these deep fades demonstrates the forecasting occurrence probabilities in the future and the possibility of forecasting the fading occurrences in dimensions of time and amplitude.

An adaptive probabilistic data-driven methodology for prognosis of the fatigue life of composite structures

Data driven probabilistic methodologies have found increasing use the last decade and provide a platform for the remaining useful life (RUL) prediction of composite structures utilizing health-monitoring data. Of particular interest is the RUL prediction of composite structures that either underperform or outperform due to unexpected phenomena that might occur during their service life. These composite structures are referred as outliers and the prediction of their RUL is a challenge. This study addresses this challenge by proposing a new data-driven model; the Adaptive Non-Homogenous Hidden Semi Markov Model (ANHHSMM), which is an extension of the NHHSMM. The ANHHSMM uses diagnostic measures, which are estimated based on the training and testing data, and it adapts the trained parameters of the NHHSMM. The training data set consists of acoustic emission data collected from open-hole carbon–epoxy specimens, subjected to fatigue loading, while the testing data set consists of acoustic emission data collected from specimens, subjected to fatigue and in-situ impact loading, which can be considered as an unseen event for the training process. ANHHSMM provides better predictions in comparison to the NHHSMM for all the cases, demonstrating its capability to adapt to unexpected phenomena and integrate unforeseen data to the prognostics course.

RELIABILITY AND COST ANALYSIS COMPARISON BETWEEN TWO-UNIT PARALLEL SYSTEMS WITH NON-IDENTICAL AND IDENTICAL CONSUMABLE UNITS

In this paper, reliability and cost analysis of two different two-unit parallel redundant systems with non identical and identical consumable units respectively have been done by employing Semi – Markov Processes and Regenerative Point Technique and a comparison is made between both the systems. The systems being considered are two inkjet printers with different cartridge arrangements. The first system contains two non-identical consumable units, colour and black cartridges, wherein, failure of one unit results in system being operative under reduced efficiency. However, the second system contains two identical consumable units, both colour cartridges, wherein, failure of any one of the units does not affect efficiency of system. The system may also fail due to other technical failures. Various system efficiency indicators such as Mean Time to System Failure (MTSF), Availability and Expected Down-Time of the System, Busy Time Period of Repairman, and Average Number of (damaged) Unit Replacements are estimated. Using data collected from industry, the parameters are used to estimate reliability parameters of both the printers and a comparison of profits of both systems with respect to time has been done. Subsequently, the differences in behavior of Availability and Profitability of both systems due to variations in its parameters have been analyzed.

Artificial bee colony optimization based energy-efficient wireless network interface selection for industrial mobile devices

Management of energy is a crucial challenge for mobile devices, having the network activity frequently taking up a considerable part of the energy in the overall system. The important goal of this work is to offer energy efficiency for mobile handhelds. The industrial mobile devices in the recent times are fitted with several wireless network interfaces, these devices function with less battery power. In mobile handhelds, each user is able to run multiple applications with the purpose of have particular Quality of Service (QoS) requirements. Hence, it would be beneficial for a multi-interface terminal to simultaneously use two or more interfaces to gain in performance. So satisfying the QoS becomes very difficult task. An energy-effective Adaptive Wireless Network Interface Selection (AWNIS) with Artificial Bee Colony (ABC) is proposed in this paper. AWNIS-ABC algorithm chooses the best of the wireless network interface in regard to the energy consumed by taking the QoS of the link into consideration as well as following a hi-speed network interface-selection interval based on the scenario in the network. Then Hidden Semi Markov Model (HSMM) is introduced for searching the detouring paths if the energy state of a given relay node is very less to utilize the required interface, which ensures timely delivery. Also, it provides the diversification of the route paths in the network with the purpose of extending the lifetime of the network. An alternate path is proposed for reliable data delivery beforehand the route path encounters a breakage owing to energy exhaustion is found. A number of wireless network interfaces in mobile devices are utilized in improving the energy efficiency, satisfaction of QoS occurring during data transfer. The simulation of the network is implemented via the Network Simulator 2(NS2).

