机会网络中能量有效的副本分布状态感知路由机制

针对机会网络中的节点能耗与消息扩散程度之间存在矛盾,基于节点能耗与副本状态感知的路由机制可以显著地改善资源受限场景的网络性能,综合考虑消息扩散程度与节点剩余能量,并结合节点相遇概率预测方法,提出能量有效的副本分布状态感知路由机制,为消息分布式地选择合理的中继节点。仿真结果表明,所提出的路由机制能够有效降低网络负载,并大幅改善消息投递率和平均时延性能。     

基于社会特征周期演化的机会移动网络路由转发策略

针对分布式k团社区检测引起的超大社区问题,提出了具有节点退出机制的?-window社区检测方法,相应提出了?-window中心性估计。通过实验发现?-window社区和?-window中心性具有周期演化特性,利用该特性,提出TTL(time to live)社区检测和TTL中心性估计,以更准确预测消息生存期上节点的相遇。随后,利用TTL社区和TTL中心性作为转发测度,设计了新的机会移动网络路由算法PerEvo。实验结果表明,与现有的基于社会特征的路由算法比较,PerEvo在保持基本不变的传输开销的同时,有效提高了机会移动网络消息投递的成功率。 关键词：机会移动网络;社区;中心性;周期演化;消息转发     

基于节点社会特征的机会网络最优发送策略

建立了基于节点社会特征的机会网络信息传输模型,使用庞特里亚金极大值定理求得最优发送策略,该策略服从阈值形式,设停止时间为h,当t < h时,节点以最大概率发送信息,当t > h时,节点停止发送信息。实验表明,该策略优于最优静态策略。进一步分析发现,节点的平均朋友数目越多,最优发送策略的停止时间越小,同时,其性能也越好。     

基于网络编码的机会网络高效路由算法

针对采用Epidemic机制的机会网络路由算法在数据分组传送阶段存在通信冗余的问题,提出了一种基于网络编码的高效路由算法--NCBER(network-coding-based epidemic routing）。NCBER在传送数据分组的过程中使用主动异或网络编码和多播,并取消了 Request（请求）控制分组,从而减少数据分组的转发次数和控制分组数量,降低网络开销,缩短分组传输时延。理论分析和仿真结果表明,NCBER 算法在网络开销和数据分组端到端时延性能方面优于经典的Epidemic路由算法及其改进算法MRRMR（message redundancy removal of multi-copy routing）,并且使数据传送成功率保持在100%。     

Message trust‐based secure multipath routing protocol for opportunistic networks

Opportunistic networks (OppNets) are composed of wireless nodes opportunistically communicating with each other. These networks are designed to operate in a challenging environment characterized by high delay, intermittent connectivity, and no guarantee of fixed path between the sender and the destination nodes. One of the most vital issues in designing and maintaining practical networks over a time period is the security of the messages flowing in OppNets. This paper proposes a new method called message trust‐based secure multipath routing protocol (MT‐SMRP) for opportunistic networks. Various routing protocols such as ProPHet, Epidemic, and HiBOp, to name a few, have been proposed for OppNets, but none of these have applied a secure multipath routing technique. The proposed MT‐SMRP scheme relays the message to the destination through the disjoint paths, each applying a soft‐encryption technique to prevent message fabrication attacks. Simulations are conducted using the Haggle Infocom'06 real mobility data traces, showing that when time‐to‐live is varied, (1) the proposed MT‐SMRP scheme outperforms D‐MUST by 18.10%, 7.55%, 3.275%, respectively, in terms of delivery probability, messages dropped, and average latency; (2) it also outperforms SHBPR by 21.30%, 7.44%, and 4.85%, respectively, in terms of delivery probability, messages dropped, and average latency. Under similar performance metrics, the performance of MT‐SMRP is also shown to be better than that of D‐MUST and SHBPR when the buffer size (respondents. the message generation interval) is varied.     

