基于移动代理和动态拓扑的分布式入侵检测

运行中网络的拓扑结构是动态变化的,因此提出了一种针对WAN环境设计的基于移动代理和动态拓扑的分布式入侵检测模型.该模型使用移动代理来实现入侵证据的收集、分析,通信和同步机制,并通过移动代理来跟踪运行中网络拓扑结构的动态变化以及网络入侵检测,并讨论了该模型的更新机制.     

稀疏无线传感器网络的路由算法研究

为保证移动代理节点在能量受限条件下总路由最短,给出一种基于能量受限移动代理节点的稀疏无线传感器网络路由算法.通过建立移动代理节点路由数学模型,首先根据移动代理节点能量限制条件将整个无线传感器网络划分为不同子集,然后利用旅行商问题的解决方法计算每个子集最短路径,最后所获得的子路径之和即为最优路由.实验仿真结果分析表明:随着网络节点数目增多和移动代理节点能量增加,所给方案的总路由比较接近于理想情况,在实际应用中比较有效且适于推广.     

动态传感器网络移动代理路由算法

提出一种基于蚁群优化的动态传感器网络移动代理能量有效路由算法.该算法设计了一种新的路径选择概率模型,使移动代理能找到一条从处理节点到目标节点之间的能量有效路径,该路径兼顾了路径能量消耗和节点剩余能量情况;该算法还制定了新的蚁群局部信息素再初始化规则,该规则在网络中发生动态变化的节点附近进行局部信息素再初始化,快速有效地更新最优路径.与其他算法相比,该算法能找到一条能量消耗较小,并且节点剩余能量较多的有效路径.     

基于Java-RMI技术的移动代理系统设计

移动代理是一种新型的分布式计算技术，有着许多传统分布式计算模型无法比拟的优势，是目前研究和应用热点。RMI（远程方法调用）是Java平台解决远程调用的方案，基于RMI技术设计了移动代理系统，对移动代理系统的关键问题进行了分析，通过RMI技术和动态类加载技术实现了移动代理系统，并设计实验验证了移动代理的移动性和自主性。     

自组网中基于移动代理的具有移动和能量感知的AODV路由协议

自组网是由一组带有无线收发装置的移动节点组成的一个能够支持多跳的临时性的计算机通信网络．通过引入移动代理，结合本地移动和能量感知策略，该文提出了一种基于移动代理的AODV节能路由协议．仿真结果表明，该方案通过移动代理的漫游来更新途径节点路由表，大大减少路由请求报文发起数，降低了端到端的平均数据传输时延，并延长了网络的生存时间．     

基于移动代理的网络拓扑发现技术的研究

随着Internet/Intranet的迅猛发展,网络结构日趋复杂,各种应用服务不断增多,网络规模也随之不断扩大,得到一个完整准确的网络拓扑结构图对于网络管理、网络优化、定位故障等应用越来越重要.国内在网络拓扑发现技术方面的研究仍处于初级阶段,因而必要性和紧迫性不容忽视.将移动代理这一分布式计算技术引入到网络拓扑发现领域,提出了基于移动代理网络拓扑发现算法,这种算法以TCP/IP网络管理体系为依托,将各种可以利用的协议和移动代理的特性有机地结合起来,旨在发现速度和发现准确度上有更高的要求.与传统的面向协议的方法不同,它是一种面向过程的方法,分层次地获得网络拓扑,该方法与网络分层结构相一致,能更好地体现网络层次.基于移动代理的网络拓扑发现方案不同于以往的拓扑发现算法,它体现了集中与分布的统一,适合于当今在地理上越来越分布的网络环境,具有很好的实用和研究价值.     

