【作者】 林川；

【导师】 何炎祥；

【作者基本信息】 武汉大学 ， 计算机软件与理论， 2007， 博士

【摘要】 无线传感器网络(WSNs)是新一代的传感器网络,综合了传感器技术、嵌入式计算技术、现代网络及无线通信技术、分布式信息处理技术等。传感器信息获取技术已经从单一化渐渐向集成化、微型化和网络化方向发展。无线传感器网络的出现和发展实现了物理世界、计算机世界以及人类社会三元世界的连通,并将会带来一场信息革命。尽管针对无线传感器网络的某些技术难点问题进行了许多的技术革新,但是困扰无线传感器网络发展的关键问题是到目前为止还没有一个统一的、标准的网络体系结构。建立一个通用、抽象的体系结构是设计无线传感器网络的基础和前提。在该体系结构框架下,针对无线传感器网络中的能量有效、网络拓扑、网络安全等问题才有可能进一步的规范和标准化。由于传感器节点的能量、处理能力、存储容量和通信带宽都十分有限,其能量问题是整个网络的核心问题,直接决定了传感器网络的生命周期。这使得在传感器网络中能量优化显得十分重要,几乎包括从底层的物理层到应用层的所有层面都要考虑其能量有效性问题。其中,动态能量管理(DPM)是一种重要的节能思想。采用合理有效的动态能量管理策略有利于减少无线传感器网络的能量消耗,从而延长网络的生命周期。另外,在满足全覆盖和连通性前提下,如何达到网络的能量有效性是传感器网络研究中的另一个重要问题。围绕传感器网络的能量问题,本文的主要研究有以下几个方面:1.提出了一个基于立体跨层(CCL)的传感器网络体系结构。其基本思想是将OSI参考模型的分层思想与无线传感器网络的跨层设计相结合。一方面,CCL具有传统分层设计的优点;另一方面,它又满足传感器网络某些跨层服务的需要,如能量管理、定位、同步、安全以及移动管理和任务管理等。在CCL的设计中,无线传感器网络结构被分成了物理层、传感器服务协议层(SSP)和应用层。其中,传感器服务协议(SSP)层类似Culler所提出的“细腰-SP”概念。SSP可以看作是整个网络中的一个中间件服务层的通用抽象,同时对网络中的服务进行协同调配。所有这些服务都能够被整个网络的各层所访问,而不是封装成一个独立的层,仅仅为其上或其下的层所见。2.针对Sinha提出的动态能量管理策略和算法进行了修改,考虑了节点电池的能量状态和唤醒节点所需额外能量等多种因素。这些因素直接决定了节点的休眠状态和休眠周期。同时,针对时间阈值条件(即休眠节点在休眠状态所保持的最短时间)进行了修正。模拟实验表明,在新阈值时间条件下的动态能量管理更具有能量效率,延长了传感器网络的生命周期。针对休眠状态转换和休眠节点唤醒机制,本文分别提出了一种基于能量状态转换策略的混合自动机模型和一种基于消息驱动的休眠节点唤醒算法。该唤醒算法可以有效地避免包传输失败造成的大量能量损失,同时可以大量地减少网络中的冲突。3.本文讨论了在保持全覆盖和连通性前提下,利用最小工作节点集来获得能量优化的传感器网络的覆盖问题。在高密度的传感器网络中寻找最小工作节点集(即密度控制)是覆盖问题的核心内容。该最小工作节点集能够同时满足网络的全覆盖和连通性要求。当传感器节点的通信距离至少是其感知范围的两倍以上时,凸型目标区域的完全覆盖就意味着工作节点之间的完全连通。这里,将动态能量管理与传感器网络的覆盖问题相结合,提出了一种基于最优几何密度控制(OGDC)的动态能量管理算法OGDC-DPM。该算法采用后退时间(backoff)方法,可以有效地避免在同一个时间段内多个邻居节点同时成为初始节点的可能。模拟实验表明,该算法具有能量有效性,延长了无线传感器网络的生命周期。4.本文就无线传感器网络中关于网络生命周期的上限问题展开了讨论和对无线传感器网络的诊断进行了研究。尽管传感器网络可以应用到环境监测中,也可以起到一定的环境保护作用,但是由失效网络或失效节点所产生的大量废电池本身又对环境产生了巨大的威胁。本文首次提出了由传感器节点所产生的环境污染问题,并就此问题提出了初步的解决方法和思路。其中,包括采用高能电池、生物电池、自我能量收集技术和电池无线充电技术等先进和未来的节能技术,以及针对不同应用场景进行利用生命周期分析方法和诊断技术对失效节点进行收集等。更多还原

