基于热释电无线传感网络的人体目标识别系统

文章主要分析了热释电红外传感器搭建无线传感的网络,同时讲解了热释电红外传感器的主要特征、原理以及实现到人体目标识别功能的可行性,望能为有关人员提供到一定的参考和帮助.     

无线网络多网协同下的规划优化

本文首先探究了无线网络规划中多网通信的特点以及无线网络多网协同下规划设计的原则,重点分析了增强多网协同下的无线网络规划优化的措施,以此来降低移动通信的建设成本,提高网络质量与安全.     

无线传感器网络中的最优感知节点分布及分布优化算法

无线传感器网络中，感知节点的合理分布以及网络拓扑的动态调整对于更加有效地进行信息收集以及提高网络的生存期限都具有重要的作用。为此，针对传感器网络的初始规划提出了一种基于遗传算法的最优分布。仿真结果表明，算法能够针对特定的目标区域获得较好的节点分布。在最佳分布的基础上，结合传感器网络的拓扑管理和节点定位，引入了一种有效的传感器网络拓扑和节点分布优化方法，为传感器网络的拓扑性能管理提供了有效的算法保证。     

Lifetime maximized data gathering in wireless sensor networks using limited-order distributed source coding

This paper addresses the problem of efficient data gathering in wireless sensor networks with a complexity constrained data gathering node. Due to the complexity constraint, the data gathering node employs an asymmetric DSC that (de)compresses the data of a given node exploiting its dependency with a limited number of other nodes. This is characterized in a DSC rate allocation structure that is referred to as limited-order DSC. Within this structure, we investigate the problem of rate allocation for the nodes to maximize the network lifetime. To this end, an algorithm is proposed that is proven optimal with polynomial complexity in terms of number of network nodes. Numerical results demonstrate that the algorithm, even with limited complexity, allows for exploiting most of the achievable compression gain.     

UWB技术在无线传感器网络中的应用

通信技术的发展推动了无线传感器网络技术的发展,以低功耗为显著特点的UWB技术的出现,更是把现代通信技术推向新的发展高度.文章介绍了无线传感器网络的结构及特点,并结合UWB技术的特点,分析了UWB技术应用于无线传感器网络的优势,同时给出了应用的基本模型结构.     

基于改进蚁群算法的无线传感器网络路由

为了降低节点能量消耗,延长网络生存的时间,提出了一种改进蚁群算法的无线传感器网络路由机制。首先将无线传感器网络服务质量分为3类,然后利用蚁群算法可以自适应网络状况动态性的优势,构建传感器节点转移函数和信息素更新规则,自适应构建数据路由,最后采用仿真模拟实验对算法性能进行检验。实验结果表明,相对于与现有无线传感器路由算法,本文通过引入蚁群优化机理挖掘传感器节点之间的关联性,数据传输延迟、可靠性和能量开销上具有更好的性能,使整个网络性能保持最优。     

Inception neural network for human activity recognition using wearable sensor

The experience from computer vision was learned,an innovative neural network model called InnoHAR (inception neural network for human activity recognition) based on the inception neural network and recurrent neural network was put forward,which started from an end-to-end multi-channel sensor waveform data,followed by the 1×1 convolution for better combination of the multi-channel data,and the various scales of convolution to extract the waveform characteristics of different scales,the max-pooling layer to prevent the disturbance of tiny noise causing false positives,combined with the feature of GRU helped to time-sequential modeling,made full use of the characteristics of data classification task.Compared with the state-of-the-art neural network model,the InnoHAR model has a promotion of 3% in the recognition accuracy,which has reached the state-of-the-art on the dataset we used,at the same time it still can guarantee the real-time prediction of low-power embedded platform,also with more space for future exploration.     

