Distributed sensor networks have been discussed for more than 30 years, but the vision of wireless sen-
sor networks has been brought into reality only by the recent advances in wireless communications and
electronics, which have enabled the development of low-cost, low-power and multi-functional sensors that
are small in size and communicate over short distances. Today, cheap, smart sensors, networked through
wireless links and deployed in large numbers, provide unprecedented opportunities for monitoring and
controlling homes, cities, and the environment. In addition, networked sensors have a broad spectrum
of applications in the defence area, generating new capabilities for reconnaissance and surveillance as
well as other tactical applications.
Localization (location estimation) capability is essential in most wireless sensor network applications.
In environmental monitoring applications such as animal habitat monitoring, bush fire surveillance, water
quality monitoring and precision agriculture, the measurement data are meaningless without an accu-
rate knowledge of the location from where the data are obtained. Moreover, the availability of location
information may enable a myriad of applications such as inventory management, intrusion detection,
road traffic monitoring, health monitoring, reconnaissance and surveillance.
Wireless sensor network localization techniques are used to estimate the locations of the sensors
with unknown positions in a network using the available a priori knowledge of positions of, typically,
a few specific sensors in the network and inter-sensor measurements such as distance, time difference
of arrival, angle of arrival and connectivity. Sensor network localization techniques are not just trivial
extensions of the traditional localization techniques like GPS or radar-based geolocation techniques.
They involve further challenges in several aspects: (1) a variety of measurements may be used in sensor
network localization; (2) the environments in which sensor networks are deployed are often complicated,
involving urban environments, indoor environments and non-line-of-sight conditions; (3) wireless sen-
sors are often small and low-cost sensors with limited computational capabilities; (4) sensor network
localization techniques are often required to be implemented using available measurements and with
minimal hardware investment; (5) sensor network localization techniques are often required to be suit-
able for deployment in large scale multi-hop networks; and (6) the choice of sensor network localization
techniques to be used often involves consideration of the trade-off among cost, size and localization
accuracy to suit the requirements of a variety of applications. It is these challenges that make localiza-
tion in wireless sensor networks unique and intriguing.
This book is intended to cover the major techniques that have been widely used for wireless sensor
network localization and capture the most recent developments in the area. It is based on a number of
stand-alone chapters that together cover the subject matter in a fully comprehensive manner. However,
despite its focus on localization in wireless sensor networks, many localization techniques introduced
in the book can be applied in a variety of wireless networks beyond sensor networks.
The targeted audience for the book includes professionals who are designers and/or planners for
wireless localization systems, researchers (academics and graduate students), and those who would like
to learn about the field. Although the book is not exactly a textbook, the format and flow of information
have been organized such that it can be used as a textbook for graduate courses and research-oriented
courses that deal with wireless sensor networks and wireless localization techniques.
Guoqiang Mao received his bachelors degree in electrical engineering from Hubei University of Technology (China), his masters degree in engineering from South East University (China) and PhD in telecommunications engineering from Edith Cowan University (Australia) in 1995, 1998, and 2002 respectively. After graduation from PhD, he worked in the US-based industrial company, Intelligent Pixel Incorporation, as a senior research engineer for one year. He joined the School of Electrical and Information Engineering, the University of Sydney in December 2002 where he is a senior lecturer now. He is seconded to National ICT Australia as a senior researcher since 2003. He has published over fifty papers in prestigious journals and refereed conference proceedings. He has been a regular reviewer for many of the leading journals in the area. He has served as a program committee member in a number of international conferences and was the publicity co-chair of 2007 ACM Conference on Embedded Networked Sensor Systems. He was listed in the 25th Anniversary Edition of Marquis Whos Who in the World (2008) and in the 9th (2007) and 10th (2008) Anniversary Edition of Marquis Whos Who in Science and Engineering. His research interests include wireless localization techniques, wireless multihop networks, graph theory and its application in networking, telecommunications traffic measurement, analysis and modeling, and network performance analysis.
Bar s Fidan received his BS degrees in electrical engineering and mathematics from Middle East Technical University (Turkey, 1996), his MS degree in electrical engineering from Bilkent University, (Turkey, 1998), and the PhD degree in electrical engineering at the University of Southern California, Los Angeles (USA, 2003). After working as a postdoctoral research fellow at the University of Southern California for one year, he joined the National ICT Australia and the Research School of Information Sciences and Engineering of the Australian National University, Canberra, Australia in 2005, where he is currently a senior researcher. His research interests include autonomous multi-agent dynamical systems, sensor networks, cooperative localization, adaptive and nonlinear control, switching and hybrid systems, mechatronics, and various control applications including high performance and hypersonic flight control, semiconductor manufacturing process control, and disk-drive servo systems. He is coauthor of more than seventy publications, including the textbook Adaptive Control Tutorial (SIAM, 2006).
