Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets

In this paper, a fuzzy based distributed power aware routing scheme considering both energy and bandwidth constraints, especially for query driven applications in the asynchronous duty-cycled wireless sensor networks are devised. The proposed multi-constraint, multi-objective routing optimization approach under strict resource constraints guarantees reliability and fast data delivery along with efficient power management in spite of unreliable wireless links and limited power supply. In query driven applications, the request from the sink to the individual sensor node will be a broadcast message, whereas the individual sensor nodes replies back to sink as unicast messages. In the proposed work, the fuzzy approach and “A Star” algorithm are utilized for satisfying energy and bandwidth constraints to route the broadcast messages of the sink while querying all the sensor nodes in the network. Every node will be provided with a guidance list, which is used to decide the next best neighbor node with good route quality for forwarding the received multi-hop broadcast messages. The route quality of the every node is estimated with fuzzy rules based on the network parameters such as maximum remaining energy, minimum traffic load and better link quality to increase the network lifetime. The provision of overhearing the broadcast messages and acknowledgements within the transmission range minimizes the effort to search for the active time of nodes while routing the broadcast messages with asynchronous scheduling. Further, in the proposed work only the time slot of its nearest neighbor relay node (to which packets are to be forwarded) is learnt to reduce the number of message transmissions in the network. For the unicast message replies, the fuzzy membership function is modified and devised based on the routing metrics such as higher residual energy, minimum traffic loads and minimum hop count under energy and bandwidth constraints. Also, the multi-hop heuristic routing algorithm called Nearest Neighbor Tree is effectively used to reduce the number of neighbors in the guidance list that are elected for forwarding. This helps to increase the individual sensor node’s lifetime, thereby maximizes the network lifetime and guarantees increased network throughput. The simulation results show that the proposed technique reduces repeated transmissions, decreases the number of transmissions, shortens the active time of the sensor nodes and increases the network lifetime for query driven sensor network applications invariant to total the number of sensor nodes and sinks in the network. The proposed algorithm is tested in a small test bed of sensor network with ten nodes that monitors the room temperature.     

基于深度学习的无线传感器网络数据融合

在无线传感器网络数据融合算法中,BP神经网络被广泛用于节点数据的特征提取和分类。为了解决BP神经网络收敛慢,易陷入局部最优值且泛化能力差从而影响数据融合效果的问题,提出一种将深度学习技术和分簇协议相结合的数据融合算法SAESMDA。SAESMDA用基于层叠自动编码器(SAE)的深度学习模型SAESM取代BP神经网络,算法首先在汇聚节点训练SAESM并对网络分簇,接着各簇节点通过SAESM对采集数据进行特征提取,之后由簇首将分类融合后的特征发送至汇聚节点。仿真实验表明,和采用BP神经网络的BPNDA算法相比,SAESMDA在网络能耗大致相同的情况下具有更高的特征提取分类正确率。     

A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems

Wireless sensor networks (WSNs) as one of the key technologies for delivering sensor-related data drive the progress of cyber-physical systems (CPSs) in bridging the gap between the cyber world and the physical world. It is thus desirable to explore how to utilize intelligence properly by developing the effective scheme in WSN to support data sensing and fusion of CPS. This paper intends to serve this purpose by proposing a prediction-based data sensing and fusion scheme to reduce the data transmission and maintain the required coverage level of sensors in WSN while guaranteeing the data confidentiality. The proposed scheme is called GM–KRLS, which is featured through the use of grey model (GM), kernel recursive least squares (KRLS), and Blowfish algorithm (BA). During the data sensing and fusion process, GM is responsible for initially predicting the data of next period with a small number of data items, while KRLS is used to make the initial predicted value approximate its true value with high accuracy. The KRLS as an improved kernel machine learning algorithm can adaptively adjust the coefficients with every input, while making the predicted value more close to actual value. And BA is used for data encoding and decoding during the transmission process due to its successful applications across a wide range of domains. Then, the proposed secure data sensing and fusion scheme GM–KRLS can provide high prediction accuracy, low communication, good scalability, and confidentiality. In order to verify the effectiveness and reasonableness of our proposed approach, we conduct simulations on actual data sets that are collected from sensors in the Intel Berkeley research lab. The simulation results have shown that the proposed scheme can significantly reduce redundant transmissions with high prediction accuracy.     

