Resource management is one of the challenges in satellite networks due to their high mobility, wide coverage, long propagation distances, and stringent constraints on energy, communication, and computation resources. Traditional resource allocation approaches rely only on hard and rigid system performance metrics. Meanwhile, incentive mechanisms, which are based on game theory and auction theory, investigate systems from the "economic" perspective in addition to the "system" perspective. Particularly, incentive mechanisms are able to take into account rationality and other behavior of human users into account, which guarantees benefits/utility of all system entities, thereby improving the scalability, adaptability, and fairness in resource allocation. This paper presents a comprehensive survey of incentive mechanism design for resource management in satellite networks. The paper covers key issues in the satellite networks, such as communication resource allocation, computation offloading, privacy and security, and coordination. We conclude with future research directions including learning-based mechanism design for satellite networks.

Existing autonomous vehicles (AVs) utilize neither cloud computing for execution of their deep learning-based driving tasks due to the long vehicle-to-cloud communication latency, nor solar energy to offset the energy usage of car-borne computing. They are in general equipped with the resource-constrained edge computing devices which may be unable to execute the compute-intensive deep learning models in real time. The increasing data transmission speed of the commercial mobile networks sheds light upon the feasibility of using the cloud computing for autonomous driving, which is demonstrated by our city-scale real-world measurements over the fifth generation (5G) mobile networks. Moroever, the cost and form factor declines of solar harvesting systems make the quest of integrating them with AVs for improving carbon efficiency more promising. In this paper, we present the design and implementation of ECSeg, an edge-cloud switched low-carbon image segmentation system for AVs equipped with roof-mounted solar panels. ECSeg dynamically switches between edge and cloud processing to execute semantic segmentation models in real time while aiming to decarbonize AV computing by maximizing the use of harvested solar energy. The switching decision is challenging due to the complex interdependencies among various factors, including dynamic wireless channel conditions, vehicle motion, visual scene changes, and available renewable energy. To tackle this, ECSeg employs deep reinforcement learning to learn an optimal switching policy. Extensive evaluations based on real-world experiments and trace-driven simulations demonstrate that ECSeg outperforms six baseline approaches, achieving 98.8% reduction in computing’s carbon emission, while maintaining high image segmentation accuracy, compared with our earlier design without integrating the solar panel.

Existing autonomous vehicles have not utilized the cloud computing for execution of their deep learning-based driving tasks due to the long vehicle-to-cloud communication latency. The increasing data transmission speed of the commercial mobile networks sheds light upon the feasibility of using the cloud computing for autonomous driving. In this demo, we introduce the design and implementation of ECSeg, an edge-cloud switched image segmentation system that dynamically selects between the edge and cloud to execute deep learning-based semantic segmentation models. This enables realtime understanding of a vehicle's visual scenes while adapting to dynamic wireless conditions and changing environments.

To maintain driving safety, the execution of neural network-based autonomous driving pipelines must meet the dynamic deadlines in response to the changing environment and vehicle’s velocity. To this end, this article proposes a real-time neural architecture adaptation approach, called TimelyNet, which uses a supernet to replace the most compute-intensive neural network module in an existing end-to-end autonomous driving pipeline. From the supernet, TimelyNet samples subnets with varying inference latency levels to meet the dynamic deadlines during run-time driving without fine-tuning. Specifically, TimelyNet employs a one-shot prediction method that jointly uses a lookup table and an invertible neural network to periodically determine the optimal hyperparameters of a subnet to meet its execution deadline while achieving the highest possible accuracy. The lookup table stores multiple subnet architectures with different latencies, while the invertible neural network models the distribution of the optimal subnet architecture given the latency. Extensive evaluation based on hardware-in-the-loop CARLA simulations shows that TimelyNet-integrated driving pipelines achieve the best driving safety, characterized by the lowest wrong-lane driving rate and zero collisions, compared with several baselines, including the state-of-the-art driving pipelines.

The 3D-printed mesh screen with dense penetrating pores is a new structure for massive manufacturing of molded pulp package products. However, some of the pores may be clogged by the printing material powder during the printing process. Such defects negatively affect the quality of the pulp packages produced using the mesh screen mold. To pinpoint the defects, we design a model-based robotic visual sensing system, called RoboCam, which uses a robotic arm to carry a high-resolution camera for full inspection of a mold consisting of joined mesh screens. To inspect the entire mold, RoboCam plans the camera poses to capture multiple images of the mold and render synthesized images as references for identifying the clogged pores. In particular, we propose novel designs to rectify the inherent run-time pose errors of the robotic system for ensuring the reference quality and to accelerate the reference rendering for reducing inspection latency. Extensive evaluation shows that RoboCam’s design outperforms various baselines, including three existing computer vision and convolution neural network-based inspection systems. RoboCam achieves a recall rate of 94.95% within 528 seconds latency for inspecting an entire mold with 13,000 designed pores.

