Distributed Object Recognition Research Articles

AI-Powered Disinfection Force: Harnessing 5G for Intelligent Distributed Object Recognition and Sanitation Fleet

With the increasing prevalence of embedded cameras in devices such as phones, robots, and vehicles, there’s a growing need to enhance their perception capabilities through advanced deep learning techniques. These systems, however, often struggle under suboptimal conditions like environmental noise. The paper addresses these challenges by proposing a 5G-enabled solution that uses a fleet of unmanned ground vehicles (UGVs) for coordinated disinfection tasks in hospital environments. Utilizing high-bandwidth, low-latency 5G networks, these UGVs can share high-quality images for distributed object recognition tasks and execute precise disinfection routines using onboard UV lamps. The proposed system includes an optimal formation algorithm for UGVs to maintain effective positioning and coordination. By leveraging 5G connectivity, the fleet can efficiently exchange sensor data and perform real-time, innovative object recognition and disinfection, presenting a significant advancement in intelligent sanitation technology.

Uncertainty-Aware Autonomic Resource Provisioning for Mobile Cloud Computing

Mobile platforms are becoming the predominant medium of access to Internet services due to the tremendous increase in their computation and communication capabilities. However, enabling applications that require real-time in-the-field data collection and processing using mobile platforms is still challenging due to i) the insufficient computing capabilities and unavailability of complete data on individual mobile devices and ii) the prohibitive communication cost and response time involved in offloading data to remote computing resources such as cloud datacenters for centralized computation. A novel resource provisioning framework for organizing the heterogeneous sensing, computing, and communication capabilities of static and mobile devices in the vicinity in order to form an elastic resource pool—a hybrid static/mobile computing grid (also called a loosely-coupled mobile device cloud)—is presented. This local computing grid can be harnessed to enable innovative data- and compute-intensive mobile applications such as ubiquitous context-aware health and wellness monitoring of the elderly, distributed rainfall and flood-risk estimation, distributed object recognition and tracking, and content-based distributed multimedia search and sharing. In order to address challenges such as the inherent uncertainty in the hybrid grid (in terms of network connectivity and device availability), the proposed role-based resource provisioning framework is imparted with autonomic capabilities, namely, self-organization, self-optimization, and self-healing. A thorough experimental analysis aimed at verifying and demonstrating the benefits brought by autonomic capabilities of the framework is also presented in detail.

A low-bandwidth camera sensor platform with applications in smart camera networks

Smart camera networks have recently emerged as a new class of sensor network infrastructure that is capable of supporting high-power in-network signal processing and enabling a wide range of applications. In this article, we provide an exposition of our efforts to build a low-bandwidth wireless camera network platform, called CITRIC, and its applications in smart camera networks. The platform integrates a camera, a microphone, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a wireless camera mote. With reasonably low power consumption and extensive algorithmic libraries running on a decent operating system that is easy to program, CITRIC is ideal for research and applications in distributed image and video processing. Its capabilities of in-network image processing also reduce communication requirements, which has been high in other existing camera networks with centralized processing. Furthermore, the mote easily integrates with other low-bandwidth sensor networks via the IEEE 802.15.4 protocol. To justify the utility of CITRIC, we present several representative applications. In particular, concrete research results will be demonstrated in two areas, namely, distributed coverage hole identification and distributed object recognition.

Distributed Sensor Perception via Sparse Representation

In this paper, sensor network scenarios are considered where the underlying signals of interest exhibit a degree of sparsity, which means that in an appropriate basis, they can be expressed in terms of a small number of nonzero coefficients. Following the emerging theory of compressive sensing (CS), an overall architecture is considered where the sensors acquire potentially noisy projections of the data, and the underlying sparsity is exploited to recover useful information about the signals of interest, which will be referred to as distributed sensor perception. First, we discuss the question of which projections of the data should be acquired, and how many of them. Then, we discuss how to take advantage of possible joint sparsity of the signals acquired by multiple sensors, and show how this can further improve the inference of the events from the sensor network. Two practical sensor applications are demonstrated, namely, distributed wearable action recognition using low-power motion sensors and distributed object recognition using high-power camera sensors. Experimental data support the utility of the CS framework in distributed sensor perception.