泊松点距离约束下无人机辅助地面应急网络覆盖性能研究

在用户数量激增的应急通信场景下,为保证地面用户的通信质量,提出了基于距离约束的用户自适应接入方案。首先采用泊松点距离约束策略(Poisson Point under Distance Constraint, PPDC)对无人机(Unmanned Aerial Vehicle, UAV)的位置进行建模,避免无人机区域重叠带来的干扰问题。其次,引入基站负载传输协议(Base Station Load Transfer Protocol, BSLTP),当接入基站的用户数量超过给定阈值时,超载用户由无人机提供服务。此外,分别分析了地面基站和无人机的覆盖性能,得到了系统整体覆盖概率,并研究了无人机高度、覆盖半径、激增用户密度对网络覆盖性能的影响。最后,通过仿真验证了理论结果的正确性,且所提部署方案能够有效提升网络覆盖性能。     

基于邻居节点平均距离的网络覆盖判别模型

在 SITE(信息技术与工程学院)算法基础上,提出了 ADDM(基于邻居节点平均距离的网络覆盖判别模型)算法,通过计算所有邻居节点与本节点的平均距离和交叠区域的圆心角来判别该节点是否为冗余节点。理论分析与仿真实验表明,AD-DM算法较大地改善了 SITE算法,成功解决了 SITE算法没有考虑距离大于感知半径的节点对覆盖冗余贡献的问题,从而用更少的节点实现了区域的覆盖,节省了网络节点的活动数目,延长了网络的生存时间。     

混合传感器网络中基于向量代数的覆盖补偿算法

针对混合传感器网络中,能量耗尽造成的覆盖空洞与休眠冗余节点能量剩余可能并存的矛盾现象,提出一种基于向量代数的移动节点覆盖补偿方法.算法包含所有可能参与补偿的冗余节点对各自位移量的计算方法,空洞边缘节点在平衡节点剩余能量的基础上,对最佳冗余节点的选择启用策略,保证网络能充分利用遗留能量资源的同时实现完全覆盖补偿.实验分析表明,本算法在平均移动距离、能量开销等方面均优于COA算法.     

面向图表示社区检测的新型聚类覆盖算法

图表示社区检测使用图表示方法学习网络节点的向量表示,然后对节点向量进行聚类获得社团结构.然而经典的聚类算法在聚类节点向量时,得到的结果往往不能够体现社区的特性.提出一种新型的聚类覆盖算法,将聚类所得覆盖视为社区划分结果.首先在节点向量空间中计算得到每个簇的覆盖中心;然后根据覆盖中心到同类样本的平均距离作为覆盖半径,在向量空间中形成覆盖;最后对未覆盖的点做二次划分得到社区结构.在多个有真实和无真实标签网络的实验表明,所提出的算法可以得到更合理的社区结果.     

基于改进RRT算法的无人机航迹规划

针对快速扩展随机树（RRT）算法用于无人机自主在线航迹规划时,只能快速获得可行的航迹,无法获得接近于最短航迹的较优航迹的缺点,提出了一种改进的RRT算法.该算法将无人机动力学约束融入到节点扩展过程中,通过改进离随机采样点最近的根节点的选取策略和引入航迹距离约束,搜索树将沿着航迹距离较短的方向朝着目标点进行扩展,使得规划出来的航迹接近最优,并采用基于B样条曲线的航迹平滑方法生成平滑可跟踪的航迹.仿真结果表明该算法能够快速地搜索安全并且满足无人机动力学约束的较优航迹.     

物联网数据收集中无人机路径智能规划

为解决无人机在数据收集过程中的路径规划问题,将其分为全局路径规划和局部路径规划。针对全局路径规划,将其建模为一个定向问题,定向问题是背包问题和旅行商问题2种经典优化问题的组合。采用指针网络深度学习对该模型进行求解,并在无人机能量约束下得到其服务节点集合及服务顺序。针对局部路径规划,基于无人机接收到节点的参考信号强度,通过深度Q网络学习对无人机局部飞行路径进行规划,使无人机逼近节点位置并服务各节点。仿真结果表明,所提方案能够在无人机能量约束下有效提升其数据收集的收益。     

基于ISODATA约束聚类的多无人机动态任务分配

战场环境的动态性和不确定性以及任务分配问题的复杂性,使得无人机飞行前预先规划难以满足时间敏感性要求。针对多无人机动态任务分配问题,建立了数学模型,并设计了一种求解该问题的ISODATA约束聚类法。运用分组基础上的任务再分配策略,首先通过ISODATA约束聚类实施任务分组,然后在分组基础上利用免疫粒子群算法进行组内任务重分配。仿真实验和分析表明该方法简单有效,能够应对战场环境中的多种突发情况,具有较好的时间性能。     

