传感器网络中基于局部信号重建的目标计数算法

针对传感器网络中现有目标计数算法的不足问题,提出了一种基于局部信号重建的目标计数算法。该算法首先通过局部峰值搜索找出目标可能存在的区域;然后根据信号衰减特性和节点分布情况建立信号重建模型,并基于此模型寻求局部区域内的最佳目标分布估计;最后对各局部区域内的估计结果进行汇总,得到全网范围内的目标数量。仿真实验结果表明,该算法在计数精度、抗噪性和通信开销方面具有较好性能。     

Hybrid data dissemination protocol (HDDP) for wireless sensor networks

Wireless Networks - Wireless sensor networks (WSNs) are often used for monitoring environmental conditions. One of the most important tasks in a WSN is to gather sensed data for the users to...     

Relay sensor placement in wireless sensor networks

This paper addresses the following relay sensor placement problem: given the set of duty sensors in the plane and the upper bound of the transmission range, compute the minimum number of relay sensors such that the induced topology by all sensors is globally connected. This problem is motivated by practically considering the tradeoff among performance, lifetime, and cost when designing sensor networks. In our study, this problem is modelled by a NP-hard network optimization problem named Steiner Minimum Tree with Minimum number of Steiner Points and bounded edge length (SMT-MSP). In this paper, we propose two approximate algorithms, and conduct detailed performance analysis. The first algorithm has a performance ratio of 3 and the second has a performance ratio of 2.5. Xiuzhen Cheng is an Assistant Professor in the Department of Computer Science at the George Washington University. She received her MS and PhD degrees in Computer Science from the University of Minnesota - Twin Cities in 2000 and 2002, respectively. Her current research interests include Wireless and Mobile Computing, Sensor Networks, Wireless Security, Statistical Pattern Recognition, Approximation Algorithm Design and Analysis, and Computational Medicine. She is an editor for the International Journal on Ad Hoc and Ubiquitous Computing and the International Journal of Sensor Networks. Dr. Cheng is a member of IEEE and ACM. She received the National Science Foundation CAREER Award in 2004. Ding-Zhu Du received his M.S. degree in 1982 from Institute of Applied Mathematics, Chinese Academy of Sciences, and his Ph.D. degree in 1985 from the University of California at Santa Barbara. He worked at Mathematical Sciences Research Institutea, Berkeley in 1985-86, at MIT in 1986-87, and at Princeton University in 1990-91. He was an associate-professor/professor at Department of Computer Science and Engineering, University of Minnesota in 1991-2005, a professor at City University of Hong Kong in 1998-1999, a research professor at Institute of Applied Mathematics, Chinese Academy of Sciences in 1987-2002, and a Program Director at National Science Foundation of USA in 2002-2005. Currently, he is a professor at Department of Computer Science, University of Texas at Dallas and the Dean of Science at Xi’an Jiaotong University. His research interests include design and analysis of algorithms for combinatorial optimization problems in communication networks and bioinformatics. He has published more than 140 journal papers and 10 written books. He is the editor-in-chief of Journal of Combinatorial Optimization and book series on Network Theory and Applications. He is also in editorial boards of more than 15 journals. Lusheng Wang received his PhD degree from McMaster University in 1995. He is an associate professor at City University of Hong Kong. His research interests include networks, algorithms and Bioinformatics. He is a member of IEEE and IEEE Computer Society. Baogang Xu received his PhD degree from Shandong University in 1997. He is a professor at Nanjing Normal University. His research interests include graph theory and algorithms on graphs.     

Collision Free Mobility Adaptive (CFMA) MAC for wireless sensor networks

In this paper we propose high throughput collision free, mobility adaptive and energy efficient medium access protocol (MAC) called Collision Free Mobility Adaptive (CFMA) for wireless sensor networks. CFMA ensures that transmissions incur no collisions, and allows nodes to undergo sleep mode whenever they are not transmitting or receiving. It uses delay allocation scheme based on traffic priority at each node and avoids allocating same backoff delay for more than one node unless they are in separate clusters. It also allows nodes to determine when they can switch to sleep mode during operation. CFMA for mobile nodes provides fast association between the mobile node and the cluster coordinator. The proposed MAC performs well in both static and mobile scenarios, which shows its significance over existing MAC protocols proposed for mobile applications. The performance of CFMA is evaluated through extensive simulation, analysis and comparison with other mobility aware MAC protocols. The results show that CFMA outperforms significantly the existing CSMA/CA, Sensor Mac (S-MAC), Mobile MAC (MOB-MAC), Adaptive Mobility MAC (AM-MAC), Mobility Sensor MAC (MS-MAC), Mobility aware Delay sensitive MAC (MD-MAC) and Dynamic Sensor MAC (DS-MAC) protocols including throughput, latency and energy consumption.     

