A Light-Weight Opportunistic Forwarding Protocol with Optimized Preamble Length for Low-Duty-Cycle Wireless Sensor Networks

In wireless sensor networks, sensed information is expected to be reliably and timely delivered to a sink in an ad-hoc way. However, it is challenging to achieve this goal because of the highly dynamic topology induced from asynchronous duty cycles and temporally and spatially varying link quality among nodes. Currently some opportunistic forwarding protocols have been proposed to address the challenge. However, they involve complicated mechanisms to determine the best forwarder at each hop, which incurs heavy overheads for the resource-constrained nodes. In this paper, we propose a light-weight opportunistic forwarding (LWOF) scheme. Different from other recently proposed opportunistic forwarding schemes, LWOF employs neither historical network information nor a contention process to select a forwarder prior to data transmissions. It confines forwarding candidates to an optimized area, and takes advantage of the preamble in low-power-listening (LPL) MAC protocols and dual-channel communication to forward a packet to a unique downstream node towards the sink with a high probability, without making a forwarding decision prior to data transmission. Under LWOF, we optimize LPL MAC protocol to have a shortened preamble (LWMAC), based on a theoretical analysis on the relationship among preamble length, delivery probability at each hop, node density and sleep duration. Simulation results show that LWOF, along with LWMAC, can achieve relatively good performance in terms of delivery reliability and latency, as a receiver-based opportunistic forwarding protocol, while reducing energy consumption per packet by at least twice.     

一种基于节点位置余弦相似度的机会网络转发算法

针对机会网络中数据送达率较低的问题,文中根据节点历史接触信息即节点相遇次数、相遇时间长度、节点关系稳定性来计算节点转发效用值。首先选择通信范围内效用值最大的邻居节点作为初始转发节点,再根据余弦相似度选择其他转发节点,使得转发节点能够尽可能均匀地分布。在此基础上,提出一种基于节点位置余弦相似度的机会网络转发算法(Opportunistic Network forwarding algorithm based on Node Cosine Similarity,ONNCS)。该算法使得转发节点能够均匀地分布,因此数据报文能够尽快地被转发到目的节点。实验结果表明,ONNCS具有较高的转发成功率和较低的转发能耗,转发成功率高出其他算法5%～8%。     

实时响应物联网中基于查询的数据转发方案

在许多基于传感器网络技术的物联网应用中,用户需要快速的查询响应,比如智能交通物联网应用中,行驶在路上的司机即时查询附近的空停车位信息.如何为此类物联网设计一种符合传感器网络特性(如能量有效等)的快速数据转发方案是一项重要的挑战性工作.已有的传感器网络实时数据转发协议大都因未解决好转发断路带来的额外开销、孤立节点处理耗时、难以适应网络拓扑动态变化等关键性问题而未取得理想的实时性效果.为此,该文提出一种新的基于查询的快速数据转发方案,利用查询消息为每个传感器节点建立最快速的数据转发路径(有向无环图),此外文中给出的综合路径代价模型可以均衡网络能量和减少网络拥塞延时,最后设计了贪婪的分布式数据转发算法及其改进算法,并用仿真实验验证了该方案的有效性和高效性.     

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.     

Autonomic Application-Level Message Delivery Using Virtual Magnetic Fields

Mobility has emerged as an important new trend in the arena of distributed computing bringing along deep social and technical transformations. These changes have created an increasing demand for applications and middleware that exploit and support mobility. This article proposes the use of the concept of magnetic fields borrowed from physics to enable self-organizing message forwarding on overlay networks over physical networks with possibly mobile nodes. Though autonomic, a network that employs the proposed message forwarding mechanism still needs a cognitive management to accomplish end-to-end goals. After formalizing the generic model of virtual magnetic fields, a distributed self-stabilizing propagation algorithm is defined and applied to the load-balancing problem. Simulation results are then discussed and implementation issues are detailed considering extensions of existing platforms. At last, conclusions are drawn and the main future works are listed.     

