Probabilistic Scheme Research Articles

Increasing the Safety of People Activity in Aggressive Potential Locations, Analyzed through the Probability Theory, Modeling/Simulation and Application in Underground Coal Mining

This paper deals with the increasing safety of working in aggressive potential locations, having SCADA system and WSN sensors, using a “probabilistic strategy” in comparison with a “deterministic” one, modeling/simulation and application in underground coal mining. In general, three conditions can be considered: 1) an unfriendly environment that facilitates the risk of accidents, 2) aggressive equipments that can compete to cause accidents and 3) the work security breaches that can cause accidents. These conditions define the triangle of accidents and are customized for an underground coal mining where the methane gas is released with the exploitation of the massive coal. In this case, the first two conditions create an explosive potential atmosphere. To allow people to work in a safe location it needs: first, a continuing monitoring through SCADA system of the explosive potential atmosphere and second, the use of antiexplosive equipment. This method, named “deterministic strategy”, increases the safety of working, but the explosions have not been completely eliminated. In order to increase the safety of working, the paper continues with the presentation of a new method based on hazard laws, named “probabilistic strategy”. This strategy was validated through modeling/simulation using CupCarbon software platform, and application of WSN networks implemented on Arduino equipments. At the end of the paper the interesting conclusions are emphases which are applicable to both strategies.

Ensuring the stability of rail transport under cargo turnover uncertainty

railway transportation as a system element of an integrated intellectual production system is considered. The models of cargo turnover planning under uncertainty of execution of contracts on shipment and supply of manufactured products are formalized. The models and methods of ensuring the stability of rail transport under cargo turnover uncertainty are proposed. The formal statement of task, characteristics of uncertainty and methods based on probabilistic scheme, the scheme of "Game with Nature" and a zero-sum game with an active enemy are reviewed. The methods of risk assessment in the implementation of the developed cargo turnover plan are proposed. It’s suggested to use this scheme for the engineering of management «New Silk Way » through the countries of the Eurasian Economic Union.

An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance

Most conventional heterogeneous network selection strategies applied in heterogeneous vehicular network regard the performance of each network constant in various traffic scenarios. This assumption leads such strategies to be ineffective in the real-world performance-changing scenarios. To solve this problem, we propose an optimal game approach for heterogeneous vehicular network selection under conditions in which the performance parameters of some networks are changing. Terminals attempting to switch to the network with higher evaluation is formulated as a multi-play non-cooperative game. Heterogeneous vehicular network characteristics are thoroughly accounted for to adjust the game strategy and adapt to the vehicular environment for stability and rapid convergence. A multi-play non-cooperative game model is built to formulate network selection. A probabilistic strategy is used to gradually drive players toward convergence to prevent instability. Furthermore, a system prototype was built at the Connected and Automated Vehicle Test bed of Chang'an University (CAVTest). Its corresponding test results indicate that the proposed approach can effectively suppress the ping-pong effect caused by massive handoffs due to varying network performance and thus well outperforms the single-play strategy.

Performance Evaluation and Analysis of IEC 62351-6 Probabilistic Signature Scheme for Securing GOOSE Messages

Cyber security is a growing concern in power systems. To achieve security requirements such as authentication and integrity for generic object-oriented substation event (GOOSE) messages, IEC 62351-6 standard recommends using digital signatures. Furthermore, it explicitly specifies to use RSASSA-Probabilistic Signature Scheme (PSS) digital signature algorithm based on RFC 3447. Power systems run in real-time and implemented cybersecurity measures have to strictly meet timing requirements. Therefore, it is very important to study performances of such methods and contrast them with the timing requirements stipulated by grid operations, e.g., power system protection enforces a maximum delay of 3 ms. In this fashion, it can be analyzed whether a recommended cyber security mechanism is fit for use in power systems. In previous works, only RSA digital signatures were studied and its performance evaluation in terms of computational times for securing GOOSE messages have been studied. This paper analyses the timing performance of RSASSA-PSS digital signature algorithm for securing the GOOSE messages. This is important to assess its feasibility for IEC 61850-based networks, as specified by the IEC 62351-6 standard. RSASSA-PSS digital signature algorithm is implemented in Python and verification times are calculated. The results show that RSASSA-PKCS1-v1_5 1024 key digital signatures provide improved performance compared with other RSA digital signature schemes. That being said, none of the algorithms is fast enough to be implemented for time-critical operations such as protection coordination.