一种鲁棒性强的无线传感器网络位置隐私保护方案

传统无线传感器网络（WSNs）位置隐私保护方案难以解决安全性与网络能耗之间的均衡,为了提高网络隐私信息的安全性,提出一种鲁棒性强的无线传感器网络位置隐私保护方案.首先通过增加伪源节点和伪汇聚节点防止攻击者获得关键节点的位置信息;然后采用伪汇聚节点分组、概率丢弃冗余数据包降低网络资源消耗;最后在Matlab 2012平台下进行仿真对比实验.结果表明：该方案可以提高网络攻击事件检测率,降低网络时延,有效地保护源节点和汇聚节点的位置隐私.     

无线传感器网络数据融合协议比较

传感器网络由电池能量受限的节点组成,必须采用一种能量有效的方法收集节点感知的信息,如果每个节点都采用单跳方式将其感知的数据直接传输给汇聚节点,则与汇聚节点距离较远的节点能量将很快被耗尽。应用于无线传感器网络的LEACH协议提出了通过分簇实现数据融合的方法,簇头在接收到本簇成员的数据后进行融合处理,最终,将融合结果传输到汇聚节点。另一种应用数据融合的PEGASIS协议,是一种接近理想的基于链状的协议,它在LEACH协议的基础上做出了改进。在PEGASIS中,每个节点只与一个位置最近的邻居进行通信,并且,轮流传输数据到汇聚节点,然后,降低每一轮中的能量消耗。模拟结果表明:采用PEGASIS协议有效地延长了网络的生存时间。     

无线传感器网络中基于区域再生树汇聚的路由算法

传感器网络具有严格的能量限制,冗余的低速数据流和多对一的通信方式等不同于传统Ad Hoc网络的特点,针对这些特点,提出一种区域再生树汇聚的路由算法。算法中将传感区域内部的所有传感器节点采集的数据沿区域再生树的父子关系层层汇聚到传感区域内离Sink点最近的区域汇聚点,再将汇聚的数据通过全局路由树形成的最短路径传递给Sink节点。仿真结果显示区域再生树的数据汇聚能够减少数据传输量,并具有较小的传输时延。     

无线传感器网络基于数据汇聚的路由

提出了一种针对无线传感器网络的路由协议,该路由采用最小传输成本生成树的数据汇聚机制。具体方法是首先将传感区域内的传感器节点采集的数据传送给传感区域内离汇聚点最近的节点,将这些数据进行汇聚操作后,将汇聚的结果通过最短路径传递给网络汇聚点。仿真结果显示,采用最小传输代价生成树的路由协议能减少数据传输量50%-80%,并具有较小的传输时延。     

Rendezvous based routing protocol for wireless sensor networks with mobile sink

In wireless sensor networks, the sensor nodes find the route towards the sink to transmit data. Data transmission happens either directly to the sink node or through the intermediate nodes. As the sensor node has limited energy, it is very important to develop efficient routing technique to prolong network life time. In this paper we proposed rendezvous-based routing protocol, which creates a rendezvous region in the middle of the network and constructs a tree within that region. There are two different modes of data transmission in the proposed protocol. In Method 1, the tree is directed towards the sink and the source node transmits the data to the sink via this tree, whereas in Method 2, the sink transmits its location to the tree, and the source node gets the sink’s location from the tree and transmits the data directly to the sink. The proposed protocol is validated through experiment and compared with the existing protocols using some metrics such as packet delivery ratio, energy consumption, end-to-end latency, network life time.     

无线传感器网络的数据汇聚机制

针方法是首先将传感区域内部的所有传感器节点采集的数据都传送对传感器网络的特点，提出了一种最小传输成本生成树的数据汇聚机制。具体实现给传感区域内离汇聚点最近的节点，经过数据汇聚后，将汇聚的数据通过最短路径传递给汇聚点。仿真结果显示最小传输代价生成树的数据拒聚能够减少数据传输量50％-80％，并具有较小的传输时延。     

