移动社会网络中基于多维上下文匹配的数据转发算法

通过研究移动社会网络中的多种上下文信息对节点移动模式的影响,提出了基于多维上下文认知的数据转发算法MCMF。该算法综合考虑物理邻接性、社会相似性以及社会交互性3个维度的上下文信息来进行动态数据转发决策。首先消息携带者节点通过物理邻接匹配获得邻居节点集合;然后通过社会相似性匹配在邻居节点集合中选出候选节点子集,并基于社会网络的社群特征,采用马尔可夫预测方法在候选节点子集中选出最优中继节点;最后设计高效的数据转发算法。仿真实验表明,相比于其他3种著名算法,该算法在交付比率和开销比率方面具有较好的性能。     

基于模糊身份密码学的机会网络身份认证方案

针对机会网络的自组织性、开放性、连通性差等特点以及现有的基于上下文的路由协议中可能存在的隐私泄露等安全问题,提出了一种基于模糊身份密码学的身份认证方案F-ONIAS(Identity Authentication Scheme in Opportunistic Network Based on Fuzzy-IBE)。该方案通过一个无需实时在线的PKG为用户颁发私钥来解决机会网络中因节点无法实时连通而导致的传统非对称密码学方案不适用的问题。同时,将节点的生物信息作为身份标识,避免了传统身份密码学中身份信息可能被伪造而带来的安全隐患。仿真实验表明,在存在恶意节点的网络环境下,本方案比现有的经典路由协议方案拥有更高的报文投递率和更低的路由开销率,并且未对报文平均时延造成明显影响。     

基于机会网络的消息传输系统

机会网络不需要源节点和目标节点之间存在完整链路,利用节点移动带来的相遇机会实现通信,是目前的研究热点之一。设计了一种基于机会网络的信息采集与消息传输系统BlueChat。一方面,系统采集节点相遇信息,构建路由信息表,并且可以作为机会网络中节点移动轨迹建模、社团划分、消息转发策略研究、网络QOS研究等的原始数据;另一方面,系统可以采用不同的消息队列管理策略,通过节点的移动,以“存储-携带-转发”的路由模式实现节点间消息的传输。通过对系统进行连续3个月,50个以上节点,长时间、大规模的测试,并分析采集到的数据,得出消息交付率在95%以上,满足特殊情况下的节点通信需求。     

移动社会网络中基于社区的消息机会传输策略

移动社会网络是一种由大量具有社会特征的节点组成的机会网络.已有的基于社区的路由算法大多选用社会性最优的节点参与转发,而没有考虑到社区分布对节点移动的影响,将这些算法直接用于移动社会网络中会导致网络资源消耗高、传输成功率低等问题.针对这些问题,提出一种基于社区的消息机会传输算法,在社区间根据节点到目标社区的传输概率选择社区间的最优传输路径,在社区内选择与目标节点相遇概率较高的节点完成社区内传输.仿真实验结果表明,在移动社会网络中,该算法与 Prophet,Spray and Wait 等经典算法相比,提高了消息传输成功率,降低了网络开销.     

面向机会社会网络的服务广告分发机制

提出面向机会社会网络的服务广告分发机制,解决移动网络环境中由于节点移动性、拓扑动态性引起的集中式服务注册库失效的问题.首先分析机会社会网络中服务表现出的社会特征,提出服务社会上下文及其参数度量方法;然后提出基于社会上下文的服务广告分发机制,该机制根据服务行业相关度判断用户对服务的感兴趣程度,以确定广告目标节点,根据可靠度和活跃度计算节点效用,并根据行业时空共存关系预测节点和服务行业相遇概率,以动态选择服务广告代理;最后在社区移动模型下进行了仿真实验,验证了算法的正确性和有效性.     

机会网络中的安全与信任技术研究进展

机会网络是一种通信连接经常中断的移动自组织网络,是利用节点移动形成的通信机会逐跳传输消息,以“存储-携带-转发”的路由模式实现节点间通信,然而机会网络作为移动自组织网络的极端版本,其特有的节点高移动性、网络链路经常变化和延迟容忍特性,对数据的机密性和完整性、路由安全性与隐私性以及节点认证与合作性等提出了更高的要求与挑战.首先系统地描述了机会网络的安全威胁与安全需求,深入分析了机会网络中的安全路由及其隐私保护机制;其次研究了机会网络节点认证及其合作激励机制,并对各种相关安全与信任技术解决方案进行了综合比较分析;最后对未来进一步的研究工作作出了展望.     

