Ruofan Jin

Network performance of smart mobile handhelds in a university campus WiFi network

Smart mobile handheld devices (MHDs) such as smartphones have been used for a wide range of applications. Despite the recent flurry of research on various aspects of smart MHDs, little is known about their network performance in WiFi networks. In this paper, we measure the network performance of smart MHDs inside a university campus WiFi network, and identify the dominant factors that affect the network performance. Specifically, we analyze 2.9TB of data collected over three days by a monitor that is located at a gateway router of the network, and make the following findings: (1) Compared to non-handheld devices (NHDs), MHDs use well provisioned Akamai and Google servers more heavily, which boosts the overall network performance of MHDs. Furthermore, MHD flows, particularly short flows, benefit from the large initial congestion window that has been adopted by Akamai and Google servers. (2) MHDs tend to have larger local delays inside the WiFi network and are more adversely affected by the number of concurrent flows. (3) Earlier versions of Android OS (before 4.X) cannot take advantage of the large initial congestion window adopted by many servers. On the other hand, the large receive window adopted by iOS is not fully utilized by most flows, potentially leading to waste of resources. (4) Some application-level protocols cause inefficient use of network and operating system resources of MHDs in WiFi networks. Our observations provide valuable insights on content distribution, server provisioning, MHD system design, and application-level protocol design.

Malware Detection for Mobile Devices Using Software-Defined Networking

The rapid adoption of mobile devices comes with the growing prevalence of mobile malware. Mobile malware poses serious threats to personal information and creates challenges in securing network. Traditional network services provide connectivity but do not have any direct mechanism for security protection. The emergence of Software-Defined Networking (SDN) provides a unique opportunity to achieve network security in a more efficient and flexible manner. In this paper, we analyze the behaviors of mobile malware, propose several mobile malware detection algorithms, and design and implement a malware detection system using SDN. Our system detects mobile malware by identifying suspicious network activities through real-time traffic analysis, which only requires connection establishment packets. Specifically, our detection algorithms are implemented as modules inside the OpenFlow controller, and the security rules can be imposed in real time. We have tested our system prototype using both a local testbed and GENI infrastructure. Test results confirm the feasibility of our approach. In addition, the stress testing results show that even unoptimized implementations of our algorithms do not affect the performance of the OpenFlow controller significantly.

Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

As the number of electric vehicles (EVs) grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a decentralised online EV charging scheduling algorithm for large populations of EVs, where the EVs can be highly heterogeneous and may join the charging system dynamically. The algorithm couples a clustering-based strategy that dynamically classifies heterogeneous EVs into multiple groups and a sliding-window iterative approach that schedules the charging demand for the EVs in each group in real time. Extensive simulation results demonstrate that our approach provides near-optimal solutions at significantly reduced complexity and communication overhead. It flattens the aggregated load on the power grid and reduces the costs of both the users and the utility.

Deploying Energy Routers in an Energy Internet Based on Electric Vehicles

An energy internet is a system that enables energy sharing in a distribution system such as the Internet. It has been attracting a lot attention from both academia and industry. The main purpose of this paper is to develop a model of an electric vehicle (EV) energy network to transmit, distribute, and store energy by EVs from renewable energy sources to places that need the energy. We describe the EV energy internet in detail and then formulate an optimization problem to place charging stations in an EV energy internet. We develop two solutions: one using a greedy heuristic and another based on diffusion. Simulation results using real-world data show that the greedy heuristic requires less charging stations, whereas the diffusion-based algorithm incurs less energy transmission loss.

Enabling Resilient Microgrid Through Programmable Network

In this paper, we integrate programmable networks in microgrid (MG) to provide flexible and easy-to-manage communication solutions, thus enabling resilient MG operations in face of various cyber and physical disturbances. Specifically, two contributions have been made: 1) establish a novel software-defined networking (SDN) based communication architecture that abstracts the network infrastructure from the upper-level applications to significantly expedite the development of MG applications and 2) create a hardware-in-the-loop cyber-physical platform for evaluating and validating the performance of the presented architecture, control techniques, and SDN-based functionalities. Test results have demonstrated that the new architecture can significantly enhance MG resilience, particularly for those that have high penetration of renewable energy sources.

Optimal renewable energy transfer via electrical vehicles

Using electrical vehicles (EV) in transportation systems is more cost effective and environmental friendly than using conventional vehicles. In addition, the energy storage capability and mobility of EVs provide a convenient way to transfer energy from renewable energy sources to locations that have no direct access to renewable energy. As an example, an EV can be charged by a renewable energy source and discharge energy at a charge station; other EVs passing by the charge station can get charged, and hence indirectly use the energy from the renewable energy source. In this paper, we investigate the optimal renewable energy transfer problem in a bus system. Specifically, the goal is to determine how much energy a bus should deposit or withdraw at a charge station so that the total amount of renewable energy used by the bus system is maximized. We formulate and solve the above optimization problem using linear programming. Simulation results using the Manhattan city bus system demonstrate that our approach significantly outperforms a baseline scheme and provides an effective way for distributing renewable energy in bus systems.

