Panlong Yang

False Negative Problem of Counting Bloom Filter

Bloom filter is effective, space-efficient data structure for concisely representing a data set and supporting approximate membership queries. Traditionally, researchers often believe that it is possible that a Bloom filter returns a false positive, but it will never return a false negative under well-behaved operations. By investigating the mainstream variants, however, we observe that a Bloom filter does return false negatives in many scenarios. In this work, we show that the undetectable incorrect deletion of false positive items and detectable incorrect deletion of multiaddress items are two general causes of false negative in a Bloom filter. We then measure the potential and exposed false negatives theoretically and practically. Inspired by the fact that the potential false negatives are usually not fully exposed, we propose a novel Bloom filter scheme, which increases the ratio of bits set to a value larger than one without decreasing the ratio of bits set to zero. Mathematical analysis and comprehensive experiments show that this design can reduce the number of exposed false negatives as well as decrease the likelihood of false positives. To the best of our knowledge, this is the first work dealing with both the false positive and false negative problems of Bloom filter systematically when supporting standard usages of item insertion, query, and deletion operations.

R-TTWD: Robust Device-Free Through-The-Wall Detection of Moving Human With WiFi

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human–computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit–receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.

FEMO: A Platform for Free-weight Exercise Monitoring with RFIDs

Regular free-weight exercise helps to strengthen the bodys natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue tooth) for activity sensing, recognition and countingetc.. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.

Shake and Walk: Acoustic Direction Finding and Fine-grained Indoor Localization Using Smartphones

We propose an accurate acoustic direction finding scheme, Swadloon, according to the arbitrary pattern of phone shaking in rough horizontal plane. Swadloon tracks the displacement of smartphone relative to the acoustic direction with the resolution less than 1 millimeter. The direction is then obtained by combining the velocity from the displacement with the one from the inertial sensors. Major challenges in implementing Swadloon are to measure the displacement precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, and apply Swadloon to several case studies: Phone-to-Phone direction finding, indoor localization and tracking. Our extensive experiments show that the mean error of direction finding is around 2.1° within the range of 32 m. For indoor localization, the 90-percentile errors are under 0.92 m. For real-time tracking, the errors are within 0.4 m for walks of 51 m.

Almost optimal accessing of nonstochastic channels in cognitive radio networks

We propose joint channel sensing, probing, and accessing schemes for secondary users in cognitive radio networks. Our method has time and space complexity O(N·k) for a network with N channels and k secondary users, while applying classic methods requires exponential time complexity. We prove that, even when channel states are selected by adversary (thus non-stochastic), it results in a total regret uniformly upper bounded by Θ(√TN log N), w.h.p, for communication lasts for T timeslots. Our protocol can be implemented in a distributed manner due to the nonstochastic channel assumption. Our experiments show that our schemes achieve almost optimal throughput compared with an optimal static strategy, and perform significantly better than previous methods in many settings.

Swadloon: Direction Finding and Indoor Localization Using Acoustic Signal by Shaking Smartphones

We propose an accurate acoustic direction finding scheme, Swadloon, according to the arbitrary pattern of phone shaking in a rough horizontal plane. Swadloon leverages sensors of the smartphone without the requirement of any specialized devices. Our Swadloon design exploits a key observation: the relative displacement and velocity of the phone-shaking movement corresponds to the subtle phase and frequency shift of the Doppler effects experienced in the received acoustic signal by the phone. Swadloon tracks the displacement of smartphone relative to the acoustic direction with the resolution less than 1 millimeter. The direction is then obtained by combining the velocity from the displacement with the one from the inertial sensors. Major challenges in implementing Swadloon are to measure the displacement precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, and apply Swadloon to several case studies: Phone-to-Phone direction finding, indoor localization and tracking. Our extensive experiments show that the mean error of direction finding is around 2.1 degree within the range of 32 m. For indoor localization, the 90-percentile errors are under 0.92 m. For real-time tracking, the errors are within 0.4 m for walks of 51 m.