基于机会网络中社交关系的转发机制

由于现有的机会网络转发机制不能完全适应社会网络中基于社交关系的判断与分析,为此通过建立社交关系的分类,构建机会网络中的社会关系树结构模型,提出了一种新的转发机制。该机制设置了节点权重值,由节点所处的层次和社会关系的可利用性及可靠性决定。通过路径权重值,选择最有价值的路径进行消息的传递。通过仿真实验,所提出的转发机制有效地降低了网络中的消息副本数和降低了路由开销。     

基三分层网络中的受限多播路由算法

多播路由算法对互连网络的通信性能和多处理机系统性能的发挥起着重要作用。针对基三分层互连网络，在权衡性能、成本和实现的基础上，提出一种基于树的受限多播路由算法TRMA。该算法充分利用基三分层互连网络的层次特性和节点编码中所含的网络拓扑信息实现消息路由，算法设计简单，易于硬件实现。和其他基于树的多播路由算法相比，TRMA算法不需要源节点在发送消息前构建多播树,并将多播树的信息存放在消息中，大大降低了源节点的工作负载，提高整个系统的性能。通过仿真比较了TRMA和基于单播的多播路由算法，结果表明TRMA具有较低的网络延迟和较小的网络流量。     

基于上下文信息匹配的消息传输算法的安全策略

在不影响消息传输算法自身传输效率的情况下,如何在不可信任的机会网络节点环境中,保护可能暴露的上下文信息一直是业界研究的重点。提出了借助一种基于上下文信息的、可搜索的对称密码算法,来对数据传输进行加密。该算法将包含目的节点关键信息的关键字转换成为可搜索、可加密的消息头,将加密算法与信息数据的关键字进行优化配对,这样既能够向目的节点进行安全可靠的数据传输,亦可在数据传输过程中对其进行加密处理,以保证其安全性。基于ONE仿真软件进行模拟实验,本文的结果表明,利用对称密码学实施安全策略更符合机会网络节点特征。本文采用的安全策略并不会对其数据到达率及消息延迟产生较大的影戏;本文所建立的算法较之现有已报道的算法,具有较高的数据包转发成功率以及较小的消息延迟率,二者可以达到平衡。     

机会社会网络中基于社区的消息传输算法

针对机会网络中的主流转发协议未考虑到节点移动社会性的问题,提出一种基于副本限制和人类社会特性的路由算法。该算法根据节点间的社会关系强度,动态自适应地将节点划分为不同的社区,通过限制消息副本数来减少网络中消息的冗余,并利用活跃性高的节点带动消息的转发和传递。仿真结果表明,该协议能够保证较高的消息传输成功率和相对较低的传输时延,降低网络的资源消耗。     

移动机会网络组播路由的研究进展

在移动机会网络中,节点进行机会式的连接通信,该方式被广泛应用于移动社交网络、车载智能网络、移动自组网络等场景中。移动机会网络群组通信在抢险救灾、社区消息分发及智能交通等领域具有极高的应用价值,组播路由技术是其关键技术之一。将移动机会网络组播路由分为传统组播路由和智能组播路由两类,重点介绍了智能组播路由的研究现状,并采用通用的性能指标对各组播路由策略进行了比较分析。分析结论为:智能组播路由由于考虑了移动机会网络的社会特性、节点缓存、能量及安全等特性,因此具有较好的综合性能。最后展望了组播路由在大数据缓存管理、群组安全策略、车载自组网及动态感知方面的研究前景。     

A direction-based geographic routing scheme for intermittently connected mobile networks

In an intermittently connected mobile network, a complete routing path from a source to a destination cannot be guaranteed most of the time. Therefore, traditional routing methods for mobile ad hoc networks are not applicable in such a network. Current approaches for intermittently connected mobile networks are primarily based on redundant transmission and single-copy opportunistic routing. However, they incur either high overhead due to excessive transmissions, or long delay due to incorrect path choices during forwarding. In this paper, we propose a direction-based geographic (DIG) routing scheme for intermittently connected mobile networks. Relying on geographic location information, the packets are routed in a path approximately to the shortest path from the source node to the destination, which significantly reduces the overhead in redundant transmission and decreases the transmission delay in the single-copy opportunistic routing. Theoretical analysis and trace-driven experimental results show that DIG provides low transmission delay with low overhead in comparison with the schemes in the redundant transmission and single-copy opportunistic routing.     

ML-SOR: Message routing using multi-layer social networks in opportunistic communications

Opportunistic networks are a generalization of DTNs in which disconnections are frequent and encounter patterns between mobile devices are unpredictable. In such scenarios, message routing is a fundamental issue. Social-based routing protocols usually exploit the social information extracted from the history of encounters between mobile devices to find an appropriate message relay. Protocols based on encounter history, however, take time to build up a knowledge database from which to take routing decisions. While contact information changes constantly and it takes time to identify strong social ties, other types of ties remain rather stable and could be exploited to augment available partial contact information. In this paper, we start defining a multi-layer social network model combining the social network detected through encounters with other social networks and investigate the relationship between these social network layers in terms of node centrality, community structure, tie strength and link prediction. The purpose of this analysis is to better understand user behavior in a multi-layered complex network combining online and offline social relationships. Then, we propose a novel opportunistic routing approach ML-SOR (Multi-layer Social Network based Routing) which extracts social network information from such a model to perform routing decisions. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. Trace driven simulations show that a routing metric combining social information extracted from multiple social network layers allows users to achieve good routing performance with low overhead cost.     

