On-board Processing Capabilities Research Articles

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network’s capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of 74.68% in model storage requirements and 55.93% in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology.

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%.

Joint Object Detection and Re-Identification for 3D Obstacle Multi-Camera Systems

The growing on-board processing capabilities have led to more complex sensor configurations, enabling autonomous car prototypes to expand their operational scope. Nowadays, the joint use of LiDAR data and multiple cameras is almost a standard and poses new challenges for existing multi-modal perception pipelines, such as dealing with contradictory or redundant detections caused by inference on overlapping images. In this paper, we address this last issue in the context of sequential schemes like F-PointNets, where object candidates are obtained in the image space, and the final 3D bounding box is then inferred from point cloud information. To this end, we propose the inclusion of a re-identification branch into the 2D detector, i.e., Faster R-CNN, so that objects seen from adjacent cameras can be handled before the 3D box estimation takes place, removing duplicates and completing the object’s cloud. Extensive experimental evaluations covering both the 2D and 3D domains affirm the effectiveness of the suggested methodology. The findings indicate that our approach outperforms conventional Non-Maximum Suppression (NMS) methods. Particularly, we observed a significant gain of over 5% in terms of accuracy for cars in camera overlap regions. These results highlight the potential of our upgraded detection and re-identification system in practical scenarios for autonomous driving.

Lane-Level Map Generation and Management Framework Using Connected Car Data

This study proposes a lane-level map generation and management framework using connected sensor data to reduce the manpower and time required for producing and updating high-definition (HD) maps. Unlike previous studies that relied on the onboard processing capabilities of vehicles to collect map-constructing elements, this study offloads computing for map generation to the cloud, assigning vehicles solely the role of transmitting sensor data. For efficient data collection, we divide the space into a grid format to define it as a partial map and establish the state of each map and its transition conditions. Lastly, tailored to the characteristics of the road elements composing the map, we propose an automated map generation technique and method for selectively collecting data. The map generation method was tested using data collected from actual vehicles. By transmitting images with an average size of 350 KB, implementation was feasible even with the current 5G upload bandwidth. By utilizing 12,545 elements, we were able to achieve a position accuracy and regression RMSE of less than 0.25 m, obtaining 651 map elements to construct the map. We anticipate that this study will help reduce the manpower and time needed for deploying and updating HD maps.

Towards Reliable Space-Ground Integrated Networks: From System-Level Design to Implementation

The integration of space and terrestrial networks results in a promising future network architecture, which exploits the high data rate and low latency of terrestrial networks as well as the wide-range coverage of satellite networks. However, spaceground integrated networks (SGINs) face some unprecedented challenges, including the rapidly fluctuating network topology, limited resources, intermittent connections, lossy links, asymmetric bandwidth allocation, and so on. The traditional endto- end (E2E) transport protocols designed for reliable terrestrial networks exhibit inherent limitations in the context of dynamic SGIN. A promising solution is to decouple the E2E perfect reception confirmation into hop-by-hop acknowledgments, which results in prompt packet loss recovery and high transmission resilience in the face of high-dynamic topologies. Hence, in this paper we propose a reliable cache-enabled transport system, which enhances the efficiency of reliable hop-by-hop transmission, and supports multi-orbit breakpoint transmission, while at the same time facilitates the reliability of intra-satellite communication with a novel transport protocol. In addition to unveiling a compelling system-level design, we demonstrate the benefits of the proposed reliable transport system (RTS) in a real SGIN prototype relying on satellites having onboard processing capability. Our experimental results validate the feasibility of the proposed RTS in the face of lossy links, intermittent connections and intra-satellite transmission failure. Moreover, one of the satellites has been launched in April 2021, while the other two will be launched to perform on-orbit test.

