Distributed optimization of tactical actions by mobile intelligent agents

In this article we compare the performance of a learning controller with and without a flexible and incrementally expandable distribution scheme. The learning method is modeled after Holland's learning classifier system, but it uses Q-learning as the reinforcement method instead of the “bucket-brigade.” The method for distributing the learning among sub-processes is modular, so that learning can be accomplished monolithically, as a redundant system of peers, or as a conceptual hierarchy of competence levels with varying degrees of abstraction. They can also communicate using short messages transmitted quickly between modules running in separate processes, on separate machines, or across a network connection. The system is applied to a mobile agent whose task is to defend itself against other mobile agents trying to attack it. As a simulation example, we consider a naval combat scenario wherein a surface vessel is considered as a mobile root with various radar and communications sensors. Its task is to detect other agents such as other ships and planes, identify and classify them, and devise optimal tactical decision policies. The results suggest that the learning method employed is useful in general for any sensor-based autonomous robot in dynamic, unstructured environments. © 1997 John Wiley & Sons, Inc.     

Distributed connected coverage control for groups of mobile agents

For a group of mobile agents, each agent can sense the local environment around it and directly communicate with others in its communication range. This article deals with the self-deployment problem of mobile agents to achieve a sensing coverage over the environment as large as possible, while keeping the underlying time-variant communication topology connected all the time. For this problem, a self-deployment algorithm is developed for each agent. It guarantees network connectivity by preserving the group core structure, which is extracted from the communication topology in a completely distributed, local manner and is renewed in each iteration period. Note that maintaining all the connecting edges in the core structure still remains a sufficient degree of freedom for each agent to traverse over a maximal distance from its neighbours. Thus, the distributed connected coverage objective can be achieved by spreading out the group members with the constraints of preserving the core structure. The performance of the self-deployment algorithm is demonstrated by both theoretical analysis and numerical simulations.     

Distributed directory service and message routing for mobile agents

Research about networks and agents has identified the need for a layer that provides a uniform protocol to communicate with fixed and mobile agents. In order to preserve the compatibility with existing infrastructures, proposed solutions have involved a “home agent”, which forwards messages to a mobile entity. The mechanism of a home agent puts a burden on the infrastructure, which may hamper the scalability of the approach, in particular, in massively distributed systems, such as the amorphous computer or the ubiquitous/pervasive computing environment. Free from any compatibility constraint, we have designed an algorithm to route messages to mobile agents that does not require any fixed location. The algorithm has two different facets: a distributed directory service that maintains distributed information about the location of a mobile agent, and a message router that uses the directory service to deliver messages to a mobile agent. Two properties of the algorithm were established. Safety ensures that messages are delivered to the agent they were aimed at, whereas liveness guarantees that messages eventually get delivered. A mechanical proof of the properties was carried out using the proof assistant Coq.     

面向依赖关系约束的移动群智感知任务协作

现有移动群智感知中,大多研究将每个任务作为独立个体进行处理,对任务间约束关系缺乏研究,为此,提出了基于感知质量优先级的在线任务协作方法（online task collaboration method based on sensing quality priority,TCSP）。该方法首先使用贪婪算法计算感知质量优先级,对全部任务进行筛选以保证任务完成率;然后将选出任务中存在时间先后或执行逻辑前后关系的多个子任务构建为任务协作图,并将其协作过程建模为有约束的马尔可夫决策过程,通过强化学习算法求出最优协作策略。实验结果表明,与现有基线方法相比,所提出的任务协作方法能够减少依赖任务的平均完成时间,有效降低平台的平均感知成本。     

移动群智感知中面向用户区域的分布式多任务分配方法

多数群智感知（MCS）任务分配方法针对单个任务,难以适用于多任务实时并发的现实场景,而且往往需要实时获取用户位置,不利于保护参与者隐私。针对上述问题,提出了一种面向用户区域的分布式多任务分配方法Crowd-Cluster。该方法首先通过贪心启发算法将全局感知任务及用户区域进行分簇;其次,基于空间关联性采用Q-learning算法将并发任务组合构成任务路径;接着,构建符合玻尔兹曼分布的用户意愿模型对任务路径进行动态定价;最后,基于历史信誉记录贪心优选参与者实现任务分配。基于真实数据集mobility的实验结果表明,Crowd-Cluster能有效减少参与者总人数及用户总移动距离,并且在低人群密度场景下,还能降低感知资源不足对任务完成度的影响。     

Distributed observer-based cooperative control of multiple nonholonomic mobile agents

In this article, cooperative output feedback control of a group of nonholonomic mobile agents is considered. Distributed observer-based cooperative control laws are proposed with the aid of Lyapunov techniques and results from graph theory. Robustness of the stability of the closed-loop systems with the proposed control laws is considered. The effectiveness of the proposed controllers is verified by simulation results.     