Routing with multi-level cross-community social groups in mobile opportunistic networks

Mobile opportunistic networks (MONs) are intermittently connected networks, such as pocket switched networks formed by human-carried mobile devices. Routing in MONs is very challenging as it must handle network partitioning, long delays, and dynamic topology. Flooding is a possible solution but with high costs. Most existing routing methods for MONs avoid the costly flooding by selecting one or multiple relays to deliver data during each encounter. How to pick the “good” relay from all encounters is a non-trivial task. To achieve efficient delivery of messages at low costs, in this paper, we propose a novel group-based routing protocol in which the relay node is selected based on multi-level cross-community social group information. We apply a simple group formation method to both historical encounters (social relationships in physical world) and/or social profiles of mobile users (social relationships in social world) and build multi-level cross-community social groups, which summarize the wide range of social relationships among all mobile participants. Our simulations over several real-life data sets demonstrate the efficiency and effectiveness of the proposed method by comparing it with several existing MON routing schemes.     

Exploiting users’ social relations to forward data in opportunistic networks: The HiBOp solution

Opportunistic networks, in which nodes opportunistically exploit any pair-wise contact to identify next hops towards the destination, are one of the most interesting technologies to support the pervasive networking vision. Opportunistic networks allow content sharing between mobile users without requiring any pre-existing Internet infrastructure, and tolerate partitions, long disconnections, and topology instability in general. In this paper we propose a context-aware framework for routing and forwarding in opportunistic networks. The framework is general, and able to host various flavors of context-aware routing. In this work we also present a particular protocol, HiBOp, which, by exploiting the framework, learns and represents through context information, the users’ behavior and their social relations, and uses this knowledge to drive the forwarding process. The comparison of HiBOp with reference to alternative solutions shows that a context-aware approach based on users’ social relations turns out to be a very efficient solution for forwarding in opportunistic networks. We show performance improvements over the reference solutions both in terms of resource utilization and in terms of user perceived QoS.     

Evaluating opportunistic networks in disaster scenarios

Forwarding data in scenarios where devices have sporadic connectivity is a challenge. An example scenario is a disaster area, where forwarding information generated in the incident location, like victims' medical data, to a coordination point is critical for quick, accurate and coordinated intervention. New applications are being developed based on mobile devices and wireless opportunistic networks as a solution to destroyed or overused communication networks. But the performance of opportunistic routing methods applied to emergency scenarios is unknown today. In this paper, we compare and contrast the efficiency of the most significant opportunistic routing protocols through simulations in realistic disaster scenarios in order to show how the different characteristics of an emergency scenario impact in the behaviour of each one of them.     

一种基于混合社区的移动机会网络数据传输机制

由人携带或使用智能设备组成的移动机会网络采用"存储-携带-转发"的通信机制,通过中继转发来实现节点之间的数据传递。为了改善网络性能,往往需要借助节点的社会属性来进行中继选择和制定转发策略。然而,在已有的传输机制中,往往只是利用节点的部分社会属性,不能完全反映出节点之间的社会关系。为此,提出了一种新的基于混合社区的数据传输机制,并给出了混合社区的内涵、混合社区的构建方法和基于混合社区的数据传输算法。基于几种真实轨迹数据集的实验结果表明,与已有的几种经典路由算法相比,该方法传输成功率较高,传输延迟较短,性能更好。     

稀疏机会网络中基于固定中继节点与消息相关性的缓存管理策略

针对稀疏机会网络中固定中继节点和移动节点间的相遇频率以及缓存资源的差异性问题,在多副本路由协议中,提出一种结合固定中继节点重要性和消息相关性的缓存管理策略。该策略通过交互固定节点和移动节点的接触信息和消息队列信息,减少固定中继节点缓存中冗余消息的数量,从而合理地利用固定中继节点的缓存资源。仿真结果表明,所提的缓存管理策略能够在保证消息传输成功率的基础上提高固定中继节点缓存空间的利用率。     

一种基于区域朋友关系的机会路由算法

基于社会的机会网络中,节点趋向于和自己关系亲密的节点进行接触,因此可以通过评价节点之间的亲密程度来帮助预测节点之间的接触概率。由于节点亲密性的时间相关性和区域相关性,利用节点的历史接触信息构造节点亲密程度的评价模型时,考虑节点接触区域和节点接触时间对亲密程度的影响,从而得到节点在相应区域的亲密节点——区域朋友(Regional Friends,RF)。最后结合节点当前位置和区域朋友关系提出一种基于区域朋友关系的机会路由算法(Opportunistic Routing algorithm based on Regional Friendship,ORRF)。实验数据表明,在节点分布密度以及数据可容忍时延变化的网络环境下,该算法得到较高的数据送达率和较低的转发能耗。