Increasing supply air temperatures is a rule-of-thumb approach to reduce cooling energy usage of data centers (DCs). However, colocation DCs are short of incentive programs to move tenants from the current over-cooling strategy despite the expanding allowable temperature ranges of the computing equipment. This paper considers an essential incentive mechanism, in which the DC operator offers monetary incentives to offset tenants’ electricity payments. We propose an encoder-embedded multi-agent reinforcement learning solution to let the operator agent and tenant agents collaboratively find their policies for deciding the incentives and supply air temperatures, respectively, which are coupled in determining the DC’s total cooling power usage. The solution does not require the cooling power model, which is complex and in general unavailable in practice. Moreover, as each tenant agent learns in the other tenants’ latent state spaces defined by their pre-trained variational autoencoders, only encoded tenants’ states are exchanged, thereby mitigating information leakage concerns. Extensive trace-driven evaluation and comparison with three baselines show that our solution effectively incentivizes tenants to move from the over-cooling strategy and achieves substantial cooling power savings.

Existing autonomous vehicles have not utilized the cloud computing for execution of their deep learning-based driving tasks due to the long vehicle-to-cloud communication latency. Meanwhile, the vehicles are in general equipped with the resource-constrained edge computing devices which may be unable to execute the compute-intensive deep learning models in real time. The increasing data transmission speed of the commercial mobile networks sheds light upon the feasibility of using the cloud computing for autonomous driving. Our city-scale real-world measurements show that the vehicles can partially use the cloud computing via the fifth generation (5G) mobile network with the low data transmission latency. In this paper, we present the design and implementation of ECSeg, an edge-cloud switched image segmentation system that dynamically switches between the edge and cloud for executing the deep learning-based semantic segmentation models to understand the vehicle's visual scenes in real time. The switching decision-making is challenging due to the intricate interdependencies among various factors including the dynamic wireless channel condition, vehicle's movement and visual scene change. To this end, we employ deep reinforcement learning to learn an optimal switching policy. Extensive evaluation based on both real-world experiments and trace-driven simulations demonstrates that ECSeg achieves superior image segmentation accuracy for autonomous vehicles, compared with four baseline approaches.

Visual sensing has been widely adopted for quality inspection in production processes. This article presents the design and implementation of a smart collaborative camera system, called BubCam , for automated quality inspection of manufactured ink bags in Hewlett-Packard (HP) Inc.’s factories. Specifically, BubCam estimates the volume of air bubbles in an ink bag, which may affect the printing quality. The design of BubCam faces challenges due to the dynamic ambient light reflection, motion blur effect, and data labeling difficulty. As a starting point, we design a single-camera system that leverages various deep learning (DL)-based image segmentation and depth fusion techniques. New data labeling and training approaches are proposed to utilize prior knowledge of the production system for training the segmentation model with a small dataset. Then, we design a multi-camera system that additionally deploys multiple wireless cameras to achieve better accuracy due to multi-view sensing. To save power of the wireless cameras, we formulate a configuration adaptation problem and develop the single-agent and multi-agent deep reinforcement learning (DRL)-based solutions to adjust each wireless camera’s operation mode and frame rate in response to the changes of presence of air bubbles and light reflection. The multi-agent DRL approach aims to reduce the retraining costs during the production line reconfiguration process by only retraining the DRL agents for the newly added cameras and the existing cameras with changed positions. Extensive evaluation on a lab testbed and real factory trial shows that BubCam outperforms six baseline solutions including the current manual inspection and existing bubble detection and camera configuration adaptation approaches. In particular, BubCam achieves 1.3x accuracy improvement and 300x latency reduction compared with the manual inspection approach.

Various deep neural networks (DNNs) including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have shown appealing performance in various classification tasks. However, due to their large sizes, a single DNN often cannot fit into the memory of resource-constrained smart IoT sensors. This paper presents a DNN splitting framework calledNNFacetthat aims to run a DNN-based classification task on a total of$N$concurrent battery-based sensors observing the same physical process. We begin with determining the importance of all CNN filters or RNN units in learning each class. Then, an optimization problem divides the class set into$N$subsets and assigns them to the sensors, where the important CNN filters or RNN units associated with a class subset form a small model that is deployed to a sensor. Lastly, a multilayer perceptron is trained and deployed to a cloud or edge server, which yields the final classification result based on the low-dimensional features extracted by the sensors using their small models for the same observation. We apply NNFacet to three case studies of voice sensing, vibration sensing, and visual sensing. Extensive evaluation shows that NNFacet outperforms four baseline approaches in terms of system lifetime, latency, and classification accuracy.

Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers interest of applying Artificial Intelligence of Things (AIoT) systems for industrial applications. The in situ inference and decision made based on the sensor data allow the industrial system to address a variety of heterogeneous, local-area non-trivial problems in the last hop of the IoT networks. Such a scheme avoids the wireless bandwidth bottleneck and unreliability issues, as well as the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer lessons for the relevant research and industry communities. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of HP Inc.’s ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the work, which could be useful to the development of other industrial AIoT systems for quality control in manufacturing.

Data centers (DCs) are power-intensive facilities which use a significant amount of energy for cooling the servers. Increasing the temperature and relative humidity (RH) setpoints is a rule-of-thumb approach to reducing the DC energy usage. However, the high temperature and RH may undermine the server's reliability. Before we can choose the proper temperature and RH settings, it is essential to understand how the temperature and RH setpoints affect the DC power usage and server's reliability. To this end, we constructed and experimented with an air-cooled DC testbed in Singapore, which consists of a direct expansion cooling system and 521 servers running real-world application workloads. This paper presents the key measurement results and observations from our 11-month experiments. Our results suggest that by operating at a supply air temperature setpoints of 29${}^{\circ }$C, our testbed achieves substantial cooling power saving with little impact on the server's reliability. Furthermore, we present a total cost of ownership (TCO) analysis framework which guides settings of the temperature and RH for a DC. Our observations and TCO analysis framework will be useful to future efforts in building and operating air-cooled DCs in tropics and beyond.

Visual sensing has been increasingly employed in various industrial applications including manufacturing process monitoring and worker safety monitoring. This paper presents the design and implementation of a wireless camera system, namely, EFCam, which uses low-power wireless communications and edge-fog computing to achieve cordless and energy-efficient visual sensing. The camera performs image pre-processing and offloads the data to a resourceful fog node for advanced processing using deep models. EFCam admits dynamic configurations of several parameters that form a configuration space. It aims to adapt the configuration to maintain desired visual sensing performance of the deep model at the fog node with minimum energy consumption of the camera in image capture, pre-processing, and data communications, under dynamic variations of the monitored process, the application requirement, and wireless channel conditions. However, the adaptation is challenging due to the complex relationships among the involved factors. To address the complexity, we apply deep reinforcement learning to learn the optimal adaptation policy when a fog node supports one or more wireless cameras. Extensive evaluation based on trace-driven simulations and experiments show that EFCam complies with the accuracy and latency requirements with lower energy consumption for a real industrial product object tracking application, compared with five baseline approaches incorporating hysteresis-based and event-triggered adaptation.

At present, a co-location data center often applies an identical and low temperature setpoint for its all server rooms. Although increasing the temperature setpoint is a rule-of-thumb approach to reducing the cooling energy usage, the tenants may have different mentalities and technical constraints in accepting higher temperature setpoints. Thus, supporting distinct temperature setpoints is desirable for a co-location data center in pursuing higher energy efficiency. This calls for a new cooling power attribution scheme to address the inter-room heat transfers that can be up to 9% of server load as shown in our real experiments. This article describes our approaches to estimating the inter-room heat transfers, using the estimates to rectify the metered power usages of the rooms’ air handling units, and fairly attributing the power usage of the shared cooling infrastructure (i.e., chiller and cooling tower) to server rooms by following the Shapley value principle. Extensive numeric experiments based on a widely accepted cooling system model are conducted to evaluate the effectiveness of the proposed cooling power attribution scheme. A case study suggests that the proposed scheme incentivizes rational tenants to adopt their highest acceptable temperature setpoints under a non-cooperative game setting. Further analysis considering distinct relative humidity setpoints shows that our proposed scheme also properly and inherently addresses the attribution of humidity control power.

