基于改进主动学习的HWSN网络入侵检测方法

针对HWSN网络拓扑结构复杂、多源异构数据交叉分布的特点,提出一种经过改进的主动学习的入侵检测方法。首先,构建五元组的主动学习模型,利用SVM分类器改变异构传感网络中输入数据和输出数据之间的单纯线性关系,提升算法的泛化能力;其次,对网络中的原始数据做归一化处理并主动定制标签,划分数据集的类型,将训练集中的训练数据和测试集中的测试数据类型全部转换为数值型数据;最后,利用主成分分析法提出入侵数据集的特征。仿真结果显示,提出的改进检测算法在检测率和召回率方面均具有优势,在训练耗时和检测效率等方面也优于传统方法。

六边形覆盖条件下的HWSN最大寿命模型

提出一种在六边形覆盖约束条件下异构无线传感器网络的"突然死亡"能量消耗模型,该模型可以保证能量均衡消耗和网络寿命最大化。在该能耗模型基础上给出一种考虑节点能耗约束和数据完整性等因素的网络寿命模型,并求得网络寿命模型的最优解。仿真结果表明,构建在六边形覆盖条件下的异构监测传感器模型可以均衡网络能耗,且使网络寿命最大化。

基于HWSN虚拟蜂窝网格的能量剖分算法

随着传感器应用范围的扩展,在复杂地形部署不同类型异构节点已是一个必然趋势。首先对同构蜂窝网格的网络传输功耗进行了研究;其次从拓扑学角度把同构蜂窝网格结构延伸到异构网络环境中,严格计算理想状态下无缝覆盖监测区域下两类节点的通信半径比例,提出异构蜂窝网格结构;最后利用数学建模、几何理论和数据归纳法,建立异构环境下的能量模型,对异构蜂窝网格的能量消耗方式进行了分析,并比较同构和异构网络下的能量优劣。仿真实验表明,异构蜂窝网格环境下节点收发数据的能量消耗更少。

面向无线异构传感器网络的三维覆盖研究

【目的】为达到增强无线异构传感器网络(HWSN)三维覆盖能力的目的,提出了一种基于改进蜜獾优化算法(IHBA)的无线异构传感器网络三维部署方法。【方法】首先,结合自适应果蝇优化算法,增强算法的随机搜索性,便于算法得到全局最优解,然后引入替换最差个体策略,避免适应度过低的个体占据种群位置,提高算法收敛速度,同时引入新个体提高种群多样性,避免算法个体早熟。【结果】将该算法应用于无线异构传感器网络的覆盖优化,相比标准蜜獾算法,其网络覆盖率提升18.1%。【结论】仿真结果表明,该算法收敛速度更快,可以有效提高无线异构传感器的网络覆盖能力,整个网络的节点分布也更加均匀。

异构传感器网络覆盖势力剖分算法

对于普遍存在的异构传感器网络,目前尚缺乏有力的方法解决其覆盖势力的剖分问题。对此,该文提出一种本地化的覆盖势力剖分算法—CFA(Coverage Force Algorithm)。该算法根据节点感应能力的差异,构建基于感应异构性的"通用Voronoi"图,能有效对网络中异构节点的覆盖势力范围进行剖分。实验证明,CFA算法解决了异构网络覆盖性能分析问题,和传统的Voronoi图方法相比,具有广普性和本地化的特点。