基于属性证书的移动代理权限管理方案

移动代理技术是一种新型的分布式计算技术,具有广阔的应用前景。权限管理是保证移动代理安全性的主要技术之一。本文讨论了移动代理中的安全问题,并基于X.509V4属性证书介绍了一种移动代理权限管理方案,能够安全、灵活地控制移动代理系统中的实体行为。     

LON网络远程监控的移动Agent实现方案

提出了一种基于移动代理的Lon网络远程监控系统的实现方案,介绍了系统的构成和设计技术。     

基于策略代理的无线传感器网络主动管理研究

无线传感器网络管理需要针对节点有限的能量和动态拓扑,完成通信资源的维持并对传感数据进行有效控制.在移动代理的基础上设计基于策略代理逐层控制的管理模型,提出一种基于策略代理的主动管理体系,建立本地策略库来提高网络拓扑管理的自适应能力,并通过主动数据监控机制实现对数据的分层扫描和策略化管理.实验表明,此方案能够有效地降低节点能源和通信开销,延长网络生存期,优化数据管理服务,提升无线传感器网络管理的主动性和策略性控制.     

传感器网络中基于移动代理的数据融合框架设计

使用移动代理进行数据融合相比于传统的数据融合方法拥有诸多优势.设计了一种基于移动代理的数据融合框架;通过定义目标函数,采用遗传算法求解框架中移动代理的最优路由策略;提出了一种基于分辨率的并行量化交叠的数据融合算法RPQO作为框架中的融合策略.仿真结果表明基于移动代理的数据融合框架能够有效地将融合策略和基于移动代理的路由策略整合起来,取得比传统数据融合算法更好的性能,其优势随着网络节点规模的增长更为明显.     

Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.     

Collaboration among mobile agents for efficient energy allocation in mobile grid

The use of mobile devices in grid environments may have two interaction aspects: devices are considered as users of grid resources or as grid resources providers. Due to the limitation constraints on energy and processing capacity of mobile devices, their integration into the Grid is difficult. In this paper, we investigate the cooperation among mobile devices to balance the energy consumption and computation workloads. Mobile devices can have different roles such as buyer devices and seller devices. In the mobile grid, the energies of mobile devices are uneven, energy-poor devices can exploit other devices with spare energy. Our model consists of two actors: A buyer device agent represents the benefits of mobile buyer device that intends to purchase energy from other devices. A seller device agent represents the profits of mobile seller device that is willing to sell spare energy to other devices. The objective of optimal energy allocation in mobile grid is to maximize the utility of the system without exceeding the energy capacity, expense budget and the deadline. A collaboration algorithm among mobile agents for efficient energy allocation is proposed. In the simulation, the performance evaluation of collaboration algorithm among mobile agents is conducted.     

Optimal control of mobile monitoring agents in immune-inspired wireless monitoring networks

This paper studies optimal control of mobile monitoring agents in artificial-immune-system-based (AIS-based) monitoring networks. In AIS-based structural health monitoring (SHM) networks, the active structural health monitoring is performed by a group of mobile monitoring agents equipped with damage pattern recognition algorithms. The mobile monitoring agents mimic immune cells in the natural immune system and patrol a structure to detect damage patterns using their receptors (feature vectors), damage pattern recognition algorithms, and the dynamic response data of the structure. The optimal control of mobile monitoring agents includes agent generation and distribution. The generation of mobile monitoring agents is optimized to minimize the response time for the mobile monitoring agents to diagnose structural damage in a sub-network and maximize the average affinity of monitoring agents′ receptors to the damaged sensor data feature vector. The objective functions for distributing mobile monitoring agents are to increase the detection probability and extend network life by balancing energy consumption of sensor nodes in the network. The presented optimization algorithms are developed using multi-objective genetic algorithms. The impact of the algorithm parameters on the performance of the algorithm is also investigated.     

移动自组织网络中基于移动Agent 的多约束QoS 多播路由算法

提出了一种移动自组织网络中基于移动Agent 的多约束QoS 多播路由算法．算法采用移动Agent 采集 网络信息,每个源节点构造一棵具有带宽和能量约束的多播树,选择节点剩余能量多的作为中间转发节点,以延长 网络的生存时间．当数据传输过程中链路发生断裂时,算法修复局部路径,而不是重新发送数据包,有效地降低了 路由重构和数据重传的可能性．利用网络仿真工具NS2 系统进行了仿真实验,实验结果证明新算法具有很高的效率 和鲁棒性．     

An economics-based negotiation scheme among mobile devices in mobile grid

In this paper, we propose an economics-based distributed negotiation scheme among mobile devices in mobile grid. In our model, there are energy negotiation and transactions between buyer devices and seller devices. Dynamic allocation of energy resources in mobile grid is performed through online transactions within markets. Mobile devices can be sellers and buyers that use optimization algorithms to maximize predefined utility functions during their transactions. Seller device agents sell the underlying energy resources of the mobile device. Buyer device agent makes buying decisions within the budget constraints to acquire energy resources. An economics-based negotiation algorithm among mobile devices is proposed. The proposed algorithm decomposes mobile grid system optimization problem into a sequence of two sub-problems. In the simulation, the performance evaluation of economics-based negotiation algorithm is evaluated.     

