A management scheme for distributed cross-layer reconfigurations in the context of cognitive B3G infrastructures

Current research efforts in wireless communications are targeted at the evolution of B3G (Beyond the 3rd Generation) wireless infrastructures. The operation of B3G infrastructures envisions dynamic adaptations to external stimuli, which can be facilitated through the exploitation of cognitive networking potentials. Cognitive networks dispose mechanisms for dynamically selecting their configuration (algorithms and parameter values, at different layers of the protocol stack), through appropriate management functionality that takes into account the context of operation (environment characteristics and requirements), profiles, goals, policies and knowledge that derives from previous experience. This paper focuses on such management functionality and it addresses a problem, dealing with “Distributed, Cross-Layer Reconfigurations” (DCLR), which aims at assessing and selecting the most appropriate configuration per network element in a cognitive network. In essence, this work contributes in four main areas. First, a fully distributed formulation and solution to the DCLR problem is provided, which is important for the management of a particular reconfigurable element in a cognitive context. Second, robust learning and adaptation, strategies are proposed, for estimating and gaining knowledge of the performance potentials of alternate reconfigurations. Third, a computationally efficient solution to the problem of exploiting the performance potentials of reconfigurations is provided, in order to rate reconfigurations and finally select the best ones. Finally, results that expose the behaviour and efficiency of the proposed schemes, are presented.     

A General Probability Formula of the Number of Location Areas Boundaries Crossed by a Mobile Between Two Successive Call Arrivals

Mobility management is a challenging topic in mobile computing environment. Studying the situation of mobiles crossing the boundaries of location areas is significant for evaluating the costs and performances of various location management strategies. Hitherto, several formulae were derived to describe the probability of the number of location areas' boundaries crossed by a mobile. Some of them were widely used in analyzing the costs and performances of mobility management strategies. Utilizing the density evolution method of vector Markov processes, we propose a general probability formula of the number of location areas' boundaries crossed by a mobile between two successive calls. Fortunately, several widely-used formulae are special cases of the proposed formula.     

Solving the stochastic dynamic lot-sizing problem through nature-inspired heuristics

We investigate the dynamic lot-size problem under stochastic and non-stationary demand over the planning horizon. The problem is tackled by using three popular heuristic methods from the fields of evolutionary computation and swarm intelligence, namely particle swarm optimization, differential evolution and harmony search. To the best of the authors' knowledge, this is the first investigation of the specific problem with approaches of this type. The algorithms are properly manipulated to fit the requirements of the problem. Their performance, in terms of run-time and solution accuracy, is investigated on test cases previously used in relevant works. Specifically, the lot-size problem with normally distributed demand is considered for different planning horizons, varying from 12 up to 48 periods. The obtained results are analyzed, providing evidence on the efficiency of the employed approaches as promising alternatives to the established Wagner–Whitin algorithm, as well as hints on their proper configuration.     

Analytical Model to Evaluate the Performance of Mobile Agents in a Generic Network Topology

The constant growth of computer and telecommunication networks and the variety of topologies being interconnected are making the efficient management of these networks a hard task. Centralized management, currently the most used model, is becoming inflexible and inefficient in view of this growth. On the other hand, code mobility is being considered as a possible solution to this problem. However, no research proposes a way to identify wich technique is the best to perform any management task in a network with any configuration. In this context, the present work proposes an analytical model to evaluate the performance of Mobile Agents in the decentralization of the management compared with the traditional and centralized management model in a generic network topology.     

Integrated approach to network design and frequency setting problem in railway rapid transit systems

This work presents an optimization-based approach to simultaneously solve the Network Design and the Frequency Setting phases on the context of railway rapid transit networks. The Network Design phase allows expanding existing networks as well as building new ones from scratch, considering infrastructure costs. In the Frequency Setting phase, local and/or express services are established considering transportation resources capacities and operation costs. Integrated approaches to these phases improve the transit planning process. Nevertheless, this integration is challenging both at modeling and computational effort to obtain solutions. In this work, a Lexicographic Goal Programming problem modeling this integration is introduced, together with a solving strategy. A solution to the problem is obtained by first applying a Corridor Generation Algorithm and then a Line Splitting Algorithm to deal with multiple line construction. Two case studies are used for validation, including the Seville and Santiago de Chile rapid transit networks. Detailed solution reports are shown and discussed. Conclusions and future research directions are given.     

