Self-managing energy-efficient multicast support in MANETs under end-to-end reliability constraints

Dynamic networks, e.g. Mobile Ad hoc NETworks (MANETs), call for self-healing routing protocols to tolerate topological changes imposed by node mobility. Moreover, emerging time-critical MANET applications such as disaster response and rescue, and battlefield operations, require support for real-time, reliable data streaming, while maintaining energy efficiency. However, most of the energy-efficient routing protocols rely on configuration parameters which need to be estimated and specified before the deployment phase. This paper proposes a self-managing, energy-efficient multicast routing suite based on the self-stabilization paradigm. This suite uses (i) WECM, a Waste Energy Cost Metric designed for energy-efficient route selection, (ii) SS-SPST-E, a Self-Stabilizing, Shortest-Path Spanning Tree protocol for Energy efficiency based on WECM to maintain an energy-efficient, self-healing routing structure, (iii) SS-SPST-Efc, an enhanced SS-SPST-E with fault containment to decrease stabilization latency, (iv) AMO, an Analytical Model for Optimization framework to reduce the energy overhead of the route maintenance mechanism, and (v) self-configuration mechanisms that observe, estimate and disseminate the optimization parameters.The WECM’s innovation is that it considers the overhearing energy wasted. The AMO framework considers the link state change rate, application data traffic intensity, application packet delivery requirements, and the stabilization latency. Numerical evaluations show that SS-SPST-E slightly increases the energy consumption when compared with non-adaptive energy-efficient protocols such as EWMA because of its mechanism to handle mobility. Simulation results show that SS-SPST-Efc achieves the maximum balance between the energy-reliability trade-off while conforming to the end-to-end packet delivery requirement with an accuracy between 80% and 100%. The energy-reliability balance, measured in terms of the packet delivery ratio (PDR) per millijoules of energy expended, is at least 24% and 27% higher in SS-SPST-E and SS-SPST-Efc, respectively, when compared to the MAODV and ODMRP protocols.     

Distributed workflow mapping algorithm for maximized reliability under end-to-end delay constraint

A distributed scientific workflow mapping algorithm for maximized reliability under certain end-to-end delay (EED) bound is proposed. It is studied in a heterogeneous distributed computing environment, where computing node and communication link failures are inevitable. The mapping decision and the stored table information is distributed among various nodes in order to achieve scalability and robustness, which are especially important for large-scale distributed systems. This Distributed Reliability Maximization workflow mapping algorithm under End-to-end Delay constraint (dis-DRMED) considers both the maximum reliability and the minimum EED objectives in a two-step procedure. In the first step, a mapping algorithm combining iterative Critical Path search and Layer-based priority assigning techniques (CPL) is adopted to minimize the EED by focusing on the optimal allocation of tasks on the critical path. In the second step, tasks on noncritical paths are remapped to improve the overall execution reliability. Simulation results under various system setups demonstrated that dis-DRMED achieved considerably higher reliability values under the same EED constraint compared with some representative workflow mapping algorithms.     

e-Transactions: end-to-end reliability for three-tier architectures

A three-tier application is organized as three layers: human users interact with front-end clients (e.g., browsers), middle-tier application servers (e.g., Web servers) contain the business logic of the application, and perform transactions against back-end databases. Although three-tier applications are becoming mainstream, they usually fail to provide sufficient reliability guarantees to the users. Usually, replication and transaction-processing techniques are applied to specific parts of the application, but their combination does not provide end-to-end reliability. The aim of this paper is to provide a precise specification of a desirable, yet realistic, end-to-end reliability contract in three-tier applications. The paper presents the specification in the form of the Exactly-Once Transaction (e-Transaction) abstraction: an abstraction that encompasses both safety and liveness properties in three-tier environments. It gives an example implementation of that abstraction and points out alternative implementations and tradeoffs     

