Random Departures Research Articles

Distributed control for large-scale plug-in electric vehicle charging with a consensus algorithm

As the development of plug-in-electric vehicles (PEVs) accelerates, the effective integration of large-scale PEVs with power grid becomes an urgent problem for utilities and PEV users. Mainly considering grid side preference, this paper proposes a distributed control method with a consensus algorithm to deal with large-scale PEV charging coordination. The objectives of the proposed coordination strategy are: minimizing the charging power loss and maximizing the available PEV power for vehicle-to-grid (V2G) services. The incremental cost functions are formulated for these objectives. The graph theory is applied to specify the communication network for PEVs. An interaction rule, the consensus algorithm is applied to realize the local information updating and external information exchanging among neighboring PEVs. A variable-size stochastic matrix is defined to specify the communication topology among PEVs, as well as guarantee the convergence of the consensus targets. The simulation results show that, with limited communication, the large-scale PEV charging/discharging can be efficiently coordinated to reduce total charging power loss or provide V2G service with maximum available PEV power. Meanwhile the PEV charging requirements specified by drivers can be satisfied. In addition, the convergence of the proposed distributed consensus algorithm can always be guaranteed upon random PEV departures.

Reliable and Secure Vehicular Fog Service Provision

Vehicular fog computing (VFC) complements vehicular cloud computing as a promising solution for accommodating the surge of mobile traffic and reducing latency. This paper considers vehicular fog service (VFS) provided by a vehicular fog (VF), which is formed on-the-fly by integrating computing and storage resources of parked vehicles. VF dynamicity, due to vehicles’ random arrivals and departures, poses a number of challenges for reliable and secure VFS provision to client vehicles. We propose a novel mechanism which consists of a VF construction method and a VFS access method to ensure VFS reliability and security without sacrificing performance. The reliability and security of VFS under our mechanism are discussed in detail. Moreover, we investigate the impact of the proposed mechanism on VF throughput and show that the mechanism is lightweight enough to be used in the latency-sensitive VFC.

Multi-Objective Optimization for Cyber-Physical-Social Systems: A Case Study of Electric Vehicles Charging and Discharging

Cyber-physical-social system (CPSS), an integration of cyberspace, physical space, and social space, has revolutionized the relationship between human, computers, and physical environments. Electrical cyber-physical system integrating human behaviors and interactions is a typical CPSS, and the study of electric vehicles (EVs) charging and discharging in the CPSS has become a research hotspot due to its environmental advantages and social benefits. This paper focuses on simultaneously optimizing the power load stability and the total cost of vehicle owners in the CPSS. First, the optimal scheduling problem is formulated as a multi-objective optimization problem with system's technical constraints, and then a hierarchical optimal algorithm is proposed to solve the problem. The convergence analysis of the proposed algorithm is presented to ensure its optimal performance. Further, this paper combines the proposed algorithm with a moving horizon method to deal with the EV owners random arrival and departure case based on the owners' convenience and preferences. It shows that the proposed algorithm not only enhances stable performance of the power load but also improves satisfaction of the vehicle owners in the CPSS framework integrating power grids, energy consumption areas, and social components. Numerical simulations are presented to demonstrate the effectiveness of the proposed algorithm.

Optimization of Aggregate Capacity of PEVs for Frequency Regulation Service in Day-Ahead Market

An aggregator can coordinate plug-in electric vehicles (PEVs) to provide frequency regulation service to an independent system operator (ISO). The aggregator can participate in the electricity markets of ISOs which provide economic incentives for PEV frequency regulation service. While the ISOs typically use forward market [e.g., day-ahead market (DAM)] to trade frequency regulation service, the available regulation capacity of an aggregator is subject to the random arrival and departure of the PEVs. In the DAM, the aggregator submits a bid to indicate its available capacity on the next day. This motivates us to study the problem of how an aggregator determines its bid in the DAM, given the uncertainty of the available regulation capacity of the PEVs. The DAM is used to trade the frequency regulation capacity in California ISO (CAISO) and New York ISO (NYISO). We consider two types of DAMs based on the market rules of CAISO and NYISO. For the first type, the exact amount of regulation capacity submitted in the DAM needs to be fulfilled on the next day. For the second type, a market participant can settle a shortage of capacity by paying a penalty to the ISO. In both cases, the aggregator can participate in the real-time market to sell extra capacity on the next day. We formulate the problem for determining the bid using stochastic programming. As PEVs have uncertain arrival and departure times, our problem formulation incorporates risk management using the conditional value at risk. Efficient algorithms are proposed for solving the formulated problem. PEV charging data collected in Vancouver, BC, Canada, is used in our simulations. We compare the profit of the aggregator when it participates in the markets of CAISO and NYISO. Our simulation results show that the uncertainty of the PEVs’ available capacity has less effect on the profit and financial risk as the number of PEVs increases.