一种基于多策略差分进化的分解多目标进化算法

为了提高多目标优化问题非支配解集合的分布性和收敛性,根据不同差分进化策略的特点,基于切比雪夫分解机制,提出一种基于多策略差分进化的分解多目标进化算法(MOEA/D-WMSDE).该算法首先采用切比雪夫分解机制,将多目标优化问题转化为一系列单目标优化子问题;然后引入小波基函数和正态分布实现差分进化算法的参数控制,探究一种...     

Deformation monitoring over large areas with multipass differential SAR interferometry: a new approach based on the use of spatial differences

Multipass differential synthetic aperture radar interferometry is becoming increasingly important as a powerful technique for ground deformation retrieval compared with classical geodetic techniques such as levelling and global positioning system (GPS). It proves superior in terms of costs, coverage, data accessibility and availability of historical archives. Application to different areas of risk management such as monitoring of volcanoes and slope instabilities, tectonic movements, urban areas and infrastructure, has already been successfully demonstrated. In this work we discuss a new multipass differential synthetic aperture radar interferometry processing technique that makes extensive use of spatial differences. The results obtained demonstrate that this technique allows the monitoring of ground deformation over wide areas. The processed data were acquired from ERS–1 and ERS–2 sensors over three partially overlapping tracks related to a region, approximately 240 km N–S and 80 km E–W wide, located in the centre‐south of Italy, along the west coast. Comparison of the mean deformation velocity results over the different tracks and of the deformation evolution with levelling measures, available in some areas, shows good agreement and goes to validate the technique.     

一种基于移动的Ad Hoc网络分层位置管理方案

位置管理是AdHoc网络研究中新的热点问题。论文提出一种新颖的AdHoc网络位置管理策略,结合了SLALoM和HGRID两种位置管理策略的优点,根据节点的移动特性,引入更新阈值M来控制节点的位置更新,可以在一定程度上避免乒乓效应,大大减轻了系统控制信息的开销,在大型的AdHoc网络中有较好的应用和良好的可扩展性。     

一种改进的无线传感器网络质心定位算法

在无线传感器网络中,确定节点位置或事件发生的位置对其监测活动至关重要。节点自身的准确定位不仅是提供监测事件或监测目标位置信息的前提,也是提供网络拓扑自配置、提高路由效率、向部署者报告网络的覆盖质量以及为网络提供命名空间等网络功能的基础。为此,本文对无线传感器网络定位技术中的质心定位算法进行了改进,对未知节点大致位置的算法做了新的修正,并对未知节点位置确定算法中的加权因子进行了优化,使未知节点的定位误差和定位精度更加精确。相比原加权质心定位算法,本文仿真结果表明,改进的质心定位算法无论在定位误差还是在定位精度方面都有很大的提高。     

A comparison of three artificial life techniques for reporting cell planning in mobile computing

Location management is a very important and complex problem in today's mobile computing environments. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of location management scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper compares several well-known artificial life techniques to gauge their suitability for solving location management problems. Due to their popularity and robustness, a genetic algorithm (GA), tabu search (TS), and ant colony algorithm (ACA) are used to solve the reporting cells planning problem. In the reporting cell location management scheme, some cells in the network are designated as reporting cells; mobile terminals update their positions (location update) upon entering one of these reporting cells. To create such a planner, a GA, TS, as well as several different AC algorithms are implemented. The effectiveness of each algorithm is shown for a number of test problems.     

Location-Based IPv6 Address Configuration for Vehicular Networks

Due to long latency and considerable packet loss, stateful and stateless address configuration standards cannot efficiently work in vehicular networks, so this paper proposes an IPv6 address configuration scheme for vehicular networks based on location information. This scheme combines a stateless address configuration mechanism with a stateful one. In the stateless algorithm, duplicate address detection is performed within a road domain instead of a network. A random ID is introduced to reduce the address conflict rate, so the address configuration cost and delay are reduced. In the stateful algorithm, a vehicle acquires a unique address from a neighbor vehicle without recording the address allocation states, so the address configuration task is distributed around all vehicles and the distributed address configuration is achieved. This paper evaluates the performance of this scheme, and the data results show that this scheme reduces the addressing costs and delays.     