一种基于动态概率转发的AODV路由协议

在Ad hoc网络(自组织网络)的路由建立过程中,由于AODV(Ad hoc按需距离矢量)路由采用洪泛方式转发RREQ(路由请求)消息会产生很多不必要的重传,很可能引发广播风暴而影响网络性能。文章提出了一种基于动态概率转发RREQ消息的改进方案,该方案根据邻居节点的个数选择不同的概率转发RREQ消息。仿真结果表明,在网络节点较多且网络比较稠密的情况下,改进方案能有效减少网络端到端时延,提高网络吞吐量和分组投递率。     

MrFbP: Markov random field-driven spatial entropy-based routing protocol in opportunistic networks

Recently, research towards technologies associated with the 5G communication is in full operation. Amongst these enabling technologies, device-to-device(D2D) communication is one of the critical factors for scaling up the network efficiency in 5G communication. Intermittent connections due to higher mobility lead to frequent path breaks, and hence a mobility-based opportunistic routing is suitable enough to control the forwarding process. Opportunistic networks (OppNet) use the pairwise opportunistic contacts and higher mobility to rely on the store-carry and forward mechanism for routing purpose. In this work, a novel mobility-induced context-based routing process has been designed to support D2D communication. The designed Markov random field-induced protocol (MrFbP) is based on spatial entropy for capturing the coverage span of the forwarding node in the network. The work relies on the monitored historic mobility of a node and is then used to capture the utility metric for taking forwarding decision. MrFbP is compared against the established Direct Delivery (DD), Epidemic (EP), Spray & Wait (SW), and PropHet (PR) on parameters like throughput, delay, hops, overheads, and energy consumption. Simulation has been carried out using ONE simulator to validate an improvement in the design of a designed protocol against the baseline protocols.     

基于马尔可夫链的网络蠕虫传播模型

提出了网络蠕虫的随机传播模型。首先,基于马尔可夫链对于网络蠕虫进行了建模,并且讨论了模型的极限分布以及平稳分布的存在性。然后,讨论了网络蠕虫在传播初期灭绝的充要条件以及在传播后期灭绝的必要条件。最后,讨论了网络蠕虫的传播规模。仿真实验对于模型进行了验证,讨论了模型中传播参数,时间参数以及漏洞主机数等相关参数对于网络蠕虫传播的影响,并且与G-W模型进行了数据对比,说明了本模型的优势。     

Routing protocol based on social characteristics for opportunistic networks简

Opportunistic networks are derived from delay tolerant networks, where mobile nodes have no end-to-end connections. Nodes are represented by people, which means that opportunistic networks can be regarded as social networks. Human mobility plays an important role in affecting the performance of forwarding protocols in social networks, furthermore, the trajectory of people＇s movements are driven by social characteristics. However, current routing protocols rely on simple mobility models, and rarely consider social characteristics. Considering two heterogeneous network models, an social opportunistic networks routing（SONR） was proposed which brings an adapted discrete Markov chain into nodes＇ mobility model and calculates the transition probability between successive status. Comparison was made between Spray, Wait and Epidemic protocol. Simulation show that SONR can improve performance on delivery ratio, delivery latency and network overhead, meanwhile. SONR approaches the performance of Epidemic routing.     

An altruism‐based trust‐dependent message forwarding protocol for opportunistic networks

In opportunistic networks (OppNets), which are characterized by intermittent end‐to‐end connections, the messages are routed in a store‐carry‐and‐forward fashion using the locally inferred knowledge about the behavior of nodes. As such, most OppNets routing protocols use social metrics that are dependent on the nodes' past information. But the participation of nodes in the message forwarding process is not guaranteed without incentivizing them because most nodes are reluctant in sharing their private resources for public uses. In this paper, some socially derived psychological attributes of a node are introduced to ensure their trustworthy participation in the message forwarding process, leading to the design of an altruism‐dependent trust‐based data forwarding mechanism for OppNets (called ATDTN). In this protocol, each node is associated with a dynamically changing altruism value representing its trust in the network, which is used to determine its status with regard to its participation in message forwarding. Through trace‐driven simulations using the ONE simulator, it is shown that ATDTN outperforms IronMan and SimBet protocols for routing in OppNets (respectively, 18% and 48% improvement), in terms of delivery ratio, end‐to‐end delay, overhead count, and average number of hops, under varying buffer size and time‐to‐live.     