Multiconstrained QoS multipath routing in wireless sensor networks

Sensor nodes are densely deployed to accomplish various applications because of the inexpensive cost and small size. Depending on different applications, the traffic in the wireless sensor networks may be mixed with time-sensitive packets and reliability-demanding packets. Therefore, QoS routing is an important issue in wireless sensor networks. Our goal is to provide soft-QoS to different packets as path information is not readily available in wireless networks. In this paper, we utilize the multiple paths between the source and sink pairs for QoS provisioning. Unlike E2E QoS schemes, soft-QoS mapped into links on a path is provided based on local link state information. By the estimation and approximation of path quality, traditional NP-complete QoS problem can be transformed to a modest problem. The idea is to formulate the optimization problem as a probabilistic programming, then based on some approximation technique, we convert it into a deterministic linear programming, which is much easier and convenient to solve. More importantly, the resulting solution is also one to the original probabilistic programming. Simulation results demonstrate the effectiveness of our approach. This work was supported in part by the U.S. National Science Foundation under grant DBI-0529012, the National Science Foundation Faculty Early Career Development Award under grant ANI-0093241 and the Office of Naval Research under Young Investigator Award N000140210464. Xiaoxia Huang received her BS and MS in the Electrical Engineering from Huazhong University of Science and Technology in 2000 and 2002, respectively. She is completing her Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Florida. Her research interests include mobile computing, QoS and routing in wireless ad hoc networks and wireless sensor networks. Yuguang Fang received a Ph.D. degree in Systems Engineering from Case Western Reserve University in January 1994 and a Ph.D degree in Electrical Engineering from Boston University in May 1997. He was an assistant professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology from July 1998 to May 2000. He then joined the Department of Electrical and Computer Engineering at University of Florida in May 2000 as an assistant professor, got an early promotion to an associate professor with tenure in August 2003 and to a full professor in August 2005. He holds a University of Florida Research Foundation (UFRF) Professorship from 2006 to 2009. He has published over 200 papers in refereed professional journals and conferences. He received the National Science Foundation Faculty Early Career Award in 2001 and the Office of Naval Research Young Investigator Award in 2002. He has served on several editorial boards of technical journals including IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing and ACM Wireless Networks. He have also been activitely participating in professional conference organizations such as serving as The Steering Committee Co-Chair for QShine, the Technical Program Vice-Chair for IEEE INFOCOM’2005, Technical Program Symposium Co-Chair for IEEE Globecom’2004, and a member of Technical Program Committee for IEEE INFOCOM (1998, 2000, 2003–2007).     

Privacy preservation in wireless sensor networks: A state-of-the-art survey

Much of the existing work on wireless sensor networks (WSNs) has focused on addressing the power and computational resource constraints of WSNs by the design of specific routing, MAC, and cross-layer protocols. Recently, there have been heightened privacy concerns over the data collected by and transmitted through WSNs. The wireless transmission required by a WSN, and the self-organizing nature of its architecture, makes privacy protection for WSNs an especially challenging problem. This paper provides a state-of-the-art survey of privacy-preserving techniques for WSNs. In particular, we review two main categories of privacy-preserving techniques for protecting two types of private information, data-oriented and context-oriented privacy, respectively. We also discuss a number of important open challenges for future research. Our hope is that this paper sheds some light on a fruitful direction of future research for privacy preservation in WSNs.     

Fault-tolerant monitor placement for out-of-band wireless sensor network monitoring

Monitoring a sensor network to quickly detect faults is important for maintaining the health of the network. Out-of-band monitoring, i.e., deploying dedicated monitors and transmitting monitoring traffic using a separate channel, does not require instrumenting sensor nodes, and hence is flexible (can be added on top of any application) and energy conserving (not consuming resources of the sensor nodes). In this paper, we study fault-tolerant out-of-band monitoring for wireless sensor networks. Our goal is to place a minimum number of monitors in a sensor network so that all sensor nodes are monitored by k distinct monitors, and each monitor serves no more than w sensor nodes. We prove that this problem is NP-hard. For small-scale network, we formulate the problem as an Integer Linear Programming (ILP) problem, and obtain the optimal solution. For large-scale network, the ILP is not applicable, and we propose two algorithms to solve it. The first one is a ln(kn) approximation algorithm, where n is the number of sensor nodes. The second is a simple heuristic scheme that has much shorter running time. We evaluate our algorithms using extensive simulation. In small-scale networks, the latter two algorithms provide results close to the optimal solution from the ILP for relatively dense networks. In large-scale networks, the performance of these two algorithms are similar, and for relatively dense networks, the number of monitors required by both algorithms is close to a lower bound.     

