Yanyan Zhuang

Minimizing Energy Consumption with Probabilistic Distance Models in Wireless Sensor Networks

Minimizing energy consumption in wireless sensor networks has been a challenging issue, and grid-based clustering and routing schemes have attracted a lot of attention due to their simplicity and feasibility. Thus how to determine the \textit{optimal grid size} in order to minimize energy consumption and prolong network lifetime becomes an important problem during the network planning and dimensioning phase. So far most existing work uses the average distances within a grid and between neighbor grids to calculate the average energy consumption, which we found largely underestimates the real value. In this paper, we propose, analyze and evaluate the energy consumption models in wireless sensor networks with probabilistic distance distributions. These models have been validated by numerical and simulation results, which shows that they can be used to optimize grid size and minimize energy consumption accurately. We also use these models to study variable-size grids, which can further improve the energy efficiency by balancing the relayed traffic in wireless sensor networks.

Time and Location-Critical Emergency Message Dissemination for Vehicular Ad-Hoc Networks

One promise of Vehicular Ad-hoc Networks (VANET) is to considerably increase road safety and travel comfort by enabling inter-vehicle communications. Among a vast array of potential applications, emergency message (EM) dissemination has attracted a lot of attention in the literature. In this paper, we propose a time/location-critical (TLC) framework for EM dissemination and use our scalable modulation and coding (SMC) scheme to achieve the goal. In specific, vehicles near the accident site (or the point-of-interest location) receive guaranteed, detailed messages to take proper reaction immediately (e.g., slow down or change lanes), and vehicles further away have a high probability to be informed and make location-aware decisions accordingly (e.g., detour or reroute), with the assistance of reverse traffic when possible and necessary. The efficacy of the proposed framework is analyzed and validated by extensive numerical and simulation results. The TLC framework and the use of the SMC scheme are shown to be able to disseminate EMs effectively and efficiently by taking both the time and location criticality into account, while simplifying the design of radio transceivers and media access control protocols for VANET.

On the Uplink MAC Performance of a Drive-Thru Internet

As IEEE 802.11 access points (APs) open up services to mobile clients, opportunistic access to the roadside communication infrastructures from traveling vehicles has become more prominent and has drawn considerable attention. In particular, data uploading from traveling vehicles has a great potential for many vehicular ad hoc network applications, where both the intermittent connectivity and the time-varying vehicle arrivals have presented significant challenges to the current analytical models. Our focus is on the carrier sense multiple access with collision avoidance-based medium access control (MAC) layer performance in a last-hop drive-thru Internet, considering both the contention nature of the uplink and the realistic vehicle traffic model. Analytical and simulation results show the intrinsic relationships among the vehicle density and speed, the coverage of the AP, the achievable throughput, and the total amount of data uploaded by vehicles. We further investigate the efficacy of an admission control scheme by the AP for achieving an optimal operating region that has both a high throughput and a large amount of data uploaded from each drive-thru vehicle.

A Geometric Probability Model for Capacity Analysis and Interference Estimation in Wireless Mobile Cellular Systems

Performance metrics in cellular systems, such as per-user link capacity and co-channel interference, are dependent on the statistical distances between communicating nodes. An analytical model based on geometric probability in cellular systems is presented here for capacity analysis and interference estimation. We first derive the closed-form distance distribution between cellular base stations and mobile users, giving the explicit probability density functions of the distance from a base station to an arbitrary user in the same hexagonal cell, or to the users in adjacent cells. Different from numerical methods or approximation, and the existing approaches in geometric probability, this unified approach provides explicit distribution functions that can lead to all statistical moments, and is not limited by coordinate distributions, either of base stations or subscribers. Analytical results on per-user link capacity and co-channel interference are derived and validated through simulation, which shows the high accuracy and promising potentials of this approach.

Evaluating On-Demand Data Collection with Mobile Elements in Wireless Sensor Networks

Exploring mobility to accomplish the data collection in wireless sensor networks (WSNs) has become the focus of recent studies, which can improve the energy efficiency of sensor nodes by shifting the data forwarding task from them to mobile elements (MEs). However, the data collection latency in this case can be much higher. We consider an on-demand data collection scenario in this paper, in which sensor nodes broadcast service requests when their buffer is about to be full. On receiving such requests, the ME moves toward the sensor nodes to collect data, and uploads the data to the sink when possible. An M/G/1 queue-based analytical model is presented, and analytical results on several important system performance metrics are derived. Furthermore, we propose an improved service scheme, which combines requests whenever they are in proximity. The work is evaluated through extensive simulations, which validate the accuracy of our model. The efficacy of the proposed service scheme to improve the system performance is also verified.