Distributed Decisions on TV Spectrum Allocation Considering Spatial and Temporal Variation

TV spectrum has lower path loss, longer transmission range, and higher penetration capability, resulting in a wide range of potential important applications. However, unlike Wi-Fi bands, TV spectrum is subjected to high spatial and temporal variations due to the random arrivals and departures of primary users (PUs), which results in new technical challenges in TV spectrum utilization. One important issue is how to allocate TV spectrum to secondary users (SUs) by taking the spatial and temporal variations into consideration. This has been largely ignored in previous studies. In this paper, we first formulate the TV spectrum allocation problem as a 0–1 integer optimization problem, and then we approximate our optimal objective via Log-Sum-Exp function. Thereafter, we solve this problem by implementing a Markov chain in a distributed manner. Furthermore, we extend the static problem setting to a dynamic environment where the number of vacant TV channels varies with time due to the arrivals and departures of PUs. Simulation results show that our proposed distributed algorithm can converge very fast to the optimal solution, and can achieve a close-to optimal performance with a guaranteed loss bound.

Dynamical Analysis of an Internet-Based Video System

The dissemination of video applications across the Internet still remains the driving force for Internet traffic growth. Generally, video streaming systems are characterized with irregular transmitting pattern and fluctuation which usually leads to transmission delay and frame loss. Furthermore, the democratic platform created by peer-to-peer (P2P) network for video streaming suggests complex dynamical behaviour with difficult predictable performance. The existence of flash crowd and peer churn situations in P2P streaming systems obtained from random arrival and departure of peers exhibits chaotic attributes. In this paper, a P2P streaming system(UStream) was modelled using a system of first order differential equations. The dynamics of UStream were analysed using equilibrium points and eigenvalues structure. Instability region was tracked through numerical investigation. The numerical simulation conducted using the Mathematica package software showed that UStream exhibits spiral fixed point and strange attractors with certain initial conditions which confirms experimental measurement and observations. Also, the system responds exponentially with time and reflects instability pattern for the first 20 seconds and 500 seconds when investigated with client-to-server(CS)and P2P network models respectively.

Energy Detection With Random Arrival and Departure of Primary Signals: New Detector and Performance Analysis

By taking into account the random arrival and departure of primary signals, spectrum sensing is formulated to a hypothesis testing problem with null hypothesis H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> : primary signals are absent or present but departing within a sensing interval (secondary users are allowed to access the spectrum), and alternative hypothesis H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> : primary signals are present and not departing within a sensing interval (secondary users are not allowed to access the spectrum). The above-mentioned binary hypothesis testing involves two mutually exclusive random variables (i.e., the departure and arrival time instants of primary signals). To tackle these random variables, we develop an average log-LRT (aveLLR) based energy detector (ED) by using the Bayesian criterion. The theoretical performance of the aveLLR-based ED is analyzed and numerical simulations are provided to demonstrate its superior performance. It is interesting that when the ratio of the length of sensing intervals to the holding time of channel states is small, the proposed aveLLR-based ED is approximately reduced to the conventional ED (C-ED). It was commonly believed that high primary user traffic would degrade the performance of C-ED severely, as C-ED does not consider the random arrival and departure of primary signals. However, we reveal that the ratio plays a key role, and when the ratio is small, the impact of the primary user traffic on the C-ED is marginal.

Adaptive Weighted Sensing With Simultaneous Transmission for Dynamic Primary User Traffic

In practical scenarios with random arrival and departure of primary users (PUs), existing simultaneous sensing and transmission schemes allocated the same weight to each sample, and did not consider low signal-to-noise ratio (SNR) situations. This paper proposes an adaptive weighted sensing scheme with simultaneous transmission for dynamic PU traffic. It uses a power function based on the corresponding sampling sequence and could reveal the actual PU state in near real time. The power exponent is further adjusted to the sensing situations to achieve lowest false alarm probability under a certain detection probability constraint. Then, an analytical model considering all possible PU state transitions is developed to evaluate achievable interference, throughput, and energy efficiency. Furthermore, the optimal frame duration yielding both optimal false alarm probability and throughput is computed. After that, a fast search algorithm is proposed to track the optimal duration at an exponential convergence rate. Simulation results are provided to validate the analytical model and demonstrate the improvement in low SNR. The results indicate that the proposed scheme can achieve lower false alarm probability and higher energy efficiency over a wide SNR range than that of the existing weighting schemes, which are based on probability, geometric sequence, and equal weighting.