多天线主动窃听系统的干扰机优化设计

不同于与传统无线通信物理层安全被动窃听技术,针对物理层主动窃听技术进行了研究,主要用于合法部门监听可疑用户的通信。考虑可疑发射机和合法干扰机具有多天线、可疑接收机和合法窃听者具有单天线的系统模型,在可疑通信链路增益强于窃听链路增益的情况下,通过发射干扰信号,控制可疑用户的通信速率,使合法窃听者能正确解码窃听信息。分别在合法干扰机对合法窃听者有/无干扰的两种情况下,进行最优的干扰信号设计和干扰功率控制,最大化窃听速率。仿真结果表明,在不同的应用场景下合理设置干扰机位置,能有效提高窃听速率,并且所提的干扰机设计方法均取得了比现有的两种基准方法更优的窃听性能。     

真实路况下的快递配送路径优化研究

针对快递配送过程中的实际情况,结合旅行商问题在快递配送中的应用,借助百度地图应用程序接口提取实际道路信息和行驶时间,在考虑实时路况的情况下,采用改进的遗传算法解决快递配送过程中交通环境、交通工具和配送路径要求不同的快递配送两个层次的路径优化,从而得到优化配送方案及分段路径规划,并将算法解析的配送路径显示在地图上。实例应用验证表明,该优化方法具有一定的可行性与实用性,可解决真实路况下的快递配送路径优化,具有良好的应用价值。     

Sequential optimization and reliability assessment for multidisciplinary design optimization under aleatory and epistemic uncertainties

In engineering design, to achieve high reliability and safety in complex and coupled systems (e.g., Multidisciplinary Systems), Reliability Based Multidisciplinary Design Optimization (RBMDO) has been received increasing attention. If there are sufficient data of uncertainties to construct the probability distribution of each input variable, the RBMDO can efficiently deal with the problem. However there are both Aleatory Uncertainty (AU) and Epistemic Uncertainty (EU) in most Multidisciplinary Systems (MS). In this situation, the results of the RBMDO will be unreliable or risky because there are insufficient data to precisely construct the probability distribution about EU due to time, money, etc. This paper proposes formulations of Mixed Variables (random and fuzzy variables) Multidisciplinary Design Optimization (MVMDO) and a method of MVMDO within the framework of Sequential Optimization and Reliability Assessment (MVMDO-SORA). The MVMDO overcomes difficulties caused by insufficient information for uncertainty. The proposed method enables designers to solve MDO problems in the presence of both AU and EU. Besides, the proposed method can efficiently reduce the computational demand. Examples are used to demonstrate the proposed formulations and the efficiency of MVMDO-SORA.     

Maintenance optimization in failure-prone systems under imperfect preventive maintenance

In the majority of the existing preventive optimization models only costs related to maintenance actions are accounted for, while breakdown and operational costs are usually ignored. Liao et al. (J Intell Manuf 21(6):875–884, 2010) proposed a preventive maintenance model to deal with this shortcoming. In the present paper, we revisit and discuss the results provided in Liao et al. (2010) and point out some inconsistencies in the maintenance optimization model proposed therein. Accordingly, we develop a new maintenance optimization model and discuss some of its main cost components. Furthermore, optimality conditions are also formally investigated and a solution method is provided. Numerical experiments are conducted to illustrate the validity of the proposed approach and results are compared with those provided in the original paper by Liao et al. (2010).     

Feature-based hybrid strategies for gradient descent optimization in end-to-end speech recognition

Multimedia Tools and Applications - With the increasing popularity of deep learning, deep learning architectures are being utilized in speech recognition. Deep learning based speech recognition...     

