In this paper, we focus on the use of linear sensor networks (LSNs) to monitor underwater pipelines. We propose the use of an autonomous underwater vehicle (AUV) to move along the pipeline and collect data from the sensor nodes (SNs). It then delivers the collected data to the surface sink at the end of the LSN or LSN segment. In turn, the surface sink transmits the collected data to the network control center (NCC) using any of the communication technologies that are available in the area such as WiMAX, cellular, and satellite. The use of the AUV for data collection allows for considerable advantages including flexibility in SN placement, considerable energy savings, and reduced interference, hidden terminal problems, and collisions. We also provide, different AUV movement strategies and analyze the effect of various network design parameters on network performance metrics such as packet delivery ratio and end-to-end delay.

The booming of Convolutional Neural Networks (CNNs) has empowered lots of computer-vision applications. Due to its stringent requirement for computing resources, substantial research has been conducted on how to optimize its deployment and execution on resource-constrained devices. However, previous works have several weaknesses, including limited support for various CNN structures, fixed scheduling strategies, overlapped computations, high synchronization overheads, etc. In this article, we present DeepSlicing, a collaborative and adaptive inference system that adapts to various CNNs and supports customized flexible fine-grained scheduling. As a built-in functionality, DeepSlicing has supported typical CNNs including GoogLeNet, ResNet, etc. By partitioning both model and data, we also design an efficient scheduler, Proportional Synchronized Scheduler (PSS), which achieves the trade-off between computation and synchronization. Based on PyTorch, we have implemented DeepSlicing on the testbed with real-world edge settings that consists of 8 heterogeneous Raspberry Pi's. The results indicate that DeepSlicing with PSS outperforms the existing systems dramatically, e.g., the inference latency and memory footprint are reduced up to 5.79× and 14.72×, respectively.

Mobile CrowdSensing (MCS), through which a requester can coordinate a crowd of workers to accomplish some data collection tasks, has been recognized as a promising paradigm for large-scale data acquisition in recent years. Many researches focus on the worker recruitment problem in MCS, but most of them either have the assumption that workers’ qualities are known ahead of time or cannot ensure that workers report costs honestly. In this paper, we propose an incentive mechanism based on Combinatorial Multi-Armed Bandit and reverse Auction, called CMABA, to solve the multiple unknown workers recruitment problem in MCS. Our objective is to determine a recruiting strategy to maximize the total sensing quality under a limited budget, while ensuring truthfulness and individual rationality of sensing workers. We theoretically prove that our CMABA mechanism achieves truthfulness and individual rationality, and then analyze the regret of the mechanism. Based on CMABA, we ulteriorly propose an adaptive incentive mechanism, called ACMABA, to recruit workers via the alternative worker recruitment and quality update, which can achieve a higher total sensing quality and lower regret. Additionally, we also demonstrate significant performances of the CMABA and ACMABA mechanisms through extensive simulations on real-world data traces.

Major cities worldwide have millions of cameras deployed for surveillance, business intelligence, traffic control, crime prevention, etc. Real-time analytics on video data demands intensive computation resources and high energy consumption. Traditional cloud-based video analytics relies on large centralized clusters to ingest video streams. With edge computing, we can offload compute-intensive analysis tasks to nearby servers, thus mitigating long latency incurred by data transmission via wide area networks. When offloading video frames from the front-end device to an edge server, the application configuration (i.e., frame sampling rate and frame resolution) will impact several metrics, such as energy consumption, analytics accuracy and user-perceived latency. In this paper, we study the configuration selection and bandwidth allocation for multiple video streams, which are connected to the same edge node sharing an upload link. We propose an efficient online algorithm, called JCAB, which jointly optimizes configuration adaption and bandwidth allocation to address a number of key challenges in edge-based video analytics systems, including edge capacity limitation, unknown network variation, intrusive dynamics of video contents. Our algorithm is developed based on Lyapunov optimization and Markov approximation, works online without requiring future information, and achieves a provable performance bound. We also extend the proposed algorithms to the multi-edge scenario in which each user or video stream has an additional choice about which edge server to connect. Extensive evaluation results show that the proposed solutions can effectively balance the analytics accuracy and energy consumption while keeping low system latency in a variety of settings.

In this paper, we investigate the incentive problem in Spatial Crowdsourcing (SC), where mobile social-aware workers have unknown qualities and can share their answers to tasks via social networks. The objectives are to recruit high-quality workers and maximize all parties’ utilities simultaneously. However, most existing works assume that the qualities of workers are known in advance or cannot take all parties’ utilities into account together, especially having not considered the impact of social networks. Thus, we propose an incentive mechanism based on the multi-armed bandit and three-stage Stackelberg game, called TACT. We first design a greedy arm-pulling scheme to recruit workers, which not only can solve the exploration-exploitation dilemma but also takes workers’ social relations into account. Based on the recruitment results, we further design the utility functions incorporating with social benefits for workers, and model the payment computation problem as a three-stage Stackelberg game among all participants. Next, we derive the optimal strategy group so that each party can maximize its own utility to form a multi-win situation. Moreover, we theoretically prove the unique existence of Stackelberg equilibrium and the worst regret bound. Finally, we conduct extensive simulations on a real trace to corroborate the performance of TACT.

Wireless power transfer is a promising technology used to extend the lifetime of, and thus enhance the usability of, energy-hungry battery-powered devices. It enables energy to be wirelessly transmitted from power chargers to energy-receiving devices. Existing studies have mainly focused on maximizing network lifetime, optimizing charging efficiency, minimizing charging delay, etc. In this paper, we consider wireless charging service provision in a two-dimensional target area and focus on optimizing charging quality, where the power of each charger is adjustable. We first consider the charger Placement and Power allocation Problem with Stationary rechargeable devices (SP^3): Given a set of stationary devices and a set of candidate locations for placing chargers, find a charger placement and a corresponding power allocation to maximize the charging quality, subject to a power budget. We prove that SP^3 is NP-complete, and propose an approximation algorithm. We also show how to deal with mobile devices (MP^3), cost-constrained power reconfiguration (CRP), and optimization with more candidate locations. Extensive simulation results show that, the proposed algorithms perform very closely to the optimum (the gap is no more than 4.5%, 4.4%, and 5.0% of OPT in SP^3, MP^3, and CRP, respectively), and outperforms the baseline algorithms.