Design-based mapping for finite populations of marked points

The estimation of marks for a finite population of points scattered onto a study region is considered when a sample of these points is selected by a probabilistic sampling scheme. At each point, the mark is estimated by means of an inverse distance weighting interpolator. The design-based asymptotic properties of the resulting maps are derived when the study area remains fixed, a sequence of nested populations with increasing size is considered and samples of increasing size are selected. Conditions ensuring design-based asymptotic unbiasedness and consistency are given. They essentially require that marks are the values of a pointwise or uniformly continuous deterministic function, the enlargement of the populations is rather regular and the sequence of sampling designs ensures an asymptotic spatial balance. A computationally simple mean squared error estimator is proposed. A simulation study is performed to assess the theoretical results on artificial populations. Finally, an application for mapping the values of the height of trees in a forest stand located in North Italy is reported.

Location Privacy in Usage-Based Automotive Insurance: Attacks and Countermeasures

Usage-based insurance (UBI) is regarded as a promising way to provide accurate automotive insurance rates by analyzing the driving behaviors (e.g., speed, mileage, and harsh braking/accelerating) of drivers. The best practice that has been adopted by many insurance programs to protect users’ location privacy is the use of driving speed rather than GPS data. However, in this paper, we challenge this approach by presenting a novel speed-based location trajectory inference framework. The basic strategy of the proposed inference framework is motivated by the following observations. In practice, many environmental factors, such as real-time traffic and traffic regulations, can influence the driving speed. These factors provide side-channel information about the driving route, which can be exploited to infer the vehicle’s trace. We implement our discovered attack on a public data set in New Jersey. The experimental results show that the attacker has a nearly 60% probability of obtaining the real route if he chooses the top 10 candidate routes. To thwart the proposed attack, we design a privacy preserving scoring and data audition framework that enhances drivers’ control on location privacy without affecting the utility of UBI. Our defense framework can also detect users’ dishonest behavior (e.g., modification of speed data) via a probabilistic audition scheme. Extensive experimental results validate the effectiveness of the defense framework.

Estimating the uncertainty of areal precipitation using data assimilation

We present a method to estimate spatially and temporally variable uncertainty of areal precipitation data. The aim of the method is to merge measurements from different sources, remote sensing and in situ, into a combined precipitation product and to provide an associated dynamic uncertainty estimate. This estimate should provide an accurate representation of uncertainty both in time and space, an adjustment to additional observations merged into the product through data assimilation, and flow dependency. Such a detailed uncertainty description is important for example to generate precipitation ensembles for probabilistic hydrological modelling or to specify accurate error covariances when using precipitation observations for data assimilation into numerical weather prediction models. The presented method uses the Local Ensemble Transform Kalman Filter and an ensemble nowcasting model. The model provides information about the precipitation displacement over time and is continuously updated by assimilation of observations. In this way, the precipitation product and its uncertainty estimate provided by the nowcasting ensemble evolve consistently in time and become flow-dependent. The method is evaluated in a proof of concept study focusing on weather radar data of four precipitation events. The study demonstrates that the dynamic areal uncertainty estimate outperforms a constant benchmark uncertainty value in all cases for one of the evaluated scores, and in half the number of cases for the other score. Thus, the flow dependency introduced by the coupling of data assimilation and nowcasting enables a more accurate spatial and temporal distribution of uncertainty. The mixed results achieved in the second score point out the importance of a good probabilistic nowcasting scheme for the performance of the method.

Flexible Filter Bank Multi-Carriers PON Based on Two-Dimensional Multiple Probabilistic Shaping Distribution

We propose a filter bank multi-carriers (FBMC) passive optical network (PON) system based on 2-D multiple probabilistic shaping (MPS) distribution to achieve flexible and adjustable access bit-rates. The proposed PON can assign different probabilistic shaping schemes to different time-slots and subcarriers, thus improving the system flexibility and adjustability in both the time and frequency domains. Carrier-less amplitude and phase modulation are adopted to achieve the implementation of the intensity-modulation and direct-modulation applied in our proposed PON system. Moreover, for the first time, an experiment demonstrating MPS FBMC signal transmission over a 25-km standard single mode fiber in PON system is successfully carried out to verify the favorable performance. The bit error rate performance of different MPS time-slots at bit-rates of 23.665, 20.663, 17.666, and 15.481 Gbit/s are investigated. The received optical powers at the threshold for hard-decision forward error correction are −16.13, −16.94, −17.85, and −18.81 dBm, respectively. These experiment results indicate that our proposed MPS FBMC PON system can be flexible and adjustable in acquiring different and dynamic access bit-rates, which suggests a prospective solution for the next generation passive optical network.