基于蜂窝结构的改进混合无线传感器网络覆盖优化算法

基于蜂窝结构的混合无线传感器网络(HWSN)覆盖优化算法HWSNBCS存在移动节点平均移动距离较大的问题,为此,提出一种改进的HWSN覆盖优化算法IHWSNBCS。寻找移动传感器节点初始位置与通过HWSNBCS算法得出的候选目标位置之间的最优匹配,将移动节点移动距离之和最小化问题转化为二分图最优匹配问题,利用带权二分图匹配算法KM寻找该匹配问题的最优解,从而得到移动节点最终的目标位置,并实现对HWSNBCS算法移动节点平均移动距离的进一步优化。实验结果表明,IHWSNBCS算法在取得与HWSNBCS算法相同网络覆盖率的前提下,移动节点的平均移动距离减少幅度达到38.87%~43.28%,单个移动节点的最大移动距离减少幅度达到22.65%~66.58%,降低了系统因重新部署移动传感器节点所产生的能耗以及单个传感器节点因能量耗尽而失效的概率,从而延长了网络生命周期,同时,IHWSNBCS的ΔCov-Dist性能指标为HWSNBCS算法的1.64~1.76倍,表明移动节点移动相同距离时IHWSNBCS算法的网络覆盖率提升更大。

异构无线传感器网络组密钥管理方案设计

为解决大规模异构无线传感器网络组密钥更新能耗大、效率低的问题,提出一种基于拓扑信息的异构无线传感器网络组密钥管理方案。利用节点拓扑信息构建αβ密钥管理树,在密钥管理树的生成与更新过程中对其进行结构优化,从而减少组密钥更新的能量消耗与更新时延。仿真实验表明,该方案是一种高效可扩展的组密钥管理方案,适合应用于异构无线传感器网络中。

异构无线传感器网络高效密钥管理方案

在分析现有密钥管理方案的基础上,提出一种异构无线传感器网络密钥管理方案,该方案为H-Sensor和L-Sensor产生不同数目的密钥链生成值,采用散列函数与密钥链生成值迭代生成密钥链,通过产生的密钥链和迭代次数运用分组链接技术构造会话密钥。分析表明,该方案能够利用较低的存储负载获得较高的密钥连通性,同时提高整个网络的安全性能。

基于遗传算法的异构无线传感器网络分簇算法

为进一步均衡无线传感器网络能耗,提出一种基于遗传算法改进的异构无线传感器网络分簇算法。在簇头选举阶段,分析传感器节点剩余能量、节点分布位置、节点的邻居节点密度因素,通过遗传算法寻优,选举出每轮最佳数目的最优簇头集;同时为解决周期性分簇造成的能量损失,建立簇头更新策略,降低簇头集更新频率。仿真结果表明,该算法与SEP、LEACH和TSEP算法相比较,第一死亡节点出现时间分别推迟了44.59%,69.4%和8.85%,网络寿命延长了174%,47.78%和71.33%。

A Data Transmission Path Optimization Protocol for Heterogeneous Wireless Sensor Networks Based on Deep Reinforcement Learning

Wireless sensor networks had become a hot research topic in Information science because of their ability to collect and process target information periodically in a harsh or remote environment. However, wireless sensor networks were inherently limited in various software and hardware resources, especially the lack of energy resources, which is the biggest bottleneck restricting their further development. A large amount of research had been conducted to implement various optimization techniques for the problem of data transmission path selection in homogeneous wireless sensor networks. However, there is still great room for improvement in the optimization of data transmission path selection in heterogeneous wireless sensor networks (HWSNs). This paper proposes a data transmission path selection (HDQNs) protocol based on Deep reinforcement learning. In order to solve the energy consumption balance problem of heterogeneous nodes in the data transmission path selection process of HWSNs and shorten the communication distance from nodes to convergence, the protocol proposes a data collection algorithm based on Deep reinforcement learning DQN. The algorithm uses energy heterogeneous super nodes as AGent to take a series of actions against different states of HWSNs and obtain corresponding rewards to find the best data collection route. Simulation analysis shows that the HDQN protocol outperforms mainstream HWSN data transmission path selection protocols such as DEEC and SEP in key performance indicators such as overall energy efficiency, network lifetime, and system robustness.

Study on key management scheme for heterogeneous wireless sensor networks

Heterogeneous wireless sensor network( HWSN) is composed of different functional nodes and is widely applied. With the deployment in hostile environment,the secure problem of HWSN is of great importance; moreover,it becomes complex due to the mutual characteristics of sensor nodes in HWSN. In order to enhance the network security,an asymmetric key pre-distributed management scheme for HWSN is proposed combining with authentication process to further ensure the network security; meanwhile,an effective authentication method for newly added nodes is presented. Simulation result indicates that the proposed scheme can improve the network security while reducing the storage space requirement efficiently.

