Benyuan Liu

On the capacity of hybrid wireless networks

This paper involves the study of the throughput capacity of hybrid wireless networks. A hybrid network is formed by placing a sparse network of base stations in an ad hoc network. These base stations are assumed to be connected by a high-bandwidth wired network and act as relays for wireless nodes. They are not data sources nor data receivers. Hybrid networks present a tradeoff between traditional cellular networks and pure ad hoc networks in that data may be forwarded in a multihop fashion or through the infrastructure. It has been shown that the capacity of a random ad hoc network does not scale well with the number of nodes in the system. In this work, we consider two different routing strategies and study the scaling behavior of the throughput capacity of a hybrid network. Analytical expressions of the throughput capacity are obtained. For a hybrid network of n nodes and m base stations, the results show that if m grows asymptotically slower than √n, the benefit of adding base stations on capacity is insignificant. However, if m grows faster than √n, the throughput capacity increases linearly with the number of base stations, providing an effective improvement over a pure ad hoc network. Therefore, in order to achieve nonnegligible capacity gain, the investment in the wired infrastructure should be high enough.

Mobility improves coverage of sensor networks

Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition sensors in order to achieve a static configuration with an enlarged covered area. In this paper, we study the dynamic aspects of the coverage of a mobile sensor network that depend on the process of sensor movement. As time goes by, a position is more likely to be covered; targets that might never be detected in a stationary sensor network can now be detected by moving sensors. We characterize the area coverage at specific time instants and during time intervals, as well as the time it takes to detect a randomly located stationary target. Our results show that sensor mobility can be exploited to compensate for the lack of sensors and improve network coverage. For mobile targets, we take a game theoretic approach and derive optimal mobility strategies for sensors and targets from their own perspectives.

Predicting Flu Trends using Twitter data

Reducing the impact of seasonal influenza epidemics and other pandemics such as the H1N1 is of paramount importance for public health authorities. Studies have shown that effective interventions can be taken to contain the epidemics if early detection can be made. Traditional approach employed by the Centers for Disease Control and Prevention (CDC) includes collecting influenza-like illness (ILI) activity data from “sentinel” medical practices. Typically there is a 1–2 week delay between the time a patient is diagnosed and the moment that data point becomes available in aggregate ILI reports. In this paper we present the Social Network Enabled Flu Trends (SNEFT) framework, which monitors messages posted on Twitter with a mention of flu indicators to track and predict the emergence and spread of an influenza epidemic in a population. Based on the data collected during 2009 and 2010, we find that the volume of flu related tweets is highly correlated with the number of ILI cases reported by CDC. We further devise auto-regression models to predict the ILI activity level in a population. The models predict data collected and published by CDC, as the percentage of visits to “sentinel” physicians attributable to ILI in successively weeks. We test models with previous CDC data, with and without measures of Twitter data, showing that Twitter data can substantially improve the models prediction accuracy. Therefore, Twitter data provides real-time assessment of ILI activity.

Properties of random direction models

A number of mobility models have been proposed for the purpose of either analyzing or simulating the movement of users in a mobile wireless network. Two of the more popular are the random waypoint and the random direction models. The random waypoint model is physically appealing but difficult to understand. Although the random direction model is less appealing physically, it is much easier to understand. User speeds are easily calculated, unlike for the waypoint model, and, as we observe, user positions and directions are uniformly distributed. The contribution of this paper is to establish this last property for a rich class of random direction models that allow future movements to depend on past movements. To this end, we consider finite oneand two-dimensional spaces. We consider two variations, the random direction model with wrap around and with reflection. We establish a simple relationship between these two models and, for both, show that positions and directions are uniformly distributed for a class of Markov movement models regardless of initial position. In addition, we establish a sample path property for both models, namely that any piecewise linear movement applied to a user preserves the uniform distribution of position and direction provided that users were initially uniformly throughout the space with equal likelihood of being pointed in any direction.

Barrier Coverage of Line-Based Deployed Wireless Sensor Networks

Barrier coverage of wireless sensor networks has been studied intensively in recent years under the assumption that sensors are deployed uniformly at random in a large area (Poisson point process model). However, when sensors are deployed along a line (e.g., sensors are dropped from an aircraft along a given path), they would be distributed along the line with random off- sets due to wind and other environmental factors. It is important to study the barrier coverage of such line- based deployment strategy as it represents a more realistic sensor placement model than the Poisson point process model. This paper presents the first set of results in this direction. In particular, we establish a tight lower-bound for the existence of barrier coverage under line-based deployments. Our results show that the barrier coverage of the line-based deployments significantly outperforms that of the Poisson model when the random offsets are relatively small compared to the sensors sensing range. We then study sensor deployments along multiple lines and show how barrier coverage is affected by the distance between adjacent lines and the random offsets of sensors. These results demonstrate that sensor deployment strategies have direct impact on the barrier coverage of wireless sensor networks. Different deployment strategies may result in significantly different barrier coverage. Therefore, in the planning and deployment of wireless sensor networks, the coverage goal and possible sensor deployment strategies must be carefully and jointly considered. The results obtained in this paper will provide important guidelines to the deployment and performance of wireless sensor networks for barrier coverage.