Network-aware support for mobile distributed teams

An experiment evaluated network-aware support to increase understanding of the factors that are important for successful teamwork in mobile geographically dispersed teams of first responders. Participants performed a simulated search and rescue team task and were equipped with a digitized map and real-time situation updates on the location of other participants in a simulated disaster area. The connection to a server, however, was made deliberately error-prone, leading to occasional losses of network connections. Consequently, participants were not provided with real-time situation updates. To deal with this problem we equipped team members with a network-aware application that signaled network loss to them and adapted the graphical representation of the location of fellow team members accordingly to the quality of location information present. The experiment revealed that presenting complete and reliable geospatial information improves teamwork. Teams connected to a server over a fast and reliable link showed superior performance over teams with no network connection whatsoever to a server. The present study failed, however, to demonstrate the added value of network-aware support when teams had to collaborate in the presence of an unreliable communications infrastructure. Although participants indicated a slight preference for the network-aware application over a condition without support signaling network loss, no differences were observed in team process and outcome measures.     

感知受限的移动传感器节点扫描覆盖优化算法

移动无线传感器网络（WSN）的应用中,因为传感器节点的感知范围受限,其覆盖分析就是一个针对目标区域的扫描覆盖问题。提出了一种基于多目标优化的扫描覆盖算法。在目标区域中,采用双目标优化策略对单个移动传感器节点进行路径规划,一方面使节点的覆盖面最大化,另一方面使扫描覆盖的路径最短。仿真实验在含有障碍物和不含障碍物的情况下进行,与多节点的编队覆盖算法相比,所提算法在适度降低覆盖率的情况下,可大幅降低移动能耗。     

Distributed debugging for mobile networks

Debuggers are an integral part, albeit often neglected, of the development of distributed applications. Ambient-oriented programming (AmOP) is a distributed paradigm for applications running on mobile ad hoc networks. In AmOP the complexity of programming in a distributed setting is married with the network fragility and open topology of mobile applications. To our knowledge, there is no debugging approach that tackles both these issues. In this paper we argue that a novel kind of distributed debugger that we term an ambient-oriented debugger, is required. We present REME-D (read as remedy), an online ambient-oriented debugger that integrates techniques from distributed debugging (event-based debugging, message breakpoints) and proposes facilities to deal with ad hoc, fragile networks – epidemic debugging, and support for frequent disconnections.     

Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling

This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.     

Portfolio approaches for constraint optimization problems

Within the Constraint Satisfaction Problems (CSP) context, a methodology that has proven to be particularly performant consists of using a portfolio of different constraint solvers. Nevertheless, comparatively few studies and investigations have been done in the world of Constraint Optimization Problems (COP). In this work, we provide a generalization to COP as well as an empirical evaluation of different state of the art existing CSP portfolio approaches properly adapted to deal with COP. The results obtained by measuring several evaluation metrics confirm the effectiveness of portfolios even in the optimization field, and could give rise to some interesting future research.     

Causality for debugging mobile agents

Mobile agents, i.e. pieces of programs that can be sent around networks of computers, appear more and more frequently on the Internet. These programs may be seen as an enrichment of traditional distributed computing, and structuring applications using mobile agents is destined to become the de facto way of constructing distributed systems in the near future. Since mobile agents may carry communication links with them as they move across the network, they create very dynamic interconnectio n structures that can be extremely complex to analyse. In this paper we study an example of a system based on the mobile agent principle, written in the Facile programming language. We propose a Structural Operational Semantics (SOS) for Facile, giving a proved transition system that records encodings of the derivation trees of transitions in their labels. This information enables us to easily recover non-interleaving semantics for Facile by looking only at the labels of transitions. We use the new semantics to debug an agent based system. This example is a scaled down version of a system demonstrated at the European IT Conference Exhibition in Brussels, 1995. We also adopt our causal semantics to analyse the specification of a prefetch pipeline processor. Received: 14 November 1997 / 30 April 1999     

Distributed control and constraint preconditioners

Optimization problems with constraints that involve a partial differential equation arise widely in many areas of the sciences and engineering, in particular in problems of design. The solution of such PDE-constrained optimization problems is usually a major computational task. Here we consider simple problems of this type: distributed control problems in which the 2- and 3-dimensional Poisson problem is the PDE. Large dimensional linear systems result from the discretization and need to be solved: these systems are of saddle-point type. We introduce an optimal preconditioner for these systems that leads to convergence of symmetric Krylov subspace iterative methods in a number of iterations which does not increase with the dimension of the discrete problem. These preconditioners are block structured and involve standard multigrid cycles. The optimality of the preconditioned iterative solver is proved theoretically and verified computationally in several test cases. The theoretical proof indicates that these approaches may have much broader applicability for other partial differential equations.     

The mobile groups approach for the coordination of mobile agents

We present the concept of mobile groups as a basic mechanism for the reliable coordination of mobile agents. Analogously to traditional group systems, mobile groups also provide message delivery guarantees and virtual synchrony. Furthermore, they make agent mobility not only visible for the group, but also consistently ordered with other group actions (such as crashes, joins, leaves, and other migrations). The mobile groups approach represents a novel mobility support mechanism, which can be used to handle reliability of mobile agents required at both, the application and system level (e.g., for coordinating distributed agents and for reliable agent migration, respectively). In this paper, we discuss the motivations for the mobile groups approach, formally define their properties, and present a membership protocol for such groups. We also discuss some implementation issues and related performance data, and present the advantages of mobile groups against mechanisms commonly employed for the coordination of mobile agents.     

