基于运动预测的水下传感器网络MAC协议

 在海洋环境的影响下,水下无线传感器网络(UWSN)节点始终处于运动状态;UWSN采用声信号通信,缓慢的节点移动也会造成UWSN现有的多址接入技术(MAC)协议冲突避免机制失效。该文建立了水下节点的运动模型,基于AR运动预测模型减小水下节点的时空不确定性对于MAC协议的影响,提高发送信息在预约时隙到达的概率。仿真结果表明,采用AR(5)预测可以减小74.8%的时延探测误差。提出了基于预测的预约MAC协议：P-MAC。NS-2仿真结果表明该协议在海浪运动场景下能提高收包成功率(PRR)10%-15%。     

基于扩展传染病模型和马尔可夫链的物联网可用度评估方法

为反映恶意程序传播环境下物联网可用的状态,基于扩展的SEIRD传染病模型和马尔可夫链提出一种物联网可用度评估方法。根据物联网节点的实际状态,扩展经典传染病模型SIR建立SEIRD物联网节点状态转换模型。由物联网节点各个状态之间的动态变化过程,构建物联网节点处于5种状态的概率动力学方程,得到反映各状态转换的马尔可夫矩阵,进一步得到物联网节点的可用度计算方法。以典型的星形和簇形物联网拓扑结构为例,给出整个物联网可用度的评估方法。通过实验,为管理员如何合理部署正常工作节点数、路由数提供建议。研究成果对提高物联网可用度、促进物联网成功应用具有理论指导意义。     

Contention aware mobility prediction routing for intermittently connected mobile networks

This paper introduces a novel multi-copy routing protocol, called predict and forward (PF), for delay tolerant networks, which aims to explore the possibility of using mobile nodes as message carriers for end-to-end delivery of the messages. With PF, the message forwarding decision is made by manipulating the probability distribution of future inter-contact and contact durations based on the network status, including wireless link condition and nodal buffer availability. In particular, PF is based on the observations that the node mobility behavior is semi-deterministic and could be predicted once there is sufficient mobility history information. We implemented the proposed protocol and compared it with a number of existing encounter-based routing approaches in terms of delivery delay, delivery ratio, and the number of transmissions required for message delivery. The simulation results show that PF outperforms all the counterpart multi-copy encounter-based routing protocols considered in the study.     

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.     

Finite automaton intrusion tolerance system model based on Markov

To ensure that the system could still provide normal service for legitimate users when the LAN being invaded,a finite automaton intrusion tolerance model was designed.Based on Markov’s theory,the state transformation relationship of the service provided by the system by establishing the state transition probability matrix was described,quantified the transition state and found the key nodes in the system.The maintenance of key nodes could enhance the tolerance of the system and improve the reliability of the service.Experimental comparison shows that this model not only has strong intrusion tolerance,but also has obvious advantages in the integrity of the security system when it is invaded.     

Lightweight opportunistic routing forwarding strategy based on Markov chain

A lightweight opportunistic routing forwarding strategy (MOR) was proposed based on Markov chain.In the scheme,the execute process of network was divided into a plurality of equal time period,and the random encounter state of node in each time period was represented by activity degree.The state sequence of a plurality of continuous time period constitutes a discrete Markov chain.The activity degree of encounter node was estimated by Markov model to predict its state of future time period,which can enhance the accuracy of activity degree estimation.Then,the method of comprehensive evaluating forwarding utility was designed based on the activity degree of node and the average encounter interval.MOR used the utility of node for making a routing forwarding decision.Each node only maintained a state of last time period and a state transition probability matrix,and a vector recording the average encounter interval of nodes.So,the routing forwarding decision algorithm was simple and efficient,low time and space complexity.Furthermore,the method was proposed to set optimal number of the message copy based on multiple factors,which can effectively balance the utilization of network resources.Results show that compared with existing algorithms,MOR algorithm can effectively increase the delivery ratio and reduce the delivery delay,and lower routing overhead ratio.     

带有节点状态估计的间断连接无线网络缓存管理策略

针对间断连接无线网络中的节点缓存资源有限的问题,该文提出一种适用于间断连接无线网络的缓存管理机制。根据运动过程中所获得的网络状态信息,各个节点以分布式的方式估计给定节点与其他节点直接及间接连接状态、节点服务率以及节点连通强度,动态感知各个节点服务能力的差异,同时预测当前节点成功投递该消息的概率以感知消息的效用值,从而执行缓存管理操作。结果表明,与其他缓存管理机制相比,所提出的缓存管理机制不仅能够有效降低投递开销,同时大幅度地提高了消息成功投递率。     

Stochastic Properties of Mobility Models in Mobile Ad Hoc Networks

The stochastic model assumed to govern the mobility of nodes in a mobile ad hoc network has been shown to significantly affect the network's coverage, maximum throughput, and achievable throughput-delay trade-offs. In this paper, we compare several mobility models, including the random walk, random waypoint, and Manhattan models on the basis of the number of states visited in a fixed time, the time to visit every state in a region, and the effect of the number of wandering nodes on the time to first enter a set of states. These metrics for a mobility model are useful for assessing the achievable event detection rates in surveillance applications where wireless-sensor-equipped vehicles are used to detect events of interest in a city. We also consider mobility models based on Correlated Random Walks, which can account for time dependency, geographical restrictions, and nonzero drift. We demonstrate that these models are analytically tractable by using a matrix-analytic approach to derive new, closed-form results in both the time and transform-domains for the probability that a node is at any location at any time for both semi-infinite and finite 1D lattices. We also derive first entrance time distributions for these walks. We find that a correlated random walk 1) covers more ground in a given amount of time and takes a smaller amount of time to cover an area completely than a random walk with the same average transition rate, 2) has a smaller first entrance time to small sets of states than the random waypoint and random walk models, and 3) leads to a uniform distribution of nodes (except at the boundaries) in steady state.     

Network reliability with arc failures and repairs

Arc repair probabilities are incorporated into network calculations for directed networks with independent arc failures. A discrete-time Markov chain with one absorbing state is constructed for the problem. The transition probability matrix is used to determine the probability of source-to-sink conductivity in a given time interval, the mean time to source-to-sink connectivity, the fraction of time a node is blocked, and the fraction of time the network is blocked (disconnected). Blockage probabilities aid in repair-crew allocation to the nodes of the network     

一种面向高速路车联网场景的自适应路由方法

车载自组织网络中节点的高速移动性使得网络拓扑频繁变化,造成路由效率低下.本文提出了一种面向高速路车联网场景的自适应路由方法.本方法采用了贪婪机会转发（GOF）算法,在选择下一跳转发节点时,同时考虑到目的节点的距离计算、节点间的链路状态以及下一跳的有效节点度状况来找出最优转发节点,并提出新的计算连通概率的方法.仿真实验和实际道路场景的测试表明,与相关算法相比在路由稳定性方面表现出较好的效果.