时滞分布参数系统的移动传感器/执行器防碰撞控制

针对一类状态时滞的分布参数系统,考虑了传感器/执行器间的防碰撞问题和最大通讯距离的最小通讯能耗问题,以及系统的稳定性问题.利用抽象发展方程理论和Lyapunov稳定性方法,设计了一种基于时滞分布参数系统的智能体移动控制策略,包括输出反馈控制器和移动控制力.通过理论推导和仿真实验验证,文中设计的控制策略能够使得时滞分布参数系统是渐近稳定的,同时智能体在移动过程中是防碰撞的,也验证了智能体在最大通讯距离的最小能耗.     

具有负载均衡和蚁群优化的移动P2P路由策略

分析了移动P2P网络的移动节点设备资源更加短缺,网络更加动态多变,建立健壮的路由策略是非常重要的。通过研究移动P2P网络的特征,从路由发现、路由选择、路由保持三个方面入手,提出了一种新的具有负载均衡和蚁群优化的路由策略。利用蚁群算法理论来指导移动agent的全局搜索的路由发现工作,并且结合通用的能量消耗公式计算得到的节点剩余能量和节点业务执行等候队列长度来优化路由选择工作。从仿真实验可以看到,该路由策略在平均端到端的延迟、路由控制负载方面具有性能优势。同时,节约了节点的能量,延长了节点在网络中的生存时间。     

A bio-inspired mobile agent-based integrated system for flexible autonomic job shop scheduling

This paper presents a bio-inspired mobile agent-based integrated system for flexible autonomic job shop scheduling. The system matches the autonomic system architecture, inspired by the autonomic nervous system and proposed by the IBM, and has the IBM-defined fundamental self-managing properties, so that it can manage itself with little human intervention. The system conforms to the IEEE FIPA (Foundation for Intelligent Physical Agents) standard. Therefore, the interoperability between agents of the system and agents from many active heterogeneous FIPA compliant agent platforms can be ensured. The system supports the execution of C/C++ mobile agent codes. Thus, it is applicable to a variety of applications, especially for distributed mechatronic and embedded systems. In addition, since the system is composed of agents, including stationary and mobile agents, the system has a high scalability and flexibility to integrate and adopt various scheduling models and algorithms for different scheduling requirements. An overall architecture of the system and critical implementation details about the agency and agents in the system are presented in this article. An energy saving job shop scheduling example is used to validate one autonomic property of the system.     

基于移动代理的无线传感器网络簇内融合算法

在无线传感器网络中采用移动代理技术进行数据融合。根据节点剩余能量、局部融合结果,设计节点分簇算法。优化移动代理在簇内的路由策略,进一步减少无线传感器网络的数据传输总量。通过实验得出,采用移动代理的簇内融合过程中,能在一定程度上减小路径损耗。     

A self-optimizing mobile network: Auto-tuning the network with firefly-synchronized agents

The reduction of cost and complexity is a key driver in the evolution of mobile networks. This reduction not only applies to the pre-operational state (e.g. the deployment phase of a new network), but to the operational state as well. During the operational state, self-optimization processes can be performed to reduce the operating expenses (OPEX) of telecommunications operators. In this paper, we propose an agent-based mechanism for auto-tuning mobile networks with the aim of achieving energy savings in access networks. Firefly-based synchronization is used for the coordination of mutually dependent software agents located at the operator’s base stations. Once synchronized, agents can act together to obtain a global goal – i.e. the reduction of the telecommunications operator’s costs without reducing the quality of the provided services for mobile users. These agents build a self-organized overlay network where neither centralized nor decentralized control is needed and, therefore, is more robust.