Evolving cellular automata for location management in mobile computing networks

Location management is a very important and complex problem in mobile computing. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of location management scenarios. The paper investigates the use of cellular automata (CA) combined with genetic algorithms to create an evolving parallel reporting cells planning algorithm. In the reporting cell location management scheme, some cells in the network are designated as reporting cells; mobile terminals update their positions (location update) upon entering one of these reporting cells. To create such an evolving CA system, cells in the network are mapped to cellular units of the CA and neighborhoods for the CA is selected. GA is then used to discover efficient CA transition rules. The effectiveness of the GA and of the discovered CA rules is shown for a number of test problems.     

A branch-and-cut algorithm for two-level survivable network design problems

This paper approaches the problem of designing a two-level network protected against single-edge failures. The problem simultaneously decides on the partition of the set of nodes into terminals and hubs, the connection of the hubs through a backbone network (first network level), and the assignment of terminals to hubs and their connection through access networks (second network level). We consider two survivable structures in both network levels. One structure is a two-edge connected network, and the other structure is a ring. There is a limit on the number of nodes in each access network, and there are fixed costs associated with the hubs and the access and backbone links. The aim of the problem is to minimize the total cost. We give integer programming formulations and valid inequalities for the different versions of the problem, solve them using a branch-and-cut algorithm, and discuss computational results. Some of the new inequalities can be used also to solve other problems in the literature, like the plant cycle location problem and the hub location routing problem.     

Distributed cell-centric neighborhood-related location area planning for PCS networks

An intelligent location area planning (LAP) scheme should consider the frequent replannings of location areas (LAs) due to changes in user distribution and mobility patterns along with optimization of location management costs, including location updating and paging costs. Most schemes proposed in the literature are designed through centralized techniques, thus requiring more computing time to plan the LAs. Frequent replannings to accommodate environmental changes make the situation worse. As to the optimization of location management costs, most proposed schemes consider the inter-cell crossing rate as one of the key factors in determining the optimal partitions. In some cases, the inter-cell crossing rate may lead to an unsatisfactory outcome. Another problem is the ping-pong effect which is caused by the fixed borders between any two of LAs. In this paper, we propose a distributed cell-centric neighborhood-related LAP scheme in which each cell acts as the center of an LA and in which highly correlative neighboring cells are bundled into the LA if mobile terminals (MTs) remain within the LA long enough to reduce costs. Moreover, the ping-pong effect will be alleviated because MTs always locate at the center cell of an LA whenever a new location update is performed. Finally, the scheme can be implemented in a distributed manner so the computing time incurred by frequent replannings can be reduced. Simulation results show that our scheme indeed exhibits excellent results.     

Designing a fine‐grained parallel differential evolution with Pareto tournaments for solving an optical networking problem

The future of designing optical networks focuses on the wavelength division multiplexing technology. This technology divides the huge bandwidth of an optical fiber into different wavelengths, providing different available channels per link of optical fiber. However, when it is required to establish a set of demands, a problem comes up. This problem is known as routing and wavelength assignment problem. In this work, we have tackled the static routing and wavelength assignment problem by using multiobjective evolutionary computing. The algorithm applied is the differential evolution but modified with the Pareto tournaments concept for being adapted to the multiobjective context. By using OpenMP, an application programming interface that supports multiplatform shared memory multiprocessing programming, we have demonstrated that this algorithm is highly suitable to be parallelized. We have performed several experiments in multicore systems with two, four, and eight cores, obtaining 97.57% of mean efficiency. To ensure that our heuristic obtains relevant results, we have compared it with a parallel version of the standard fast nondominated sorting genetic algorithm. Finally, in order to show the goodness and effectiveness of the differential evolution with Pareto tournaments algorithm when dealing with this problem, we present diverse multiobjective comparisons with the nondominated sorting genetic algorithm and other approaches published in the literature. Copyright © 2013 John Wiley & Sons, Ltd.     

A mobility management model based on users’ mobility profiles for IPv6 networks

Fourth-generation (4G) mobile systems provide access to a wide range of services and enable mobile users to communicate regardless of their geographical location and their roaming characteristics. Due to the growing number of mobile users, global connectivity, and the small size of cells, one of the most critical issues pertaining to these networks is location management. In recent years, several strategies have been proposed to improve the performance of the location management procedure in 3G and 4G mobile networks. In this paper, we propose a new model called Seamless Mobile IPv6 (SMIPv6) to improve the performance of the handover component in location management schemes. This model improves handover by predicting user location based on Users’ Mobility Profiles. The overall goals of SMIPv6 are to reduce both handover latency and signaling loads generated during the location update process. Simulation results show that the use of SMIPv6 produces a handover with low delay, as well as a significant drop of signaling overhead. Better results have been obtained by our protocol in all cases studied when compared to Mobile IPv6 (MIPv6) and Fast Handovers for MIPv6 (FMIPv6).     

