Yingwen Chen

Empirical study on spatial and temporal features for vehicular wireless communications

Static topology analysis is not sufficient for the dynamic vehicular ad hoc network. Understanding the evolving topology of vehicular ad hoc networkings (VANETs) caused by vehicle mobility is very important for routing protocol design and algorithm optimization. This paper explores the spatial and temporal features of vehicular network topologies based on two real taxi-trace datasets. The analysis results reveal that the whole topology of VANETs consists of a large number of small-sized connected components. Two quantitative metrics are proposed to measure the stability and location dependency of the connected components. When the communication range is greater than a threshold, a large proportion of vehicles will connect to the biggest connected component, which is relatively stable and covers the most part of the downtown region of the city. Based on the analytical results, we propose several design philosophies and new research issues for VANETs.

An energy-efficient framework for multirate query in wireless sensor networks

Minimizing the communication overhead is always a hot topic in wireless sensor networks. In a multirate query system, data sources disseminate the data streams to users at the frequency they request. However, sending data in different frequencies to individual users is very costly. We address this problem by broadcasting a single consolidated data stream, aiming at reducing the amount of transmitted data. Taking into account the data correlation, we can reconstruct the data streams at lower frequencies from the consolidated stream at a higher frequency. In this paper, we propose an energy-efficient framework to process multirate queries and investigate the path-sharing routing tree construction method together with the rate conversion mechanism. We evaluate both the accuracy and energy efficiency by simulation. Simulation results indicate that with a reasonable level of tolerance, the performance gain is significant. As far as we know, this is the first energy-efficient solution for multirate query in wireless sensor networks.

Degrading Detection Performance of Wireless IDSs Through Poisoning Feature Selection

Machine learning algorithms have been increasingly adopted in Intrusion Detection Systems (IDSs) and achieved demonstrable results, but few studies have considered intrinsic vulnerabilities of these algorithms in adversarial environment. In our work, we adopt poisoning attack to influence the accuracy of wireless IDSs that adopt feature selection algorithms. Specifically, we adopt the gradient poisoning method to generate adversarial examples which induce classifier to select a feature subset to make the classification error rate biggest. We consider the box-constrained problem and use Lagrange multiplier and backtracking line search to find the feasible gradient. To evaluate our method, we experimentally demonstrate that our attack method can influence machine learning, including filter and embedded feature selection algorithms using three benchmark network public datasets and a wireless sensor network dataset, i.e., KDD99, NSL-KDD, Kyoto 2006+ and WSN-DS. Our results manifest that gradient poisoning method causes a significant drop in the classification accuracy of IDSs about 20%.

Exploration of Human Activities Using Sensing Data via Deep Embedded Determination

Clustering analysis is one of promising techniques of uncovering different types of human activities from a set of ubiquitous sensing data in an unsupervised manner. Previous work proposes deep clustering to learn feature representations that favor clustering tasks. However, these algorithms assume that the number of clusters is known a priori, which is often impractical in the real world. Determining the number of clusters from high dimensional data is challenging. On the other hand, the lack of the number of clusters make it difficult to extract low dimensional features appropriate for clustering. In this paper, we propose Deep Embedding Determination (DED), a method that can determine the number of clusters and extract appropriate features for the high dimensional real data. Our experimental evaluation on different datasets shows the effectiveness of DED, and the excellent performance of DED in exploring the human activities using sensing data.

False Data Injection Attack Based on Hyperplane Migration of Support Vector Machine in Transmission Network of the Smart Grid

The smart grid is a key piece of infrastructure and its security has attracted widespread attention. The false data injection (FDI) attack is one of the important research issues in the field of smart grid security. Because this kind of attack has a great impact on the safe and stable operation of the smart grid, many effective detection methods have been proposed, such as an FDI detector based on the support vector machine (SVM). In this paper, we first analyze the problem existing in the detector based on SVM. Then, we propose a new attack method to reduce the detection effect of the FDI detector based on SVM and give a proof. The core of the method is that the FDI detector based on SVM cannot detect the attack vectors which are specially constructed and can replace the attack vectors into the training set when it is updated. Therefore, the training set is changed and then the next training result will be affected. With the increase of the number of the attack vectors which are injected into the positive space, the hyperplane moves to the side of the negative space, and the detection effect of the FDI detector based on SVM is reduced. Finally, we analyze the impact of different data injection modes for training results. Simulation experiments show that this attack method can impact the effectiveness of the FDI detector based on SVM.

An Adaptive MAC Protocol for Wireless Rechargeable Sensor Networks

In the existing medium access control (MAC) protocols of rechargeable sensor networks, the maximum charging threshold of sensor nodes generally set to a fixed value based on nodes battery capacity. It leads to the channel occupied due to the long time charging of node so that the data cannot be transmitted on time. In addition, the minimum charging threshold is also set to a fixed value. This will lead to the death of the nodes due to the energy depletion while nodes cannot replenish energy in time. In this paper we put forward an adaptive charging MAC protocol called AC-MAC with double adaptive thresholds to solve the above problems. The nodes can adjust the maximum and minimum charging thresholds based on the number of transmission or receiving packets, the channel idle time and the power of transmission and reception. By analyzing the protocol, it can ensure the minimum energy level of the node, which can effectively extend the network lifetime and reduce the end to end transmission delay.

Privacy-Preserving Public Auditing Together with Efficient User Revocation in the Mobile Environments

Cloud platforms can provide elastic infrastructure for mobile users. Therefore, publicly auditing the integrity of shared data outsourced on the cloud is very important since the cloud may be untrusted. However, existing remote integrity checking protocols can not preserve owner’s privacy nor provide efficient user revocation. In this paper, we analyze a previous work, and improve the protocol to get better user revocation efficiency and preserve the users’ privacy against the untrusted cloud platform and the auditor.

Empirical Study on Spatial and Temporal Features for Vehicular Wireless Communications

Traditional networking technologies based on static topology analysis are not sufficient to the dynamic Vehicular Ad hoc Network. Understanding the network dynamics caused by vehicle mobility is very important for routing protocol design and algorithm optimization. This paper explores the spatial and temporal features based on two real taxi-trace datasets. It reveals that the whole topology of VANETs consists of a large number of small-sized connected components. When the communication range is greater than a threshold, a large proportion of vehicles will connect to a largest connected component, which covers the most part of the downtown region of the city both in on-peak hour and off-peak hour. Based on the analytical results, we propose several design philosophies and new research issues for VANETs.

An Energy-Efficient Framework for Multi-Rate Query in Wireless Sensor Networks

Minimizing the communication overhead is always a hot topic in wireless sensor networks. In a multi-rate query system, data sources disseminate the data streams to users at the frequency they request. However, sending data in different frequency to individual users is very costly. We address this problem by broadcasting a single consolidated data stream, aiming at reducing the amount of transmitted data. Taking into account the data correlation, we can re-construct the data streams at lower frequencies from the consolidated stream at a higher frequency. In this paper, we propose an energy-efficient framework to process multi-rate queries and investigate rate conversion mechanism. We evaluate both the accuracy and energy efficiency by simulation. Simulation results indicate that with a reasonable level of tolerance, the performance gain is significant. As far as we know, this is the first energy-efficient solution for multi-rate query in wireless sensor networks.