Novel fault-tolerant clustering-based multipath algorithm (FTCM) for wireless sensor networks

Wireless sensor networks are designed in such a way that transfer sensed data to base station, while a part of network is faulty. This study suggests a fault-tolerant clustering-based multipath algorithm for wireless sensor networks. We have employed a hybrid energy-efficient distributed clustering approach, to cluster nodes. Then a backup node is selected to increase the fault tolerance of cluster head node so that on completing collecting data from sensor nodes, it stores a copy of data. While collecting data in clusters, hypothesis testing and majority voting in cluster head were used to detect the fault of nodes. Finally, three paths were adopted to transfer data from source to base station based on residual energy, number of hops, propagation speed, and reliability parameters. The results of the simulation reveal that our proposed method has improved in terms of energy (6.7%), correct data (53%), data loss (4%), and delay (5.6%) compared with other algorithms.

     

区块链助力电信网络反欺诈协同治理

电信网络欺诈案线索分布广、破坏跨度大、受害人群多,给案件侦破带来了难度。大数据、人工智能等技术推动了精准化侦办,但技术的不当使用也会带来个人隐私泄露、银行卡被盗刷盗用的风险。探索了基于区块链的电信网络反欺诈协同治理,进行了核心能力和反欺诈人群分析,利用区块链的智能合约和密码共识,保证了电信数据的不可篡改和安全共享,进行了反欺诈协同治理的逻辑设计,为基于区块链的电信网络反欺诈协同治理的进一步落地应用提供了思路。     

Independent sensor networks

The concept of networks of distributed sensors is a popular topic of research. Currently, many systems focus on environmental monitoring applications. Homeland security and the potential for nuclear weapons or dirty bombs has necessitated another type of environmental monitoring, that for radioisotopes. Select traffic points in the United States may be monitored for illicit isotopes by commercially available portal monitoring systems. This article discusses the research efforts at Los Alamos National Laboratory on the development of heterogeneous networks of small, low-power, easily concealed nodes and larger, more compute-capable nodes for in-situ data processing. These networks must be able to reconfigure themselves independently based on the data being collected across a number of sensor types in real time. An application pertinent to current national and global security issues to demonstrate the relevant concepts has been selected. The Los Alamos National Laboratory is developing methods of guarding against a potential terrorist attack using a simple radiological dispersal device (RDD).     

星载单光子激光雷达海洋噪声模型

星载单光子激光雷达以超高灵敏度和超高重频的优势,在海洋探测领域展现了广泛的应用前景。雷达系统中的单光子探测器件具有极高的灵敏度,可以探测光子量级的回波信号,同时也极易受太阳背景光噪声影响。由于背景噪声直接影响激光雷达的工作性能,还会对星上原始的数据量产生影响,在卫星系统设计阶段,对噪声强度的准确估计至关重要。本文综合考虑大气后向散射、水面反射及水体后向散射的贡献,建立了一个星载单光子激光雷达海洋噪声的估计模型。以全球首个对地观测星载激光雷达ATLAS为例,在输入系统参数和环境参数后,模型估计的噪声与ATLAS实测噪声误差在15%以内,证实了该噪声模型的正确性。     

Energy-efficient detection in sensor networks

There is significant interest in battery-powered sensor networks to be used for detection in a wide variety of applications, from surveillance and security to health and environmental monitoring. Severe energy and bandwidth constraints at each sensor node demand system-level approaches to design that consider detection performance jointly with system-resource constraints. Our approach is to formulate detection problems with constraints on the expected cost arising from transmission (sensor nodes to a fusion node) and measurement (at each sensor node) to address some of the system-level costs in a sensor network. For a given resource constraint, we find that randomization over the choice of measurement and over the choice of when to transmit achieves the best performance (in a Bayesian, Neyman-Pearson, and Ali-Silvey sense). To facilitate design, we describe performance criteria in the send/no-send transmission scenario, where the joint optimization over the sensor nodes decouples into optimization at each sensor node.     

Security in wireless sensor networks

Recent advances in electronics and wireless communication technologies have enabled the development of large-scale wireless sensor networks that consist of many low-power, low-cost, and small-size sensor nodes. Sensor networks hold the promise of facilitating large-scale and real-time data processing in complex environments. Security is critical for many sensor network applications, such as military target tracking and security monitoring. To provide security and privacy to small sensor nodes is challenging, due to the limited capabilities of sensor nodes in terms of computation, communication, memory/storage, and energy supply. In this article we survey the state of the art in research on sensor network security.     