Channel-Adaptive Probabilistic Broadcast in Route Discovery Mechanism of MANETs

Broadcasting is the backbone of the route discovery process in on-demand routing protocols in Mobile Ad-hoc Networks (MANETs). Pure flooding is the simplest and most common broadcasting technique for route discovery in on-demand routing protocols. In pure flooding, the route request (RREQ) packet is broadcasted and each receiving node rebroadcasts it. This continues until the RREQ packet arrives at the destination node. The obvious drawback of pure flooding is excessive redundant traffic that degrades the system performance. This is commonly known as broadcast storm problem (BSP). To address BSP, various probabilistic broadcast schemes have been proposed in the literature where a node broadcasts a RREQ packet with a certain probability. However, these schemes do not consider the effects of thermal noise and co-channel interference which cannot be ignored in realistic MANETs, and therefore, these schemes do not perform well in real life MANETs. This paper presents a novel Channel Adaptive Probabilistic Broadcast (CAPB) scheme that adapts the rebroadcast probability dynamically to the current SINR (Signal to Interference plus Noise Ratio) and node density in the neighborhood. The proposed scheme and two related state of the art (SoA) schemes from the literature are implemented in the standard AODV routing protocol to replace the pure flooding based broadcast. Extensive ns-2 simulation results show that the proposed scheme outperforms the standard AODV, and the two competitors in terms of routing overhead, throughput, end-to-end delay and energy consumption significantly in noisy MANETs.

Efficient evacuation in a multi-exit environment: an agent-based decision support model

A majority of research work carried out in crowd evacuation rely on simulation due to non-availability of real and realistic trial data. In this paper, an agent-based simulation study of an evacuating crowd is presented. The model is based on the microscopic behavioural rules formulated through small-scale empirical evidence in conjunction with crowd behavioural theories. In particular, the study focuses on the possibility of efficient evacuation from the environment with limited perceptions. Extending Moore's neighbourhood model, local congestion avoidance mechanism capable of detecting the relative displacement and orientation of the all the individuals in its neighbourhood is considered. Other strategies based on exit capacity and exit population are also modelled and tested. A probabilistic exit selection strategy is also designed that considers a sensitivity of an exit as a deciding factor. The simulation results show that the enhanced exit selection strategies make the proposed system more robust and increase the evacuation efficiency substantially.

Graphical Evolutionary Game Model of Virus-Based Intrusion to Power System for Long-Term Cyber-Security Risk Evaluation

Through virus-based reconnaissances, the attacker may compromise critical power substations and induce power outages. Meanwhile, the power system operator may conduct defensive measures to prevent virus-spreading. In this paper, a graphical evolutionary game model is developed to solve the equilibrium of competitions between virus propagations and countermeasures, which helps to evaluate the cyber-security risk of power systems under long-term virus-based intrusions. In the proposed game model, the virus is supposed to propagate itself in a cyber network connecting cyber nodes belonging to power substations. Each cyber node may play as an attacker or a defender according to its state. A death-birth rule is adopted to model probabilistic strategies and state transfers, which reflects the bounded rationality of the viruses. Then, an evolution dynamic equation is derived to obtain the portion of the infected substations when the equilibrium is reached, and simulation algorithm is also given to estimate the infection probability of each substation under the persistent virus-based intrusion. Assuming a virus may trip all breakers in the infected substation, the cyber-security risk is evaluated to identify system-level vulnerabilities. The IEEE 118-bus system with a layered cyber network is used for case studies. Test results validate the proposed model and provide further insights into the virus-based intrusion.

Adaptive and probabilistic strategy evolution in dynamical networks

In the evolution of cooperation, numerous agents are playing games with each other by adopting dynamical strategies in dynamical networks. The dynamics of strategy and structure are entangled tightly, where the dynamics on networks is driven by the evolving strategies and the dynamics of networks is led by the rewiring actions. In this paper, adaptive and probabilistic strategies are studied to explore the prevalence of cooperation in Prisoner’s Dilemma (PD). Each agent will update the probability to cooperate in a distributed manner by aggregating local and historical information instead of pairwise comparison, meanwhile the agents rewire the network by redirecting the links from defective neighbours to others. Global prosperity comes along with the emergent diversity of network, when a large number of edges are absorbed into a tiny group of cooperative agents. A rich landscape is demonstrated by changing various embedding parameters, which may shed light upon the self-interested behaviour and swarm intelligence in social, biological and ecological networks.

