Traditionally, the channelization structure in IEEE 802.11-based wireless LANs has been fixed: Each access point (AP) is assigned one channel and all channels are equally wide. In contrast, it has recently been shown that even on commodity hardware, the channel-width can be adapted dynamically purely in software. Leveraging this capability, we study the use of dynamic-width channels, where every AP adaptively adjusts not only its center-frequency, but also its channel-width to match its traffic load. This gives raise to a novel optimization problem that differs from previously studied channel assignment problems. We propose efficient spectrum-distribution algorithms and evaluate their effectiveness through analysis and simulations using real-world traces. Our results indicate that by allocating more spectrum to highly-loaded APs, the overall spectrum-utilization can be substantially improved and the notorious load-balancing problem in WLANs can be solved naturally.

A number of studies have shown the abundance of unused spectrum in the TV bands. This is in stark contrast to the overcrowding of wireless devices in the ISM bands. A recent trend to alleviate this disparity is the design of Cognitive Radios, which constantly sense the spectrum and opportunistically utilize unused frequencies in the TV bands. A key challenge in the design of such networks is that of Spectrum Allocation, which enables nodes to reserve chunks of the spectrum for certain periods of time. In this paper, we introduce the concept of a time-spectrum block to model spectrum reservation, and use it to present a theoretical formalization of the spectrum allocation problem. We also present a centralized and a distributed protocol for spectrum allocation and show that these protocols are close to optimal in most scenarios. We have implemented the distributed protocol in QualNet and show that our analysis closely matches the simulation results.

Node compromise poses severe security threats in wireless sensor networks. Unfortunately, existing security designs can address only a small, fixed threshold number of compromised nodes; the security protection completely breaks down when the threshold is exceeded. In this paper, we seek to overcome the threshold limitation and achieve resiliency against an increasing number of compromised nodes. To this end, we propose a novel location-based approach in which the secret keys are bound to geographic locations, and each node stores a few keys based on its own location. The location-binding property constrains the scope for which individual keys can be (mis)used, thus limiting the damages caused by a collection of compromised nodes. We illustrate this approach through the problem of report fabrication attacks, in which the compromised nodes forge non-existent events. We evaluate our design through extensive analysis, implementation and simulations, and demonstrate its graceful performance degradation in the presence of an increasing number of compromised nodes.

The next-generation wireless technologies, e.g., 802.11n and 802.15.3a, offer a physical-layer speed at least an-order-of-magnitude higher than the current standards. However, direct application of current MACs leads to high protocol overhead and significant throughput degradation. We propose ADCA, a high-performance MAC that works with high-capacity physical layer. ADCA exploits two ideas of adaptive batch transmission and opportunistic selection of high-rate hosts to simultaneously reduce the overhead and improve the aggregate throughput. It opportunistically favors high-rate hosts by providing higher access probability and more access time, while ensuring each low-rate host certain minimum amount of channel access time. Simulations show that the ADCA design increases the throughput by 112% and reduces the average delay by 55% compared with the legacy DCF. It delivers more than 100 Mbps MAC-layer throughput as compared with 35 Mbps offered by the legacy MAC.

Several studies have recently been performed on wireless university campus networks, corporate and public networks. Yet little is known about the flow-level characterization in such networks. In this paper, we statistically characterize both static flows and roaming flows in a large campus wireless network using a recently-collected trace. For static flows, we take a two-tier approach to characterizing the flow arrivals, which results a Weibull regression model. We further discover that the static flow arrivals in spatial proximity show strong similarity. As for roaming flows, they can also be well characterized statistically.We explain the results by user behaviors and application demands, and further cross-validate the modeling results by three other traces. Finally, we use two examples to illustrate how to apply our models for performance evaluation in the wireless context.

Service discovery technologies are exploited to enable services to advertise their existence in a dynamic way, and can be discovered, configured and used by other devices with minimum manual efforts. It plays an essential role in future network scenarios especially with development of mobile ad hoc network (MANET) and emergence of pervasive computing. Because MANET allows these devices to communicate dynamically without fixed infrastructure and centralized administration, it gives rise to the challenges of the service discovery techniques. In this paper, we present a dynamic service discovery infrastructure that uses XML to describe services and match using the semantic content of service descriptions for MANET. We believe that the architecture we have designed is a necessary component of service discovery in non-infrastructure network by further exploring the secure and performance issues of this infrastructure.

We present queue length based fair queueing (QLFQ), a scheme to approximate fair bandwidth allocation without per-flow state. We have evaluated QLFQ together with CSFQ (core-stateless fair queueing) (see Stoica, I., Proc. ACM SIGCOMM'98, p.119-30, 1998) and RFQ (rainbow fair queueing) (see Cao, Z., Proc. IEEE Infocom, 2000) with several different configurations and traffic sources. The simulation results show that QLFQ is able to achieve approximately fair bandwidth sharing in all of these scenarios. The performance of QLFQ is comparable to that of CSFQ and it performs much better than RFQ. The simulations also show that in an environment with bursty traffic sources, QLFQ provides much better fairness than CSFQ.