Nonconvex resource control and lifetime optimization in wireless video sensor networks based on chaotic particle swarm optimization

Wireless video sensor networks (WVSNs) have attracted a lot of interest because of the enhancements that they offer to existing wireless sensor networks applications and their numerous potential in other research areas. However, the introduction of video raises new challenges. The transmission of video and imaging data requires both energy efficiency and quality of service (QoS) assurance in order to ensure the efficient use of sensor resources as well as the integrity of the collected information. To this end, this paper proposes a joint power, rate and lifetime management algorithm in WVSNs based on the network utility maximization framework. The optimization problem is always nonconcave, which makes the problem difficult to solve. This paper makes progress in solving this type of optimization problems using particle swarm optimization (PSO). Based on the movement and intelligence of swarms, PSO is a new evolution algorithm to look for the most fertile feeding location. It can solve discontinuous, nonconvex and nonlinear problems efficiently. First, since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the paper introduces chaos mapping into PSO with adaptive inertia weight factor to avoid the disadvantage of original PSO of easily getting to the local optimal solution in the later evolution period and keep the rapid convergence performance. Second, based on the distribution characteristics of the actual network, we decompose the resource control problem into a number of sub-problems using the hierarchical thought, where each user corresponds to a subsystem which is solved using the proposed CPSO3 method. Through the cooperative coevolution theory, these sub-optimization problems interact with each other to obtain the optimum of the system. Numerical examples show that our algorithm can guarantee fast convergence and fairness within a few iterations. Besides, it is demonstrated that our algorithm can solve the nonconvex optimization problems very efficiently.     

A Decision-Theoretic Approach to Network Fault Diagnosis and Follow-up Action

Fault management and security of computer networks present new challenges of increasing complexity. Decision procedures for fault diagnosis, security-related threats, and follow-up actions must, however, be evaluated on the basis of sound theoretical foundations and economic costs of various strategies. This paper presents a minimum expected cost solution for fault diagnosis and corrective actions. Several notions new to fault management are introduced. The methodology is applicable to both non-malicious and malicious faults. As a novel security-related application, the problem of choosing between two strategies for containing the spread of network worms is discussed.     

Non-probabilistic robust optimization approach for flood control system design

Flood risk management in floodplain systems is a long-standing problem in water resources management. Soft strategies such as land cover change are used to mitigate damages due to flooding. In this approach one chooses the best combination of land covers such that flood damage and the investment costs are minimized. Because of the uncertain nature of the problem, former studies addressed this problem by stochastic programming models which are found to be computationally expensive. In this work, a novel non-probabilistic robust counterpart approach is proposed in which the uncertainty of the rainfall events requires a new formulation and solution algorithms. Non-probabilistic methods, developed in the field of robust optimization were shown to have advantages over classical stochastic methods in several aspects such as: tractability, non-necessity of full probabilistic information, and the ability to integrate correlation of uncertain variables without adding complexity. However, unlike former studies in the field of robust optimization, the resulting optimization model in the flood risk management problem is nonlinear and discontinuous and leads to an intractable robust counterpart model. In this work, a novel iterative linearization scheme is proposed to effectively solve nonlinear robust counterpart models. This work demonstrates the tractability and applicability of non-probabilistic robust optimization to nonlinear problems similar to the flood risk management problem. The results show considerable promise of the robust counterpart approach in terms of showing the tradeoff between flood risk and cost in an efficient manner.     

Advances in evolutionary feature selection neural networks with co-evolution learning

Training neural networks is a complex task provided that many algorithms are combined to find best solutions to the classification problem. In this work, we point out the evolutionary computing to minimize a neural configuration. For this purpose, a distribution estimation framework is performed to select relevant features, which lead to classification accuracy with a lower complexity in computational time. Primarily, a pruning strategy-based score function is applied to decide the network relevance in the genetic population. Since the complexity of the network (connections, weights, and biases) is most important, the cooling state of the system will strongly relate to the entropy as a minimization function to reach the desired solution. Also, the framework proposes coevolution learning (with discrete and continuous representations) to improve the behavior of the evolutionary neural learning. The results obtained after simulations show that the proposed work is a promising way to extend its usability to other classes of neural networks.