System-Level Analysis of Heterogeneous Networks Under Imperfect Traffic Hotspot Localization

We study, in this paper, the impact of imperfect small cell positioning with respect to traffic hotspots in cellular networks. In order to derive the throughput distribution in macro and small cells, we firstly perform static level analysis of the system considering a non-uniform distribution of user locations. We secondly introduce the dynamics of the system, characterized by random arrivals and departures of users after a finite service duration, with the service rates and distribution of radio conditions outfitted from the first part of the work. When dealing with the dynamics of the system, macro and small cells are modeled by multi-class processor sharing queues. Macro and small cells are assumed to be operating in the same bandwidth. Consequently, they are coupled due to the mutual interferences generated by each cell to the other. We derive several performance metrics such as the mean flow throughput and the gain, if any, generated from deploying small cells to manage traffic hotspots. Our results show that in case the hotspot is near the macro BS (Base Station), even a perfect positioning of the small cell will not yield improved performance due to the high interference experienced at macro and small cell users. However, in case the hotspot is located far enough from the macro BS, performing errors in small cell positioning is tolerated (since related results show positive gains) and it is still beneficial in offloading traffic from the congested macrocell. The best performance metrics depend also on several other important factors such as the users' arrival intensity, the capacity of the cell and the size of the traffic hotspot.

From regulatory life tables to stochastic mortality projections: The exponential decline model

Often in actuarial practice, mortality projections are obtained by letting age-specific death rates decline exponentially at their own rate. Many life tables used for annuity pricing are built in this way. The present paper adopts this point of view and proposes a simple and powerful mortality projection model in line with this elementary approach, based on the recently studied mortality improvement rates. Two main applications are considered. First, as most reference life tables produced by regulators are deterministic by nature, they can be made stochastic by superposing random departures from the assumed age-specific trend, with a volatility calibrated on market or portfolio data. This allows the actuary to account for the systematic longevity risk in solvency calculations. Second, the model can be fitted to historical data and used to produce longevity forecasts. A number of conservative and tractable approximations are derived to provide the actuary with reasonably accurate approximations for various relevant quantities, available at limited computational cost. Besides applications to stochastic mortality projection models, we also derive useful properties involving supermodular, directionally convex and stop-loss orders.

Cooperative mobile video transmission for traffic surveillance in smart cities

Cooperation transmission among mobile devices is critical for video streaming systems. Conventional systems usually require huge storage space and bandwidth, subsequently causing a bottleneck for the development of streaming systems. To resolve such problems, this study proposes a cooperative mechanism for mobiles that can make full use of local storage space and uploading bandwidth to relieve the computational load for streaming servers. Firstly, a statistical distribution of the life cycle of devices is dynamically established based on the analysis of random arrival and departure of mobile users. Secondly, an adaptive sliding-window technology based on Scalable Video Coding (SVC) is proposed in this study to change the size of windows in real time. Thirdly, an adaptive streaming solution is introduced in the OPNET environment, which cooperatively integrates the strategies of overlay formation, quality adaptation, and data scheduling. Simulation results showed that the proposed mechanism saved power consumption of devices and ensured the video quality for networks with limited resources.

Finite-Horizon Prediction of Energy Depletions in Off-Grid Wireless Networks

In this paper, we present a simple analytic method that can be used to predict potential energy depletion in off-grid wireless backbone network nodes serving mobile users. The instantaneous energy depletion of the batteries of the network nodes is determined by random energy arrivals and departures and modeled as a G/G/1 queue. To evaluate the online energy depletion probability (EDP), an integral-free asymptotic approach is typically used by assuming that the prediction horizon approaches infinity. However, in many practical cases, the time required by user connections can be rather short. This indicates the need for proactive resource management decisions over finite horizons so that the transmission opportunities with limited energy and time horizons are not wasted. Using Holder's inequality, we obtain a novel finite-horizon upper bound for the EDP, and the result is compared with the infinite-horizon method. The accuracy of the proposed bound, which is addressed both analytically and numerically, proves to be better for shorter prediction horizons. The finite- and infinite-horizon methods are then applied for an energy provisioning admission control (EP-AC) framework using off-grid backbone network nodes. The key observation of this paper is that the proposed finite-horizon prediction approach admits significantly more users to the network when the connection times are relatively short, while retaining an integral-free closed-form structure suitable for the online evaluation of the EDP.