Multiperiod robust optimization for proactive resource provisioning in virtualized data centers

Energy management has become a significant concern in data centers for reducing operational costs. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off idle servers. This needs to consider the power state change overhead. In this paper, we investigate proactive resource provisioning in short-term planning for performance and energy management. To implement short-term planning based on workload prediction, this requires dealing with high fluctuations that are inaccurately predictable by using single value prediction. Unlike long-term planning, short-term planning can not depend on periodical patterns. Thus, we propose an adaptive range-based prediction algorithm instead of a single value. We implement and extensively evaluate the proposed range-based prediction algorithm with different days of real workload. Then, we exploit the range prediction for implementing proactive provisioning using robust optimization taking into consideration uncertainty of the demand. We formulate proactive VM provisioning as a multiperiod robust optimization problem. To evaluate the proposed approach, we use several experimental setups and different days of real workload. We use two metrics: energy savings and robustness for ranking the efficiency of different scenarios. Our approach mitigates undesirable changes in the power state of servers. This enhances servers’ availability for accommodating new VMs, its robustness against uncertainty in workload change, and its reliability against a system failure due to frequent power state changes.     

Efficient route optimization scheme for nested-NEMO

Network mobility (NEMO) basic support protocol maintains the connectivity when mobile router (MR) of a mobile network changes its point of attachment to the Internet by establishing a bi-directional tunnel between the MR and the home agent (HA). A packet from a correspondent node (CN) traverses through the tunnel to reach the mobile network. Nesting occurs in NEMO when a MR's new attachment point is in another mobile network that has also moved away from its home link. The level of tunneling increases as the level of nesting increases. Multiple levels of tunneling in nested NEMO adds multiple legs to a non-optimized routing path that the IP packets have to traverse in order to reach the final destination. As per our study, an efficient route optimization technique in NEMO, particularly in nested NEMO, is still a research challenge. In this paper, we propose an efficient route optimization scheme for nested NEMO. We use two care-of Addresses for each MR, as well as two types of entries, such as fixed and visiting, in the routing table in each MR. Our route optimization scheme removes the tunnels completely from the nested NEMO in a single step using only one binding update message irrespective of the number of levels in the nest. Our route optimization scheme also works for non-nested NEMO.     

基于多目标优化多任务学习的端到端车牌识别方法

本文针对多个车牌识别任务之间存在竞争和冲突,导致难以同时提高多个车牌的识别率的问题,提出基于多目标优化多任务学习的端到端车牌识别方法.首先,通过分析某些车牌识别任务容易占主导地位,而其他任务无法得到充分优化的问题,建立基于多任务学习的车牌识别模型.接着,针对字符分割造成车牌识别准确率较低、鲁棒性较差的问题,提出基于多任...     

Optimum random and age replacement policies for customer-demand multi-state system reliability under imperfect maintenance

This paper proposes the generalised random and age replacement policies for a multi-state system composed of multi-state elements. The degradation of the multi-state element is assumed to follow the non-homogeneous continuous time Markov process which is a continuous time and discrete state process. A recursive approach is presented to efficiently compute the time-dependent state probability distribution of the multi-state element. The state and performance distribution of the entire multi-state system is evaluated via the combination of the stochastic process and the Lz-transform method. The concept of customer-centred reliability measure is developed based on the system performance and the customer demand. We develop the random and age replacement policies for an aging multi-state system subject to imperfect maintenance in a failure (or unacceptable) state. For each policy, the optimum replacement schedule which minimises the mean cost rate is derived analytically and discussed numerically.     

Computer aided reliability for optimum maintenance planning

Computer aided Maintenance planning for mechanical equipment is presented in this work to implement the optimized extensive maintenance plan at a specific time horizon. The objective is to interchange the unscheduled corrective maintenance into scheduled preventive maintenance depending on historical data of equipment. This work is concerned with development of reliability analysis based on Weibull distribution. An optimization technique has been developed to provide the optimized preventive maintenance plan for the whole equipment during a required time horizon.     

Synthesis and optimization of the recovery route for residual products under uncertain product demand

The present work describes an optimization model for managing the recovery of residual products that originate at industrial plants. The framework for the proposed general network superstructure, where all possible process transformations, storage, transports and auxiliary operations are accounted for, is modeled using a maximal state task network representation. This framework is combined with the evaluation of a set of environmental impacts, quantified by metrics (for air, water pollution, etc.) through the minimum environment impact analysis methodology and is associated with waste generation at utility production and transportation levels. The final model is described as a mixed-integer linear programming model, which, once solved, is able to suggest the optimal processing and transport routes, while optimizing a given objective function and meeting design and environmental constraints. For each solution obtained, a stochastic flexibility index is computed, allowing for the drawing of trade-off curves for investment decision support.     