Latency Optimization for Hybrid GEO–LEO Satellite-Assisted IoT Networks

Benefiting from the development of satellite onboard processing capability, the orbital computing can be realized by deploying edge computing servers on satellites to reduce the task processing latency. However, edge computing based on geostationary earth orbit (GEO) or low earth orbit (LEO) alone can hardly meet the latency requirements of satellite assisted internet of things (SIoT) services. Moreover, the uneven distribution of tasks generated by SIoT devices will also cause the load unbalancing among different satellites. In this paper, hybrid GEO-LEO SIoT networks is investigated with joint computing and communication resource allocation. To tackle the load unbalancing problem, tasks generated by SIoT devices can be processed by collaborative LEO satellites or forwarded to gateways on ground via GEO satellite. Thus, the joint task offloading, communication and computing resources allocation for the hybrid SIoT network can be formulated as a mixed integer dynamic programming problem with satellites-ground cooperation and inter-satellite cooperation via the inter-satellite links. Then, an intelligent task offloading and multi-dimensional resources allocation algorithm (TOMRA) is proposed to minimize the latency of task offloading and processing. Firstly, a method base on deep reinforcement learning is utilized to solve the subproblem of task offloading and channel allocation. And then, convex optimization is adopted to solve the sub-problem of computing resource allocation under fixed offloading and channel allocation decisions. Simulation results show that the proposed TOMRA can achieve better performance than the reference schemes.

A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime.

In recent years, in-ear electroencephalography (EEG) was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported. Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer on-board processing capabilities. Here we overcome all these limitations, presenting an ear-EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud. The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit, and an ARM Cortex-M4 based microcontroller enabling on-board processing and Bluetooth Low Energy connectivity. The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1.3 mW power consumption for data streaming or on board processing, respectively. The latter allows for 600 hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud. Clinical relevance- The proposed ear-EEG system can be employed for diagnostic tasks such as objective hearing threshold estimations, outside of clinical settings, thereby enabling it as a point-of-care solution. The long battery lifetime is also suitable for a continuous monitoring scenario.

Intelligent Mobile Edge Caching for Popular Contents in Vehicular Cloud Toward 6G

Although vehicles are the most commonly used means of transportation in our daily lives, their computational power and memory resources are often ignored. The new paradigm, Internet of Vehicles, has turned on-road vehicles into intelligent service providers, making full use of on-board processing and storage capabilities. With the advent of 6 G technology, e.g., millimeter wave and terahertz, fast and massive data transfers become possible between moving objects. In this paper, we consider a scenario where vehicles act as mobile edge caching nodes and form a local cloud, to provide popular contents for nearby users via 6 G communication. To maximize the efficiency of content allocation under the limitation of short communication range, we first predict the driving trajectories of vehicles with a Markov chain model and estimate the popularity level of contents in each urban area. Next, we design a scoring system based on the above predictions to compare users' satisfaction and computational efficiency of different algorithms. Finally, we find a subset of vehicles with appropriate content to serve the demand in the area. The simulation experiments demonstrate that the proposed scheme outperforms the conventional methods by providing a cost-efficient content allocation solution with lower delay.

Miniature FSCV Devices: A Review

Fast-scan cyclic voltammetry (FSCV) is an electrochemical sensing method used for measuring the concentration of neurotransmitters within the brain. FSCV is typically conducted using benchtop equipment, providing accurate and high temporal resolution measurements. However, the large size and high cost of benchtop FSCV systems pose significant limitations in widespread preclinical use of FSCV. Recent technological improvements have facilitated the miniaturization of FSCV equipment, creating systems that can be mounted on the subject as they are small and lightweight. One key remaining bottleneck in the emerging miniaturized FSCV systems is that their data typically needs to be transmitted to an external computer for analysis. This paper presents a review on the current miniature FSCV devices. It discusses the latest improvements in voltage scan waveforms that help increase the sensitivity of measuring specific analytes, for use in miniaturized FSCV. It also tabulates analytes that have been detected using FSCV. It also details current issues with FSCV and the latest achievements in post-processing of FSCV data to better identify target analytes. Furthermore, the emerging miniature FSCV devices that have integrated on-board data processing capabilities to mitigate external data processing are discussed. These advances can accelerate neurochemistry research, reducing time spent analysing data on external computers, and allowing for investigation into questions on neurotransmitter dynamics in target brain regions.