Continuous time Markov chain based website navigability measure

Website navigability is acquiring a growing importance in website design and redesign, quality evaluation, and improvement.Existing navigability measures mainly depend on site link structure, so that they only consider the impact of site link structure for navigability and ignore the impact of Web page content. A continuous Markov chain model which depicts the user's surfing behavior can balance these two factors in the evaluation of website navigability, and it needs to estimate the page transition probabilities and user stay time according to user access log. In this way, we can obtain more reliable results for website navigability measure than the existed methods. Experiments show that our method is effective.     

Multiattribute‐based routing for lifetime maximization in opportunistic mobile social networks

With the advancement in wireless technology and portable devices, smart phones have become one of the belongings of human being. People share their data online, but it has limitations due to transmission range and mobility. Yet opportunistic mobile social network enables users to share data online even if there is no connected path between source and destination. The widespread use of mobile phones equipped with WiFi, Bluetooth, and several other components and contact opportunities among humans bridge the gap between internet available and nonavailable area. In this paper, we have proposed a new routing approach which utilizes both spatial and temporal attributes of user such as probability to meet a particular location and remaining intercontact time between two nodes to select better relay nodes. Generally, users visit different locations such as sports stadium and mall, with varying probability. Users with similar interest form group, and each user has different intercontact time with other users according to their point of interest and visiting pattern. By utilizing multiattributes, different forwarding strategies have been devised for both inter and intragroup routing. The proposed work “point of interest (PoI)‐based routing” is implemented in Opportunistic Network Environment simulator, and the performance is analysed in terms of delivery rate, latency, overhead, goodput, and energy consumed. The simulation results show that PoI diminishes 23% of overhead and yields 24% improvement in goodput over the state‐of‐the‐art protocol. Thus, the simulation results reveal that our proposed work provides the balance between routing performance and resource consumption.     

Poisson's probability‐based Q‐Routing techniques for message forwarding in opportunistic networks

Opportunistic Networks (OppNets) are intermittently connected infrastructure less wireless networks. There is no continuous end‐to‐end connection between the sender and the receiver, and hence nodes follow a store‐carry‐forward mechanism. The routing algorithm is required to be adaptive to the changing topology of the network. In this work, Q‐Routing technique has been used with forwarding probability and incorporated using Poisson's probability for decision making and controlling transmission energy. The algorithm refines the forwarding decision of finding the next suitable hop by exploiting the characteristics of nodes such as daily routines, mobility pattern, etc. In simulations, the performance of PBQ‐Routing is compared with Q‐Routing, Epidemic Routing, PRoPHET (Probabilistic Routing Protocol using History of Encounters and Transitivity), and HBPR (History Based Prediction Routing) for OppNets. The use of Poisson's distribution improves the effectiveness of the probabilistic forwarding decision. The findings show that the delivery probability of PBQ‐Routing almost gets doubled and overhead ratio reduces to half in comparison with that of Q‐Routing when used in OppNets. PBQ‐Routing outperforms other Q‐Routing based algorithms over Poisson's distribution. As there is less replication in case of PBQ‐Routing, it also saves the transmission energy.     

泛洪攻击对Epidemic机制下机会网络生命期的影响

机会网络应用中存在能量无法补充的场景,泛洪是机会网络中容易发生的攻击行为。从理论上分析了在Epidemic路由机制下,泛洪攻击导致的节点能量消耗以及对网络生命期的影响。分析表明恶意节点数量的增加会对网络生命期产生显著影响,而恶意节点注入的数据分组的数量仅能在特定的场景下产生影响,且影响轻微。使用ONE仿真平台对泛洪攻击进行了仿真实验,仿真结果与理论分析的结论一致。     