Asynchronous Neighbor Discovery on Duty-cycled Mobile Devices: Integer and Non-Integer Schedules

Neighbor discovery is a fundamental problem in wireless networks. In this paper, we study asynchronous neighbor discovery between duty-cycled mobile devices. Each node is duty-cycled, i.e., its radio may only be active for a small fraction of the time. The duty cycles of the nodes can be the same or different, leading to symmetric or asymmetric cases of the neighbor discovery problem. In addition, the setting is asynchronous, i.e., clocks of different nodes may not be synchronized. Most existing studies assume an integer model (where time proceeds in discrete steps); two recent studies break away from this assumption, which allows them to develop significantly more efficient schemes. Our study improves the state-of-the-art in three main fronts. Firstly, we develop a generalized non-integer model (where time is continuous) that permits unified treatment of the assumptions in existing studies. We also provide a reduction that transforms any schedule in the basic integer model to a corresponding schedule in the generalized non-integer model while improving the performance by a factor of two. Applying this reduction, an optimal schedule in the integer model becomes an optimal schedule in the non-integer model. Thirdly, we establish a new family of lower bounds for the best achievable latency guarantee in the non-integer model. They are applicable to both symmetric and asymmetric settings, and encompass the lower bounds for the integer model as special cases. Finally, we develop a novel optimal construction based on Sidon sets for the symmetric setting. Our approach differs from the approaches taken by all existing studies, and provides a new direction for constructing neighbor discovery schedules.

LinkForecast: Cellular Link Bandwidth Prediction in LTE Networks

Accurate cellular link bandwidth prediction can benefit upper-layer protocols significantly. In this paper, we investigate how to predict cellular link bandwidth in LTE networks. We first conduct an extensive measurement study in two major commercial LTE networks in the US, and identify five types of lower-layer information that are correlated with cellular link bandwidth. We then develop a machine learning based prediction framework, LinkForecast , that identifies the most important features (from both upper and lower layers) and uses these features to predict link bandwidth in real time. Our evaluation shows that LinkForecast is lightweight and the prediction is highly accurate: At the time granularity of one second, the average prediction error is in the range of 3.9 to 17.0 percent for all the scenarios we explore. We further investigate the prediction performance when using lower-layer features obtained through standard APIs provided by the operating system, instead of specialized tools. Our results show that, while the features thus obtained have lower fidelity compared to those from specialized tools, they lead to similar prediction accuracy, indicating that our approach can be easily used over commercial off-the-shelf mobile devices.

A control theoretic approach to ABR video streaming: A fresh look at PID-based rate adaptation

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. ABR streaming can be naturally modeled as a feedback control problem. There has been some initial work on using PID, a widely used feedback control technique, for ABR streaming. Existing studies, however, either use PID control directly without fully considering the special requirements of ABR streaming, leading to suboptimal results, or conclude that PID is not a suitable approach. In this paper, we take a fresh look at PID-based control for ABR streaming. We design a framework called PIA that strategically leverages PID control concepts and incorporates several novel strategies to account for the various requirements of ABR streaming. We evaluate PIA using simulation based on real LTE network traces, as well as using real DASH implementation. The results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes (reduction up to 40%) and stalls (reduction up to 85%), while incurring very small runtime overhead.

Detecting Node Failures in Mobile Wireless Networks: A Probabilistic Approach

Detecting node failures in mobile wireless networks is very challenging because the network topology can be highly dynamic, the network may not be always connected, and the resources are limited. In this paper, we take a probabilistic approach and propose two node failure detection schemes that systematically combine localized monitoring, location estimation and node collaboration. Extensive simulation results in both connected and disconnected networks demonstrate that our schemes achieve high failure detection rates (close to an upper bound) and low false positive rates, and incur low communication overhead. Compared to approaches that use centralized monitoring, our approach has up to 80 percent lower communication overhead, and only slightly lower detection rates and slightly higher false positive rates. In addition, our approach has the advantage that it is applicable to both connected and disconnected networks while centralized monitoring is only applicable to connected networks. Compared to other approaches that use localized monitoring, our approach has similar failure detection rates, up to 57 percent lower communication overhead and much lower false positive rates (e.g., 0.01 versus 0.27 in some settings).