Air free cooling is an energy-efficient cooling scheme that has been adopted in the dry and cold climate zones. To adopt this cooling scheme in Singapore's tropical condition, we designed and implemented an air free-cooled DC testbed integrating sensing and control systems for the server and room conditions. Then, we conducted extensive experiments on the testbed to understand its energy efficiency and server reliability. This paper presents the key observations, experiences, and learned lessons obtained from our testbed over a duration of nearly two years. The experiments show that (1) the air free-cooling design can achieve the power usage effectiveness of 1.05, (2) the tropics’ year-round high temperatures up to$37^\circ$C do not impede the air free-cooling, and (3) the implementation of the air free-cooled tropical DCs requires special cares to deal with airborne contaminants to avoid fast corrosion rate and dust-induced server faults. Based on our experiment data, a set of recommendations on the temperature control and the selection of IT equipment for air free-cooled tropical DCs is made. The descriptions of the learned lessons, the resulting recommendations, and the released data can be useful to the relevant research communities, governmental agencies, and standardizing bodies.

Convolutional neural networks (CNNs) are increasingly adopted on resource-constrained sensors for in-situ data analytics in Internet of Things (IoT) applications. This paper presents a model split framework, namely, splitCNN, in order to run a large CNN on a collection of concurrent IoT sensors. Specifically, we adopt CNN filter pruning techniques to split the large CNN into multiple small-size models, each of which is only sensitive to a certain number of data classes. These class-specific models are deployed onto the resource-constrained concurrent sensors which collaboratively perform distributed CNN inference on their same/similar sensing data. The outputs of multiple models are then fused to yield the global inference result. We apply splitCNN to three case studies with different sensing modalities, which include the human voice, industrial vibration signal, and visual sensing data. Extensive evaluation shows the effectiveness of the proposed splitCNN. In particular, the splitCNN achieves significant reduction in the model size and inference time while maintaining similar accuracy, compared with the original CNN model for all three case studies.

Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers wide interest of applying the resulting Artificial Intelligence of Things (AIoT) systems in industrial applications. The in situ inference and decision made based on the sensor data containing patterns with certain sophistication allow the industrial system to address a variety of heterogeneous, local-area non-trivial problems in the last hop of the IoT networks, avoiding the wireless bandwidth bottleneck and unreliability issues and also the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer important lessons for the relevant research and engineering communities, no matter the development is successful or not. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of Hewlett-Packard's ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the effort, which could be useful to the developments of other industrial AIoT systems.

Visual sensing has been increasingly employed in industrial processes. This paper presents the design and implementation of an industrial wireless camera system, namely, EFCam, which uses low-power wireless communications and edge-fog computing to achieve cordless and energy-efficient visual sensing. The camera performs image pre-processing (i.e., compression or feature extraction) and transmits the data to a resourceful fog node for advanced processing using deep models. EFCam admits dynamic configurations of several parameters that form a configuration space. It aims to adapt the configuration to maintain desired visual sensing performance of the deep model at the fog node with minimum energy consumption of the camera in image capture, pre-processing, and data communications, under dynamic variations of application requirement and wireless channel conditions. However, the adaptation is challenging due primarily to the complex relationships among the involved factors. To address the complexity, we apply deep reinforcement learning to learn the optimal adaptation policy. Extensive evaluation based on trace-driven simulations and experiments show that EFCam complies with the accuracy and latency requirements with lower energy consumption for a real industrial product object tracking application, compared with four baseline approaches incorporating hysteresis-based adaptation.

Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies’ prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in the tropics. However, due to the complex psychrometric dynamics, operating the air free-cooled DC in the tropics generally requires adaptive control of supply air condition to maintain the computing performance and reliability of the servers. This article studies the problem of controlling the supply air temperature and RH in a free-cooled tropical DC below certain thresholds. To achieve the goal, we formulate the control problem as Markov decision processes and apply deep reinforcement learning (DRL) to learn the control policy that minimizes the cooling energy while satisfying the requirements on the supply air temperature and RH. We also develop a constrained DRL solution for performance improvements. Extensive evaluation based on real data traces collected from an air free-cooled testbed and comparisons among the unconstrained and constrained DRL approaches as well as two other baseline approaches show the superior performance of our proposed solutions.

An ad-hoc mobile cloud allows a mobile user to access a potential cloud resource with a short offloading latency and a low bandwidth consumption from nearby mobile devices, namely mobile cloudlets via short-range communications. However, due to the user and cloudlet mobility, the time-varying wireless channel properties, and the cloudlet's limited computation resource, designing an effective offloading algorithm involves challenges in workload distribution, cost estimation and energy minimization. In this paper, to address these challenges, we develop an optimization-based offloading algorithm that enables the mobile user to make an optimal offloading decision. The proposed algorithm takes into account effects of the user's workload, the diverse connectivity of cloudlets, and the wireless environment on the offloading action. More specifically, we formulate and solve a Markov decision process (MDP) scheme to achieve an optimal offloading policy for the mobile user with the objective of maximizing the user's utility while minimizing the offloading cost. Extensive simulations were performed to evaluate the performance of the proposed MDP scheme. The simulation results show that the proposed scheme outperforms baseline schemes.

