In wireless sensor networks (WSNs), sensor readings have spatiotemporal correlation, and wireless links are unreliable. To balance the energy-performance trade-off and reduce the impact of packet loss, a novel approach is proposed that combines Kronecker compressed sensing (KCS) and cluster topology to exploit spatial and temporal correlations simultaneously. The head nodes generate sparse sub-measurement matrices based on gathered data to avoid measuring nodes that cannot successfully transmit readings. The sink constructs those matrices as a block diagonal matrix (BDM) to utilize the spatial correlation among clusters. Numerical results show that this scheme effectively balances the energy-performance trade-off and maintains a high reconstruction accuracy with packet loss.

Reliability and energy efficiency are two key considerations when designing a compressive sensing (CS)-based data-gathering scheme. Most researchers assume there is no packets loss, thus, they focus only on reducing the energy consumption in wireless sensor networks (WSNs) while setting reliability concerns aside. To balance the performance–energy trade-off in lossy WSNs, a distributed data storage (DDS) and gathering scheme based on CS (CS-DDSG) is introduced, which combines CS and DDS. CS-DDSG utilizes broadcast properties to resist the impact of packet loss rates. Neighboring nodes receive packets with process constraints imposed to decrease the volume of both transmissions and receptions. The mobile sink randomly queries nodes and constructs a measurement matrix based on received data with the purpose of avoiding measuring the lossy nodes. Additionally, we demonstrate how this measurement matrix satisfies the restricted isometry property. To analyze the efficiency of the proposed scheme, an expression that reflects the total number of transmissions and receptions is formulated via random geometric graph theory. Simulation results indicate that our scheme achieves high precision for unreliable links and reduces the number of transmissions, receptions and fusions. Thus, our proposed CS-DDSG approach effectively balances energy consumption and reconstruction accuracy.

With the current increasing volume and dimensionality of data, traditional data classification algorithms are unable to satisfy the demands of practical classification applications of data streams. To deal with noise and concept drift in data streams, we propose an ensemble classification algorithm based on attribute reduction and a sliding window in this paper. Using mutual information, an approximate attribute reduction algorithm based on rough sets is used to reduce data dimensionality and increase the diversity of reduced results in the algorithm. A double-threshold concept drift detection method and a three-stage sliding window control strategy are introduced to improve the performance of the algorithm when dealing with both noise and concept drift. The classification precision is further improved by updating the base classifiers and their nonlinear weights. Experiments on synthetic datasets and actual datasets demonstrate the performance of the algorithm in terms of classification precision, memory use, and time efficiency.

Reinforcement learning has recently made great progress in various challenging domains such as board game of Go and MOBA game of StarCraft II. Policy gradient based reinforcement learning method has become the mainstream due to its effectiveness and simplicity both in discrete and continuous scenarios. However, policy gradient methods commonly involve function approximation and work in an on-policy fashion, which leads to high variance and low sample efficiency. This paper introduces a novel policy gradient method to improve the sample efficiency via a pair of trajectory based prioritized replay buffers and reduce the variance in training with a target network whose weights are updated in a “soft” manner. We evaluate our method on the reinforcement learning suit of Open AI Gym tasks, and the results show that the proposed method can learn more steadily and achieve higher performance than existing methods.

In TDOA passive location tasks, the geometric configuration can greatly affect the positioning precision due to the complicated characteristics of electromagnetic environment. How to find an appropriate path to a good geometry to locate the transmitter accurately is vital in practical location tasks. This paper proposes a novel geometry optimization method based on deep reinforcement learning. In the proposed method, stations are regarded as mobile agents that can receive wireless signals decide where to go. All agents are controlled by an actor-critic learner, which is trained on the experiences collected from executing the TDOA location task repeatedly. To evaluate the trained agents, a TDOA location simulator environment with complex electromagnetic characteristics is developed. The empirical results show that, the learner mastered useful strategies and navigated to optimal geometric configurations efficiently. A visual depiction of highlights of the learner’s behavior in TDOA passive location tasks can be viewed in the video provided in the supplementary material.