Ball: An R package for detecting distribution difference and association in metric spaces

The rapid development of modern technology facilitates the appearance of numerous unprecedented complex data which do not satisfy the axioms of Euclidean geometry, while most of the statistical hypothesis tests are available in Euclidean or Hilbert spaces. To properly analyze the data of more complicated structures, efforts have been made to solve the fundamental test problems in more general spaces. In this paper, a publicly available R package Ball is provided to implement Ball statistical test procedures for K-sample distribution comparison and test of mutual independence in metric spaces, which extend the test procedures for two sample distribution comparison and test of independence. The tailormade algorithms as well as engineering techniques are employed on the Ball package to speed up computation to the best of our ability. Two real data analyses and several numerical studies have been performed and the results certify the powerfulness of Ball package in analyzing complex data, e.g., spherical data and symmetric positive matrix data

Containing denial-of-service attacks in broadcast authentication in sensor networks

Broadcast authentication is an important application in sensor networks. Public Key Cryptography (PKC) is desirable for this application, but due to the resource constraints on sensor nodes, these operations are expensive, which means sensor networks using PKC are susceptible to Denial of Service (DoS) attacks: attackers keep broadcasting bogus messages, which will incur extra costs, thus exhaust the energy of the honest nodes. In addition, the long time to verify each message using PKC increases the response time of the nodes; it is impractical for the nodes to validate each incoming message before forwarding it. In this paper we discuss this type of DoS attacks, in which the goal of the adversary is to exhaust the energy of the sensor nodes and to increase their response time to broadcast messages. We then present a dynamic window scheme, where sensor nodes determine whether first to verify a message or first to forward the message by themselves. This is made possible with the information such as how far this node is away from the malicious attacker, and how many hops the incoming message has passed. We compare the performance of the proposed scheme with other schemes, and show that it can contain the damage of DoS attacks to only a small portion of the sensor nodes

Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.Comment: 11pages, conferenc

Fine-Grained Access Control for HTML5-Based Mobile Applications in Android

HTML5-based mobile applications are becoming more and more popular because they can run on different platforms. Several newly introduced mobile OS natively support HTML5-based applications. For those that do not provide native sup-port, such as Android, iOS, and Windows Phone, developers can develop HTML5-based applications using middlewares, such as PhoneGap [17]. In these platforms, programs are loaded into a web component, called WebView, which can render HTML5 pages and execute JavaScript code. In order for the program to access the system resources, which are isolated from the content inside WebView due to its sand-box, bridges need to be built between JavaScript and the native code (e.g. Java code in Android). Unfortunately, such bridges break the existing protection that was origi-nally built into WebView. In this paper, we study the potential risks of HTML5-based applications, and investigate how the existing mobile systems ’ access control supports these applications. We fo-cus on Android and the PhoneGap middleware. However, our ideas can be applied to other platforms. Our studies indicate that Android does not provide an adequate access control for this kind of applications. We propose a fine-grained access control mechanism for the bridge in Android system. We have implemented our scheme in Android and have evaluated its effectiveness and performance. 1

Ball: An R Package for Detecting Distribution Difference and Association in Metric Spaces

The rapid development of modern technology has created many complex datasets in non-linear spaces, while most of the statistical hypothesis tests are only available in Euclidean or Hilbert spaces. To properly analyze the data with more complicated structures, efforts have been made to solve the fundamental test problems in more general spaces (Lyons 2013; Pan, Tian, Wang, and Zhang 2018; Pan, Wang, Zhang, Zhu, and Zhu 2020). In this paper, we introduce a publicly available R package Ball for the comparison of multiple distributions and the test of mutual independence in metric spaces, which extends the test procedures for the equality of two distributions (Pan et al. 2018) and the independence of two random objects (Pan et al. 2020). The Ball package is computationally efficient since several novel algorithms as well as engineering techniques are employed in speeding up the ball test procedures. Two real data analyses and diverse numerical studies have been performed, and the results certify that the Ball package can detect various distribution differences and complicated dependencies in complex datasets, e.g., directional data and symmetric positive definite matrix data

Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization

We focus on a replanning scenario for quadrotors where considering time efficiency, non-static initial state and dynamical feasibility is of great significance. We propose a real-time B-spline based kinodynamic (RBK) search algorithm, which transforms a position-only shortest path search (such as A* and Dijkstra) into an efficient kinodynamic search, by exploring the properties of B-spline parameterization. The RBK search is greedy and produces a dynamically feasible time-parameterized trajectory efficiently, which facilitates non-static initial state of the quadrotor. To cope with the limitation of the greedy search and the discretization induced by a grid structure, we adopt an elastic optimization (EO) approach as a post-optimization process, to refine the control point placement provided by the RBK search. The EO approach finds the optimal control point placement inside an expanded elastic tube which represents the free space, by solving a Quadratically Constrained Quadratic Programming (QCQP) problem. We design a receding horizon replanner based on the local control property of B-spline. A systematic comparison of our method against two state-of-the-art methods is provided. We integrate our replanning system with a monocular vision-based quadrotor and validate our performance onboard.Comment: 8 pages. Published in International Conference on Robotics and Automation (ICRA) 2018. IEEE copyrigh