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.

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。

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.

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.