Distributed fusion in sensor networks

This paper presents an overview of research conducted to bridge the rich field of graphical models with the emerging field of data fusion for sensor networks. Both theoretical issues and prototyping applications are discussed in addition to suggesting new lines of reasoning.     

Decentralized detection in sensor networks

In this paper, we investigate a binary decentralized detection problem in which a network of wireless sensors provides relevant information about the state of nature to a fusion center. Each sensor transmits its data over a multiple access channel. Upon reception of the information, the fusion center attempts to accurately reconstruct the state of nature. We consider the scenario where the sensor network is constrained by the capacity of the wireless channel over which the sensors are transmitting, and we study the structure of an optimal sensor configuration. For the problem of detecting deterministic signals in additive Gaussian noise, we show that having a set of identical binary sensors is asymptotically optimal, as the number of observations per sensor goes to infinity. Thus, the gain offered by having more sensors exceeds the benefits of getting detailed information from each sensor. A thorough analysis of the Gaussian case is presented along with some extensions to other observation distributions.     

Security in wireless sensor networks

With sensor networks on the verge of deployment, security issues pertaining to the sensor networks are in the limelight. Though the security in sensor networks share many characteristics with wireless ad hoc networks, the two fields are rapidly diverging due to the fundamental differences between the make‐up and goals of the two types of networks. Perhaps the greatest dividing difference is the energy and computational abilities. Sensor nodes are typically smaller, less powerful, and more prone to failure than nodes in an ad hoc network. These differences indicate that protocols that are valid in the context of ad‐hoc networks may not be directly applicable for sensor networks. In this paper, we survey the state of art in securing wireless sensor networks. We review several protocols that provide security in sensor networks, with an emphasis on authentication, key management and distribution, secure routing, and methods for intrusion detection. Copyright © 2006 John Wiley & Sons, Ltd.     

Low communication cost (LCC) scheme for localizing mobile wireless sensor networks

In recent years, the number of applications utilizing mobile wireless sensor networks (WSNs) has increased, with the intent of localization for the purposes of monitoring and obtaining data from hazardous areas. Location of the event is very critical in WSN, as sensing data is almost meaningless without the location information. In this paper, two Monte Carlo based localization schemes termed MCL and MSL* are studied. MCL obtains its location through anchor nodes whereas MSL* uses both anchor nodes and normal nodes. The use of normal nodes would increase accuracy and reduce dependency on anchor nodes, but increases communication costs. For this reason, we introduce a new approach called low communication cost schemes to reduce communication cost. Unlike MSL* which chooses all normal nodes found in the neighbor, the proposed scheme uses set theory to only select intersected nodes. To evaluate our method, we simulate in our proposed scheme the use of the same MSL* settings and simulators. From the simulation, we find out that our proposed scheme is able to reduce communication cost—the number of messages sent—by a minimum of 0.02 and a maximum of 0.30 with an average of 0.18, for varying node densities from 6 to 20, while nonetheless able to retain similar MSL* accuracy rates.     

Hierarchical Tree Alternative Path (HTAP) algorithm for congestion control in wireless sensor networks

Recent advances in wireless sensor networks (WSNs) have lead to applications with increased traffic demands. Research is evolving from applications where performance is not considered as a crucial factor, to applications where performance is a critical factor. There are many cases in the fields of automation, health monitoring, and disaster response that demand wireless sensor networks where performance assurances are vital, especially for parameters like power, delay, and reliability. Due to the nature of these networks the higher amount of traffic is observed when the monitored event takes place. Exactly at this instance, there is a higher probability of congestion appearance in the network. Congestion in WSNs is tackled by the employment of two methods: either by reducing the load (“traffic control”), or by increasing the resources (“resource control”). In this paper we present the Hierarchical Tree Alternative Path (HTAP) algorithm, a “resource control” algorithm that attempts, through simple steps and minor computations, to mitigate congestion in wireless sensor networks by creating dynamic alternative paths to the sink. HTAP is evaluated in several scenarios in comparison with another “resource control” algorithm (TARA), as well as with a “traffic control” algorithm (SenTCP), and also the case where no congestion control exists in the network (“no CC”). Results show that HTAP is a simple and efficient algorithm capable of dealing successfully with congestion in WSNs, while preserving the performance characteristics of the network.