Capacity of a wireless ad hoc network with infrastructure

In this paper we study the capacity of wireless ad hoc networks with infrastructure support of an overlay of wired base stations. Such a network architecture is often referred to as hybrid wireless network or multihop cellular network. Previous studies on this topic are all focused on the two-dimensional disk model proposed by Gupta and Kumarin their original work on the capacity of wireless ad hoc networks. We further consider a one-dimensional network model and a two-dimensional strip model to investigate the impact of network dimensionality and geometry on the capacity of such networks. Our results show that different network dimensions lead to significantly different capacity scaling laws. Specifically, for a one-dimensional network of n nodes and b base stations, even with a small number of base stations, the gain in capacity is substantial, increasing linearly with the number of base stations as long as b log b ≤ n. However, a two-dimensional square (or disk) network requires a large number of base stations b = Ω(√n) before we see such a capacity increase. For a 2-dimensional strip network, if the width of the strip is at least on the order of the logarithmic of its length, the capacity follows the same scaling law as in the 2-dimensional square case. Otherwise the capacity exhibits the same scaling behavior as in the 1-dimensional network. We find that the different capacity scaling behaviors are attributed to the percolation properties of the respective network models.

Data fusion improves the coverage of wireless sensor networks

Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on sensing coverage are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this paper, we attempt to bridge this gap by exploring the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors. In particular, for signal path loss exponent of k (typically between 2.0 and 5.0), rho_f=O(rho_d^(1-1/k)), where rho_f and rho_d are the densities of uniformly deployed sensors that achieve full coverage under the fusion and disc models, respectively. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.

Dynamic Coverage of Mobile Sensor Networks

We study the dynamic aspects of the coverage of a mobile sensor network resulting from continuous movement of sensors. As sensors move around, initially uncovered locations may be covered at a later time, and intruders that might never be detected in a stationary sensor network can now be detected by moving sensors. However, this improvement in coverage is achieved at the cost that a location is covered only part of the time, alternating between covered and not covered. We characterize area coverage at specific time instants and during time intervals, as well as the time durations that a location is covered and uncovered. We further consider the time it takes to detect a randomly located intruder and prove that the detection time is exponentially distributed with parameter 2λrv̅s where λ represents the sensor density, r represents the sensors sensing range, and v̅s denotes the average sensor speed. For mobile intruders, we take a game theoretic approach and derive optimal mobility strategies for both sensors and intruders. We prove that the optimal sensor strategy is to choose their directions uniformly at random between (0, 2π). The optimal intruder strategy is to remain stationary. This solution represents a mixed strategy which is a Nash equilibrium of the zero-sum game between mobile sensors and intruders.

TCP-cognizant adaptive forward error correction in wireless networks

Wireless links are characterized by high bit error rates and intermittent connectivity. This can result in significant degradation in the performance (goodput) of TCP over wireless networks since non-congestion related packet losses can be misinterpreted by TCP as indications of network congestion, resulting in unnecessary congestion control. In this paper, we propose a technique, TCP with adaptive forward error correction (TCP-AFEC), to improve TCP performance over wireless networks. TCP-AFEC combines the well-established performance characterization of TCP with an understanding of the link layer error control scheme to dynamically select the forward error correction (FEC) that maximizes TCP goodput according to the current channel condition. The benefit of coupling a characterization of TCP performance with link layer FEC to improve TCP goodput is demonstrated by comparing the performance of TCP-AFEC against those of TCP-SACK and Snoop. Simulation results show that TCP-AFEC outperforms TCP-SACK and Snoop for a wide range of wireless channel conditions.

Secret communication in large wireless networks without eavesdropper location information

We present achievable scaling results on the per-node secure throughput that can be realized in a large random wireless network of n legitimate nodes in the presence of m eavesdroppers of unknown location. We consider both one-dimensional and two-dimensional networks. In the one-dimensional case, we show that a per-node secure throughput of order 1/n is achievable if the number of eavesdroppers satisfies m = o(n/log n). We obtain similar results for the two-dimensional case, where a secure throughput of order 1/(√n log n) is achievable under the same condition. The number of eavesdroppers that can be tolerated is significantly higher than previous works that address the case of unknown eavesdropper locations. The key technique introduced in our construction to handle unknown eavesdropper locations forces adversaries to intercept a number of packets to be able to decode a single message. The whole network is divided into regions, where a certain subset of packets is protected from adversaries located in each region. In the one-dimensional case, our construction makes use of artificial noise generation by legitimate nodes to degrade the signal quality at the potential locations of eavesdroppers. In the two-dimensional case, the availability of many paths to reach a destination is utilized to handle collaborating eavesdroppers of unknown location.