Due to its advantages of providing a large geographical coverage and having no strict limits on energy and sensing and processing capabilities, a vehicular sensor network (VSN) has recently emerged as a promising paradigm for air quality monitoring in an urban area. However, designing an efficient VSN-based air monitoring system has challenges due to the vehicles' heterogeneous temporal and spatial coverage and the relatively expensive communication cost over cellular networks. In this paper, we propose a machine learning (ML)-based Air quality Monitoring (MLAirM) system which aims at reducing communication and sensing costs by allowing vehicles to process the collected data in a distributed fashion. More specifically, in MLAirM, vehicles are first assigned to take measurements at sets of locations in a sensing area. The vehicle then utilizes a distributed machine learning algorithm to learn a local model of air quality based on its collected data. Finally, the vehicle sends parameters of its model to a monitoring center which combines multiple local models to build a global air quality map. Furthermore, assigning the sensing locations to vehicles can be viewed as a successful measurement probability aware location assignment problem. An integer linear optimization problem is formulated and a heuristic algorithm is proposed to find the solution. Simulations based on realistic vehicular traces are performed to compare the proposed MLAirM system with other approaches. The simulations results show that the MLAirM can achieve a similar accuracy of building the global air quality map with a significant reduction in communication and sensing costs compared to other approaches.

The task of monitoring a moving phenomenon in an unbounded area using a mobile sensor network (MSN) brings out several challenges due to the high movement speed of phenomenon, the limited sensing/communication capabilities of mobile sensor nodes. To address the challenges and achieve a high weighted sensing coverage, in this paper we propose a monitoring algorithm, namely VirFID-MP (Virtual Force (VF)-based Interest-Driven moving phenomenon monitoring with Mobility Prediction). In VirFID-MP, the movement of phenomenon is first predicted based on its previous movements. Then, the predicted information is used to determine a global virtual force, which is utilized to speed up the MSN toward the moving phenomenon. Simulation results show that VirFID-MP outperforms original VirFID in terms of weighted coverage efficiency, when the MSN monitors a moving phenomenon.

Abstract In this paper, we study mobile sensor network (MSN) architectures and algorithms for monitoring a moving phenomenon in an unknown and open area using a group of autonomous mobile sensor (MS) nodes. Monitoring a moving phenomenon involves challenges due to limited communication/sensing ranges of MS nodes, the phenomenon’s unpredictable changes in distribution and position, and the lack of information on the sensing area. To address the challenges and meet the objective of the maximization of weighted sensing coverage, we propose a novel scheme, namely VirFID (Virtual Force (VF)-based Interest-Driven moving phenomenon monitoring). In VirFID, MS nodes move toward the positions where more interesting sensing data can be obtained by utilizing the virtual force, which is calculated based on the distance between MS nodes and sensed values in the area of interest. MS nodes also perform network-wise information sharing to increase the weighted sensing coverage. Depending on the level of information used, three variants of VirFID are evaluated: VirFID-LIB (Local Information-Based), VirFID-GHL (Global Highest and Lowest), and VirFID-IBN (Interests at Boundary Nodes). In addition, an analytical model for estimating MSN speed is designed. Simulations are performed to compare the performance of three VirFID variants with other approaches. Our simulation results show that VirFID algorithms outperform other schemes in terms of the weighted coverage efficiency, and VirFID-IBN achieves the highest weighted coverage efficiency among VirFID variants.

In ad hoc networks, wireless links are subject to a low quality due to time-varying channel properties, node mobility, and obstacles. Such low quality wireless links lead to the degraded performance of the end-to-end data transfer service, which also result in less applicability of ad hoc networks to practical deployment. In order to address the problem of low quality wireless links and provide a required quality for end-to-end data transfer services, we propose a novel routing and relaying architecture that exploits controllable mobility of robotic relays, namely RoCoMAR (Robots' Controllable Mobility Aided Routing), which repeatedly reinforces wireless links with the main objective of maximizing the network throughput. RoCoMAR first identifies the lowest quality link and replaces it with high quality links that are created by re-locating a robotic relay in an optimal position. The simulation results show that RoCoMAR outperforms existing ad hoc routing protocols in terms of network throughput and end-to-end delay.