DAR: An energy-balanced data-gathering scheme for wireless sensor networks

A wireless sensor network faces special challenges due to its inherent features, such as the limited energy. The energy constraint drives research on how to utilize energy efficiently to prolong the lifetime of the network. Because a sink node takes the responsibility of collecting data from other nodes, a usual conception is to transfer data towards the sink node by multihop. However, conventional data-gathering schemes based on the conception give rise to the hotspot problem because of the nodes that run out of their energy sooner than other nodes, which results in accelerating the end of the whole network. The closer sensor nodes are to the sink, the more quickly they exhaust their energy, which leaves an upper bound to the lifetime of the whole network. Because of the bottleneck nodes, the network loses its service ability regardless of a large amount of residual energy of the other nodes. In this paper, we propose a novel data-gathering scheme, DAR, to handle the hotspot problem, in which all the nodes participate in the workload of gathering data from the whole network and transferring the data directly to the sink. In our scheme, the forwarding behavior of all the nodes is scheduled to balance their burden of aggregating and transmitting the network data and the nodes may send their data back against the sink, which differs from the conventional schemes. We performed simulation experiments to evaluate the performance of the DAR scheme, and the results show that our data-gathering scheme can balance the energy consumption among all the nodes and extend the network lifetime notably.     

Energy-aware node placement,topology control and MAC scheduling for wireless sensor networks

In the WSNs, the nodes closer to the sink node have heavier traffic load for packet forwarding because they do not only collect data within their sensing range but also relay data for nodes further away. The unbalanced power consumption among sensor nodes may cause network partition. This paper proposes efficient node placement, topology control, and MAC scheduling protocols to prolong the sensor network lifetime, balance the power consumption of sensor nodes, and avoid collision. Firstly, a virtual tree topology is constructed based on Grid-based WSNs. Then two node-placement techniques, namely Distance-based and Density-based deployment schemes, are proposed to balance the power consumption of sensor nodes. Finally, a collision-free MAC scheduling protocol is proposed to prevent the packet transmissions from collision. In addition, extension of the proposed protocols are made from a Grid-based WSN to a randomly deployed WSN, enabling the developed energy-balanced schemes to be generally applied to randomly deployed WSNs. Simulation results reveal that the developed protocols can efficiently balance each sensor node’s power consumption and prolong the network lifetime in both Grid-based and randomly deployed WSNs.     

改进的LEACH协议在井下通信系统中的应用

无线传感器网络由能量受限的节点组成,通过部署这些节点以便收集特定监测区域内的有用信息.基于层次的LEACH协议通过将节点分簇以实现数据融合.随机选择的簇头节点接收到本簇成员的数据后进行融合处理,将结果传输到汇聚节点.避免每个节点都与远距离的汇聚节点直接通信,从而节约能耗.将无线传感器网络应用于井下通信系统,能够提高通信的安全性.LEACH的分簇结构与矿井内分坑道工作的情况相类似,把每个坑道作为一个簇,将多数传感器节点安置在坑道内的固定位置,少量节点随矿工位置移动,再将这些节点采集的数据传输至簇头节点.本文主要针对井下通信系统的特点对现有的LEACH协议进行改进,优化了簇头节点的选举方法,并允许部分节点采用多跳方式与汇聚节点通信,使其更符合矿井结构的要求,从而节约了能耗,并且有效地延长了网络的生存时间.     

无线传感器网络中分布式小波压缩

无线传感器网络中传感节点的资源十分有限,为了减少无线传感器网络在数据通信时的能量消耗,提出了基于提升格式的分布式小波数据压缩算法及其简化算法.这种方法一方面把整个小波变换所需的计算量分布于各个节点之中,通过简化消除额外的计算和数据传输,而且对于每个节点来说,计算量都很小,易于实现.另一方面又能有效地消除无线传感器网络中节点内和节点间的信息冗余,大大节省了无线传输所消耗的能量.仿真结果表明,与经典的方法相比,在能耗和重构信号质量上都获得了良好的效果.     

