Eliminating Explicit Contention from Channel Access in Distributed Wireless Networks

Emerging techniques such as WiFi direct makes the objective of always-on be true. People can easily chat and share files with nearby friends even without AP(Access Point) or cellular coverage. In this paper, we focus on the channel efficiency issue of APfree Wi-Fi networks, which can be easily constructed in the subway, in a high-speed railway, or when camping in the wild. Today IEEE 802.11 DCF is the most commonly used MAC protocol for Wi-Fi networks, however, due to the control messages and backoff time, channel efficiency in high data rate networks can be extremely low. To solve this, we propose CD-MAC, which allows control messages to be transmitted with data packets concurrently, and thus eliminates the overheads of backoff and explicit contention. To maintain the reception reliability, we redesign the control messages and use signal detection in PHY instead of bits decoding to detect them. In MAC layer, CD-MAC is built upon our Correlation Detection based PHY. We have implemented and evaluated CD-MAC using USRP N210. Evaluation results show that CD-MAC can achieve over 95.5% channel efficiency and provide throughput gains of up to 80%, 60%, and 29.1% compared with DCF, 802.11 ec, and back2F, respectively.

DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this area.To address this problem,researchers start looking for information out of the medical datasets.Previous efforts mainly leverage information from natural images via transfer learning.More recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra annotations.In this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)images.In DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the networks.We have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public dataset.The results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their performance.For example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

Acoustic Goos-H?nchen effect

We report two models of the lateral displacement of acoustic-wave scattering on a fluid-solid interface that reveal an acoustic analog of the Goos-Hainchen effect in optics. This acoustic analog is called the acoustic Goos-Hainchen effect. Using newly proposed models, we made numerical calculations for the system ofa water-Perspex interface. Specifically, in the post-critical-angle region, we observed a lateral displacement （and transition time） of the reflected P-wave with respect to the incident P-wave. The first arrival of the acoustic signal from the interface is found to be a reflected P-wave rather than the sliding-refraction P-wave usually described in traditional acoustic-logging sliding P-wave theory. For both proposed models, the effective propagation speed of the reflected P-wave along the interface depends on not only the physical properties of the interracial media but also the incident angle. These observations are intriguing and warrant further investigation.

Fault Tolerant Data Aggregation Scheduling with Local Information in Wireless Sensor Networks

We study the problem of efficient data aggregation in unreliable wireless sensor networks by designing a fault tolerant data aggregation protocol. A fault tolerant data aggregation protocol consists of two parts： basic aggregation scheduling and amendment strategies. On default, data is aggregated according to the basic aggregation scheduling strategy. The amendment strategy will start automatically when a middle sensor node is out of service. We focus our attention on the amendment strategies and assume that the network adopts a connected dominating set （CDS） based aggregation scheduling as its basic aggregation scheduling strategy. The amendment scheme includes localized aggregation tree repairing algorithms and distributed rescheduling algorithms. The former are used to find a new aggregation tree for every child of the corrupted node, whereas the latter are used to achieve interference free data aggregation scheduling after the amendment. These amendment strategies impact only a very limited number of nodes near the corrupted node and the amendment process is transparent to all the other nodes. Theoretical analyses and simulations show that the scheme greatly improves the efficiency of the data aggregation operation by reducing both message and time costs compared to rebuilding the aggregation tree and rescheduling the en- tire network.

Combined Cloud： A Mixture of Voluntary Cloud and Reserved Instance Marketplace

Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.

Guest Editorial: Special Issue on Wireless Sensor Networks, Cyber-Physical Systems, and Internet of Things

Recent convergence of information communications technology and sensing equipment is creating new de- mands and opportunities for wireless sensor networks without technological restrictions, such as cyber- physical systems and internet of things. The fast-growing number of wireless sensor networks, the variety of sensors, the different granularity of time control in cyber-physical systems,