The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

Enhancing optical-flow-based control by learning visual appearance cues for flying robots

Flying insects employ elegant optical-flow-based strategies to solve complex tasks such as landing or obstacle avoidance. Roboticists have mimicked these strategies on flying robots with only limited success, because optical flow (1) cannot disentangle distance from velocity and (2) is less informative in the highly important flight direction. Here, we propose a solution to these fundamental shortcomings by having robots learn to estimate distances to objects by their visual appearance. The learning process obtains supervised targets from a stability-based distance estimation approach. We have successfully implemented the process on a small flying robot. For the task of landing, it results in faster, smooth landings. For the task of obstacle avoidance, it results in higher success rates at higher flight speeds. Our results yield improved robotic visual navigation capabilities and lead to a novel hypothesis on insect intelligence: behaviours that were described as optical-flow-based and hardwired actually benefit from learning processes. Autonomous flight is challenging for small flying robots, given the limited space for sensors and on-board processing capabilities, but a promising approach is to mimic optical-flow-based strategies of flying insects. A new development improves this technique, enabling smoother landings and better obstacle avoidance, by giving robots the ability to learn to estimate distances to objects by their visual appearance.

Satellite Communications in the New Space Era: A Survey and Future Challenges

Satellite communications (SatComs) have recently entered a period of renewed interest motivated by technological advances and nurtured through private investment and ventures. The present survey aims at capturing the state of the art in SatComs, while highlighting the most promising open research topics. Firstly, the main innovation drivers are motivated, such as new constellation types, on-board processing capabilities, non-terrestrial networks and space-based data collection/processing. Secondly, the most promising applications are described, i.e., 5G integration, space communications, Earth observation, aeronautical and maritime tracking and communication. Subsequently, an in-depth literature review is provided across five axes: i) system aspects, ii) air interface, iii) medium access, iv) networking, v) testbeds & prototyping. Finally, a number of future challenges and the respective open research topics are described.

Future Vision for Autonomous Ocean Observations

Autonomous platforms already make observations over a wide range of temporal and spatial scales, measuring salinity, temperature, nitrate, pressure, oxygen, biomass; and many other parameters. However, the observations are not comprehensive. Future autonomous systems need to be more affordable, more modular, more capable and easier to operate. Creative new types of platforms and new compact, low power, calibrated and stable sensors are under development to expand autonomous observations. Communications and recharging need bandwidth and power which can be supplied by standardized docking stations. In situ power generation will also extend endurance for many types of autonomous platforms, particularly autonomous surface vehicles. Standardized communications will improve ease of use, interoperability, and enable coordinated behaviors. Improved autonomy and communications will enable adaptive networks of autonomous platforms. Improvements in autonomy will have three aspects: hardware, control, and operations. As sensors and platforms have more onboard processing capability and energy capacity, more measurements become possible. Control systems and software will have the capability to address more complex states and sophisticated reactions to sensor inputs, which allows the platform to handle a wider variety of circumstances without direct operator control. Operational autonomy is increased by reducing operating costs. To maximize the potential of autonomous observations new standards and best practices are needed. In some applications, focus on common platforms and volume purchases could lead to significant cost reductions. Cost reductions could enable order-of-magnitude increases in platform operations and increase sampling resolution for a given level of investment. Energy harvesting technologies should be integral to the system design, for sensors, platforms, vehicles, and docking stations. Connections are needed between the marine energy and ocean observing communities to coordinate among funding sources, researchers, and end users. Regional teams should work with global organizations such as IOC/GOOS in governance development. International networks such as EGO for the emerging glider operations should also provide a forum for addressing governance. Networks of multiple vehicles can improve operational efficiencies and transform operational patterns. There is a need to develop operational architectures at regional and global scales to provide a backbone for active networking of autonomous platforms.

