Xiuzhen Cheng

TPS: a time-based positioning scheme for outdoor wireless sensor networks

We present a novel time-based positioning scheme (TPS) for efficient location discovery in outdoor sensor networks. TPS relies on TDoA (time-difference-of-arrival) of RF signals measured locally at a sensor to detect range differences from the sensor to three base stations. These range differences are averaged over multiple beacon intervals before they are combined to estimate the sensor location through trilateration. A nice feature of this positioning scheme is that it is purely localized: sensors independently compute their positions. We present a statistical analysis of the performance of TPS in noisy environments. We also identify possible sources of position errors with suggested measures to mitigate them. Our scheme requires no time synchronization in the network and minimal extra hardware in sensor construction. TPS induces no communication overhead for sensors, as they listen to three beacon signals passively during each beacon interval. The computation overhead is low, as the location detection algorithm involves only simple algebraic operations over scalar values. TPS is not adversely affected by increasing network size or density and thus offers scalability. We conduct extensive simulations to test the performance of TPS when TDoA measurement errors are normally distributed or uniformly distributed. The obtained results show that TPS is an effective scheme for outdoor sensor self-positioning.

A polynomial-time approximation scheme for the minimum-connected dominating set in ad hoc wireless networks

A connected dominating set in a graph is a subset of vertices such that every vertex is either in the subset or adjacent to a vertex in the subset and the subgraph induced by the subset is connected. A minimum-connected dominating set is such a vertex subset with minimum cardinality. An application in ad hoc wireless networks requires the study of the minimum-connected dominating set in unit-disk graphs. In this paper, we design a (1 + 1/s)-approximation for the minimum-connected dominating set in unit-disk graphs, running in time nO((s log s)2).

Aggregation tree construction in sensor networks

Large-scale wireless sensor networks are expected to play an increasingly important role in future civilian and military settings. Collaborative microsensors could be very effective in monitoring their operations. However, low power and in-network data processing make data-centric routing in wireless sensor networks a challenging problem. In this paper we propose heuristics to construct and maintain an aggregation tree in sensor networks. This aggregation tree can be used to facilitate data-centric routing. The main idea is to turn off the radio of all leaf nodes to save power, and thereby extending the network lifetime. Therefore, in order to save the number of broadcasting messages, only the nonleaf nodes in the tree are in charge of data aggregation and traffic relaying. In this paper, we propose an efficient energy-aware distributed heuristic to generate the aggregation tree, which we refer to as EADAT. Our EADAT algorithm makes no assumption on local network topology, and is based on residual power. It makes use of neighboring broadcast scheduling and distributed competition among neighbors. These novel concepts make EADAT very efficient and effective, as demonstrated by our simulation experiments with NS2.

Connected Dominating Set in Sensor Networks and MANETs

3 Centralized CDS Construction 335 3.1 Guha and Khuller’s Algorithm . . . . . . . . . . . . . . . . . . . . . 336 3.2 Ruan’s Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 3.3 Cheng’s Greedy Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 340 3.4 Min’s Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 3.5 Butenko’s Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 341

Silent Positioning in Underwater Acoustic Sensor Networks

In this paper, we present a silent positioning scheme termed UPS for underwater acoustic sensor networks. UPS relies on the time difference of arrivals locally measured at a sensor to detect range differences from the sensor to four anchor nodes. These range differences are averaged over multiple beacon intervals before they are combined to estimate the 3-D sensor location through trilateration. UPS requires no time synchronization and provides location privacy at underwater vehicles/sensors whose locations need to be determined. To study the performance of UPS, we model the underwater acoustic channel as a modified ultrawideband Saleh-Valenzuela model: The arrival of each path cluster and the paths within each cluster follow double Poisson distributions, and the multipath channel gain follows a Rician distribution. Based on this channel model, we perform both theoretical analysis and simulation study on the position error of UPS under acoustic fading channels. The obtained results indicate that UPS is an effective scheme for underwater vehicle/sensor self-positioning.

