Rogue access point (AP) has emerged as an important security problem in WLANs. However, it is a challenge task to localize the rogue AP with both high accuracy and minimal infrastructure cost. Either expensive professional infrastructure (e.g., multiple wireless sniffers) or additional hardware (e.g., directional antenna) need to be pre-deployed for rogue AP localization with high cost. Moreover, existing methods using Received Signal Strength (RSS) result in a large error as RSS is suffered from the multipath and shadowing effects in complex wireless environment. In this work, we exploit the channel state information (CSI), which is readily available from commercial Wi-Fi devices, to locate the rogue AP with high accuracy. We use only a single off-the-shelf Wi-Fi device for rogue AP localization which involves minimal infrastructure requirement. Our proposed rogue AP localization framework consists of two components: direction determination and position estimation. The direction determination can be carried out by using the human blocking effect on the CSI amplitude or phase. The multiple antennas on the Wi-Fi devices can be further utilized to enhance the rogue AP direction estimation. Given the estimated direction, two schemes are proposed to pinpoint the position of the rogue AP: determining directions at multiple locations grounded on triangulation and walking towards the rogue AP with direction adjustment. Results from extensive experiments in both indoor and outdoor environments show that our framework can achieve more practical and accurate rogue AP localization when comparing with the existing RSS-based approach.

The problem of device-free passive localization aims on locating moving objects which do not carry any localization devices. The rationale of this problem is based on the fact that a moving object can result in the changes of received signal strength (RSS) of the wireless links. Existing studies on this problem usually do not consider the impact of dynamic target speed on device-free passive localization. However, the experiments show that the localization performance degrades substantially when an object is moving at dynamic speed. To meet this challenge, in this paper, we propose an adaptive device-free passive localization framework which has three components to detect target speed change and perform adaptive localization. This framework can be easily adapted for existing device-free localization methods which are based on the detection of signal strength changes. As demonstrated in the experiments, the proposed framework can lead to 50 and 30 percent improvement on median and maximum error respectively over the localization algorithms without considering dynamic moving speeds of the target.

Wireless localization using the received signal strength (RSS) can have tremendous savings over using specialized positioning infrastructures. In this work, we explore improving RSS localization performance in multipath environments by varying the transmitter's signal power and frequency. We first derive and analyze the Cramér-Rao Lower Bound (CRLB) of RSS-based localization based on the frequency dependent path loss propagation model that considers the transmitter's signal power and frequency. The derived CRLB shows the feasibility of improving localization performance by applying frequency and power level selection for RSS-based localization. Using this analysis, we develop two new selection metrics based on the observed standard deviations of RSS as well as residuals. We then show a set of selection methods that attempt to select the combinations of power and frequencies which minimize the localization error in a representative class of localization algorithms. Our simulation results confirm the proposed selection methods can improve the localization accuracy under CRLB. Additionally, using active RFID tags, we experimentally characterize the effect of using multiple signal powers and frequencies on a wide spectrum of RSS-based algorithms. We found that the performance of all the algorithms improves when leveraging on multiple power levels and frequencies, although different algorithms present different sensitivity in terms of localization accuracy under different selection methods.

Device-free passive localization enables locating targets (e.g., intruders or victims) that do not carry any radio devices nor do they actively participate in the wireless localization process. This is because the wireless environments will get affected when people move into the area, which result in the changes of Received Signal Strength (RSS) of the wireless links. In this paper, we first show that the localization performance degrades significantly when people are moving in dynamic speeds. This is because existing studies in device-free passive localization system have an implicit assumption that the target is moving at a constant speed, which is not always true in practical scenarios. To cope with targets moving with dynamic speeds, we propose an adaptive speed change detection framework including three components: speed change detection, determination of time-window size and adaptive localization. Two speed change detection schemes have been developed to capture the changes of moving speed and adjust the time-window size adaptively to facilitate effective localization. We demonstrate that our framework is flexible to work with any device-free localization method using signal strength. Results from the real experiments confirm that our approach has over 30% improvement on both median and max localization error, under dynamically changing speed of the target.

Received Signal Strength (RSS) based localization algorithms are sensitive to a set of non-cryptographic attacks. For example, the attacker can perform signal strength attacks by placing an absorbing or reflecting material around a wireless device to modify its RSS readings. In this work, we first formulate the all-around signal strength attacks, where similar attacks are launched towards all landmarks, and experimentally show the feasibility of launching such attacks. We then propose a general principle for designing RSS-based algorithms so that they are robust to all-around signal strength attacks. To evaluate our approach, we adapt two RSS-based localization algorithms according to our principle and experiment with real attack scenarios. All the experiments show that our design principle can be applied to achieve comparable performance with much better robustness.

Wireless localization using the received signal strength (RSS) can have tremendous savings over using specialized positioning infrastructures. In this work, we explore improving RSS localization performance in multipath environments by varying the transmitter's signal power and frequency. Using a theoretical analysis, we first show how selection of different signal powers and frequencies can improve localization accuracy for the least squares algorithm. We next develop a set of selection methods that attempt to select the combinations of power and frequencies which minimize the localization error. Our selection methods are based on the observed standard deviations of RSS as well as algorithm specific residuals. Using active RFID tags, we experimentally characterize the effect of using multiple signal powers and frequencies on a wide spectrum of RSS-based algorithms. We found that the performance of all the algorithms improves when leveraging on multiple power levels and frequencies, although different algorithms present different sensitivity in terms of localization accuracy under different selection methods.

The wireless data collected in mobile environments provides tremendous opportunities to build new applications in various domains such as Vehicular Ad Hoc Networks and mobile social networks. One of the biggest challenges is how to store these data. Storing the data decentralized in wireless devices is an attractive approach because of its major advantages over centralized ones. In this work, to facilitate effective access control of the wireless data in distributed data storage, we propose a fully decentralized key management scheme by utilizing a cryptography-based secret sharing method. The secret sharing method splits the keys into multiple shares and distributes them to multiple nodes, which brings the challenge that due to node mobility, these key shares may not be available in the neighborhood when they are needed for key reconstruction. To address this challenge arising from mobile environments, we propose the Transitive Prediction(TRAP) protocol that distributes key shares among devices that are traveling together. We derive a theoretical analysis of the robustness of our approach. Furthermore, inside TRAP, we develop three key distribution schemes that utilize the correlation relationship embedded among devices that are traveling together. Our key distribution schemes maximize the chance of successful key reconstruction and minimize the communication overhead. Our extensive simulation results demonstrate that our key distribution schemes are highly effective, and thus provide strong evidence of the feasibility of applying our approach to support distributed data storage in wireless networks.