Fault detection and diagnosis based on particle filters combined with interactive multiple-model estimation in dynamic process systems

A new approach to fault detection and diagnosis (FDD) is developed for nonlinear stochastic dynamic process systems in this paper. It is called PFs-IMM, which combines particle filters (PFs) and the interactive multiple model (IMM) estimation. In this method, a multiple-model estimation scheme is first formulated to describe the complex process system poorly represented by a single model. The IMM algorithm can deal with abrupt changes in the behavior of operating processes. The residuals of the multiple models are examined for the likelihood of each model. A decision rule is employed to adaptively determine which model is the most appropriate one at each time step. Then based on IMM, a set of PFs run in parallel is used to estimate the states and the reconciled measurements even when the operating mode changes. Each of the PFs utilizes a particular mode to derive the estimation of the state variables as well as the reconciliation of the measured variables based on the probabilistic weighting scheme. From the multiple filters, the interaction among PFs allows the fusing of dynamic estimates. To achieve higher sensitivity to faults and more robustness to disturbances and noises, a new fault index function is developed for FDD. The proposed PFs-IMM approach provides an integrated framework. It can estimate the current operational or faulty mode of the system and derive the overall state estimation and the measurement reconciliation as well. The simulation solutions to the problems are obtained to demonstrate the effectiveness of the proposed method in highly nonlinear dynamic processes.

A chaos-based probabilistic block cipher for image encryption

Traditional encryption is based on secrecy provided by secret-key. But this leads to generation of same cipher text when the encryption scheme is applied to same plaintext with same key. Thus, replay of messages can be effortlessly identified by an adversary which can be a weak link in any communication. Probabilistic encryption is an approach to overcome this weakness where different cipher texts are generated each time same plaintext is encrypted using the same key.Extending the probabilistic approach, which is generally employed in asymmetric encryption, this paper proposes a new chaos-based probabilistic symmetric encryption scheme with customizable block-size suitable for image encryption. It employs a Random Bits Insertion phase followed by four rounds of two-staged diffusion involving simple XOR (exclusive-OR) operation making it computationally efficient. Random Bits Insertion makes the scheme probabilistic. This phase also helps in increasing entropy and making intensity distribution more uniform in cipher. The generated cipher text is twice the size of plain text. An increase in cipher text space is inevitable for probabilistic encryption and it provides an advantage as the apparent message space for the attacker is increased. The observations show that the scheme offers high strength to resist statistical and cryptanalytic attacks.

Signatures of a probabilistic strategy in the control of saccadic eye movements

Signatures of a probabilistic strategy in the control of saccadic eye movements

A Gibbs sampling algorithm for structural modal identification under seismic excitation

SummaryIdentification of structural modal parameters under seismic excitation using operational modal analysis (OMA) is a challenging task because it violates the basic assumptions of OMA: linear time‐invariant model, stationary white noise input, and adequately long data. The consequence is significant uncertainties associated with the identified modal parameters. This study aims at developing an algorithm to quantify these uncertainties from a Bayesian perspective. Representing the structure and the seismic excitation by a state‐space model, a probabilistic OMA scheme is formulated. The analytical solution for the posterior statistics is not achievable, and a Gibbs sampling algorithm is developed to provide an efficient and robust numerical solution appropriate for practical applications. The performance of the proposed method is validated by identifying a shear‐type building using simulated response data under 4 recorded earthquake motions, and a supertall building—the One Rincon Hill in San Francisco—using field‐recorded data under seismic and ambient excitations. The computed posterior distributions of modal parameters represent the knowledge extracted from the measured data; they can be reliably used for model validation and health monitoring.