无人机覆盖搜索地域通信网交叉式航路规划

针对地域通信网的侦察搜索问题,提出一种以交叉式航路来实现对地域通信网部署区域完全覆盖搜索的无人机航路规划方法。首先,根据地域通信网结构特点以及干线节点天线波束特性,定性地分析得到无人机侦察干线节点时的最大侦察距离与盲区侦察距离,并分析得到无人机搜索干线节点的静态模型以及动态模型;其次,根据侦察模型,建立并优化无遗漏搜索的无人机交叉式搜索航路模型,该方法可保证搜索到地域通信网所有干线节点,并提高任务执行效率;最后,对无人机交叉搜索航路进行仿真,仿真结果表明,该方法能有效搜索到地域通信网每一个干线节点,并求解了无人机交叉式覆盖搜索航路代价。     

结合地理位置与语义聚类的P2P叠加网构建

针对目前P2P叠加网效率不高与拓扑失配问题,以基于超节点体系结构的文件共享应用为背景,提出了一种结合网络节点位置信息与语义聚类的P2P叠加网拓扑构建策略POCLS.POCLS中节点依据物理位置信息就近组成各自治域,域中节点再根据共享资源的语义形成多个语义聚类,利用域和聚类对P2P叠加网进行双重管理.仿真表明POCLS在保证查询成功率的同时,有效减少了查询延时与网络冗余流量,提高了P2P网络性能.     

基于聚类模型预测的无线传感网自适应采样技术研究

该文利用无线传感网(WSNs)的数据空间相关性,提出一种基于数据梯度的聚类机制,聚类内簇头节点维护簇成员节点的数据时间域自回归(AR)预测模型,在聚类内范围实施基于预测模型的采样频率自适应算法。通过自适应优化调整采样频率,在保证数据采样精度的前提下减少了冗余数据传输,提高无线传感网的能效水平。该文提出的时间域采样频率调整算法综合考虑了感知数据的时空联合相关性特点,仿真结果验证了该文算法的性能优势。     

基于UAV的移动物联网远距离通信节能策略研究

针对应急移动物联网在缺少地面基站的情况下,可靠节能地远距离传输重点区域全信息的要求,本文将无人机（Unmanned Aerial Vehicle,UAV）作为空基通信平台,研究应急移动物联网应用中大规模移动物联网设备能量受限条件下的远距离通信问题,提出基于无人机的移动物联网远距离通信节能策略.首先,对该类物联网进行系统建模;其次,根据所建模型中地面设备无序立体地分散于重点区域的特点,通过优化无人机的布署位置,在提供可靠通信的同时,降低地面设备的通信耗能;再次,根据地面设备运动性能强、活动范围广的特点,通过研究无人机与地面移动设备的联合运动策略,避免无人机频繁换簇覆盖所引起的远距离移动等大运动状态变化问题,实现无人机的移动节能.最后,通过实验,从通信耗能和运动耗能两方面验证了本文所提策略的有效性.     

Communication Coverage in Wireless Passive Sensor Networks

System lifetime of wireless sensor networks (WSN) is inversely proportional to the energy consumed by critically energy-constrained sensor nodes during RF transmission. In that regard, modulated backscattering (MB) is a promising design choice, in which sensor nodes send their data just by switching their antenna impedance and reflecting the incident signal coming from an RF source. Hence, wireless passive sensor networks (WPSN) designed to operate using MB do not have the lifetime constraints of conventional WSN. However, the communication performance of WPSN is directly related to the RF coverage provided over the field the passive sensor nodes are deployed. In this letter, RF communication coverage in WPSN is analytically investigated. The required number of RF sources to obtain interference-free communication connectivity with the WPSN nodes is determined and analyzed in terms of output power and the transmission frequency of RF sources, network size, RF source and WPSN node characteristics.     

A new method for distributing power usage across a sensor network

We present a method for more uniformly distributing the energy burden across a wireless ground-based sensor network communicating with an overhead unmanned aerial vehicle (UAV). A subset of sensor nodes, termed a transmit cluster, receives and aggregates data gathered by the entire network, and forms a distributed antenna array, concentrating the radiated transmission into a narrow beam aimed towards the UAV. Because these duties are power-intensive, the role of transmit cluster must be shifted to different nodes as time progresses. We present an algorithm to reassign the transmit cluster, specifying the time that should elapse between reassignments and the number of hops that should be placed between successive transmit clusters in order to achieve three competing goals: first, we wish to better and more broadly spread the energy load across the sensor network while, second, minimizing the energy expended in moving the transmit cluster, all the while, third, reducing to the extent practicable the time to bring the UAV and the sensor network’s beam into alignment. Additionally, we present a method for reconfiguring the communication burden between the ground-based sensor network and the UAV. We describe and analyze two alternative strategies to bring the UAV and the sensor network’s beam into alignment, while minimizing the energy expended by the sensor network. The performance of the two strategies is compared in terms of probability of beam-UAV alignment as a function of time, and the expected time to alignment. We examine the performance tradeoff between the choice of strategy and parameters of the sensor network that affect power conservation.     