Optimal service rates of a perishable inventory system with service facility

This paper studies a continuous-review perishable inventory system with service facility. We consider a finite capacity inventory system with Markovian arrivals and exponentially distributed service times. The inventory is replenished according to an (s, S) policy and the lead times follow phase type distribution. The life time of each item is assumed to be exponential. By controlling the service rate at each instance of time, we can minimise the total expected cost rate. We formulate this model as a semi Markov decision problem. The stationary optimal policy is computed using linear programming algorithm and the results are illustrated numerically.

Node Selection Strategy for Reliable Data Transmission in Manet using Semi Markov Process for Multicast Routing Protocol

Reliable data delivery is essential in the mobile adhoc network (MANET) and the devices change their positions very frequently since they do not have any fixed infrastructure. In this paper, we have used the semi Markov process to select the nodes. Semi Markov process is used to develop the node behavior model for network survivability. Then multicast routing protocols have used to form a group for data transmission. We have used Adaptive demand driven routing protocol which provides routing with fast topological changes and Neighbour supporting multicast routing protocol.

Reliable Data Delivery in Manet using Adaptive Demand Driven Routing Protocol and Semi Markov Process

Reliable data delivery is an essential feature or element in the mobile adhoc network (MANET) and the devices often change their locations as they are not allocated any fixed infrastructure. In this paper, multicast routing protocols for military communications is proposed. Data security is a serious concern in military communications with MANET. This paper attempts to prove that data security can be achieved, by applying one of the most widely used algorithms, Adaptive demand driven routing (ADMR) protocol which provides routing done with rapid topological changes. For network survivability the node behavior model is devised by incorporating Semi Markov process. On the whole, the paper shows that reliable data delivery for MANET can be obtained by estimating the current performance of the network and by using Semi-Markov process when the forwarder node is isolated.

Modelling of Transport System Operational Reliability a Markov Approach

Abstract The workability of a transport system is associated with performance and operational reliability. Operational reliability provides a measure of the probability that a transport system will realize transport process as intended. Performance reliability is an adequacy measure of transport process realization under specific environmental and traffic conditions. Transport system can be modelled as repairable, multistate, non-homogenous rectangular or dendrite system. This article provides the Markov and semi Markov models for estimation of the operational and performance reliability of city transport system. The system is semi homogenous it means that serial subsystems have the same reliability function. The reliability of any serial subsystem is exponential. The distribution of the repair time is any probability distribution. In case where the probability distribution of the repair time is exponential, the Markov process is used to construct simulation model. The simulation model was applied at Microsoft Excel. Many stochastic models in engineering, logistic and even finance or insurance are setup in a spreadsheet for simulation. The semi Markov model of the multistate reliability of repaired system is applied to the street system. The embedded Markov chain was used to count stationary probabilities. The possibility of application of the results is illustrated by an example for the systems with rectangular or dendrite shaped accordingly, consist of three types of elements.

Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries

This paper examines diagnostics and prognostics of Lithium-Polymer (Li-Po) batteries for unmanned aerial vehicles (UAVs). Several discharge voltage histories obtained during actual indoor flights constitute the training data for a data-driven approach, utilizing the Non-Homogenous Hidden Semi Markov model (NHHSMM). NHHSMM is a suitable candidate as it has a rich mathematical structure, which is capable of describing the discharge process of Li-Po batteries and providing diagnostic and prognostic measures. Diagnostics and prognostics in unseen data are obtained and compared with the actual remaining flight time in order to validate the effectiveness of the selected model.

Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification

In this paper, the discharge voltage is utilized as a critical indicator towards the probabilistic estimation of the Remaining Useful Life until the End-of-Discharge of the Lithium-Polymer batteries of unmanned aerial vehicles. Several discharge voltage histories obtained during actual flights constitute the in-house developed training dataset. Three data-driven prognostic methodologies are presented based on state-of-the-art as well as innovative mathematical models i.e. Gradient Boosted Trees, Bayesian Neural Networks and Non-Homogeneous Hidden Semi Markov Models. The training and testing process of all models is described in detail. Remaining Useful Life prognostics in unseen data are obtained from all three methodologies. Beyond the mean estimates, the uncertainty associated with the point predictions is quantified and upper/lower confidence bounds are also provided. The Remaining Useful Life prognostics during six random flights starting from fully charged batteries are presented, discussed and the pros and cons of each methodology are highlighted. Several special metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding their prognostic capabilities and potential.