Optimized localization of target nodes using single mobile anchor node in wireless sensor network

In Wireless Sensor Networks (WSNs), main challenges which restrict the performance are data computation, lifetime, routing, task scheduling, security, organization and localization. Recently, numerous Computational Intelligence (CI) based potential solutions for above mentioned challenges have been proposed to fulfill the desired level of performance in WSNs. Use of CI gives autonomous and strong solutions to ascertain precise node location (2D/3D) with least hardware necessity (position finding device, i.e., GPS empowered gadget). Localization of target nodes in static scenario can be done more precisely. However, in case of mobility, determining accurate position of each node in network is a challenging problem. In this paper, a novel idea of localizing target nodes with moving single anchor node is proposed using CI based application of Particle Swarm Optimization (PSO) and H-Best Particle Swarm Optimization (HPSO). The moving anchor node is following the Hilbert trajectory. Proposed algorithms are actualized for range-based, distributed, non-collaborative and isotropic WSNs. Only single moving anchor node is used as a reference node to localize the target nodes in the entire network. In proposed algorithms, problem of Line of Sight (LoS) is minimized due to projection of virtual anchor nodes.     

基于ACS的无线传感器网络区分服务路由算法

针对无线传感器网络中数据传输的不同要求,将QoS分为3类,根据无线链路的特点提供区分服务。利用博弈论分析了数据传输在延迟、可靠性与网络能量开销之间的关系,基于改进的蚁群优化算法ACS(ant colony system),设计了区分服务路由算法ADSGR(ant colony system based differentiated service and game-theory routing),依据不同QoS要求,选择适当的路由,提高网络的整体性能和资源利用率。实验结果表明,与现有算法相比,该算法在数据传输的延迟、可靠性和能量开销上具有更好的性能。     

Energy-efficiency clustering protocol in wireless sensor networks

The conventional clustering method has the unique potential to be the framework for power-conserving ad hoc networks. In this environment, studies on energy-efficient strategies such as sleeping mode and redirection have been reported, and recently some have even been adopted by some standards like Bluetooth and IEEE 802.11. However, consider wireless sensor networks. The devices employed are power-limited in nature, introducing the conventional clustering approach to the sensor networks provides a unique challenge due to the fact that cluster-heads, which are communication centers by default, tend to be heavily utilized and thus drained of their battery power rapidly. In this paper, we introduce a re-clustering strategy and a redirection scheme for cluster-based wireless sensor networks in order to address the power-conserving issues in such networks, while maintaining the merits of a clustering approach. Based on a practical energy model, simulation results show that the improved clustering method can obtain a longer lifetime when compared with the conventional clustering method.     

Link optimization strategy based on spectral moment in wireless sensor network

In order to prolong the life of the wireless sensor network (WSN),an adaptive local communication link optimization algorithm of nodes was proposed to optimize the network structure dynamically.By introducing the term of Laplacian spectrum moment,each node can make the decision of adding or deleting a link iteratively with their neighborsusinglimited local network structural information,and then the whole network can evolve to a predefined structure.The experimental results show that each node takes only finite iterations and then the network structure can converge quickly to the target,which prove the effectiveness of the algorithm.     

Clock synchronization in wireless sensor networks using least common multiple

In this research, we propose the Clock Synchronization by Least Common Multiple (CSLCM) method to remove the clock offset and clock skew among the sensor nodes. The proposed CSLCM enables the nodes to reach a network synchronization time by calculating the least common multiple of their Clock Time Period (CTP). The network is organized into clusters and every node reaches the network synchronization time using its own CTP. Simulation results show that, the CSLCM algorithm is more efficient compared to the Average Time Synchronization with Pairwise messages (ATSP) in terms of accuracy, communication overhead, and computation overhead.     

