Short-term traffic flow forecasting is a fundamental and challenging task due to the stochastic dynamics of the traffic flow, which is often imbalanced and noisy. This paper presents a sample-rebalanced and outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting. In this model, we adopt a new metric for the evolutionary traffic flow patterns, and reconstruct balanced training sets by relative transformation to tackle the imbalance issue. Then, we design a hybrid model that considers both local and global information to address the limited size of the training samples. We employ four real-world benchmark datasets often used in such tasks to evaluate our model. Experimental results show that our model outperforms state-of-the-art parametric and non-parametric models.

Accurate and timely short-term traffic flow forecasting plays a key role in intelligent transportation systems, especially for prospective traffic control. For the past decade, a series of methods have been developed for short-term traffic flow forecasting. However, due to the intrinsic stochastic and evolutionary trend, accurate forecasting remains challenging. In this paper, we propose a noise-immune long short-term memory (NiLSTM) network for short-term traffic flow forecasting, which embeds a noise-immune loss function deduced by maximum correntropy into the long short-term memory (LSTM) network. Different from the conventional LSTM network equipped with the mean square error loss, the maximum correntropy induced loss is a local similar metric, which is immunized to non-Gaussian noises. Extensive experiments on four benchmark datasets demonstrate the superior performance of our NiLSTM network by comparing it with the frequently used models and state-of-the-art models.

Energy balance consumption is an important research topic in the field of wireless sensor networks (WSNs). Clustering protocols are widely used to reduce WSN energy consumption. However, this many-to-one cluster head (CH) model can also easily lead to energy exhaustion of some critical nodes near the base station results in the decline of the network quality and lifetime. This article proposes a clustering election game-based and two-level management clustering protocol (CEGT) for WSNs; the network is divided into layers of equal size, and the layer heads (LHs) are selected within the layers based on the location and energy information. Each layer is further divided into clusters of equal size, and the nodes within the clusters undergo a two-stage screening followed by local clustering and multiple rounds of election games to dynamically select the staged CHs. This protocol effectively prevents low-energy nodes from participating in the CH competitions, and selects high-quality nodes through election games which are based on customized qualification values, and ensures reasonable energy utilization as well as efficient network load balancing. Finally, our simulations show that our CEGT protocol outperforms other clustering protocols in terms of the network lifetime, the number of surviving nodes, and the average number of clusters, and the throughput, which also indicate that the CEGT protocol is more suitable for large-scale networks.

In mobile wireless sensor networks (MWSNs), the mobility of nodes imposes heightened energy management demands on the network. This study proposes a game-based dynamic clustering routing (GDCR) protocol for MWSNs. The GDCR protocol employs multidimensional clustering and constructs a mixed strategy game model among heterogeneous nodes. This model considers various factors for cluster head selection, including residual energy, movement, distance to the sink, and node spacing, ensuring comprehensive decision making. In addition, the protocol incorporates detached node management to ensure network security and stability. Simulation experiments demonstrate that the proposed protocol reduces the average network energy consumption when nodes have unrestricted movements. Furthermore, it effectively curtails fluctuations in energy consumption as the network scales up, thereby prolonging network lifetime and enhancing stability.

This paper investigates the enforcement of regulator using an appropriate penalty, in the framework of game theory, to force a polluting firm to act in a socially optimal way. In most related literature, the penalty is considered to be only related t