【Abstract】 Wireless Sensor Networks is the next generation sensor networks, which integrating sensor technology, embedded computing technology, advanced networking and wireless communication technology and distributed signal information processing techniques. The sensing acquirement technology has been changed from singularity to integration, miniaturization and networking. It will realize the interconnection of physical world, computer world and human society eventually.Though many specific technical challenges remain and deserve much further study, the primary factor currently limiting progress in sensor networks is not these challenges but is instead the lack of an overall Sensor Network Architecture (SNA). Since limited processing speed, storage capacity, and communication bandwidth of sensor node, the energy consumption is the most important factor to determine the life of a sensor network. This makes energy optimization more complicated in sensor networks because it involved not only reduction of energy consumption but also prolonging the life of the network as much as possible. This can be done by having energy awareness in every aspect of design and operation through the overall SNA. Dynamic power management (DPM) can be used to reduce more energyconsumption. In addition, under the condition of full coverage and connectivity, how to reduce the energy consumption is another important issue.This paper focuses on the energy problem of wireless sensor networks and has the following researches:1. The basic idea of Cubic and Cross-Layer (CCL) sensor network architecture proposed in this thesis is combing the layering thought of OSI reference model and cross-layer design of sensor networks.In CCL, the SNA can be divided into physical layer, sensor service protocol layer (SSP) and application layer. SSP which is similar to the“narrow waist-SP”proposed by Culler, is a common abstraction as a middleware layer. All these services are considered or accessible to be in a cross-layer manner instead of being full encapsulated at one layer, only visible to the one above and below.2. The DPM policy and algorithm proposed by Sinha have been modified in this paper with considering more factors such as the battery status. In addition, the time threshold which the least time should sensor node be stayed in the sleep state is revised. The sensor network consumed less energy in our simulation than the old one. A hybrid automata model to represent our sleep state transition policy and message-driven algorithm for awakening the sleep nodes are proposed in this paper. The message-driven algorithm can effectively avoid huge energy consumption caused by to the failure of packet transmission and largely deduce the collision of the networks.3. This paper addresses the issues of maintaining sensing coverage and connectivity by keeping a minimal number of sensor nodes in the active mode in wireless sensor networks. One important issue that arises in such high-density sensor networks is density control—the function that controls the density of the working sensor set to a certain level. It is desirable to choose a minimal set of working sensors in order to reduce power consumption and prolong network lifetime. If the radio range is at least twice of the sensing range, a complete coverage of a convex area implies connectivity among the working set of nodes.This paper combines the DPM and coverage problem of sensor networks, proposes an OGDC-DPM algorithm which is a dynamic power management policy based on Optimal Geographical Density Control (OGDC). This algorithm exploits a backoff timer method, which avoids the possibility of multiple neighboring nodes volunteering themselves to be the starting node in a round. The simulation result proved that this algorithm has energy effective and hence prolong the lifetime of the sensor network.4. This paper discusses the upper bound of sensor network lifetime and the diagnosis of sensor network node. Though WSNs can be used in environment monitoring and protection, the large number of invalidated sensor nodes is a huge hazard to environment. Hence, it is the fist time to propose the environment pollution of sensor nodes in this paper. At last, several solutions to the environment pollution caused by the batteries of sensor nodes are proposes, including high energy battery, biological battery, energy self-collection and wireless recharge techniques and sensor nodes collection. The sensor nodes can be collected by using the analysis of WSNs lifetime and diagnosis technology under different application scenario.更多还原