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这本书带给我的最强烈的感受,是一种关于“感知边界”的辩证思考。它似乎在挑战“可见即已知”的传统假设。许多篇幅被用于探讨“信息视界”的概念,即每个传感器节点所能有效影响或感知的最大范围,以及当这些视界相互重叠或产生空洞时,系统如何通过集体“信念传播”来填补空白。这种对“知识边界”的描绘,非常贴近现实世界中,尤其是在环境动态变化剧烈(如自然灾害监测或军事侦察)时的系统表现。它不避讳地讨论了信息反馈回路中的“延迟陷阱”和“过度自信”现象,并提出了一套基于“最小必要信息共享”的协同策略。这套策略的精髓,似乎在于教会系统在不确定的环境下,如何以最保守、最节约能量的方式,达成对环境的共识。这种对功耗与信息增益之间复杂权衡的精细刻画,使得这本书的价值,超越了单纯的算法讨论,上升到了一种关于“生存策略”的深层探讨,尤其适用于那些追求极致能效和长期自主运行的边缘计算场景。
评分这部著作的视角着实令人耳目一新,它似乎在引导我们进入一个完全不同的信息世界,而非直接聚焦于无线传感器网络中那些耳熟能详的定位算法。我阅读的体验,更像是在探索一门关于“空间感知”的通用哲学,并从中汲取构建复杂系统的灵感。书中对“不确定性”的处理方式,展现出一种深邃的洞察力,仿佛在暗示,真正的鲁棒性并非来源于绝对的精确,而是源于对误差边界的精妙管理与动态适应。例如,作者似乎花了大量的篇幅去探讨信息熵在分布式决策中的作用,这使得原本偏向工程技术的讨论,蒙上了一层理论物理学般的思辨色彩。我尤其欣赏它在构建宏观模型时所采用的迭代方法论,那种从微观噪声中提炼出宏观秩序的叙事结构,极大地拓宽了我对“算法设计”的传统理解。它不是在教你如何调优一个PID控制器,而是在挑战你重新审视“测量”本身的意义。如果将书中的某些图示和推导过程抽象化,它们完全可以应用于金融市场的风险建模,或者生物种群的动态预测,这表明其底层逻辑具有惊人的普适性,远远超出了传统硬件通信的范畴。这种对基础理论的深刻挖掘,使得整本书读起来像是一部关于“如何用有限信息构建无限世界”的教科书,其深度远非一本技术手册所能企及。
评分阅读这本书的过程,更像是一次对人类认知偏差的深度体验。它似乎在刻意回避那些教科书上标准化的最小二乘法或其他线性回归模型,转而深入探讨那些“非理性”或“直觉性”的决策过程在群体智能中的潜在价值。我惊喜地发现,有几处讨论,竟与认知心理学中关于“锚定效应”和“框架效应”的实验结果不谋而合,只是这里的“框架”被具象化为网络的物理部署约束。作者似乎在构建一个模型,用以解释,在信息严重缺失的情况下,一个由众多低能耗节点构成的群体,如何通过微小的、局部的信息交换,集体“涌现”出一种近乎全知的能力。这种涌现机制的描述,非常精妙,它没有诉诸于任何神奇的中央协调者,而是通过对节点间“信任度”或“信息价值”的动态权重分配来实现。这种对“软信息”的量化处理,给我留下了极其深刻的印象,让我开始重新审视在资源极端受限的环境下,如何将“软性约束”提升为“硬性指导”的理论基础。这已经超越了定位本身,触及了分布式决策的本质困境。
评分这本书的风格极其严谨,但其严谨性并非建立在对现有标准的严格遵循之上,反而更像是在为未来的标准构建理论基石。我感到它像是一部关于“信息几何”的入门教材,只不过其具体的应用场景被巧妙地伪装成了网络定位问题。其中关于高维空间中的“测地线”与网络连通性的映射关系,是全书中最具挑战性也最引人入胜的部分。它没有提供现成的代码库,而是提供了一整套分析问题的数学工具箱,迫使读者从更高维度理解“距离”和“邻近性”的含义。读到后面,我发现自己思考的已经不再是“这个节点在哪里”,而是“这个节点所处的环境,在信息空间中的拓扑结构是怎样的”。这种视角上的切换,要求读者具备扎实的数学基础,但一旦掌握,便能洞悉许多传统算法表面之下的结构性缺陷。对于那些寻求从根本上重构网络范式,而非仅仅修补现有漏洞的研究者来说,这本书无疑是一座灯塔,它指引的方向,是通往更优雅、更具内在一致性的系统架构。
评分这本书的叙事节奏,简直像一出精心编排的悬疑大戏,每翻开一章,都感觉在拆解一个层层包裹的谜团,但这个谜团的核心,似乎与传统的网络拓扑优化或能效管理相去甚远。我从中捕捉到的是一种强烈的“信息流形”构建的意图。作者似乎对传统欧几里德空间中的定位概念持有一种审慎的怀疑态度,转而推崇一种基于相对关系和动态关联性的空间重构。很多章节的论述,更像是对拓扑数据分析(TDA)的深入实践,而非标准的信号处理流程。我特别留意到其中关于“时间漂移补偿”的章节,它没有采用常见的时钟同步方案,而是构建了一种基于事件序列的因果链条,这使得整个系统的可靠性评估都转向了对“历史路径依赖”的分析。这种处理方式,极大地削弱了对绝对时间基准的依赖,从而在面对大规模、异构设备环境时,展现出非凡的弹性。可以说,它提供了一种“去中心化记忆”的构建蓝图,其核心思想是:系统对自身位置的认知,是通过对过往交互的持续整合,而非瞬时测量的累积来实现的。对于那些试图构建具有长期记忆和自我修正能力的复杂自主系统的人来说,这本书提供的思维框架是无价的。
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