基于相遇节点跨层感知的机会网络高效低时延路由算法

针对基于epidemic机制的机会网络路由算法未能及时感知相遇节点以及在数据分组交换过程中存在冗余的问题,提出了一种采用跨层感知相遇节点思路的机会网络高效低时延路由算法——ERCES（epidemic routing based on cross-layer encountered-node sensing）,通过在物理层、MAC层和网络层之间的跨层信息共享与协同,实现相遇节点及时感知,并且采用节点相遇后立即广播新数据分组、收到SV（summary vector）分组后优先发送目的节点为对方的数据分组、动态自适应发送HELLO分组、借助SV删除节点缓存中已到达目的节点的分组等新机制,减少控制和存储开销,降低分组时延。理论分析验证了ERCES算法的有效性,仿真结果表明：与经典的Epidemic Routing算法及其多个改进相比,ERCES算法的控制开销和存储开销分别减少8.2%和2.1%以上,数据分组平均端到端时延至少降低了11.3%。     

基于shell命令和Markov链模型的用户伪装攻击检测

提出一种新的基于shell命令的用户伪装攻击检测方法。该方法在训练阶段充分考虑了用户行为的多变性和伪装攻击的特点,采用平稳的齐次Markov链对合法用户的正常行为进行建模,根据shell命令的出现频率进行阶梯式数据归并来划分状态,同现有的Markov链方法相比大幅度减少了状态个数和转移概率矩阵的存储量,提高了泛化能力。针对检测实时性需求和shell命令操作的短时相关性,采用了基于频率优先的状态匹配方法,并通过对状态短序列的出现概率进行加窗平滑滤噪处理来计算判决值,能够有效减少系统计算开销,降低误报率。实验表明,该方法具有很高的检测准确率和较强的可操作性,特别适用于在线检测。     

基于免疫信息SV捎带的机会网络低开销路由算法

提出一种新的低开销路由算法——LRPI（low-overhead routing with piggybacked immunity information）,它包含以下4种新机制：基于SV（summary vector）捎带的免疫信息存储和传送,免疫信息跨层快速产生,改进的SV交换,SV自适应压缩。通过采用这些新机制,LRPI算法不仅不需要专门的数据结构来存发免疫信息,而且能够更快速地实现同样的免疫功能。理论分析和仿真结果表明,LRPI算法在控制开销、节点缓存分组数和数据分组端到端时延等方面的性能均优于Epidemic路由算法和它的多个改进版本。     

基于马尔柯夫链的地面防空兵兵力需求分析

构建了基于防空作战Markov链的多层防御系统射击效用计算模型,提出了平均防御总成本的概念,在考虑费效比的基础上,给出地面防空兵兵力需求的计算和优化方法,通过算例演示了用启发式算法求解此优化问题的过程,最后给出一点有益讨论。     

基于多方议价博弈的机会网络高吞吐量低开销概率路由算法

针对含自私节点的机会网络中现有基于议价博弈的概率路由算法存在的消息单向传递影响转发概率提高、依赖虚拟货币进行交易削弱节点购买力以及消息交易过程有冗余交互的问题,提出一种基于多方议价博弈的高吞吐量低开销概率路由算法——HLPR-MG。HLPR-MG算法通过将节点两两博弈扩展为多方博弈以加快消息转发概率的提升,引入“以物易物”方式增强节点购买力,并且改进现有交互机制以减少博弈次数,从而达到提高网络吞吐量的和减小控制开销的效果。理论分析验证了HLPR-MG算法设计的有效性。仿真结果表明,与基于议价博弈的现有典型路由算法GSCP和BG相比,HLPR-MG算法的吞吐量和消息传送成功率至少提高了3.63%,而控制开销和平均端到端消息时延则分别降低超17.76%和4.03%。     

A nodes scheduling model based on Markov chain prediction for big streaming data analysis

Streaming data analysis is an important part of big data processing. However, streaming data is difficult to be analyzed and processed in real time because of the rapid data arriving speed and huge size of data set in stream model. The paper proposes a nodes scheduling model based on Markov chain prediction for analyzing big streaming data in real time by following three steps: (i) construct data state transition graph using Markov chain to predict the varying trend of big streaming data; (ii) choose appropriate cloud computing nodes to process big streaming data depending on the predicted result of the data state transition graph; and (iii) assign big streaming data to these computing nodes using the load balancing theory, which ensures that all subtasks are accomplished synchronously. Experiments demonstrate that the proposed scheduling algorithm can fast process big streaming data effectively. Copyright © 2014 John Wiley & Sons, Ltd.