We conduct an extensive experimental evaluation of a class of WiFi active power/energy consumption models for smartphones that are based on parameters readily available to the upper layers of the protocol stack. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. We study the properties of a previously proposed throughput-based model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios that have not been considered in previous studies. Our results show that the model works well in a number of scenarios, with both 802.11nand 802.11ac-equipped smartphones, and its accuracy can be largely improved with the knowledge of transport layer protocol and packet size. However, such knowledge makes the model more complex and results in largely reduced accuracy in high throughput settings or on hardware different from the one that was used for training. We further discuss a few practical issues related to the measurement and modeling methodology.

A large number of routing protocols for Cognitive radio networks (CRNs) have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. In our previous work, we conducted the first extensive empirical performance study of routing protocols for CRNs-Coolest Path, SAMER, and CRP - under the same realistic set of assumptions. Our study revealed that SAMER significantly outperforms the other protocols under low primary user activity but its performance degrades under high PU activity. In this work, we introduce an enhanced version of SAMER (SAMER+) that performs consistently well in both low and high PU activity scenarios. The core of SAMER+ is a new path metric that balances short term throughput and long term path stability. We evaluate SAMER+ using extensive simulations and compare it against SAMER and Coolest Path. Our results show that SAMER+ significantly outperforms both protocols in a variety of scenarios.

The increasing popularity of WiFi brings new challenges in the design of wireless protocols but at the same time creates exciting opportunities for novel ways of exploiting WiFi technology in a variety of applications. On one hand, the smaller form factor of today's WiFi devices allows WiFi users to access wireless networks under mobility, calling for new, mobility-aware wireless protocols that are able to sustain high performance, on the other hand, the ubiquity of WiFi devices has recently raised interest in extending WiFi's capabilities beyond communication, e.g., for human-computer interaction. In this paper, we demonstrate that it is possible to enable fine-grained human motion detection on commodity WiFi devices by exploiting PHY layer information exposed by today's WiFi chipsets. We propose two motion detection techniques for two different application scenarios and explore their benefits on protocol performance and human-device interaction, respectively.

This paper demonstrates that it is possible to leverage WiFi signals from commodity mobile devices to enable hands-free drawing in the air. While prior solutions require the user to hold a wireless transmitter, or require custom wireless hardware, or can only determine a pre-defined set of hand gestures, this paper introduces WiDraw, the first hand motion tracking system using commodity WiFi cards, and without any user wearables. WiDraw harnesses the Angle-of-Arrival values of incoming wireless signals at the mobile device to track the user's hand trajectory. We utilize the intuition that whenever the user's hand occludes a signal coming from a certain direction, the signal strength of the angle representing the same direction will experience a drop. Our software prototype using commodity wireless cards can track the user's hand with a median error lower than 5 cm. We use WiDraw to implement an in-air handwriting application that allows the user to draw letters, words, and sentences, and achieves a mean word recognition accuracy of 91%.

Cognitive radio networks (CRNs) have emerged as a promising solution to the ever-growing demand for additional spectrum resources and more efficient spectrum utilization. A large number of routing protocols for CRNs have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. However, little is known about the relative performance of all these protocols, let alone the tradeoffs among their different design goals. In this paper, we conduct the first detailed, empirical performance comparison of three representative routing protocols for CRNs, under the same realistic set of assumptions. Our extensive simulation study shows that the performance of routing protocols in CRNs is affected by a number of factors, in addition to PU activity, some of which have been largely ignored by the majority of previous works. We find that different protocols perform well under different scenarios, and investigate the causes of the observed performance. Furthermore, we present a generic software architecture for the experimental evaluation of CRN routing protocols on a testbed based on the USRP2 platform, and compare the performance of two protocols on a 6 node testbed. The testbed results confirm the findings of our simulation study.

With the proliferation of smartphones and tablets, mobile devices are soon becoming a preferred medium of Internet access in Wireless LANs (WLANs). Due to their smaller form factor, these truly mobile devices allow the users to access the wireless networks while undergoing different types of mobility, posing new challenges to wireless protocols. Current history-based protocols that maximize performance in static settings do not work well in mobile settings where wireless conditions change rapidly. Thus, today's WLANs need to be able to determine the type of the client's mobility and employ appropriate strategies in order to sustain high performance. While previous work tried to detect mobility using hints from sensors available in today's mobile devices, in this work, we demonstrate how different mobility modes can be distinguished by using physical layer information -- Channel State Information (CSI) and Time-of-Flight (ToF) -- available at commodity APs, with no modifications on the client side. In addition, we demonstrate how fine-grained mobility determination can be exploited to improve performance of client roaming, rate control, frame aggregation, and MIMO beamforming. Our testbed experiments show that our mobility classification algorithm achieves more than 92% accuracy in a variety of scenarios, and the combined throughput gain of all four mobility-aware protocols over their mobility-oblivious counterparts can be more than 100%.

We conduct the first detailed measurement study of the properties of a class of WiFi active power/energy consumption models based on parameters readily available to smartphone app developers. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. Using a large dataset and an 802.11n-equipped smartphone, we build four versions of a previously proposed linear power-throughput model, which allow us to explore the fundamental trade off between accuracy and simplicity. We study the properties of the model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios which have not been considered in previous studies. Our study shows that the model works well in a number of scenarios but its accuracy drops with high throughput values or when tested on different hardware. We further show that a non-linear model can greatly improve the accuracy in these two cases.

The continuously increasing number of smartphones and tablets allow the users to access Wireless LANs (WLANs) while undergoing different types of mobility, posing new challenges to wireless protocols. Current history-based WLAN protocols do not work well in mobile settings where wireless conditions change rapidly. Thus, today's WLANs need to be able to determine the type of the client's mobility and employ appropriate strategies in order to sustain high performance. While previous work tried to detect mobility using hints from sensors available in mobile devices, in this work, we demonstrate how different mobility modes can be distinguished by using physical layer information - Channel State Information (CSI) and Time-of-Flight (ToF) - available at commodity APs, with no modifications on the client side. Our testbed experiments show that our mobility classification algorithm achieves more than 92% accuracy in a variety of scenarios. In addition, we demonstrate how fine-grained mobility determination can be exploited to greatly improve performance of client roaming and MIMO beamforming.

Cognitive radio networks (CRNs) have emerged as a promising solution to the ever-growing demand for additional spectrum resources and more efficient spectrum utilization. A large number of routing protocols for CRNs have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. However, little is known about the relative performance of all these protocols, let alone the tradeoffs among their different design goals. In this paper, we conduct the first detailed, empirical performance comparison of three representative routing protocols for CRNs, under the same realistic set of assumptions. Our extensive simulation study shows that the performance of routing protocols in CRNs is affected by a number of factors, in addition to PU activity, some of which have been largely ignored by the majority of previous works. We find that different protocols perform well under different scenarios, and investigate the causes of the observed performance. Furthermore, we present a generic software architecture for the experimental evaluation of CRN routing protocols on a test bed based on the USRP2 platform, and compare the performance of two protocols on a 6 node test bed. The test bed results confirm the findings of our simulation study.