基于委托转发技术的延迟容忍网络组播路由算法*

针对延迟容忍网络中的组播路由问题,提出了一种基于委托转发技术的组播路由算法。该算法是在详细分析组播路由设计需求的基础上,结合延迟容忍网络中节点移动特性,对委托转发技术中节点属性值和节点对转发标准进行重新设计。其节点属性值是面向组播会话的,节点对转发标准是动态适应网络状态的。仿真结果表明,相比于其他基于复制方式的组播路由算法,该算法具有更好的性能,尤其是在对网络开销的控制方面,因此,更适用于延迟容忍网络。     

降低Ad hoc网络信息泄露的路由算法

分析了Ad-hoc网络信息传输过程中信息泄露的途径和原因,提出了Ad-hoc网络信息泄露模型,设计了一种降低信息泄露的路由算法RARIL。该算法在加权图模型的基础上,加入节点位置信息和身份认证,减少组外节点和组内非信任节点窃听信息,优先信任节点转发信息,降低信息泄露概率。通过计算非信任节点信息泄露概率,选择信息泄露概率最小的节点作为转发节点,组建可控转发节点集合,保证集合中转发节点的信息泄露概率最小。最后,根据算法设计约束条件,以算法性能的主要影响因素设定算法评估指标,通过仿真比较路由算法在降低信息泄露方面的优越性。     

传感器网络基于两跳邻居信息的实时路由设计

提出了无线传感器网络基于两跳邻居信息的实时路由.包的截止期要求映射为速率要求的路由决策方法在SPEED 中首先提出,而本文提出的路由决策是基于两跳速率.如果没有节点能够满足给定速率,采用节能的概率丢包策略降低截止期错失率,可以提高能效.如果截止期松弛,嵌入平衡能耗的代价函数缓解某些节点被频繁选为转发节点的情况,以达到网络的能耗平衡.在仿真中模拟了物理层和MAC 层,采用了通过Mica2 Motes 实验得到的损耗链路模型.仿真结果显示,提出的路由方案比基于单跳信息的SPEED-S 路由的截止期错失率更低,能量效率更高,而且在端到端延迟的性能总体上没有降低.该设计可用于对服务质量要求较高的基于传感器网络的实时应用中.     

一种面向机会网络路由的最优停止决策方法

投递延迟是机会网络的一个重要指标,给定节点缓存和消息副本数目限制,如何选择合适的节点复制消息成为一个关键问题.提出一种基于最优停止理论的路由决策方法（OSDR）.OSDR 将每个时隙上所遇节点和目标节点的平均相遇时间看做一个随机变量,根据该随机变量的统计特性得到一个停止观察、复制消息的规则,该规则呈现简单的阈值结构,即当某个时隙上所遇节点和目标节点的平均相遇时间小于给定阈值时即复制消息. OSDR 可以在较小的相遇间隔和等待成本之间进行折衷,实现数学期望意义上的最小消息投递延迟.介绍了OSDR 的网络模型、最优停止规则的存在性证明过程以及计算方法.模拟实验结果表明,OSDR 相对其他方法,在投递成功率、投递延迟等方面具有明显优势.     

Integration of sensing and computing in an intelligent decision support system for homeland security defense

We propose an intelligent decision support system based on sensor and computer networks that incorporates various component techniques for sensor deployment, data routing, distributed computing, and information fusion. The integrated system is deployed in a distributed environment composed of both wireless sensor networks for data collection and wired computer networks for data processing in support of homeland security defense. We present the system framework and formulate the analytical problems and develop approximate or exact solutions for the subtasks: (i) sensor deployment strategy based on a two-dimensional genetic algorithm to achieve maximum coverage with cost constraints; (ii) data routing scheme to achieve maximum signal strength with minimum path loss, high energy efficiency, and effective fault tolerance; (iii) network mapping method to assign computing modules to network nodes for high-performance distributed data processing; and (iv) binary decision fusion rule that derive threshold bounds to improve system hit rate and false alarm rate. These component solutions are implemented and evaluated through either experiments or simulations in various application scenarios. The extensive results demonstrate that these component solutions imbue the integrated system with the desirable and useful quality of intelligence in decision making.     

