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Nowadays, the deluge of heterogeneous data generated by various cloud-based industrial applications often has to be delivered to the data center for analysis and storage. To speed up data processing thus facilitating application performance, the modern data center network offers rich parallel paths and super high bisection bandwidth for data communications between servers, expecting to provide good transmission performance for the heterogeneous data traffic caused by cloud-based industrial applications. Due to high path diversities, however, balancing the heterogeneous traffic load across multiple parallel paths for fully utilizing the offered super high bisection bandwidth is full of challenges (i.e., how to achieve high path utilization without incurring adverse impact). Although prior studies demonstrate that the flowlet-based solutions are promising to fill the bill, we argue that their rerouting operations are still inappropriate in timing and manner. This article presents HG, a load balancing scheme adopting hybrid switching granularity to make traffic rerouting. HG embeds the flow-fragment-based and flowcell-based path switching into the flowlet-based path switching, and employs state-weighted path measurement to choose paths for newly appeared flow fragments, flowcells, and flowlets. The results of numerous NS2 tests show that, compared with the state-of-the-art data center load balancing schemes, HG significantly reduces the average and tail-flow completion times for delay-sensitive flows, and the throughput of throughput-oriented flows is always maintained at high level.

Cloud computing has become an increasingly popular option for users to store and share data. Encryption prior to outsourcing data to the cloud is the best way to protect data security and privacy; however, it hinders sharing of the data that was encrypted. In addition, users in many real-world organizations (e.g., enterprises) have multiple level structures and a higher-level user should have the privilege to decide which data can be shared with a lower-level user. Most solutions in the literature suffer from inefficiency or inflexibility in tackling this problem. In this article, we propose a fine-grained hierarchical data sharing (FHDS) scheme in clouds. With FHDS, the data owner can encrypt data with his public key, and then selectively share encrypted data with users in a hierarchy; if necessary, the users can disseminate the owner's data to their subordinates in the lower levels by generating access keys. In particular, the higher-level users could puncture the keys with some tags such that the part of the owner's data which is labeled by the punctured tags will not be accessible to the lower-level users. The proposed scheme is provable secure under our security model and performance analyses show the efficiency of the scheme.

Nowadays, with the features of low energy consumption and flexible networking, the pyroelectric sensor has been applied widely in areas such as network instruction detection or human body target tracking recognition. However, due to the deficiency of prominent biological morphological characteristics, such as fingerprint, iris or facial features, in the human target signals captured by pyroelectric sensor networks node, the precise classification and identification of multi-human targets in pyroelectric sensor networks become very difficult. In this paper, we apply the wavelet packet to multi-resolution to decompose the signals of pyroelectric infrared sensors and extract the energy variation of reconstructed signals to compose feature vectors to represent various characteristics of different moving human targets. Then, we generate a classifier using those feature vectors. Through some simulation experiments, it proves that this method not only can better extract the characteristics of human targets that are in detection zone of pyroelectric infrared sensor, but also an ideal classification and recognition result can be obtained of multiple human targets by training the support vector machine classifier with proper kernel function and parameters.

This paper aims to improve the accuracy of pyroelectric infrared sensor-based human body positioning system by studying the characteristics of the human body radiation signal. We designed the amplifying and filtering circuits to capture the signals. We also built a human body positioning system. Considering the impact of external interference, the body velocity, the distance between sensors and human target, we propose a new algorithm for the system to interpret the target information in the sensing area considering several impacts. Besides, in the system, the sensing area was divided into smaller grids to make human positioning more accurate. We evaluate the proposed algorithm based on real test-bed and we repeated the experiments for 480 times and validated that the proposed human positioning system can track people with the tracking error for only 0.46 m.