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks (SMPISCS)

In Wireless Sensor Network (WSN), energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery. This quantified level of co-operation signifies the performance of the network in terms of increased throughput, packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead. The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding. The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate. Semi Markov Process Inspired Selfish aware Co-operative Scheme (SMPISCS) is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network. SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish. Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes, number of sensor nodes and range of detection threshold.

Optimal Service Rates of a perishable Inventory System with Service Facility

This paper studies a continuous-review perishable inventory system with service facility. We consider a finite capacity inventory system with Markovian arrivals and exponentially distributed service times. The inventory is replenished according to an (s, S) policy and the lead times follow phase type distribution. The life time of each item is assumed to be exponential. By controlling the service rate at each instance of time, we can minimise the total expected cost rate. We formulate this model as a semi Markov decision problem. The stationary optimal policy is computed using linear programming algorithm and the results are illustrated numerically.

Restless Video Bandits: Optimal SVC Streaming in a Multi-User Wireless Network

In this paper, we consider the problem of optimal scalable video delivery to mobile users in wireless networks given arbitrary Quality Adaptation (QA) mechanisms. In current practical systems, QA and wireless channel scheduling are performed independently by the content provider and network operator, respectively. While most research has been focused on jointly optimizing these two tasks, the high complexity that comes with a joint approach makes the implementation impractical. Therefore, we present a scheduling mechanism that takes the QA logic of each user as input and optimizes the scheduling accordingly. Hence, there is no need for centralized QA and cross-layer interactions are minimized. We model the QA-adaptive scheduling and the jointly optimal problem as a Restless Bandit and a Multi-user Semi Markov Decision Process, respectively, in order to compare the loss incurred by not employing a jointly optimal scheme. We then present heuristic algorithms in order to achieve the optimal outcome of the Restless Bandit solution, assuming the base station has knowledge of the underlying quality adaptation of each user (QA-Aware). We also present a simplified heuristic without the need for any knowledge of the QA logic at the base station (QA-Blind). We show that our QA-Aware strategy can achieve up to two times improvement in network utilization compared to popular baseline algorithms such as Proportional Fairness. We also show that the QA-Blind strategy performs very close to the QA-Aware scheme while providing significant reduction in complexity. Furthermore, we provide a testbed implementation of the QA-Blind scheme in order to compare it with baseline algorithms in a real network setting.

Application of Hidden Markov Models and Hidden Semi Markov Models as Diagnostic Aid in Chronic Mastitis in Cows

Application of Hidden Markov Models and Hidden Semi Markov Models as Diagnostic Aid in Chronic Mastitis in Cows

Optimal joint RAN selection and resource allocation strategy in HetNet under Co-channel and user centric deployment

In this article, a joint Radio Access Network selection strategy and a sub-channel resource allocation scheme are introduced, where the downlink of a two-tier HetNet system is deployed in a co-channel deployment. With this scenario in mind, a Semi Markovian Decision Process based-model is proposed to find an optimal policy for user association the channel allocation, where the long-term expected reward is maximized. For the proposed strategy, the sub-channel allocation is only considered for those new and handoff petitions that potentially can be allocated in one of the SCs. In addition, with the aim to contribute to the enhancement of the overall usage of radio resources, a traffic offloading process is considered at the departure of the sessions. In order to motivate the session offloading from the MC to the SCs at departure times, we have defined that two sub-channels will be allocated in the SC for each offloaded session. Analytical results quantify and show the effectiveness of the proposed strategy.

A Simplified Stochastic SMP Model on Beacon Message Dissemination

Beacon message dissemination presumes the top priority in the smart transportation network. The successful transmission of the sent messages without collision or loss in the DSRC medium needs to be modeled. This communication is attained by IEEE 802.11 DCF protocol. A semi Markov Processes model is proposed to attain the expected throughput in the broadcast of the beacon messages.

