We present a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic. This tool does not require any controlled experiments, injection of probe traffic in the network, or even access to the network nodes. Our approach requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed using a machine learning approach to infer the carrier-sense relationship between network nodes. This coupled with an estimation of collision probabilities helps us to deduce the interference relationships. We also demonstrate an important application of this tool-detection of selfish carrier-sense behavior. This is based on identifying any asymmetry in carrier-sense behavior between node pairs and finding multiple witnesses to raise confidence. We evaluate the effectiveness of the tool for both the applications using extensive experiments and simulation. Experimental and simulation results demonstrate that the proposed approach of estimating interference relations is significantly more accurate than simpler heuristics and quite competitive with active measurements. We also validate the approach in a real Wireless LAN environment. Evaluations using a real testbed as well as ns2 simulation studies demonstrate excellent detection ability of the selfish behavior. On the other hand, the metric of selfishness used to estimate selfish behavior matches closely with actual degree of selfishness observed.

With the advent of programmability in radios, it is becoming easier for wireless network nodes to cheat to obtain an unfair share of the bandwidth. In this work we study the widely used 802.11 protocol and present a solution to detect selfish carrier-sensing behavior where a node raises the CCA (clear channel assessment) threshold for carrier-sensing, or simply does not sense carrier (possibly randomly to avoid detection). Our approach is based on detecting any asymmetry in carrier-sense behavior between node pairs and finding multiple such witnesses to raise confidence. The approach is completely passive. It requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed by using a machine learning approach to infer carrier-sense relationships between network nodes. Evaluations using a real testbed as well as ns2 simulation studies demonstrate excellent detection ability. The metric of selfishness used to estimate selfish behavior matches closely with actual degree of selfishness observed.

The demand for capacity in WiFi networks is driving a new look at transmission scheduling-based link layers. One basic issue here is the use of accurate interference models to drive transmission scheduling algorithms. However, experimental work in this space has been limited. In this work, we use commodity WiFi hardware (specifically, 802.11a) for a comprehensive study of interference modeling for transmission scheduling on a mesh network setup. We focus on the well-known physical interference model for its realism. We propose use of the "graded" version of the model where feasibility of a link is probabilistic, as opposed to using the more traditional "thresholded" version, where feasibility is binary. We show experimentally that the graded model is significantly more accurate (80 percentile error 0.2 vs. 0.55 for thresholded model). We develop transmission scheduling experiments using greedy scheduling algorithms for the evacuation model for both interference models. We also develop similar experiments for optimal scheduling performance for the simplified one-shot scheduling. The scheduling experiments demonstrate clearly superior performance for the graded model, often by a factor of 2. We conclude by promoting use of this model for scheduling studies.

In November 2008, the FCC ruled that the digital TV whitespaces be used for unlicensed access. This is an exciting development because DTV whitespaces are in the low frequency range (50-698 MHz) compared to typical cellular and ISM bands, thus resulting in much better propagation characteristics and much higher spectral efficiencies. The FCC has also mandated certain guidelines for short range unlicensed access, so as to avoid any interference to DTV receivers. We consider the problem of WiFi like access (popularly referred to as WiFi 2.0) for enterprizes. We assume that the access points and client devices are equipped with cognitive radios, i.e., they can adaptively choose the center frequency, bandwidth and ower of operation. The access points can be equipped with one or more radios. Our goal is to design a complete system, which (i) does not violate the FCC mandate, (ii) dynamically assigns center frequency and bandwidth to each access point based on their demands and (iii) squeezes the maximum efficiency from the available spectrum. This problem is far more general than prior work that investigated dynamic spectrum allocation in cellular and ISM bands, due to the non-homogenous nature of the whitespaces, i.e., different whitespace widths in different parts of the spectrum and the large range of frequency bands with different propagation characteristics. This calls for a more holistic approach to system design that also accounts for frequency dependent propagation characteristics and radio frontend characteristics. In this paper, we first propose design rules for holistic system design. We then describe an architecture derived from our design rules. Finally we propose demand based dynamic spectrum allocation algorithms with provable worst case guarantees. We provide extensive simulation results showing that (i) the performance of our algorithm is within 94% of the optimal in typical settings and (ii) and the DTV whitespaces can provide significantly higher data rates compared to the 2.4GHz ISM band. Our approach is general enough for designing any system with access to a wide range of spectrum.

Accurate interference models are important for use in transmission scheduling algorithms in wireless networks. In this work, we perform extensive modeling and experimentation on two 20-node TelosB motes testbeds – one indoor and the other outdoor – to compare a suite of interference models for their modeling accuracies. We first empirically build and validate the physical interference model via a packet reception rate vs. SINR relationship using a measurement driven method. We then similarly instantiate other simpler models, such as hop-based, range-based, protocol model, etc. The modeling accuracies are then evaluated on the two testbeds using transmission scheduling experiments. We observe that while the physical interference model is the most accurate, it is still far from perfect, providing a 90-percentile error about 20-25 % (and 80 percentile error 7-12%), depending on the scenario. The accuracy of the other models is worse and scenario-specific. The second best model trails the physical model by roughly 12-18 percentile points for similar accuracy targets. Somewhat similar throughput performance differential between models is also observed when used with greedy scheduling algorithms. Carrying on further, we look closely into the the two incarnations of the physical model – ‘thresholded ’ (conservative, but typically considered in literature) and ‘graded ’ (more realistic). We show via solving the one shot scheduling problem, that the graded version can improve ‘expected throughput ’ over the thresholded version by scheduling imperfect links. Categories and Subject Descriptors C.2.1 [Network architecture and design]: Wireless communication;

We propose a novel algorithm for detecting worm-hole attacks in wireless multi-hop networks. The algorithm uses only connectivity information to look for forbidden substructures in the connectivity graph. The proposed approach is completely localized and, unlike many techniques proposed in literature, does not use any special hardware artifact or location information, making the technique universally applicable. The algorithm is independent of wireless communication models. However, knowledge of the model and node distribution helps estimate a parameter used in the algorithm. We present simulation results for three different communication models and two different node distributions, and show that the algorithm is able to detect wormhole attacks with a 100% detection and 0% false alarm probabilities whenever the network is connected with high probability. Even for very low density networks where chances of disconnection is very high, the detection probability remains very high.

In this paper, we propose two new MAC protocols for multichannel operation in wireless ad hoc and mesh networks. The first protocol, extended receiver directed transmission protocol (xRDT) is based on a previously known multichannel solution called receiver directed transmission (RDT) that uses a notion of quiescent channel. xRDT solves the problems faced by RDT, such as multichannel hidden terminal and deafness, by using an additional busy tone interface and few additional protocol operations. We also develop a novel single interface solution, called local coordination-based multichannel MAC (LCM MAC). LCM MAC performs coordinated channel negotiations and channel switching to provide multichannel support. We demonstrate the effectiveness of these two protocols over two other well-known multichannel protocols - MMAC and DCA - via extensive ns2 simulations