Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking frameworks have two key limitations: 1) They are monolithic, proprietary, and lack the ability to rapidly incorporate sophisticated tracking models; and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog and cloud abstractions. We address these gaps using Anveshak, a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera-network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance and the active camera-set size. We illustrate the concise expressiveness of the programming model for 4 tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance and quality trade-offs enabled by our dynamic tracking, batching and dropping strategies.
@cite_27 is specifically for video surveillance using wireless networks, which have limited bandwidth. They assume an Edge computing node (ECN) is co-located with the cameras and is used to reduce redundant data from being sent to the Cloud. The authors assign a utility score to each frame to ascertain its importance, similar to our flag. Our model and platform offer more active controls over the logic running on the ECN, and the runtime tuning.
@cite_27
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We present a novel framework for robustly delivering video data from distributed wireless camera networks that are characterized by packet drops. The main focus in this work is on robustness which is imminently needed in a wireless setting. We propose two alternative models to capture interview correlation among cameras with overlapping views. The view-synthesis-based correlation model requires at least two other camera views and relies on both disparity estimation and view interpolation. The disparity-based correlation model requires only one other camera view and makes use of epipolar geometry. With the proposed models, we show how interview correlation can be exploited for robustness through the use of distributed source coding. The proposed approach has low encoding complexity, is robust while satisfying tight latency constraints and requires no intercamera communication. Our experiments show that on bursty packet erasure channels, the proposed H.263+1 based method outperforms baseline methods such as H.263+ with forward error correction and H.263+ with intra refresh by up to 2.5 dB. Empirical results further support the relative insensitivity of our proposed approach to the number of additional available camera views or their placement density. Recent advancements in manufacturing low-cost wireless battery operated cameras has made their application in Wireless Video Sensor Networks (WVSN) increasingly more feasible and affordable. The application of robotic sensing agents equipped with cameras in WVSNs, seems particularly promising in performing coverage tasks for ad hoc surveillance. Their application in this context can be specifically useful for surveying areas with little to no available or affordable infrastructure, or where quick deployment is necessary. In this paper, we address the target coverage problem for finding the minimum number of cameras, their placement, and orientation to cover arbitrarily located targets in an area of interest. We propose a computationally light-weight heuristic, where the number of used mobile cameras is close to those found by near-optimal algorithms. Specifically, we address this problem for non-uniform target distributions that naturally form clusters. Having light-weight heuristics will be particularly useful when the application is required to adapt to target mobility and or is implemented in embedded systems. Our simulation study shows that when clusters are sufficiently separated, the required number of cameras found by our proposed method is very close to those acquired by the near-optimal algorithm, whereas the computational complexity of our algorithm is about ten times less. We also deploy our algorithm on a drone testbed using off-the-shelf components to verify its feasibility. We consider decentralized estimation of a noise-corrupted deterministic signal in a bandwidth-constrained sensor network communicating through an insecure medium. Each sensor collects a noise-corrupted version, performs a local quantization, and transmits a 1-bit message to an ally fusion center through a wireless medium where the sensor outputs are vulnerable to unauthorized observation from enemy third-party fusion centers. In this paper, we introduce an encrypted wireless sensor network (eWSN) concept where stochastic enciphers operating on binary sensor outputs are introduced to disguise the sensor outputs, creating an eWSN scheme. Noting that the plaintext (original) and ciphertext (disguised) messages are constrained to a single bit due to bandwidth constraints, we consider a binary channel-like scheme to probabilistically encipher (i.e., flip) the sensor outputs. We first consider a symmetric key encryption case where the "0" and "1" enciphering probabilities are equal. The key is represented by the bit enciphering probability. Specifically, we derive the optimal estimator of the deterministic signal approached from a maximum-likelihood perspective and the Cramer-Rao lower bound for the estimation problem utilizing the key. Furthermore, we analyze the effect of the considered cryptosystem on enemy fusion centers that are unaware of the fact that the WSN is encrypted (i.e., we derive the bias, variance, and mean square error (MSE) of the enemy fusion center). We then extend the cryptosystem to admit unequal enciphering schemes for "0" and "1", and analyze the estimation problem from both the prospectives of ally (that has access to the enciphering keys) and (third-party) enemy fusion centers. The results show that when designed properly, a significant amount of bias and MSE can be introduced to an enemy fusion center with the cost to the ally fusion center being a marginal increase [factor of (1-Omega1-Omega0 )-2, where 1-Omegaj, j=0, 1 is the "j" enciphering probability in the estimation variance (compared to the variance of a fusion center estimate operating in a vulnerable WSN). Cognitive Radio Network (CRN) is regarded as a promising way to address the increasing demand for wireless channel resources. It solves the channel resource shortage problem by allowing a Secondary User (SU) to access the channel of a Primary User (PU) when the channel is not occupied by the PU. The latest FCC's rule in May 2012 enforces database-driven CRNs, in which an SU queries a database to obtain spectrum availability information by submitting a location based query. However, one concern about database-driven CRNs is that the queries sent by SUs will inevitably leak the location information. In this study, we identify a new kind of attack against location privacy of database-drive CRNs. Instead of directly learning the SUs' locations from their queries, our discovered attacks can infer an SU's location through his used channels. We propose Spectrum Utilization based Location Inferring Algorithm that enables the attacker to geo-locate an SU. To thwart location privacy leaking from query process, we propose a novel Private Spectrum Availability Information Retrieval scheme that utilizes a blind factor to hide the location of the SU. To defend against the discovered attack, we propose a novel prediction based Private Channel Utilization protocol that reduces the possibilities of location privacy leaking by choosing the most stable channels. We implement our discovered attack and proposed scheme on the data extracted from Google Earth Coverage Maps released by FCC. Experiment results show that the proposed protocols can significantly improve the location privacy.