Dynamic spectrum access: from cognitive radio to network radio

Dynamic spectrum access is a new spectrum sharing paradigm that allows secondary users to access the abundant spectrum holes or white spaces in the licensed spectrum bands. DSA is a promising technology to alleviate the spectrum scarcity problem and increase spectrum utilization. While DSA has attracted many research efforts recently, in this article, we discuss the challenges of DSA and aim to shed light on its future. We first give an introduction to the state-of- the-art in spectrum sensing and spectrum sharing. Then, we examine the challenges that prevent DSA from major commercial deployment. We believe that, to address these challenges, a new DSA model is critical, where the licensed users cooperate in DSA and hence much more flexible spectrum sharing is possible. Furthermore, the future DSA model should consider the political, social, economic, and technological factors all together, to pave the way for the commercial success of DSA. To support this future DSA model, the future cognitive radio is expected to have additional components and capabilities, to enforce policy, provide incentive and coexistence mechanisms, etc. We call the future cognitive radio with the expanded capabilities a network radio, and discuss its architecture as well as the design issues for future DSA.

Insider Attacker Detection in Wireless Sensor Networks

Though destructive to network functions, insider attackers are not detectable with only the classic cryptography-based techniques. Many mission-critic sensor network applications demand an effective, light, flexible algorithm for internal adversary identification with only localized information available. The insider attacker detection scheme proposed in this paper meets all the requirements by exploring the spatial correlation existent among the networking behaviors of sensors in close proximity. Our work is exploratory in that the proposed algorithm considers multiple attributes simultaneously in node behavior evaluation, with no requirement on a prior knowledge about normal/malicious sensor activities. Moreover, it is application-friendly, which employs original measurements from sensors and can be employed to monitor many aspects of sensor networking behaviors. Our algorithm is purely localized, fitting well to the large-scale sensor networks. Simulation results indicate that internal adversaries can be identified with a high accuracy and a low false alarm rate when as many as 25% sensors are misbehaving.

Localized Outlying and Boundary Data Detection in Sensor Networks

Given a set of sparsely distributed sensors in the Euclidean plane, a mobile robot is required to visit all sensors to download the data and finally return to its base. The effective range of each sensor is specified by a disk, and the robot must at least reach the boundary to start communication. The primary goal of optimization in this scenario is to minimize the traveling distance by the robot. This problem can be regarded as a special case of the traveling salesman problem with neighborhoods (TSPN), which is known to be NP-hard. In this paper, we present a novel TSPN algorithm for this class of TSPN, which can yield significantly improved results compared to the latest approximation algorithm.

Strong minimum energy topology in wireless sensor networks: NP-completeness and heuristics

Wireless sensor networks have recently attracted lots of research effort due to the wide range of applications. These networks must operate for months or years. However, the sensors are powered by battery, which may not be able to be recharged after they are deployed. Thus, energy-aware network management is extremely important. In this paper, we study the following problem: Given a set of sensors in the plane, assign transmit power to each sensor such that the induced topology containing only bidirectional links is strongly connected. This problem is significant in both theory and application. We prove its NP-completeness and propose two heuristics: power assignment based on minimum spanning tree (denoted by MST) and incremental power. We also show that the MST heuristic has a performance ratio of 2. Simulation study indicates that the performance of these two heuristics does not differ very much, but; on average, the incremental power heuristic is always better than MST.

Underwater Localization in Sparse 3D Acoustic Sensor Networks

We study the localization problem in sparse 3D underwater sensor networks. Considering the fact that depth information is typically available for underwater sensors, we transform the 3D underwater positioning problem into its two- dimensional counterpart via a projection technique and prove that a non-degenerative projection preserves network localizability. We further prove that given a network and a constant k, all of the geometric k-lateration localization methods are equivalent. Based on these results, we design a purely distributed localization framework termed USP. This framework can be applied with any ranging method proposed for 2D terrestrial sensor networks. Through theoretical analysis and extensive simulation, we show that USP preserves the localizability of the original 3D network via a simple projection and improves localization capabilities when bilateration is employed. USP has low storage and computation requirements, and predictable and balanced communication overhead.