A probabilistic model for message propagation in two-dimensional vehicular ad-hoc networks

Vehicular ad-hoc networks (VANET) promise to enhance the road safety and travel comfort significantly in both highway and city scenarios. Message propagation, either for emergency or pleasure purposes, constitutes a major category of VANET applications, and is particularly challenging in infrastructure-less vehicle-to-vehicle communication scenarios. In this paper, we study the connectivity property of message propagation in two-dimensional VANET. We first derive the exact expression for the average size of the connected components in the one-dimensional case, i.e., messages propagating along a main street, and give a close approximation to the size distribution. We further derive the connectivity of message propagation in the two-dimensional ladder case, i.e., covering the main and two side streets, and formulate the problem for the two-dimensional lattice case to cover all the blocks in a district. Extensive simulation has been conducted to verify the analytical model and provide further insights in message propagation with and without geographic constraints, respectively. The simulation results show the efficacy of the model and the tradeoff between these two message forwarding strategies, and provide guidelines for future network planning and protocol development.

It's the psychology stupid: how heuristics explain software vulnerabilities and how priming can illuminate developer's blind spots

Despite the security communitys emphasis on the importance of building secure software, the number of new vulnerabilities found in our systems is increasing. In addition, vulnerabilities that have been studied for years are still commonly reported in vulnerability databases. This paper investigates a new hypothesis that software vulnerabilities are blind spots in developers heuristic-based decision-making processes. Heuristics are simple computational models to solve problems without considering all the information available. They are an adaptive response to our short working memory because they require less cognitive effort. Our hypothesis is that as software vulnerabilities represent corner cases that exercise unusual information flows, they tend to be left out from the repertoire of heuristics used by developers during their programming tasks. To validate this hypothesis we conducted a study with 47 developers using psychological manipulation. In this study each developer worked for approximately one hour on six vulnerable programming scenarios. The sessions progressed from providing no information about the possibility of vulnerabilities, to priming developers about unexpected results, and explicitly mentioning the existence of vulnerabilities in the code. The results show that (i) security is not a priority in software development environments, (ii) security is not part of developers mindset while coding, (iii) developers assume common cases for their code, (iv) security thinking requires cognitive effort, (v) security education helps, but developers can have difficulties correlating a particular learned vulnerability or security information with their current working task, and (vi) priming or explicitly cueing about vulnerabilities on-the-spot is a powerful mechanism to make developers aware about potential vulnerabilities.

Experience with Seattle: A Community Platform for Research and Education

Hands-on experience is a critical part of research and education. Todays distributed testbeds fulfill that need for many students studying networking, distributed systems, cloud computing, security, operating systems, and similar topics. In this work, we discuss one such testbed, Seattle. Seattle is an open research and educational testbed that utilizes computational resources provided by end users on their existing devices. Unlike most other platforms, resources are not dedicated to the platform which allows a greater degree of network diversity and realism at the cost of programmability. Seattle is designed to preserve user security and to minimally impact application performance. We describe the architectural design of Seattle, and summarize our experiences with Seattle over the past few years as both researchers and educators. We have found that Seattle is very easy to adopt due to cross-platform support, and is also surprisingly easy for students to use. While there are programmability limitations, it is possible to construct complex applications integrated with real devices, networks, and users with Seattle as a core component. From an educational standpoint, Seattle has been shown not only to be useful as a teaching tool, it has been successful in variety of different systems classes at a variety of different types of schools. In our experience, when low-level programmability is not the main requirement, Seattle can supersede many existing testbeds for diverse educational and research tasks.

Distributed Robust Channel Assignment for Multi-Radio Cognitive Radio Networks

Cognitive radio users are allowed to utilize the unused portions of the licensed spectrum, which leads to performance enhancement. However, they need to carefully inspect the environment and make intelligent decisions. Secondary Users (SUs) are required to vacate the channel when a Primary User (PU) appears on the same licensed channel. This may cause interruptions in secondary network transmissions. In this paper we propose a distributed channel assignment scheme for cognitive radio networks. We consider a multi-radio node architecture in order to better utilize the multiple available channels. Our RIMCA (Robust Interference Minimizing Channel Assignment) scheme includes a collaborative sensing mechanism as well as channel assignment. We also consider channel reclaim by a primary user. When making decisions, secondary users consider the interference imposed on primary users as well as the total interference in the secondary network. Simulation results show that our RIMCA outperforms the most related channel assignment schemes. Moreover, our channel assignment scheme is robust to PU activities.

A geometrical probability approach to location-critical network performance metrics

Node locations and distances are of profound importance for the operation of any communication networks. With the fundamental inter-node distance captured in a random network, one can build probabilistic models for characterizing network performance metrics such as k-th nearest neighbor and traveling distances, as well as transmission power and path loss in wireless networks. For the first time in the literature, a unified approach is developed to obtain the closed-form distributions of inter-node distances associated with hexagons. This approach can be degenerated to elementary geometries such as squares and rectangles. By the formulation of a quadratic product, the proposed approach can characterize general statistical distances when node coordinates are interdependent. Hence, our approach applies to both elementary and complex geometric topologies, and the corresponding probabilistic distance models go beyond the approximations and Monte Carlo simulations. Analytical models based on hexagon distributions are applied to the analysis of the nearest neighbor distribution in a sparse network for improving energy efficiency, and the farthest neighbor distribution in a dense network for minimizing routing overhead. Both the models and simulations demonstrate the high accuracy and promising potentials of this approach, whereas the current best approximations are not applicable in many scenarios. This geometrical probability approach thus provides accurate information essential to the successful network protocol and system design.