无人机辅助车联网的网络传输链路优化

无人机(UAV)通信技术的快速发展与智能车联网应用需求的极速增长促进了无人机辅助车联网系统的产生与发展。在无人机辅助车联网系统中,如何节省能量的同时最大化系统性能对于能量有限的节点十分重要。基于此,本文主要考虑无人机辅助车联网通信过程中如何选择最优的通信网络链路,从而最大化能量效率的问题。首先建立通信网络链路选择问题为混合整数规划问题,然后提出基于能量效率最大化的网络传输链路优化算法获得最优的传输链路及对应的能量分配,最后通过数值实验仿真验证了所提算法的有效性。     

基于聚类的无线传感网络的合作MIMO传输方案

Wireless sensor network is becoming more and more popular in recent years, but energy-constrained characteristic of sensor nodes is one of the critical issues that we must consider in system design. In this paper, a cluster-based virtual V-BLAST transmission scheme is proposed to achieve energy savings for energy-constrained wireless sensor networks. In the proposed scheme, instead of using cluster member as cooperative nodes, multiple cluster heads cooperate to form virtual antenna array so that V-BLAST based virtual MIMO transmission can be implemented. Based on the communication energy consumption model, a way to optimize the parameters for the scheme is given. In addition, detailed simulation is performed to evaluate the performance of the proposed scheme for both densely and sparsely deployed sensor networks. Theoretical analysis and simulation results verify the energy efficiency of the proposed scheme.     

EHCR-FCM: Energy Efficient Hierarchical Clustering and Routing using Fuzzy C-Means for Wireless Sensor Networks

Wireless Sensor Network (WSN) is a part of Internet of Things (IoT), and has been used for sensing and collecting the important information from the surrounding environment. Energy consumption in this process is the most important issue, which primarily depends on the clustering technique and packet routing strategy. In this paper, we propose an Energy efficient Hierarchical Clustering and Routing using Fuzzy C-Means (EHCR-FCM) which works on three-layer structure, and depends upon the centroid of the clusters and grids, relative Euclidean distances and residual energy of the nodes. This technique is useful for the optimal usage of energy by employing grid and cluster formation in a dynamic manner and energy-efficient routing. The fitness value of the nodes have been used in this proposed work to decide that whether it may work as the Grid Head (GH) or Cluster Head (CH). The packet routing strategy of all the GHs depend upon the relative Euclidean distances among them, and also on their residual energy. In addition to this, we have also performed the energy consumption analysis, and found that our proposed approach is more energy efficient, better in terms of the number of cluster formation, network lifetime, and it also provides better coverage.

     

Load balancing clustering and routing for IoT-enabled wireless sensor networks

Internet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy-efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c-means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO-based clustering (GWO-C) and Cat Swarm Optimization based Energy-Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.     

Energy cooperation in communication of energy harvesting tags

Energy harvesting wireless devices are recently emerging as a viable infrastructure for internet of things (IoT) applications. In this paper, an energy cooperative transmission strategy is proposed for a network energy harvesting tags, that is adapted to the available energy resource and identification request. We develop an optimal transmission policy to maximize the long-term average throughput performance via a Markov decision process (MDP) formulation. Numerical results are provided to show the performance of the energy cooperative transmission policy under various scenarios.     

基于自适应巡视算法的工业物联网异常行为检测

针对当前物联网节点易受攻击的问题,提出一种基于自适应巡视算法的工业物联网异常行为检测方法。根据数据包完整性、数据包传输率和传输延迟等异常行为动态更新物联网节点信誉度,结合信誉阈值自适应调整巡视间隔。该方法能够实现低能耗、高效率的工业物联网安全防护,提升异常行为检测的自适应性。实验证明,该方法与传统LEACH-C机制相比,能更好降低网络能耗、延长节点生存时间,因此该方法具有一定的可用性。     

End-to-End Energy–Bandwidth Tradeoff in Multihop Wireless Networks

In this paper, energy-constrained wireless multihop networks with a single source-destination pair are considered. A network model that incorporates both the energy radiated by the transmitter and the energy consumed by the circuits that process the received signals is proposed. The rate of communication is the number of information bits transmitted (end-to-end) per coded symbol transmitted by any node in the network that is forwarding the data. The tradeoff between the total energy consumption and the end-to-end rate of communication is analyzed. The performance (either energy or rate) depends on the transmission strategy of each node, the location of the relay nodes, and the data rate used by each node. Communication strategies include the rate of transmission on each link, the scheduling of links, and the power used for each link. Strategies that minimize the total energy consumption for a given rate are found. Two communication strategies that capture the inherent constraints of some practical networks are also considered and compared with the optimum strategies. In the case of equispaced relays, analytical results for the tradeoff between the energy and the end-to-end data rate are provided. The minimum energy over all possible data rates is also obtained. Low rates incur a significant penalty because the receiver is on for a long time period while high rates require high transmission energy. At high rates routes with fewer hops minimize the energy consumption while at lower rates more hops minimize the energy consumption.