Energy problem is one of the most important challenges in the application of sensor networks. With the development of wireless charging technology and intelligent mobile charger (MC), the energy problem can be solved by wireless charging strategy. In the practical application of Wireless Rechargeable Sensor Networks (WRSNs), the energy consumption rate of nodes is dynamically changed due to many uncertainties such as the death and different transmission tasks of sensor nodes. However, existing works focus on on-demand schemes, which not fully consider real-time global charging scheduling. In this paper, a novel Dynamic Charging Scheme (DCS) in WRSN based on Actor-Critic Reinforcement Learning (ACRL) algorithm is proposed. In the ACRL, we introduce Gated Recurrent Units (GRU) to capture the relationships of charging actions in time sequence. Using the Actor network with one GRU layer, we can pick up an optimal or near-optimal sensor node from candidates as the next charging target more quickly and speed up the training of the model. Meanwhile, we take the tour length and the number of dead nodes as the reward signal. Actor and Critic networks are updated by the error criterion function of R and V. Compared with current on-demand charging scheduling algorithms, extensive simulations show that the proposed ACRL algorithm surpasses heuristic algorithms such as the Greedy, DP, NJNP and TSCA in the average lifetime and tour length, especially against the size and complexity increasing of WRSNs.

A novel coronavirus disease (COVID-19) is a pandemic disease has caused 4 million deaths and more than 200 million infections worldwide (as of August 4, 2021). Rapid and accurate diagnosis of COVID-19 infection is critical to controlling the spread of the epidemic. In order to quickly and efficiently detect COVID-19 and reduce the threat of COVID-19 to human survival, we have firstly pro- posed a detection framework based on reinforcement learn- ing for COVID-19 diagnosis, which constructs a mixed loss function that can integrate the advantages of multiple loss functions. This paper uses the accuracy of the validation set as the reward value, and obtains the initial model for the next epoch by searching the model corresponding to the maximum reward value in each epoch. We also have pro- posed a prediction framework that integrates multiple de- tection frameworks using parameter sharing to predict the progression of patients' disease without additional training. This paper also constructed a higher-quality version of the CT image dataset containing 247 cases screened by profes- sional physicians, and obtained more excellent results on this dataset. Meanwhile, we used the other two COVID-19 datasets as external verifications, and still achieved a high accuracy rate without additional training. Finally, the exper- imental results show that our classification accuracy can reach 98.31%, and the precision, sensitivity, specificity, and AUC (Area Under Curve) are 98.82%, 97.99%, 98.67%, and0.989, respectively. The accuracy of external verification can reach 93.34% and 91.05%. What's more, the accuracy of our prediction framework is 91.54%. A large number of experiments demonstrate that our proposed method is effective and robust for COVID-19 detection and prediction.

The basic operation of delay tolerant mobile sensor network (DTMSN) is for pervasive data gathering in networks with intermittent connectivity, where traditional data gathering methods can not be applied. In this paper, an efficient mobility prediction-based adaptive data gathering protocol (MPAD) based on the random waypoint mobility model tailored for DTMSN is proposed. In MPAD, a node independently makes decision to replicate messages and send them to the neighbor sensor nodes with a higher probability of meeting the sink node. MPAD consists of two components for data transmission and queue management. Data transmission makes decisions on when and where to transmit data messages according to the node delivery probability, and the queue management employs the message survival time to decide whether the message should be transmitted or dropped for minimizing the transmission overhead. Simulation results show that the proposed MPAD achieves the longer network lifetime and the higher message delivery ratio with the lower transmission overhead and data delivery delay than some other previous solutions designed for DTMSN, such as direct transmission, flooding and message fault tolerance-based data delivery protocol (FAD).

In Delay Tolerant Mobile Sensor Networks (DTMSNs) that have the inherent features of intermitted connectivity and frequently changing network topology it is reasonable to utilize multi-replica schemes to improve the data gathering performance. However, most existing multi-replica approaches inject a large amount of message copies into the network to increase the probability of message delivery, which may drain each mobile node’s limited battery supply faster and result in too much contention for the restricted resources of the DTMSN, so a proper data gathering scheme needs a trade off between the number of replica messages and network performance. In this paper, we propose a new data gathering protocol called DRADG (for Distance-aware Replica Adaptive Data Gathering protocol), which economizes network resource consumption through making use of a self-adapting algorithm to cut down the number of redundant replicas of messages, and achieves a good network performance by leveraging the delivery probabilities of the mobile sensors as main routing metrics. Simulation results have shown that the proposed DRADG protocol achieves comparable or higher message delivery ratios at the cost of the much lower transmission overhead than several current DTMSN data gathering schemes.

In heterogeneous wireless sensor networks (HWSNs), heterogeneous sensors may follow various data-generating distributions, which makes data collection a very challenging task. Although mobile sinks (MSs) are widely used to collect data from wireless sensors, existing MS-assisted approaches are often based on the assumption of synchronized data, for example, all sensors generate data at the same time, in which data are considered delay-tolerant and -sensitive data according to delay limits, while the generation time of data is ignored. This article focuses on unsynchronized data collection for HWSNs, in which unsynchronized data generation of sensors is allowed as real as actual monitoring applications. First, to reflect the timeliness of data, we use a rigid collection window to represent the lifetime of sensing data to refine the visiting time of the MS. Second, a graph attention network (GAT) structure is adopted to describe node locations, accessible paths, and data with collection windows for path planning. Third, a new deep reinforcement learning (DRL)-based MS path planning (MSPP) framework is proposed to tackle the path of the MS by minimizing total energy cost while satisfying data lifetime constraints. MSPP first uses the Target Selector module to plan the moving targets and adopts the MS Controller module to control MS mobility for achieving fast convergence and better optimality. Finally, extensive simulations show that our scheme provides explicit data collection guarantees and minimum energy consumption.