Analytical modeling of flexible structures for health monitoring under environmentally induced loads

Wireless structural health monitoring (SHM) represents an emerging paradigm in non-destructive testing evaluation, which is regularly performed within the framework of structural maintenance of critical infrastructure. While traditional cable-based SHM strategies have largely relied on centralized structural response data collection and processing, in wireless SHM the sensor nodes essentially operate as stand-alone processing units. Furthermore, the on-board processing capabilities of wireless sensor nodes have been widely exploited for decentralizing SHM tasks, thus avoiding power-consuming wireless transmission of entire structural response data sets. Evidently, on-board processing requires approaches tailored to the limited computational resources of wireless sensor nodes, particularly for computationally intensive, state-of-the-art strategies for SHM that rely on artificial intelligence (AI). Specifically, in model-based SHM/AI strategies, AI algorithms are trained by running the so-called what-if scenarios using numerical models of monitored structures. However, the use of numerical models in a decentralized wireless SHM scheme might be prohibitive from a computational resources point of view. To overcome this limitation, analytical modeling techniques using elastic waveguides are developed here that carry a low computational burden. These are specifically tailored for flexible structures of variable cross section, which comprise typical components of critical infrastructure such as masts, antennas and pylons, and can be used for simulating axial, torsional and flexural vibrations. The accuracy and efficiency of the proposed analytical modeling approach are then demonstrated through comparisons with conventional numerical models based on the finite element method.

IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings.

Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.

Communicating Multi-UAV System for Cooperative SLAM-based Exploration

In the aerial robotic community, a growing interest for Multi-Robot Systems (MRS) appeared in the last years. This is thanks to i) the technological advances, such as better onboard processing capabilities and higher communication performances, and ii) the promising results of MRS deployment, such as increased area coverage in minimum time. The development of highly efficient and affordable fleet of Unmanned Aerial Vehicles (UAVs) and Micro Aerial Vehicles (MAVs) of small size has paved the way to new large-scale applications, that demand such System of Systems (SoS) features in areas like security, disaster surveillance, inundation monitoring, search and rescue, infrastructure inspection, and so on. Such applications require the robots to identify their environment and localize themselves. These fundamental tasks can be ensured by the exploration mission. In this context, this thesis addresses the cooperative exploration of an unknown environment sensed by a team of UAVs with embedded vision. We propose a multi-robot framework where the key problem is to cooperatively choose specific regions of the environment to be simultaneously explored and mapped by each robot in an optimized manner in order to reduce exploration time and, consequently, energy consumption. Each UAV is able to performSimultaneous Localization And Mapping (SLAM) with a visual sensor as the main input sensor. To explore the unknown regions, the targets – selected from the computed frontier points lying between free and unknown areas – are assigned to robots by considering a trade-off between fast exploration and getting detailed grid maps. For the sake of decision making, UAVs usually exchange a copy of their local map; however, the novelty in this work is to exchange map frontier points instead, which allow to save communication bandwidth. One of the most challenging points in MRS is the inter-robot communication. We study this part in both topological and typological aspects. We also propose some strategies to cope with communication drop-out or failure. Validations based on extensive simulations and testbeds are presented.

Satellite IoT Edge Intelligent Computing: A Research on Architecture

As the number of satellites continues to increase, satellites become an important part of the IoT and 5G/6G communications. How to deal with the data of the satellite Internet of Things is a problem worth considering and paying attention to. Due to the current on-board processing capability and the limitation of the inter-satellite communication rate, the data acquisition from the satellite has a higher delay and the data utilization rate is lower. In order to use the data generated by the satellite IoT more effectively, we propose a satellite IoT edge intelligent computing architecture. In the article, we analyze the current methods of satellite data processing, combined with the development trend of future satellites, and use the characteristics of edge computing and machine learning to describe the satellite IoT edge intelligent computing architecture. Finally, we verify that the architecture can speed up the processing of satellite data. By demonstrating the performance of different neural network models in the satellite edge intelligent computing architecture, we can find that the lightweight of neural networks can promote the development of satellite IoT edge intelligent computing architecture.