A secured MANET using position‐based opportunistic routing and SEMI MARKOV process

SummaryInthe mobile ad hoc network (MANET), the nodes are moving very frequently and they do not have any fixed infrastructure. For reliable data delivery in MANET, we have analyzed the efficiency of data delivery using Position‐based Opportunistic routing method and greedy forwarding technique, which is based on geographic routing method. Initially, the MANET is divided into a number of provinces to identify the location of the receiver, and then Geographic routing is applied on the network for transmission of data. Based on the performance and throughput, the best forwarder node will be identified. Semi Markov process is used to develop the node behavior model for network survivability. In this work, reliable data delivery is obtained for the MANET by estimating the present performance of the network through isolating the forwarder node in Semi Markov process.

A Model to Predict Employee Turnover Rate: Observing a Case Study of Chinese Enterprises

Employee turnover is a main focus in human resource management (HRM) because understanding it can be useful in providing decision support to managers. The goal of our study was to predict the amount of employee turnover based on certain variables. The properties of employees and job positions are represented by two variables. A state-transition mode is applied to describe an employee?s job state and the type of turnover. We propose a conditional semi-Markov (SMK) model for calculating the conditional amount of employee turnover. Then, we provide a data set of employee records to illustrate how these models work in reality and how the proposed method can be valuable to managers for developing recruitment plans, promotion rules, and retirement regulations.

Extreme Prognostics for Remaining Useful Life Analysis of Composite Structures

The procedure of fatigue damage accumulation in composite structures is still unknown and depends on several parameters such as type and frequency of loading, stacking sequence and material properties. Additionally, the nonhomogeneous and anisotropic nature of composites result to a stochastic activation of the different failure mechanisms and make the estimation of remaining useful life (RUL) very complex but interesting task. Data driven probabilistic methodologies have found increasing use the last decade and provide a platform for reliable estimations of RUL utilizing condition monitoring (CM) data. However, the fatigue life of a specific composite structure has a quite significant scatter, with specimens that either underperform or outperform. These specimens are often referred as outliers and the estimation of their RUL is challenging. This study proposes a new RUL probabilistic model, the Extreme Non-Homogenous Hidden Semi Markov Model (ENHHSMM) which is an extension of the Non-Homogenous Hidden Semi Markov Model (NHHSMM). The ENHHSMM uses dynamic diagnostic measures, which are estimated based on the training and testing CM data and adapts dynamically the trained parameters of the NHHSMM. The available CM data are acoustic emission data recorded throughout fatigue testing of open-hole carbon–epoxy specimens. RUL estimations from the ENHHSMM and NHHSMM are compared. The ENHHSMM is concluded as the preferable option since it provides more accurate outlier prognostics.

A novel enhanced bat optimization based energy efficiency and imperfect channel state information in cooperative MIMO‐AF systems

SummaryEnergy Efficiency (EE) is a vital design standard for the future generation of communication systems. Similarly, cooperative communication is extremely efficient for improving the performance of such systems. Current research presents a relay precoding optimization algorithm in order to resolve EE problem in the Multiple‐Input‐Multiple‐Output (MIMO) Amplify‐And‐Forward (AF) Relay Networks containing correct Channel State Information (CSI) only. With the intension of focus on imperfect, this work presents a novel design of MIMO AF Relay Networks under Imperfect CSI (ICSI) by taking Channel Estimation (CE) errors and feedback delay errors, deprived of taking the straight link from source to destination. For this reason, we present a Hidden Semi Markov Model (HSMM) under ICSI, in which the source and destination based relay links are identified through destination. After that, we implement an Enhanced Bat Optimization (EBO) algorithm to enhance the EE of MIMO relay‐based non‐regenerative cooperative communication systems by means of improving the source and relay precoders by taking relay and direct links into consideration. Simulation outcomes prove that the EE‐ EBO optimization is capable of enhancing the outcomes of MIMO‐AF systems while matched up with direct/relay link with EE precoding optimization and precoding optimization algorithms.