Energy vs. Cyclostationarity-based Detection of Random Arrival and Departure of LTE SC-FDMA Signals for Cognitive Radio Systems

Abstract In cognitive radio systems, the random nature of primary users’ activities heavily influences the performance of spectrum sensing techniques. Considering both of the primary user random arrival and departure in one model is essential for mitigating the performance degradation in spectrum sensing. In this paper, we apply the probability based weighting energy detector (ED) to overcome the effect of random arrival and departure of primary user (PU), on a single carrier-frequency division multiple access (SC-FDMA) signal used in LTE uplink physical channel. Then compare the performance of weighted ED to the second order Cyclostationarity detector with/without the random arrival and departure of primary user signal during the secondary user’s sensing period. The simulation shows that the weighting ED approaches the performance of Cyclostationarity detector in the normal case, where there are no random activities of primary user signals during the secondary user’s sensing period, with less computational complexity. Moreover, the weighted ED outperforms the Cyclostationarity detector in the case of random arrival and departure of PU.

Herbivory causes increases in leaf spinescence and fluctuating asymmetry as a mechanism of delayed induced resistance in a tropical savanna tree

Background and aims – Leaf spinescence is thought to be a defense against herbivory and may increase in years following high herbivory pressure (DIR – delayed induced resistance). Besides DIR, herbivory also causes plant stress, which can be assessed using fluctuating asymmetry analysis (FA – random departures from leaf bilateral symmetry). In this two-year field study we first investigated the relationships between herbivory, FA and spinescence in Solanum lycocarpum trees during an outbreak of moth caterpillars in 2010; then in 2011 we examined whether plants with intense herbivory the previous year presented changes in FA (herbivory induced stress) and spinescence. Key results – In 2010, leaves damaged by moths were significantly more asymmetrical than undamaged leaves. In addition, moths preferentially fed on leaves with fewer and shorter spines. In 2011, plants severely attacked by moths the year before showed significant increases in FA (28%), spine abundance (21%) and spine length (84%), compared to less damaged plants. Conclusions – Our data suggest a delayed response of S. lycocarpum to herbivory, in that plants subjected to high leaf damage the previous year maximized their display of anti-herbivore mechanisms, in this case, leaf spinescence. This is one of the few studies to show that FA increased as a response to herbivore attack. We conclude that FA can be used as an indicator of plant stress following leaf damage.

Host-feeding sources and habitats jointly affect wing developmental stability depending on sex in the major Chagas disease vector Triatoma infestans

Fluctuating asymmetry (FA), a slight and random departure from bilateral symmetry that is normally distributed around a 0 mean, has been widely used to infer developmental instability. We investigated whether habitats (ecotopes) and host-feeding sources influenced wing FA of the hematophagous bug Triatoma infestans. Because bug populations occupying distinct habitats differed substantially and consistently in various aspects such as feeding rates, engorgement status and the proportion of gravid females, we predicted that bugs from more open peridomestic habitats (i.e., goat corrals) were more likely to exhibit higher FA than bugs from domiciles. We examined patterns of asymmetry and the amount of wing size and shape FA in 196 adult T. infestans collected across a gradient of habitat suitability and stability that decreased from domiciles, storerooms, kitchens, chicken coops, pig corrals, to goat corrals in a well-defined area of Figueroa, northwestern Argentina. The bugs had unmixed blood meals on human, chicken, pig and goat depending on the bug collection ecotope. We documented the occurrence of FA in wing shape for bugs fed on all host-feeding sources and in all ecotopes except for females from domiciles or fed on humans. FA indices for wing shape differed significantly among host-feeding sources, ecotopes and sexes. The patterns of wing asymmetry in females from domiciles and from goat corrals were significantly different; differences in male FA were congruent with evidence showing that they had higher mobility than females across habitats. The host-feeding sources and habitats of T. infestans affected wing developmental stability depending on sex.

Efficient Traffic Engineering Strategies for Optimizing Network Throughput in WDM All-Optical Networks