Virtual Agent Clustering Based Mobility Management Over the Satellite Networks

Due to the high dynamic characteristics of the low-orbit satellite networks, the frequent handovers of the users led to heavy mobility management load and large handover delay. To solve these problems, one mobility management mechanism based on the virtual agent domain (VAD) is proposed. In this mechanism, a virtual agent cluster (VAC) is designed to co-manage the network architecture of users in the corresponding VAD. With the on-board processing and switching capabilities, the architecture of the distributed mobility management mechanism is adopted to support the information sharing between the VACs, which reduces the performance requirements for single satellite and improves the system scalability. Then, we construct the home mobile-agent-anchor (HMAA) and the local MAA. In this way, the MN triggers a binding update to the HA only when the home MAA is lost, and the MN's switching within the VAD only needs to update its intra-domain relations, which reduces the overhead of mobility management and switching delay. Furthermore, the proposed scheme is theoretically evaluated in terms of the signaling overhead and handover latency. Finally, the numerical simulation results are presented to verify the efficacy of our scheme. The experimental platform also demonstrates the availability and efficiency of the new mechanism.

A USB 3.0 readout system for Timepix3 detectors with on-board processing capabilities

Timepix3 is a high-speed hybrid pixel detector consisting of a 256×256 pixel matrix with a maximum data rate of up to 5.12 Gbps (80 MHit/s). The ASIC is equipped with eight data channels that are data driven and zero suppressed making it suitable for particle tracking and spectral imaging. In this paper, we present a USB 3.0-based programmable readout system with online pre-processing capabilities. USB 3.0 is present on all modern computers and can, under real-world conditions, achieve around 320 MB/s, which allows up to 40 MHit/s of raw pixel data. With on-line processing, the proposed readout system is capable of achieving higher transfer rate (approaching Timepix4) since only relevant information rather than raw data will be transmitted. The system is based on an Opal Kelly development board with a Spartan 6 FPGA providing a USB 3.0 interface between FPGA and PC via an FX3 chip. It connects to a CERN TIMEPIX3 chipboard with standard VHDCI connector via a custom designed mezzanine card. The firmware is structured into blocks such as detector interface, USB interface and system control and an interface for data pre-processing. On the PC side, a Qt/C++ multi-platform software library is implemented to control the readout system, providing access to detector functions and handling high-speed USB 3.0 streaming of data from the detector. We demonstrate equalisation, calibration and data acquisition using a Cadmium Telluride sensor and optimise imaging data using simultaneous ToT (Time-over-Threshold) and ToA (Time-of-Arrival) information. The presented readout system is capable of other on-line processing such as analysis and classification of nuclear particles with current or larger FPGAs.

Solar-powered, wireless smart camera network: An IoT solution for outdoor video monitoring

In this paper, we present SlugCam, a solar-powered, wireless smart camera network that can be used in a variety of outdoor applications including video surveillance of public spaces, habitat and environmental monitoring, wildfire prevention and detection, to name a few. SlugCam was designed such that it can be deployed and left unattended for extended periods without requiring regular maintenance, e.g., frequent battery replacement. The system is built with off-the-shelf components which not only keeps it modular and low cost, but also facilitates its prototyping, rapid duplication, and evolution. SlugCam’s on-board processing capability allows computer vision software to run locally and autonomously. Energy efficiency in SlugCam is accomplished both in: (1) hardware by micro-managing low-power components; as well as in (2) software by having the system’s operation duty cycles automatically adapt to the current state of the battery in order to balance the trade-off between application-level requirements and power awareness. For example, SlugCam’s smart camera node changes its monitoring behavior based on how much battery charge remains. Additionally, using its computer vision software, the system only records and transmits information upon event detection which contributes both to the system’s energy efficiency as well as its low network bandwidth requirements. SlugCam’s networking functionality enables camera nodes to transfer video files, as well as collaborate on tasks such as visual processing, event detection, and object tracking. It allows node-to-node, as well as scoped- or full broadcast communication. For point-to-point communication, SlugCam uses on-demand power-aware multi-path routing to transfer video files efficiently. Another important contribution of SlugCam is to provide an open-source wireless camera network that can adapt to address the requirements of future outdoor video monitoring applications. SlugCam also includes a Web-based server where video data is stored as well as a Web-based user interface that allows end users to interact with the system, tag, query and retrieve video files, and manage SlugCam nodes remotely. In addition to a detailed description of SlugCam, this paper presents an extensive power characterization of the system’s operation and showcases its deployment in a lab testbed and a real world scenario.