不确定可靠性优化问题的多目标粒子群优化算法

针对元件可靠性为区间值的系统可靠性优化问题, 提出一种区间多目标粒子群优化方法. 首先, 建立问题的区间多目标优化模型; 然后, 利用粒子群算法优化该模型, 定义一种不精确Pareto 支配关系, 并给出编码、约束处理、外部存储器更新、领导粒子选择等关键问题的解决方法; 最后, 将该方法应用于可靠性优化问题实例, 验证了方法的有效性.

     

Intra domain route optimization for ubiquitous network

The main advantage of a wireless network is user mobility, which calls for efficient routing support at the network layer. An architecture combines hierarchical mobile IPv6 and network mobility for a network mobile and mobile nodes move in tandem and make a hierarchy in the wireless network to management of micro-mobility and seamless handoff. But the capability of the architecture for intra domain route optimization is impaired. So we propose functionality in domain nodes to enable intra domain path optimization for ubiquitous network. It is shown that intra domain cost effect is beneficial in every hierarchical domain that spans mesh network topology. We address the key function for our proposed scheme and analyze the usefulness of our proposed method using mathematically. We show that our proposed scheme performs much better than Network Mobility protocol, especially when the number of mobile nodes or mobile routers or correspondent nodes increases in hierarchically nested in ubiquitous networks.     

A human reliability assessment of marine auxiliary machinery maintenance operations under ship PMS and maintenance 4.0 concepts

Cognition, Technology & Work - Maintenance is one of the core technical aspects on board ships, which is required for the ready availability, reliability, and efficiency of machinery equipment....     

Dynamic route maintenance for geographic forwarding in mobile ad hoc networks

Traditional route maintenance requires mobile nodes periodically exchange beacon messages with their neighbors in geographic forwarding algorithms. The interval at which these nodes broadcast their beacon messages is typically fixed. However, determining an appropriate value for this interval is challenging. A longer interval reduces the number of beacons needed, but may result in significant location errors. Conversely, a shorter interval guarantees more accurate location information, but induces heavier control overheads. Additionally, since a fixed value is assigned to the lifetime of each routing entry, the forwarding algorithm cannot adapt well to different mobility environments. Therefore, this paper presents a dynamic route maintenance algorithm (DRM) for beacon-based geographic routing. In the approach, the mobile nodes dynamically adjust their beacon intervals based on their speed of movement. Moreover, the routing information can be well managed using the mobility prediction. The simulation results show that DRM not only significantly decreased the routing overheads in a low mobility scenario but also guaranteed the high quality packet delivery in high mobility environments.     

Network reliability optimization problem of interconnection network under node-edge failure model

The network reliability optimization problem for an interconnection network is to maximize the network reliability subjected to some constraints such as the total cost of the network. Even though, the problem is NP-Hard, many researchers have solved this problem in different ways but with a common assumption that nodes are perfect. But, this assumption is quite unrealistic in nature. In this paper, a new method based on artificial neural network is proposed to solve the network reliability optimization problem considering both the nodes and links of the interconnection networks to be imperfect. The problem is mapped onto an artificial neural network by constructing an energy function whose minimization process drives the neural network into one of its stable states. This stable state corresponds to a solution for the network reliability problem. Some existing methods are studied and compared with proposed method in evaluating the network reliability of some fully connected networks. The comparison reports the proposed method to be better than its counterparts in maximizing the network reliability. The proposed method is used to maximize the reliability of few fully connected networks subjected to some predefined total cost, where the node as well as the links of the networks may fail. Further, the behaviors of the cost as well as the time on the network reliability are discussed.