General capacity scaling of wireless networks

We study the general scaling laws of the capacity for random wireless networks under the generalized physical model. The generality of this work is embodied in three dimensions denoted by (λ ∈ [1, n], n_d ∈ [1, n], n_s ∈ (1, n]). It means that: (1) We study the random network of a general node density λ ∈ [1, n], rather than only study either random dense network (RDN, λ = n) or random extended network (REN, λ = 1) as in the literature. (2) We focus on the multicast capacity to unify unicast and broadcast capacities by setting the number of destinations for each session as a general value n_d ∈ [1, n]. (3)We allow the number of sessions changing in the range n_s ∈ (1, n], rather than assume that n_s = Θ(n) as in the literature.We derive the general lower bounds on the capacity for the arbitrary case of (λ, n_d, n_s). Particularly, we show that for the special cases (λ = 1, n_d ∈ [1, n], n_s= n) and (λ = n, n_d ∈ [1, n], n_s = n), our schemes achieve the highest multicast throughputs proposed in the existing works.

Surface Coverage Algorithm in Directional Sensor Networks for Three-Dimensional Complex Terrains

Coverage is an important issue in the area of wireless sensor networks, which reflects the monitoring quality of the sensor networks in scenes. Most sensor coverage research focuses on the ideal two-dimensional (2-D) plane and full three-dimensional (3-D) space. However, in many real-world applications, the target field is a 3-D complex surface, which makes conventional methods unsuitable. In this paper, we study the coverage problem in directional sensor networks for complex 3-D terrains, and design a new surface coverage algorithm. Based on a 3-D directional sensing model of nodes, this algorithm employs grid division, simulated annealing, and local optimum ideas to improve the area coverage ratio by optimizing the position coordinates and the deviation angles of the nodes, which results in coverage enhancement for complex 3-D terrains. We also conduct extensive simulations to evaluate the performance of our algorithms.

Quality-Aware Sensing Coverage in Budget-Constrained Mobile Crowdsensing Networks

Mobile crowdsensing has shown elegant capacity in data collection and has given rise to numerous applications. In the sense of coverage quality, marginal works have considered the efficient (less cost) and effective (considerable coverage) design for mobile crowdsensing networks. We investigate the optimal quality-aware coverage in mobile crowdsensing networks. The difference between ours and the conventional coverage problem is that we only select a subset of mobile users so that the coverage quality is maximized with constrained budget. To address this new problem, which is proved to be NP-hard, we first prove that the set function of coverage quality is nondecreasing submodular. By leveraging the favorable property in submodular optimization, we then propose an (1 - (1/e)) approximation algorithm with O(nk+2) time complexity, where k is an integer that is greater than or equal to 3. Finally, we conduct extensive simulations for the proposed scheme, and the results demonstrate that ours outperforms the random selection scheme and one of the state of the art in terms of total coverage quality by, at most, 2.4× and 1.5× and by, on average, 1.4× and 1.3×, respectively. Additionally, ours achieves a near-optimal solution, compared with the brute-force search results.

Sparsest Random Scheduling for Compressive Data Gathering in Wireless Sensor Networks

Compressive sensing (CS)-based in-network data processing is a promising approach to reduce packet transmission in wireless sensor networks. Existing CS-based data gathering methods require a large number of sensors involved in each CS measurement gathering, leading to the relatively high data transmission cost. In this paper, we propose a sparsest random scheduling for compressive data gathering scheme, which decreases each measurement transmission cost from O(N) to O(log(N)) without increasing the number of CS measurements as well. In our scheme, we present a sparsest measurement matrix, where each row has only one nonzero entry. To satisfy the restricted isometric property, we propose a design method for representation basis, which is properly generated according to the sparsest measurement matrix and sensory data. With extensive experiments over real sensory data of CitySee, we demonstrate that our scheme can recover the real sensory data accurately. Surprisingly, our scheme outperforms the dense measurement matrix with a discrete cosine transformation basis over 5 dB on data recovery quality. Simulation results also show that our scheme reduces almost 10 × energy consumption compared with the dense measurement matrix for CS-based data gathering.