Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced.

This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In the actual application scenario, the data collected previously and the data collected later may have different data distributions due to the sensor drift. If the dataset without sensor drift is treated as a source domain and the dataset with sensor drift as a target domain, a domain correction based on kernel transformation (DCKT) method is proposed to compensate the sensor drift. The proposed method makes the distribution consistency of two domains greatly improved through mapping to a high-dimensional reproducing kernel space and reducing the domain distance. A public benchmark sensor drift dataset is used to verify the effectiveness and efficiency of the proposed DCKT method. The experimental result shows that the proposed method yields the highest average accuracies compared to other considered methods.

In this paper, we propose a W-band 8-quadrature-amplitude-modulation (8QAM) frequency tripling photonic vector millimeter-wave (mm-wave) signal generation method by using a phase modulator (PM) with only amplitude pre-coding. The PM is driven by 2-Gbaud 8QAM-modulated amplitude precoded signal at 25 GHz. Instead of phase pre-coding, we only need to reverse the real part of the signal at the receiver after photo detector (PD). The structure of the transmitter and the structure of the receiver are simple. The frequency tripling scheme is completed by numerical simulation, the output optical spectrum is consistent with the theoretical analysis, and bit-error-ratio (BER) curves indicate that the put forward 75 GHz 8QAM vector signal generation technique has good performance. The BER of 6 Gbit/s 75 GHz 8QAM vector mm-wave signal is below 3.8 x 10-3 after 12 km optical fiber transmission. For all we know, it is the first time to report on the generation of high-order QAM photonic mm-wave signal with odd times of RF frequency by using a single external modulator.

Human activity recognition and classification have been widely used in various fields. To make full use of wireless body area networks (WBAN), multiple human activities classification based on dynamic on-body propagation characteristics is presented in this paper. Four on-body links are established to collect propagation dataset for four groups of activities, which include hand activities, arm activities, leg activities and head activities. A total of 30 human activities are considered. The line-of-sight (LOS) and non-line-of-sight (NLOS) on-body propagation characteristics are analyzed in detail by comparing measurement with full-wave simulation. Multiple human activities including some fine-grained motions can be accurately classified using the propagation data collected by the on-body antennas. Based on the similar propagation mechanism among different links, an inter-link transfer learning framework is proposed by pre-training the deep convolutional neural network (DCNN) on one link before training it on other links. The results show that after pre-trained in source domain, the inter-link transfer learning can improve classification accuracy and accelerate model convergence in target domain with a small amount of training data, which alleviates the complex and time-consuming data collection. The results of this paper are particularly useful for the deployment of WBAN integrated communication and sensing.

The replica strategy in distributed cache can effectively reduce user access delay and improve system performance. However, developing a replica strategy suitable for varied application scenarios is still quite challenging, owing to differences in user access behavior and preferences. In this paper, a replication strategy for spatiotemporal data (RSSD) based on a distributed caching system is proposed. By taking advantage of the spatiotemporal locality and correlation of user access, RSSD mines high popularity and associated files from historical user access information, and then generates replicas and selects appropriate cache node for placement. Experimental results show that the RSSD algorithm is simple and efficient, and succeeds in significantly reducing user access delay.

With the rapid development of smart health, the health sensors and wearable devices bring huge amounts of small files of spatiotemporal data, which are distributed in different servers, and affect the I/O performance of the system seriously. There are many methods to solve the problems of a small file, but most of them are applied to specific applications. However, due to the influence of user access behavior and data type, these methods are not effective when applied to sensors data in smart health. In this paper, a novel small file merging strategy for smart health is proposed. By analyzing the features of health sensors data and the preferences of user access, the strategy uses spatiotemporal clustering for the historical user access information. Then, weights are applied to these clusters based on the access density to determine the access-related spatiotemporal range. Finally, the spatiotemporal range is used to merge small files. The experimental results show that the merging strategy is simple but efficient, and it can effectively reduce user access delay for small files of spatiotemporal data in smart health.