Optimal Control of Heterogeneous-Susceptible-Exposed-Infectious-Recovered-Susceptible Malware Propagation Model in Heterogeneous Degree-Based Wireless Sensor Networks

Heterogeneous wireless sensor networks(HWSNs)are vulnerable to malware propagation,because of their low configuration and weak defense mechanism.Therefore,an optimality system for HWSNs is developed to suppress malware propagation in this paper.Firstly,a heterogeneous-susceptible-exposed-infectious-recovered-susceptible(HSEIRS)model is proposed to describe the state dynamics of heterogeneous sensor nodes(HSNs)in HWSNs.Secondly,the existence of an optimal control problem with installing antivirus on HSNs to minimize the sum of the cumulative infection probabilities of HWSNs at a low cost based on the HSEIRS model is proved,and then an optimal control strategy for the problem is derived by the optimal control theory.Thirdly,the optimal control strategy based on the HSEIRS model is transformed into corresponding Hamiltonian by the Pontryagin’s minimum principle,and the corresponding optimality system is derived.Finally,the effectiveness of the optimality system is validated by the experimental simulations,and the results show that the infectious HSNs will fall to an extremely low level at a low cost.

异构传感器网络中的一种混合密钥预分配方案

针对异构无线传感器网络,提出一种混合密钥预分配方案。该方案针对异构节点采用不同密钥预分配策略:高级节点采用基于身份的公钥密码机制进行节点间的认证和密钥协商;普通节点主要采用基于单向散列链的随机密钥预分配策略,在高级节点的辅助下进行对偶密钥的建立。在对偶密钥建立过程中,采用共享密钥发现和密钥协商相结合的方法,充分发挥公钥密码机制和随机密钥预分配策略的优势,达到较好的安全连通性和抗毁性。理论分析和仿真试验结果证明方案的可用性和安全性。

基于部署知识的异构WSN密钥管理算法

现有算法在抗节点捕获攻击方面的性能较差,为此,提出一种基于部署知识的密钥预管理算法,将部署区域划分为若干个互不重叠的三角形网格,并将多密钥空间算法用于相邻的网格。理论分析和仿真实验证明,与现有方案相比,该算法可以使网络在保持较高密钥连通度的同时,较好地抵御节点捕获攻击。

能量异构无线传感器网络分簇路由算法研究

无线传感器网络作为物联网(Internet of Things,IOT)收集信息的重要一环,分为异构和同构网络,本文研究的主要是能量异构网络(Energy Heterogeneous Wireless Sensor Networks,EHWSN)。DEEC算法(Distribute Energy-Efficient Clustering Algorithm)是应用于能量异构网络的基本算法,由于无线传感器网络受硬件限制,能量是非常有限的。因此,尽可能延长网络的生命周期,降低能量消耗是分簇算法的首要功能。提出了一种改进的DEEC算法—DEEC-BD(Distribute Energy-Efficient Clustering Algorithm based on distance),其核心思想是引入距离因子改进概率Pi,并在数据传输阶段使用多跳的方式,通过对路径质量参数的大小的比较来选择下一跳,达到降低能耗的目的。仿真结果表明,DEEC-BD与DEEC、CREEP相比,生命周期分别提高了79%、37.5%,数据传输量分别提高了450%、41.5%,网络能耗分别降低了52%、16.7%。

异构WSN中基于改进哈里斯鹰的分簇算法

针对无线传感器网络中传统的低功耗自适应集簇分层型协议存在的节点能耗过高、网络生存周期短以及负载不均衡等问题,本文提出了一种异构传感网络下的多目标簇头选举和基于模拟退火的哈里斯鹰路由优化算法(LEACH-MHO).这种改进算法首先在计算节点最优阈值的基础上,构建新的考量能耗和负载的适应度函数,找到最优簇首节点,保证簇首节点的均匀分布;再建立基于哈里斯鹰优化器的路径选择策略,同时嵌入模拟退火算法,防止过早陷入局部最优;最后使用评估函数筛选出可加入到最佳路径的簇头,缩短簇头节点到基站的通信距离.仿真实验数据表明,与CREEP、LEACH-C、LEACH算法相比,本文算法的网络生存寿命分别延长了22.18%、77.83%和180.52%,能更有效地延长网络生存寿命.

