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Wireless power transfer (WPT) promises to deliver energy to devices that are otherwise hard to charge or replace batteries for. This paper presents a new power transfer approach by aligning the phases of a collection of radio frequency (RF) energy chargers at the target receiver device. Our approach can ship energy over tens of meters and to mobile targets. More importantly, our approach leads to a highly asymmetric energy density distribution in the charging area: the energy density at the target receiver is much higher than the energy density at other locations. It is a departure from existing beamforming based WPT systems that have high energy along the energy beam path. Such a technology can enable a large array of batteryless Internet of Things applications and render them much more robust and long-running. Thanks to its asymmetric energy distribution, our approach potentially can be scaled up to ship higher level of energy over longer distances. In this paper, we design, prototype, and evaluate the proposed energy transfer approach, referred to as Energy-Ball. We implement an Energy-Ball testbed that consists of 17 N210 and 4 B210 Universal Software Radio Peripheral (USRP) nodes, yielding a 20 x 20 m2 energy delivery area. We conduct carefully designed experiments on the testbed. We demo that the energy density of Energy-Ball at the target spot is considerably higher than the energy density elsewhere, with the peak to average power ratio of 8.72. We show that Energy-Ball can transfer energy to any point within the area. When the receiver moves at a speed of 0.5 m/s, Energy-Ball can transfer 80% of optimal power to the mobile receiver. Further, our results also show Energy-Ball can deliver over 0.6mw RF power that enables batteryless sensors at any point across the area.

In this work, we propose a system that detects approaching cars for smartphone users. In addition to detecting the presence of a vehicle, it can also estimate the vehicle’s driving direction, as well as count the number of cars around the user. We achieve these goals by processing the acoustic signal captured by microphones embedded in the user’s mobile phone. The largest challenge we faced involved addressing the fact that vehicular noise is predominantly due to tire-road friction, and therefore lacked strong (frequency) formant or temporal structure. Additionally, outdoor environments have complex acoustic noise characteristics, which are made worse when the signal is captured by non-professional grade microphones embedded in smartphones. We address these challenges by monitoring a new feature: maximal frequency component that crosses a threshold. We extract this feature with a blurred edge detector. Through detailed experiments, we found our system to be robust across different vehicles and environmental conditions, and thereby support unsupervised car detection and counting. We evaluated our system using audio tracks recorded from seven different models of cars, including SUVs, medium-sized sedans, compact cars, and electric cars. We also tested our system with the user walking in various outdoor environments including parking lots, campus roads, residential areas, and shopping centers. Our results show that we can accurately and robustly detect cars with low CPU and memory requirements.

Recently, it has been noted that localization algorithms that use signal strength are susceptible to noncryptographic attacks, which consequently threatens their viability for sensor applications. In this work, we examine several localization algorithms and evaluate their robustness to attacks where an adversary attenuates or amplifies the signal strength at one or more landmarks. We study both point-based and area-based methods that employ received signal strength for localization, and propose several performance metrics that quantify the estimator's precision, bias, and error, including Hölder metrics, which quantify the variability in position space for a given variability in signal strength space. We then conduct a trace-driven evaluation of a set of representative algorithms, where we measured their performance as we applied attacks on real data from two different buildings. We found the median error degraded gracefully, with a linear response as a function of the attack strength. We also found that area-based algorithms experienced a decrease and a spatial-shift in the returned area under attack, implying that precision increases though bias is introduced for these schemes. Additionally, we observed similar values for the average Hölder metric across most of the algorithms, thereby providing strong experimental evidence that nearly all the algorithms have similar average responses to signal strength attacks with the exception of the Bayesian Networks algorithm.

Radio interference, whether intentional or otherwise, represents a serious threat to assuring the availability of sensor network services. As such, techniques that enhance the reliability of sensor communications in the presence of radio interference are critical. In this article, we propose to cope with this threat through a technique called channel surfing, whereby the sensor nodes in the network adapt their channel assignments to restore network connectivity in the presence of interference. We explore two different approaches to channel surfing: coordinated channel switching, in which the entire sensor network adjusts its channel; and spectral multiplexing, in which nodes in a jammed region switch channels and nodes on the boundary of a jammed region act as radio relays between different spectral zones. For coordinated channel switching, we examine an autonomous strategy where each node detects the loss of its neighbors in order to initiate channel switching. To cope with latency issues in the autonomous strategy, we propose a broadcast-assisted channel switching strategy to more rapidly coordinate channel switching. For spectral multiplexing, we have devised both synchronous and asynchronous strategies to facilitate the scheduling of nodes in order to improve network fidelity when sensor nodes operate on multiple channels. In designing these algorithms, we have taken a system-oriented approach that has focused on exploring actual implementation issues under realistic network settings. We have implemented these proposed methods on a testbed of 30 Mica2 sensor nodes, and the experimental results show that channel surfing, in its various forms, is an effective technique for repairing network connectivity in the presence of radio interference, while not introducing significant performance-overhead.

We propose a distributed scheme by which nodes select an appropriate access point to associate with using each individual device's channel utilization. Specifically, we define a new metric, channel utilization, which is defined as the ratio of required bandwidth to available bandwidth estimation. By incorporating channel utilization into the access point selection protocol, we effectively reduce unnecessary reassociations and improve upper layer performance in terms of throughput, packet delivery delay, etc. We further enhance our protocol by using reinforcement learning to adapt the scheduling of probing neighboring access points (APs), ultimately reducing probing overhead by learning from past experience whether the current operational scenario would suffer from undesirable overhead. When channel utilization is combined with adaptive probing, we observed a significant performance improvement compared to traditional association approaches.

Wireless sensor networks are usually deployed to monitor environmental fields. In this work, we take a different viewpoint by dual-using the wireless sensor networks beyond their original purpose and exploit the general spatial information fields associated with wireless networks to assist position verification and refine conventional localization results. We developed the Flex - EP algorithm. And further our experimental evaluation results provide strong evidence that our approach can achieve the similar performance as traditional localization algorithms, without requiring the deployment of a localization infrastructure.