Heather Zheng

TRUST: A General Framework for Truthful Double Spectrum Auctions

We design truthful double spectrum auctions where multiple parties can trade spectrum based on their individual needs. Open, market-based spectrum trading motivates existing spectrum owners (as sellers) to lease their selected idle spectrum to new spectrum users, and provides new users (as buyers) the spectrum they desperately need. The most significant challenge is how to make the auction economic-robust (truthful in particular) while enabling spectrum reuse to improve spectrum utilization. Unfortunately, existing designs either do not consider spectrum reuse or become untruthful when applied to double spectrum auctions. We address this challenge by proposing TRUST, a general framework for truthful double spectrum auctions. TRUST takes as input any reusability-driven spectrum allocation algorithm, and applies a novel winner determination and pricing mechanism to achieve truthfulness and other economic properties while significantly improving spectrum utilization. To our best knowledge, TRUST is the first solution for truthful double spectrum auctions that enable spectrum reuse. Our results show that economic factors introduce a tradeoff between spectrum efficiency and economic robustness. TRUST makes an important contribution on enabling spectrum reuse to minimize such tradeoff.

eBay in the Sky: strategy-proof wireless spectrum auctions

Market-driven dynamic spectrum auctions can drastically improve the spectrum availability for wireless networks struggling to obtain additional spectrum. However, they face significant challenges due to the fear of market manipulation. A truthful or strategy-proof spectrum auction eliminates the fear by enforcing players to bid their true valuations of the spectrum. Hence bidders can avoid the expensive overhead of strategizing over others and the auctioneer can maximize its revenue by assigning spectrum to bidders who value it the most. Conventional truthful designs, however, either fail or become computationally intractable when applied to spectrum auctions. In this paper, we propose VERITAS, a truthful and computationally-efficient spectrum auction to support an eBay-like dynamic spectrum market. VERITAS makes an important contribution of maintaining truthfulness while maximizing spectrum utilization. We show analytically that VERITAS is truthful, efficient, and has a polynomial complexity of O(n3k) when n bidders compete for k spectrum bands. Simulation results show that VERITAS outperforms the extensions of conventional truthful designs by up to 200% in spectrum utilization. Finally, VERITAS supports diverse bidding formats and enables the auctioneer to reconfigure allocations for multiple market objectives.

Physical Interference Driven Dynamic Spectrum Management

Dynamic spectrum management can drastically improve the performance of wireless networks struggling under increasing user demands. However, performing efficient spectrum allocation is a complex and difficult process. Current proposals make the problem tractable by simplifying interference constraints as conflict graphs, but they face potential performance degradation from inaccurate interference estimation. In this paper, we show that conflict graphs, if optimized properly, can produce spectrum allocations that closely match those derived from the physical interference model. Thus we propose PLAN, a systematic framework to produce conflict graphs based on physical interference characteristics. PLAN first applies an analytical framework to derive the criterion for identifying conflicting neighbors, capturing the cumulative effect of interference. PLAN then applies a local conflict adjustment algorithm to address heterogeneous interference conditions and improve spectrum allocation efficiency. Through detailed analysis and experimental evaluations, we show that PLAN builds a conflict graph to effectively represent the complex interference conditions and allow the reuse of efficient graph-based spectrum allocation solutions. PLAN also significantly outperforms the conventional graph model based solutions.

Preamble Design for Non-Contiguous Spectrum Usage in Cognitive Radio Networks

Cognitive radios can significantly improve spectrum efficiency by using locally available spectrum. The efficiency, however, depends heavily on their transceiver design. In particular, being able to use non-contiguously aligned spectrum bands simultaneously is a critical requirement. Prior work in this area requires a control channel so that transmitter/receiver pairs can synchronize on their spectrum usage patterns. However, this approach can suffer from high cost and control congestion. In this paper, we propose an in-band solution for informing receivers the spectrum usage patterns. By judiciously designing packet preambles, we embed the spectrum usage patterns in each data packet. Using the legacy 802.11 preamble structure, we focus on choosing the appropriate preamble sequences to maintain reliable packet detection in the presence of noise and interference. We verify our design using simulation and show that it can lead to reliable packet transmissions comparable to those of contiguous spectrum usage. We also identify the impact of interference on our design and propose refinements to choose the preamble sequence using information on the interference.