面向IPv6物联子网的轻量级树型转发模型

在IPv6 物联网中,RPL 路由模型已得到广泛的认可.然而对于规模较大的多跳网络结构,RPL 面临着部分转发节点路由容量较大的问题.而且物联子网中扁平化的地址结构使得这一问题更为突出.设计了支持IPv6 地址自动分配的轻量级树型转发模型TFAD（tree forwarding model with address automatically distributed）,将物联子网中的节点构造成一棵层次转发树,树节点的IPv6 地址在子树范围内高度聚合.各节点只需存储与其子节点数相当的转发项,即可完成TFAD 模型的数据转发.此外,设计了TFAD 模型的备份父节点机制,当网络出现故障时能够以子树为单位进行网络拓扑重构,实现物联子网的快速路由恢复.实验验证了TFAD 模型的高效路由存储性能以及快速的路由学习能力和故障后路由恢复能力.     

基于移动医疗大数据平台下深度最优匹配算法的机会网络转发机制

通过分析在移动医疗大数据平台下,机会网络中节点传递信息方式的特点,遍历所有邻接节点,对两节点的数据进行比较,通过最优匹配方式,选择匹配结果最优的邻接节点作为下一跳的节点,从而找出一条使数据高效转发的路径。根据此过程,提出一种基于移动医疗大数据平台下深度最优匹配算法的机会网络转发机制,即DOM算法,用来匹配节点中的数据分组,从而选择一条数据高效转发的路径。通过与机会网络中的经典算法比较,表明DOM算法能够在数据传播的过程中减少冗余数据并且显著提高传输成功率。     

Fairness-related challenges in mobile opportunistic networking

The fundamental challenge in opportunistic networking, regardless of the application, is when and how to forward a message. Rank-based forwarding techniques currently represent one of the most promising methods for addressing this message forwarding challenge. While these techniques have demonstrated great efficiency in performance, they do not address the rising concern of fairness amongst various nodes in the network. Higher ranked nodes typically carry the largest burden in delivering messages, which creates a high potential of dissatisfaction amongst them. In this paper, we adopt a real-trace driven approach to study and analyze the trade-offs between efficiency, cost, and fairness of rank-based forwarding techniques in mobile opportunistic networks.Our work comprises three major contributions. First, we quantitatively analyze the trade-off between fair and efficient environments. Second, we demonstrate how fairness coupled with efficiency can be achieved based on real mobility traces. Third, we propose FOG, a real-time distributed framework to ensure efficiency–fairness trade-off using local information. Our framework, FOG, enables state-of-the-art rank-based opportunistic forwarding algorithms to ensure a better fairness–efficiency trade-off while maintaining a low overhead. Within FOG, we implement two real-time distributed fairness algorithms; Proximity Fairness Algorithm (PFA), and Message Context Fairness Algorithm (MCFA). Our data-driven experiments and analysis show that mobile opportunistic communication between users may fail with the absence of fairness in participating high-ranked nodes, and an absolute fair treatment of all users yields inefficient communication performance. Finally our analysis shows that FOG-based algorithms ensure relative equality in the distribution of resource usage among neighbor nodes while keeping the success rate and cost performance near optimal.     

移动自组网自私行为闭环惩罚模型设计

针对移动自组网MAC层开环模型缺乏自私行为惩罚机制的不足,设计一种包括报文信息统计、统计信息共享和判决惩罚模块的闭环改进协议PDCF。报文信息统计定义转发率、丢弃度等指标衡量节点行为,修改仿真软件内置函数实现节点行为信息的统计。统计信息共享定义行为量化等级,改进DCF预留字段和CSMA/CA机制,实现节点行为信息的全网覆盖。判决惩罚模块对节点行为建立一维马尔科夫链惩罚模型,限制节点自私行为。仿真结果表明,与DCF相比,PDCF时延、丢包率和控制开销基本不变,但稳态时自私节点比例得到有效控制,且吞吐量增加2.7Mbps,网络性能得到改善。     

A Distributed and Efficient Flooding Scheme Using 1-Hop Information in Mobile Ad Hoc Networks