Integrating transient behavior as a new dimension to WHPA delineation

Abstract The protection of groundwater abstraction is primarily based on the delineation of “time-of-travel capture zones” known as Well Head Protection Areas (WHPAs). Their delineation is subject to several uncertainties: the scarcity of hydrogeological data due to limited budgets for exploration and additional uncertainty from time-varying flow conditions. While the former has lead to the use of probabilistic strategies, no existing approaches for WHPA delineation consider jointly geological uncertainties, transient flow conditions and the associated additional uncertainties. We propose to extend existing steady-state probabilistic approaches for WHPA delineation to account for transient flow conditions and their related uncertainties. In our synthetic scenario, transiency is represented through four selected transient drivers: (I) regional groundwater flow direction, (II) regional strength of the hydraulic gradient, (III) natural groundwater recharge and (IV) pumping rate. As a result of our proposed probabilistic framework, we obtain probabilistic catchment maps that consider the joint effect of (uncertain) transient flow conditions and geological uncertainty. To represent hydraulic conductivity we use a random space function using a Matern covariance function. In order to reduce computational costs, so that we can conduct Monte Carlo analysis over both sources of uncertainty, we represent transiency as a dynamic superposition of steady-state flow solutions. Overall, we show that (1) each transient driver defines a distinctive pattern of temporal catchment membership, with the ambient flow direction driving most of the changes. (2) The transient analysis enhances decision support for probabilistic WHPA delineation by providing additional information in terms of time reliability levels. Now, a WHPA can be defined from this analysis by selecting a reliability level for time and one for geological uncertainty. (3) Time reliability addresses a different kind of risk when compared to geological reliability: While a lack of time reliability implies a known risk to the well for a fraction of time, a lack of geological reliability means that one does not know whether the well is at risk. (4) In the presence of uncertain transient and geological conditions, high time reliability levels are cheap compared to high reliability levels against geological uncertainties. (5) The combined use of time-geological reliability information with land use information leads towards more targeted risk reduction measures that align with the two different types of risk addressed by time reliability and geological reliability.

Dissimilarity Metric Based on Local Neighboring Information and Genetic Programming for Data Dissemination in Vehicular Ad Hoc Networks (VANETs).

This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios.

Capacity Regions of Two-Receiver Broadcast Erasure Channels With Feedback and Memory

The two-receiver broadcast packet erasure channel with feedback and memory is studied. Memory is modeled using a finite-state Markov chain representing a channel state. Two scenarios are considered: 1) when the transmitter has causal knowledge of the channel state (i.e., the state is visible) and 2) when the channel state is unknown at the transmitter, but observations of it are available at the transmitter through feedback (i.e., the state is hidden). In both scenarios, matching outer and inner bounds on the rates of communication are derived and the capacity region is determined. It is shown that similar results carry over to channels with memory and delayed feedback and memoryless compound channels with feedback. When the state is visible, the capacity region has a single-letter characterization and is in terms of a linear program. Two optimal coding schemes are devised that use feedback to keep track of the sent/received packets via a network of queues: a probabilistic scheme and a deterministic backpressure-like algorithm. The former bases its decisions solely on the past channel state information and the latter follows a max-weight queue-based policy. The performance of the algorithms is analyzed using the frameworks of rate stability in networks of queues, max-flow min-cut duality in networks, and finite-horizon Lyapunov drift analysis. When the state is hidden, the capacity region does not have a single-letter characterization and is, in this sense, uncomputable. Approximations of the capacity region are provided and two optimal coding algorithms are outlined. The first algorithm is a probabilistic coding scheme that bases its decisions on the past $L$ acknowledgments and its achievable rate region approaches the capacity region exponentially fast in $L$ . The second algorithm is a backpressure-like algorithm that performs optimally in the long run.

Neighbor Trigger for Hovering Information

Hovering information is generated by specific area in a mobile ad hoc network(MANET) to be attached to the anchor area for some time. MANET is a self-organizing and dynamic topology network without infrastructure. Some nodes maybe not receive the hovering information due to possible partitioning of the network in some areas with sparse nodes, on the other hand broadcast packets lead to broadcast storm problem and high latency in high traffic density network. To improve the reachability and deduce the number of broadcast of hovering information, the adaptive probabilistic flooding scheme is proposed by Andreas Xeros. But the calculation of probability p is too complex. So, this paper proposes a new adaptive probability flooding scheme based on random graph and neighbour trigger (algorithm of neighbour trigger, ANT) for floating the information in the hovering area. When a new node enters the range of hovered node and become a neighbour of the node that has received the hovering information, the informed node checks its neighbour list to decide whether to trigger the broadcast program with probability p. I analyse the performance of the proposed algorithm. The results show that the ANT outperforms other candidate hovering information algorithms in terms of the number of broadcast messages.