传感器网络中基于树的感知器分布优化

无线传感器网络中,感知节点的合理分布对于提高网络的感知能力和信息收集能力以及提高网络的生存期限都具有重要的作用。对于随机分布方式产生的感知网络,可以利用节点的移动性对特定感知节点的位置进行调整从而改善网络整体的感知覆盖范围。为此,利用 Voronoi 图以及相关 Delaunay 三角网定义了传感器网络中以sink 节点为中心的伸展树,并提出了基于遗传算法的感知节点分布优化算法。仿真结果表明,算法能够以较小代价对传感器网络进行节点的分布优化,从而有效提高网络整体的感知能力。     

无线传感器网络中比特错误率估计方法

由于无线传感器网络中干扰检测的性能依赖于比特错误率(Bit Error Rate,BER)估计的准确性,而现有的比特错误率估计方法或者难以实现,或者准确性差。针对这个问题,本文提出了采用差异化思想来提高比特错误率估计准确度的方法DEE(Differentiated Error Estimation)。其主要是发送方在数据包中插入具有不同估错能力的多级估错位,并随机均匀地分布所有估错位。接收方借助BER与奇偶校验失败概率的理论关系来估计BER。同时,DEE利用BER非均匀分布特征来优化各级估错位的能力,提高出现概率高的BER的估计准确度,以降低平均估计误差。实验结果表明,与现有方法EEC相比,DEE可将估计误差平均减少约44%。当估错冗余较低时,DEE可将估计误差减少约68%。     

Energy-optimized cluster head selection based on enhanced remora optimization algorithm in underwater wireless sensor network

With the technological advancements, wireless sensor network (WSN) has played an impeccable role in monitoring the underwater applications. Underwater WSN (UWSN) is supported by WSN but subjected to data dissemination in an acoustic medium. Due to challenging conditions in underwater scenario, the limited battery resources of these sensor nodes stem to a crucial research problem that needs to address the energy-efficient routing in UWSN. In this research work, we intend to propose an energy-optimized cluster head (CH) selection based on enhanced remora optimization algorithm (ECERO) in UWSN. Since CH devours the maximum energy among the nodes, we perform selection of CH based on EROA while considering energy, Euclidean distance from sink, node density, network's average energy, acoustic path loss model and lastly, the adaptive quantity of CHs in the network. Further, to reduce the load on CH node, we introduce the concept of sleep scheduling among the closely located cluster nodes. The proposed work improves the performance of recently proposed EOCSR algorithm by great magnitude which claims to mitigate hot-spot problem, but EOCSR still suffers from the same due to relaying a large magnitude of data.     

GCRP: Grid-cycle routing protocol for wireless sensor network with mobile sink

Energy conservation is a critical issue in resource constraint wireless sensor networks. Employing mobile sink to deliver the sensed data becomes pervasive approach to conserve sensors’ limited energy. However, mobile sink makes data delivery a hard nut to crack since nodes need to know its latest location. Providing sink’s latest location by traditional flooding, erode the energy conservation goal. In this paper, we propose a Grid-Cycle Routing Protocol (GCRP) with the aim of minimizing the overhead of updating the mobile sink’s latest location. In GCRP, sensor field is partitioned into grid of cells and for each cell a grid cell head (GCH) is elected. Cycles of four GCHs is formed. Cycle(s) involving border GCHs is called exterior cycle and said to belong to a region. Another cycle involving non-boundary GCHs is called interior cycle, connecting GCHs of different regions. When sink stays at one location, it updates the nearest GCH, which in turn updates the other GCHs through exterior and interior cycle. Moreover, we propose a set of sharing rules that govern GCHs when and with who share sink’s latest location information. The performance of GCRP is evaluated at different number of nodes and compared with existing work using NS-2.31.     

太阳能无线传感器网络节点传输功率策略优化

针对配备太阳能采集设备的无线传感器网络节点,以如何合理使用采集到的能量使得时间期限内点对点传输的数据量最大为优化目的,结合必要的约束条件（能量获取电池只有有限的能量存储能力以及能量因果关系）,建立了数据传输的能量通道优化模型,通过能量通道策略优化算法得到了最优传输功率策略。与当前普遍使用的能量即到即用方法进行了性能对比,仿真发现系统累积传输了更多的数据量,性能提升明显。在此基础上,考虑了实际太阳能获取量与预测值的偏差,提出了改进的能量通道算法,使得其更加适应于实际场景中。