Distributed compressed video sensing based on the optimization of hypothesis set update technique

Multimedia Tools and Applications - Compressed Sensing (CS) breakthroughs the limitation of Nyquist sampling rate and realizes the sampling and compression of data simultaneous. Hence, it is widely...     

Distributed patching for mobile wireless sensor networks

Mobile wireless sensor networks (MWSNs), the continuous movement of sensor nodes, may cause complete disconnection of the network or at best a part of it. The design of such networks should guarantee that all sensor nodes at all times have a path to the sink node(s). Prolonging the lifetimes of the MWSNs is a crucial design issue but should not be at the expense of other essential functions such as connectivity. In this paper, we propose an energy efficient distributed framework for connectivity maintenance of MWSNs. In the proposed framework, sensor nodes of the MWSNs schedule and control their radio frequency (RF) modules based on dynamic coordinated reconstruction mechanism, some senor nodes may lose their connectivity with the network due to mobility, depletion of energy and/or incurred faults. Two protocols are developed to patch up the disconnection of the MWSNs. The first protocol reestablishes the network connection consuming far less energy than state-of-the-art alternatives. However, it is capable of reestablishing the connection when no more than 20% of the MWSN backbone nodes lose connection concurrently. The second protocol extends this limit to operate when up to 35% of the MWSN backbone nodes lose connection simultaneously.     

面向移动终端的分布并行化渲染

目的 随着移动互联技术和实时渲染技术的快速发展,面向移动终端的3维展示提供了远程交互式的模型渲染,但较高的渲染计算复杂度与较大的数据处理规模,影响了移动终端3维展示的渲染质量和实时性。针对以上不足,提出一种面向移动终端的分布并行化渲染方法。方法 该方法将渲染任务分布到服务端与终端,服务端采用层次细节模型控制场景复杂度,生成初次渲染图像;终端采用基于图像的渲染技术再次绘制图像,提高渲染质量,同时在渲染过程中利用CUDA(compute unified device architecture)并行计算加速渲染数据处理。结果 本方法有效提高了渲染速度,降低了数据传输量,并保证了图像质量,帧率和数据传输量优化了大约10.8%。结论 本文方法为面向移动终端的3维展示提供了很好的解决途径,在移动网络环境中,能够有效降低服务端负载压力,提高资源利用率并改善用户体验。     

Distributed dynamic mobile multicast

Traditional mobile multicast schemes have either high multicast tree reconfiguration cost or high packet delivery cost. The former affects service disruption time while the latter affects packet delivery delay. Although existing region-based mobile multicast schemes offer a trade-off between two costs to some extent, most of them do not determine the size of the service range, which is critical to network performance. In this paper, we propose a novel approach, called Distributed Dynamic Mobile Multicast (D²M²), to dynamically determine the optimal service range according to the mobility and service characteristics of a user. We derive an analytical model to formulate the costs of multicast tree reconfiguration and multicast packet delivery. The model is based on a Markov chain that analyzes a mobile node’s movement in a 2D mesh network. As the complexity of computing steady probability is high, we aggregate the Markov states by leveraging mobility symmetry. Simulation shows that the network performance is enhanced through D²M².     

A survey of theories for mobile agents

This paper presents a comparative survey of formalisms related to mobile agents. It describes the -calculus and its extensions, the Ambient calculus, Petri nets, Actors, and the family of generative communication languages. Each of these formalisms defines a mathematical framework that can be used to reason about mobile code; they vary greatly in their expressiveness, in the mechanisms they provide to specify mobile code based applications and in their practical usefulness for the validation and the verification of such applications. In this paper we show how these formalisms can be used to represent the mobility and communication aspects of two mobile code environments: Obliq and Messengers. We compare and classify the different formalisms with respect to mobility and discuss some shortcomings and desirable extensions. We also point to other emerging concepts in formalisms for mobile code systems.     

A constraint sampling approach for multi-stage robust optimization

We propose a tractable approximation scheme for convex (not necessarily linear) multi-stage robust optimization problems. We approximate the adaptive decisions by finite linear combinations of prescribed basis functions and demonstrate how one can optimize over these decision rules at low computational cost through constraint randomization. We obtain a-priori probabilistic guarantees on the feasibility properties of the optimal decision rule by applying existing constraint sampling techniques to the semi-infinite problem arising from the decision rule approximation. We demonstrate that for a suitable choice of basis functions, the approximation converges as the size of the basis and the number of sampled constraints tend to infinity. The approach yields an algorithm parameterized in the basis size, the probability of constraint violation and the confidence that this probability will not be exceeded. These three parameters serve to tune the trade-off between optimality and feasibility of the decision rules and the computational cost of the algorithm. We assess the convergence and scalability properties of our approach in the context of two inventory management problems.