Ensuring stable and high-quality real-time video analytics for computationally constrained mobile agents is essential. However, limited computing resources and network bandwidth present significant challenges in meeting the objective of low response time and high inference accuracy. In this paper, we present DiVE, an edge-assisted video analytics system that utilizes motion vectors calculated by video codec to extract foregrounds and differentially encode frames. DiVE removes rotational components from motion vectors by solving over-determined linear equations and filters noisy motion vectors based on the observation that motion vectors of static objects point to the same point when the ego agent purely translates. To distinguish foregrounds from backgrounds, DiVE estimates the ground based on observations that all foregrounds stand on the ground and motion vectors on static objects at the same height have the same normalized magnitude. DiVE then uses region-growing-based clustering to identify foreground objects. An adaptive bitrate allocation method is applied to optimize accuracy under estimated bandwidth. We implement a prototype and conduct extensive experiments to evaluate the performance of DiVE. The results demonstrate that DiVE can improve detection accuracy by up to 19.0% and reduce response time by up to 56.0% compared with other video analytics schemes in real-world traces.

A wide range of user perception applications leverage inertial measurement unit (IMU) data for online prediction. However, restricted by the non-i.i.d. nature of IMU data collected from mobile devices, most systems work well only in a controlled setting (e.g., for a specific user in particular postures), limiting application scenarios. To achieve uncontrolled online prediction on mobile devices, referred to as the flexible user perception (FUP) problem, is attractive but hard. In this paper, we propose a novel scheme, called Prism, which can obtain high FUP accuracy on mobile devices. The core of Prism is to discover task-aware domains embedded in IMU dataset, and to train a domain-aware model on each identified domain. To this end, we design an expectation-maximization (EM) algorithm to estimate latent domains with respect to the specific downstream perception task. Finally, the best-fit model can be automatically selected for use by comparing the test sample and all identified domains in the feature space. We implement Prism on various mobile devices and conduct extensive experiments. Results demonstrate that Prism can achieve the best FUP performance with a low latency.

Video analytics is the killer workload in edge computing, which involves the scheduler’s complex decisions to balance analysis performance (latency and accuracy) and resource consumption (network, computation, and energy). Traditional schedulers address this as a single-objective optimization problem with fixed weights, unable to precisely capture unknown system preferences due to intricate pricing rules across various service levels and resource costs, consequently leading to suboptimal system benefit like monetary gain. In this paper, we propose a Bayesian optimization-driven multi-objective scheduler, PaMO, that can proactively explore the system pricing preference by pairwise comparing outcome vectors of all objectives. Moreover, PaMO designs a heuristic scheduling algorithm with a zero-delay jitter guarantee to avoid performance degradation caused by resource contention and uses a revised Bayesian optimization algorithm to make video configuration and scheduling decisions. Experiments on real video analytics workloads show that PaMO can achieve up to 53.9% benefit gain compared to state-of-the-art scheduling methods.

Efficient one-hop multicasting (OHM) of high-volume sensor data plays a pivotal role in the success of cooperative autonomous driving applications. Although millimeter-Wave (mmWave) bands demonstrate huge potential for high- bandwidth OHM data transmission, the challenge lies in enabling individual vehicles to locate and communicate with suitable neighbors in a fully distributed and highly dynamic scenario. This paper introduces mmV2V, a fully distributed OHM scheme designed for vehicular networks, comprising three tightly integrated protocols. Initially, synchronized vehicles perform a probabilistic neighbor discovery procedure, wherein randomly divided transmitters (or receivers) clockwise scan (or listen to) the surroundings in synchronization with heterogeneous Tx (or Rx) beams. This approach facilitates the identification of the vast majority of neighbors within a few repeated rounds. Subsequently, vehicles engage in negotiations with their neighbors to establish an optimal communication schedule in evenly distributed slots. Finally, matched pairs of neighboring vehicles commence high data rate transmissions using refined beams. We implement a prototype testbed to validate the feasibility of the main components of mmV2V. Extensive simulations based on generated and real-world traffic traces are conducted and the results demonstrate that mmV2V consistently achieves a high completion ratio in demanding OHM tasks across various traffic conditions.

One-hop multicasting (OHM) of high-volume sensor data is essential for cooperative autonomous driving applications. While millimeter-Wave (mmWave) bands can be utilized for high-bandwidth OHM data transmission, it is very challenging for individual vehicles to find and communicate with a proper neighbor in a fully distributed and highly dynamic scenario. In this paper, we propose a fully distributed OHM scheme in vehicular networks, called mmV2V, which consists of three highly integrated protocols. Specifically, synchronized vehicles first conduct a probabilistic neighbor discovery procedure, in which randomly divided transmitters (or receivers) clockwise scan (or listen to) the surroundings in pace with heterogeneous Tx (or Rx) beams. In this way, the vast majority of neighbors can be identified in a few repeated rounds. Furthermore, vehicles negotiate with each of their neighbors about the optimal communication schedule in evenly distributed slots. Finally, each agreed pair of neighboring vehicles start high data rate transmissions with refined beams. We conduct extensive simulations and the results demonstrate that mmV2V can achieve a high completion ratio in rigid OHM tasks under various traffic conditions.

Acquiring the geographical distribution of neighbors can support more adaptive media access control (MAC) protocols and other safety applications in Vehicular ad hoc network (VANETs). However, it is very challenging for each vehicle to estimate its own neighbor distribution in a fully distributed setting. In this paper, we propose an online distributed neighbor distribution estimation scheme, called PeerProbe, in which vehicles collaborate with each other to probe their own neighborhood via simultaneous symbol-level wireless communication. An adaptive compressive sensing algorithm is developed to recover a neighbor distribution based on a small number of random probes with non-negligible noise. Moreover, the needed number of probes adapts to the sparseness of the distribution. We implement a prototype system to verify the feasibility of PeerProbe in various typical vehicular channel conditions. We further conduct extensive simulations and the results demonstrate that PeerProbe is lightweight and can accurately recover highly dynamic neighbor distributions in critical channel conditions.

Acquiring the geographical distribution of neighbors can support more adaptive media access control (MAC) protocols and other safety applications in Vehicular ad hoc network (VANETs). However, it is very challenging for each vehicle to estimate its own neighbor distribution in a fully distributed setting. In this paper, we propose an online distributed neighbor distribution estimation scheme, called PeerProbe, in which vehicles collaborate with each other to probe their own neighborhood via simultaneous symbol-level wireless communication. An adaptive compressive sensing algorithm is developed to recover a neighbor distribution based on a small number of random probes with non-negligible noise. Moreover, the needed number of probes adapts to the sparseness of the distribution. We conduct extensive simulations and the results demonstrate that PeerProbe is lightweight and can accurately recover highly dynamic neighbor distributions in critical channel conditions.

Security is an important issue in research and appliance of grid computing. Grid security model is composed of a series of mechanism and strategy to solve various practical security problems. This paper presents a grid security model based on virtual organization referring to GSI (a security component in Globus). The model consists of a logical model and a physical model.

Grid is a distributed computing and resource environment. Security is an important issue in the grid environment. In this paper, we present a prototype of the grid security architecture. It shows that the architecture is scalable, and meets the security requirements of the grid.