In this paper we investigate traffic-engineering issues in Wavelength Division Multiplexing (WDM) all- optical networks. In such networks, the wavelength continuity constraint along with the wavelength clash constraint, lead to poor network performances when dealing with the lightpath provisioning problem. The impact of these constraints is especially severe when traffic demands are unpredictable and characterized by random arrivals and departures. In order to alleviate the impact of these constraints, we propose to employ intentional/active rerouting. Active lightpath rerouting is to intentionally reroute already established lightpaths, during their life period, so as to achieve a better blocking performance. We here assume that due to the large geographic area an optical WDM network can cover, upgrading such a network to support the huge demand for network bandwidth can be costly. Thereby, it is extremely important for network operators to apply traffic-engineering strategies to cost-effectively optimize network throughput. Two new routing and wavelength assignment (RWA) algorithms applying intentional rerouting are proposed. Both algorithms dynamically reroute some already established lightpaths from longer paths to vacant shorter ones so as to reduce the network resources consumption and hence improve the network throughput. The first algorithm, namely, Timer-Based Active Lightpath Rerouting (TB-ALR) initiates the rerouting procedure every time a timer expires. The second algorithm, namely, Sequential Routing with Active Lightpath Rerouting (SeqRwALR) initiates the rerouting procedure when a connection leaves and its lightpaths are released. To the best of our knowledge, our global approach has not already been investigated in the literature. Simulation results show that the proposed active rerouting algorithms yield much lower connection rejection ratios than rerouting algorithms previously presented in the literature while rerouting a small number of already established lightpaths. By rerouting a small number of existing lightpaths, we hope that the disruption period incurred due to rerouting is minimized.

Sensing-Throughput Tradeoff in Cognitive Radio With Random Arrivals and Departures of Multiple Primary Users

This letter analyzes the sensing-throughput tradeoff for a secondary user (SU) under random arrivals and departures of multiple primary users (PUs). We first study the case where PUs change their status only during SU's sensing period. We then generalize to a case where PUs change status anytime during SU frame, and compare the latter case with the former in terms of the optimal sensing time and SU throughput. We also investigate the effects of PU traffic parameters and the number of PUs on the sensing-throughput tradeoff for SU. Results show that, though the increase in the number of PUs reduces the optimal sensing time for SU, the opportunity to find a vacant PU channel reduces simultaneously, in turn, reducing SU throughput. Finally, we validate the analysis by Monte Carlo simulations.

Incorporating Departure Time Choice into High-occupancy/toll (HOT) Algorithm Evaluation

This paper presents an evaluation framework for high-occupancy/toll (HOT) lanes, extending previous frameworks by incorporat- ing departure time choice alongside stochasticity in demand. Drivers are divided into two classes, strategic and captive drivers. Strategic drivers time their departures to minimize expected generalized cost and can choose either the free lanes or HOT lane. Captive drivers have random departure times, and must choose the free lanes. Generalized cost for strategic drivers takes the form of the well-known schedule delay penalty function combined with travel time. The traffic flow model involves two parallel bot- tlenecks, one for each lane group. Four toll algorithms are compared: a fixed toll which is constant in time and unresponsive; a dynamic, but unresponsive toll based on expected demand; a dynamic and responsive toll which adapts based on congestion in the HOT lane; and a “full-information” toll which is aware of all captive driver inflows.

Robust and Optimal Opportunistic Scheduling for Downlink Two-Flow Network Coding With Varying Channel Quality and Rate Adaptation

This paper considers the downlink traffic from a base station to two different clients. When assuming infinite backlog, it is known that inter-session network coding (INC) can significantly increase the throughput. However, the corresponding scheduling solution (when assuming dynamic arrivals instead and requiring bounded delay) is still nascent. For the two-flow downlink scenario, we propose the first opportunistic INC + scheduling solution that is provably optimal for time-varying channels, i.e., the corresponding stability region matches the optimal Shannon capacity. In particular, we first introduce a new binary INC operation, which is distinctly different from the traditional wisdom of XORing two overheard packets. We then develop a queue-length-based scheduling scheme and prove that it, with the help of the new INC operation, achieves the optimal stability region with time-varying channel quality. The proposed algorithm is later generalized to include the capability of rate adaptation. Simulation results show that it again achieves the optimal throughput with rate adaptation. A byproduct of our results is a scheduling scheme for stochastic processing networks with random departure, which relaxes the assumption of deterministic departure in the existing results.

Markets with random lifetimes and private values: mean reversion and option to trade

We consider a market in which traders arrive at random times, with random private values for the single-traded asset. A trader’s optimal trading decision is formulated in terms of exercising the option to trade one unit of the asset at the optimal stopping time. We solve the optimal stopping problem under the assumption that the market price follows a mean-reverting diffusion process. The model is calibrated to experimental data taken from Alton and Plott (Principles of continuous price determination in an experimental environment with flows of random arrivals and departures. Working paper, Caltech, 2010), resulting in a very good fit. In particular, the estimated long-term mean of the traded prices is close to the theoretical long-term mean at which the expected number of buys is equal to the expected number of sells. We call that value long-term competitive equilibrium, extending the concept of flow competitive equilibrium of Alton and Plott (Principles of continuous price determination in an experimental environment with flows of random arrivals and departures. Working paper, Caltech, 2010).