A Clustering-tree Topology Control Based on the Energy Forecast for Heterogeneous Wireless Sensor Networks

How to design an energy-efficient algorithm to maximize the network lifetime in complicated scenarios is a critical problem for heterogeneous wireless sensor networks (HWSN). In this paper, a clustering-tree topology control algorithm based on the energy forecast (CTEF) is proposed for saving energy and ensuring network load balancing, while considering the link quality, packet loss rate, etc. In CTEF, the average energy of the network is accurately predicted per round (the lifetime of the network is denoted by rounds) in terms of the difference between the ideal and actual average residual energy using central limit theorem and normal distribution mechanism, simultaneously. On this basis, cluster heads are selected by cost function (including the energy, link quality and packet loss rate) and their distance. The non-cluster heads are determined to join the cluster through the energy, distance and link quality. Furthermore, several non-cluster heads in each cluster are chosen as the relay nodes for transmitting data through multi-hop communication to decrease the load of each cluster-head and prolong the lifetime of the network. The simulation results show the efficiency of CTEF. Compared with low-energy adaptive clustering hierarchy (LEACH), energy dissipation forecast and clustering management (EDFCM) and efficient and dynamic clustering scheme (EDCS) protocols, CTEF has longer network lifetime and receives more data packets at base station. © 2014 Chinese Association of Automation.

基于LEACH的低能耗路由协议研究

本文基于LEACH协议提出一种改进的路由协议N-LEACH,调整了节点竞选簇头的阈值函数T(n),引入加权函数cost完成簇的构造过程,在簇构造过程中实现节点的认证,并通过多跳机制建立簇头节点与基站之间的路由。仿真结果表明本文提出的改进协议N-LEACH与传统的LEACH协议相比较好地实现了网络的负载均衡,延长了节点与网络的寿命,提高了网络的安全性。

基于多项式异构无线传感器网络密钥预分配方案

针对现有随机密钥预分配方案中不同节点间能够建立相同共享密钥的不足,提出了一种基于多项式的异构无线传感器网络密钥预分配方案。从网络连通性、节点内存需求和安全性等方面对方案进行性能评价和模拟仿真。结果表明,本方案能够有效地提高网络的连通性,减小节点所需的存储空间,并增强网络抗攻击能力。

Low-latency Data Gathering with Reliability Guaranteeing in Heterogeneous Wireless Sensor Networks

In order to achieve low-latency and high-reliability data gathering in heterogeneous wireless sensor networks(HWSNs),the problem of multi-channel-based data gathering with minimum latency(MCDGML),which associates with construction of data gathering trees,channel allocation,power assignment of nodes and link scheduling,is formulated as an optimization problem in this paper.Then,the optimization problem is proved to be NP-hard.To make the problem tractable,firstly,a multi-channel-based low-latency(MCLL)algorithm that constructs data gathering trees is proposed by optimizing the topology of nodes.Secondly,a maximum links scheduling(MLS)algorithm is proposed to further reduce the latency of data gathering,which ensures that the signal to interference plus noise ratio(SINR)of all scheduled links is not less than a certain threshold to guarantee the reliability of links.In addition,considering the interruption problem of data gathering caused by dead nodes or failed links,a robust mechanism is proposed by selecting certain assistant nodes based on the defined one-hop weight.A number of simulation results show that our algorithms can achieve a lower data gathering latency than some comparable data gathering algorithms while guaranteeing the reliability of links,and a higher packet arrival rate at the sink node can be achieved when the proposed algorithms are performed with the robust mechanism.