Traffic-aware dynamic spectrum access

Demand-driven spectrum allocation can drastically improve performance for WiFi access points struggling under increasing user demands. While their frequency agility makes cognitive radios ideal for this challenge, performing adaptive spectrum allocation is a complex and difficult process. In this work, we propose FLEX, an efficient spectrum allocation architecture that efficiently adapts to dynamic traffic demands. FLEX tunes network-wide spectrum allocation by access points coordinating with peers, minimizing network resets through local adaptations. Through detailed analysis and experimental evaluation, we show that FLEX converges quickly, provides users with proportional-fair spectrum usage and significantly outperforms existing spectrum allocation proposals.

Securing Structured Overlays against Identity Attacks

Structured overlay networks can greatly simplify data storage and management for a variety of distributed applications. Despite their attractive features, these overlays remain vulnerable to the Identity attack, where malicious nodes assume control of application components by intercepting and hijacking key-based routing requests. Attackers can assume arbitrary application roles such as storage node for a given file, or return falsified contents of an online shoppers shopping cart. In this paper, we define a generalized form of the Identity attack, and propose a lightweight detection and tracking system that protects applications by redirecting traffic away from attackers. We describe how this attack can be amplified by a Sybil or Eclipse attack, and analyze the costs of performing such an attack. Finally, we present measurements of a deployed overlay that show our techniques to be significantly more lightweight than prior techniques, and highly effective at detecting and avoiding both single node and colluding attacks under a variety of conditions.

Traffic-Driven Dynamic Spectrum Auctions

Wireless growth has been limited by the shortage of radio spectrum. While the spectrum assigned to legacy technologies remain unused, new prominent technologies such as Mesh/WiFi networks are forced to crowd into a small unlicensed band, suffering from significant interference and degraded performance. Using economic incentives, dynamic spectrum auctions redistribute spectrum to make it available to new technologies while providing financial benefits to legacy owners. In this paper, we investigate the performance of dynamic spectrum auctions under traffic dynamics. Using measured traffic traces from deployed WiFi access points, we evaluate the advantages and artifacts of dynamic auctions over plain channel sharing, and investigate the impact of bidding formats and auction intervals. Our results show that short-term dynamic auctions with traffic-aware bidding can significantly improve system throughput and provide bidders with cost-effective spectrum usage.

Group-Mobility-Aware Spectrum Management for Future Digital Battlefields

Efficient access to spectrum is critical to maintain reliable and robust communication in military networks, especially for ground vehicles on the move. High mobility and frequent topology variations make spectrum management in battlefields challenging. In this paper, we present a decentralized, hierarchical approach to assign spectrum channels among vehicles in a fair and efficient manner. Making use of group mobility, we propose a low-complexity allocation scheme with minimum communication and computation overhead. Experimental results demonstrate that the proposed algorithm can reduce the overhead by 10+ fold with maintaining reasonable spectrum utilization

Malware in IEEE 802.11 wireless networks

Malicious software (malware) is one of the largest threats facing the Internet today. In recent years, malware has proliferated into wireless LANs as these networks have grown in popularity and prevalence. Yet the actual effects of malware-related network traffic in open wireless networks has never been examined. In this paper, we provide the first study to quantify the characteristics of malware on wireless LANs. We use data collected from the large wireless LAN deployment at the 67th IETF meeting in San Diego, California as a case study. The measurements in this paper demonstrate that even a single infected host can have a dramatic impact on the performance of a wireless network.

Understanding cross-band interference in unsynchronized spectrum access

We consider the problem of cross-band interference when devices from the same or different networks share radio spectrum. Cross-band interference occurs when unsynchronized transmissions create harmful interference among each other although they use non-overlapping frequency bands. We perform an in-depth study to analytically quantify the degree of interference and its impact on OFDMA-based packet transmissions. The analysis also takes into account practical artifacts including temporal sampling mismatch, frequency offset and power heterogeneity. Using insights from the analysis, we build and compare three methods that apply temporal and frequency redundancy to reduce the interference and add robustness against it. Experimental and analytical results show that adding frequency guardband is the most efficient solution to tackle cross-band interference.