This paper presents, for the first time, a method to extract both range and Doppler information from commercial Wi-Fi Channel State Information (CSI) using a monostatic (single transceiver) setup. Utilizing the CSI phase in Wi-Fi sensing from a Network Interface Card (NIC) not designed for full-duplex operation is challenging due to (1) Hardware asynchronization, which introduces significant phase errors, and (2) Proximity of transmit (Tx) and receive (Rx) antennas, which creates strong coupling that overwhelms the motion signal of interest. We propose a new signal processing approach that addresses both challenges via three key innovations: Time offset cancellation, Phase alignment correction, and Tx/Rx coupling mitigation. Our method achieves cm-level accuracy in range and Doppler estimation for moving targets, validated using a commercial Intel Wi-Fi AX211 NIC. Our results show successful detection and tracking of moving objects in realistic environments, establishing the feasibility of high-precision sensing using standard Wi-Fi packet communications and off-the-shelf hardware without requiring any modification or specialized full-duplex capabilities.

Although WiFi-based contactless sensing has made significant progress in the past decade, most prior work still focus on the reflection zone far from WiFi transceivers, while few studies explore the diffraction zone near transceivers. Additionally, previous diffraction models only consider the CSI amplitude signal and ignore the impact of multipath. In this work, we develop an accurate diffraction model to characterize the relationship between both CSI amplitude and phase and target's movement in the diffraction zone. We further put forward the deformation forms of the model under static multipath conditions and find that the CSI patterns vary significantly with multipath. Consequently, the common assumption of a one-to-one mapping between CSI patterns and activities in existing work fails due to multipaths, degrading sensing performance when multipath changes. To address this challenge, we propose to extract a relative change pattern from CSI signals to recover the one-to-one mapping relations and eliminate the impact of static multipath. Extensive experiments under various multipath conditions demonstrate an accuracy higher than 96% for the coarse-grained intrusion detection and an average error rate of 0.6 bpm for the fine-grained respiration monitoring.

WiFi-based device-free motion tracking systems track persons without requiring them to carry any device. Existing work has explored signal parameters such as time-of-flight (ToF), angle-of-arrival (AoA), and Doppler-frequency-shift (DFS) extracted from WiFi channel state information (CSI) to locate and track people in a room. However, they are not robust due to unreliable estimation of signal parameters. ToF and AoA estimations are not accurate for current standards-compliant WiFi devices that typically have only two antennas and limited channel bandwidth. On the other hand, DFS can be extracted relatively easily on current devices but is susceptible to the high noise level and random phase offset in CSI measurement, which results in a speed-sign-ambiguity problem and renders ambiguous walking speeds. This paper proposes WiTraj, a device-free indoor motion tracking system using commodity WiFi devices. WiTraj improves tracking robustness from three aspects: 1) It significantly improves DFS estimation quality by using the ratio of the CSI from two antennas of each receiver, 2) To better track human walking, it leverages multiple receivers placed at different viewing angles to capture human walking and then intelligently combines the best views to achieve a robust trajectory reconstruction, and, 3) It differentiates walking from in-place activities, which are typically interleaved in daily life, so that non-walking activities do not cause tracking errors. Experiments show that WiTraj can significantly improve tracking accuracy in typical environments compared to existing DFS-based systems. Evaluations across 9 participants and 3 different environments show that the median tracking error$<2.5\%$for typical room-sized trajectories.

Determining the mode of transport of an individual is an important element of contextual information. In particular, we focus on differentiating between different forms of motorized transport such as car, bus, subway etc. Our approach uses location information and features derived from transit route information (schedule information, not real-time) published by transit agencies. This enables no up-front training or learning of routes and can be deployed instantly to a new place since most transit agencies publish this information. Combined with motion detection using phone accelerometers, we obtain a classification accuracy of around 90% on 50+ hours of car and transit data.

Proper frequency planning is essential to enablemultiple IEEE 802.15.4 networks to coexist in the samespace, otherwise the network performance can beadversely affected due to interference. While this maybe feasible for static networks, it is not really an optionfor mobile networks such as body area networks due tothe dynamic nature of these interactions. This workdemonstrates one approach to detecting and mitigatinginterference in IEEE 802.15.4 networks from other802.15.4 networks. Interference is detected byobserving packets from other networks while themitigation strategy leverages collaboration betweeninterfering networks to determine which networkshould switch to a different channel. The approach canalso work with legacy networks that do not implementthis protocol. The system is implemented anddemonstrated on Intel Mote2 devices running TinyOS.

In this paper, a detailed study of the performance of geographic routing protocols in the presence of localization errors is carried out. Both analytical and simulation results illustrate the major impact or localization errors on the protocol goodput and route discovery energy. The performance metrics observed were the packet delivery ratio and the power consumed at a node for routing. It is shown that significant performance deterioration occurs with location errors as low as 20% of a node's radio range with no other obstacles in the network. To counteract this degradation, an enhancement is proposed that increases the error tolerance to about 40% radio range and in addition improves the performance consistently for any location error. Furthermore, the effect of obstacles in conjunction with location errors on the routing performance is also investigated.

Opportunistic routing protocols have been proposed as efficient methods to exploit the high node densities in sensor networks to mitigate the effect of varying channel conditions and non-availability of nodes that power down periodically. They work by integrating the network and data link layers so that they can take a joint decision as to the next hop forwarding node based on its availability and suitability as a forwarder. This cross-layer integration makes it harder to optimize the protocol due to the dependencies among the different components of the protocol stack. In this paper, we provide a framework to model opportunistic routing that breaks up the functionality into three separate components and simplifies analysis. The framework is used to model two variants of opportunistic routing and is shown to match well with simulation results. In addition, using the model for performance analysis yields important guidelines for the future design of such protocols.

The recent interest in sensor networks has led to a number of routing schemes that use the limited resources available at sensor nodes more efficiently. These schemes typically try to find the minimum energy path to optimize energy usage at a node. In this paper we take the view that always using lowest energy paths may not be optimal from the point of view of network lifetime and long-term connectivity. To optimize these measures, we propose a new scheme called energy aware routing that uses sub-optimal paths occasionally to provide substantial gains. Simulation results are also presented that show increase in network lifetimes of up to 40% over comparable schemes like directed diffusion routing. Nodes also burn energy in a more equitable way across the network ensuring a more graceful degradation of service with time.

One of the most compelling challenges of the next decade is the "last-meter" problem, extending the expanding data network into end-user data-collection and monitoring devices. PicoRadio supports the assembly of an ad hoc wireless network of self-contained mesoscale, low-cost, low-energy sensor and monitor nodes. While technology advances have made it conceivable to deploy wireless networks of heterogeneous nodes, the design of a low-power, low-cost, adaptive node in a reduced time to market is still a challenge. We present a design methodology for PicoRadio Networks, from system conception and optimization to silicon platform implementation. For each phase of the design, we demonstrate the applicability of our methodology through promising experimental results.