Flooding is one of the most fundamental operations in mobile ad hoc networks. Traditional implementation of flooding suffers from the problems of excessive redundancy of messages, resource contention, and signal collision. This causes high protocol overhead and interference with the existing traffic in the networks. Some efficient flooding algorithms were proposed to avoid these problems. However, these algorithms either perform poorly in reducing redundant transmissions or require each node to maintain 2-hop (or more) neighbors information. In the paper, we study the sufficient and necessary condition of 100 percent deliverability for flooding schemes that are based on only 1-hop neighbors information. We further propose an efficient flooding algorithm that achieves the local optimality in two senses: 1) the number of forwarding nodes in each step is minimal and 2) the time complexity for computing forwarding nodes is the lowest, which is O(nlogn), where n is the number of neighbors of a node. Extensive simulations have been conducted and simulation results have shown the excellent performance of our algorithm     

基于最优连通功率控制的WSNs跨层路由优化算法

针对非连通区域节点空洞效应和热点区域节点间通信干扰导致的路由服务质量（QoS）下降问题,提出了一种基于最优连通功率控制的无线传感器网络（WSNs）跨层路由优化算法。算法采用自适应最优连通功率控制策略,在避免路由空洞产生和保证网络连通性条件下,降低热点区域节点数据转发竞争干扰;通过位置信息、剩余能量和干扰等级的跨层信息交互,动态选取最优转发节点,提高网络整体性能。仿真实验表明：算法能够提高路由（QoS）、优化网络生命周期和降低热点区域通信干扰。     

Dynamic Cesaro-Wardrop equilibration in networks

We analyze a routing scheme for a broad class of networks which converges (in the Cesaro sense) with probability one to the set of approximate Cesaro-Wardrop equilibria, an extension of the notion of a Wardrop equilibrium. The network model allows for wireline networks where delays are caused by flows on links, as well as wireless networks, a primary motivation for us, where delays are caused by other flows in the vicinity of nodes. The routing algorithm is distributed, using only the local information about observed delays by the nodes, and is moreover impervious to clock offsets at nodes. The scheme is also fully asynchronous, since different iterates have their own counters and the orders of packets and their acknowledgments may be scrambled. Finally, the scheme is adaptive to the traffic patterns in the network. The demonstration of convergence in a fully dynamic context involves the treatment of two-time scale distributed asynchronous stochastic iterations. Using an ordinary differential equation approach, the invariant measures are identified. Due to a randomization feature in the algorithm, a direct stochastic analysis shows that the algorithm avoids non-Wardrop equilibria. Finally, some comments on the existence, uniqueness, stability, and other properties of Wardrop equilibria are made.     

Multivariate context collection in mobile sensor networks

We focus on the treatment of quality-stamped contextual information in mobile sensor networks. Sensing nodes capture and forward context for consumption by mobile context aware applications. Due to the dynamic network topology the quality indicators seen by consumers vary over time. Context quality is a decreasing function of time and context can be consumed with a certain delay from its capturing time. We propose the sequential assessment of the network-circulated context information according to the Generalized Secretary Problem, a known paradigm in the Optimal Stopping Theory. The consumer node delays the processing (consumption) of incoming context until better quality is attained. We extend this basic model to include the cardinality of contextual components (i.e., different types of measurements coming from, possibly, different sources). Hence, the consumer node is interested not only in the higher possible quality of context but also in the widest possible range of context parameters (context “quantity”). We compare our findings to simple consumption strategies and pinpoint the advantages of the proposed model.     

基于Shapley值的自组网合作机制分析

在无线移动自组网中,每个节点都独立进行决定,不可能完全自发合作。针对该问题,基于移动自组网的特殊性,将节点间的信息传递看作二人博弈,提出一个约束协议,使节点只有在合作时才能到达各自的收益最大,且使网络性能最优。同时分析网络传输失败原因是网络噪声干扰还是参与者的恶意行为。     

基于异或运算的机会网络高效转发策略

通过对机会网络中节点传递信息的方式进行研究分析,遍历可以通信的邻居节点,将两节点的信息作比较。通过交集的形式,选择节点中携带信息异或程度最大的邻居节点作为下一跳进行信息传递,从而形成一条有效性最大的通信路径。基于这样的分析过程,提出了一种基于异或运算的机会网络高效转发策略FSXO。通过与机会网络中的经典算法对比,仿真结果表明,FSXO策略能够在高传输成功率的情况下,减少网络中无效数据副本的存在,从而有效地降低路由开销,减少资源的消耗。