分布式混合压缩感知无线传感器网络数据收集

在传感器网络数据收集过程中,降低网络传输量对于网络传输效率和生命周期的延长具有重要意义。结合压缩感知思想,设计了一种分布式混合压缩感知的无线传感器网络数据收集方法。首先通过基于k-means++的方法均匀聚类形成簇,各簇进行基于混合压缩感知的分布式数据收集,完成后通过建立骨干树将数据传输至sink节点。仿真结果表明,在给定的仿真工况下（压缩率为10,节点数为800）,与最短路径树混合压缩感知和最优树混合压缩感知算法相比,分别能减少40%和10%以上的传输量,与不使用混合压缩感知的收集方法相比减少70%以上的传输量;同时,节点传输量标准差由14.07和14.37和降低至11.85,置信区间大小由322.66和131.75降低至39.12,证明网络鲁棒性和负载均衡度均有提升。     

WSN数据收集中移动sink的路径规划和蔟头节点选取问题的综合研究

针对较大规模的无线传感器网络通过多跳传输进行数据收集而引起的能量空洞问题,本文提出了一种基于移动sink的簇头节点数据收集算法（MSRDG）,该算法基于图论原理,在满足时延性的条件下,综合考虑了普通节点到簇头节点路由和移动sink遍历路经选取的问题,构建了一条通过的簇头节点尽可能多的移动轨迹。通过NS-2仿真软件对算法的性能进行评估,结果显示出该算法能减少数据的多跳传输,降低无线传感器网络节点的能量消耗,延长网络寿命。     

无线传感网中一种智能数据融合算法的实现及仿真分析

在无线传感网中,传感器节点一般都由自身装配的电池供电,难以进行电量补充,因此节约电量对于无线传感网来说至关重要.为了提高无线传感网能量使用效率,延长网络生存时间,提出了一种结合遗传算法和粒子群算法优化BP神经网络的智能数据融合算法 GAPSOBP(BP Neural Network Data Fusion algorithm optimized by Genetic algorithm and Particle swarm).GAPSOBP算法将无线传感网的节点类比为BP神经网络中的神经元,通过神经网络提取无线传感网采集的感知数据并结合分簇路由对收集的传感数据进行融合处理,从而大幅减少发往汇聚节点的网络数据量.仿真结果表明,与经典LEACH算法和PSOBP算法相比,GAPSOBP算法能有效减少网络通信量,节约节点能量,显著延长网络生存时间.     

Coordinated Workload Scheduling in Hierarchical Sensor Networks for Data Fusion Applications

To minimize the execution time of a sensing task over a multi-hop hierarchical sensor network,we present a coordinated scheduling method following the divisible load scheduling paradigm.The proposed scheduling strategy builds on eliminating transmission collisions and idle gaps between two successive data transmissions.We consider a sensor network consisting of several clusters.In a cluster,after related raw data measured by source nodes are collected at the fusion node, in-network data aggregation is fu...     

数据传输时延和跳数受限的Sink节点移动路径选择算法

考虑实际无线传感网系统中数据传输时延和跳数受限情况,且为降低算法的时间复杂度,提出一种移动无线传感网的Sink节点移动路径选择算法（MPSA）。在MPSA算法中,Sink节点采用分布式最短路径树算法收集k+1跳通信范围内传感节点的相关信息和感知数据,采用虚拟力理论计算边界、障碍物和空洞区域的虚拟斥力、第k+1跳未覆盖传感节点的虚拟引力和所有虚拟力的合力,根据停留次数、合力大小和方向等信息计算当前网格中心的停留时间和下一个停留网格中心。仿真结果表明：MPSA算法根据传感节点的位置、剩余能量等信息,寻找到一条较优的移动路径,从而提高Sink节点的数据收集量和节点覆盖率,降低传感节点的感知数据丢弃量。总之,在数据传输时延和跳数受限下,MPSA算法比RAND算法、GMRE算法和EASR算法更优。     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

一种WSN网关节点的设计与实现

无线传感器网络WSN是由分布在给定区域内大量无线传感器节点构成的一种新型信息获取系统,而无线传感器硬件节点的设计与实现是其应用的关键和基础工作.针对将无线传感器网络应用在青藏铁路沿线多年冻土区典型段进行地温、变形监测方面的特殊要求,设计了一种无线传感器网络系统,该网络由大量普通传感器节点、若干网关节点及一台计算机构成.无线传感器节点布撒在需要监测的区域内.将所探测到的有用信息通过初步的数据处理和信息融合之后,通过相邻节点接力的方式传送给网关节点.网关节点通过无线方式接收各传感器节点的数据并以有线的方式将数据传送给最终用户计算机.本文详细介绍了一种基于CC2431的网关节点以及基于C8051F320的USB接口的软硬件设计与实现.