With the rapid development of smart mobile terminals (MTs), various industrial Internet of things (IIoT) applications can fully leverage them to collect and share data for providing certain services. However, two key challenges still remain. One is how to achieve high-quality data collection with limited MT energy resource and sensing range. Another is how to ensure security when sharing and exchanging data among MTs, to prevent possible device failure, network communication failure, malicious users or attackers, etc. To this end, we propose a blockchain-enabled efficient data collection and secure sharing scheme combining Ethereum blockchain and deep reinforcement learning (DRL) to create a reliable and safe environment. In this scheme, DRL is used to achieve the maximum amount of collected data, and the blockchain technology is used to ensure security and reliability of data sharing. Extensive simulation results demonstrate that the proposed scheme can provide higher security level and stronger resistance to attack than a traditional database based data sharing scheme for different levels/types of attacks.

Blockchain-Enabled Data Collection and Sharing for Industrial IoT With Deep Reinforcement Learning

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 the nearby server, thus mitigating long latency incurred by data transmission via wide area networks. When offloading frames from the front-end device to the edge server, the application configuration (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 adaption 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. Simulation results show that JCAB can effectively balance the analytics accuracy and energy consumption while keeping low system latency.

Joint Configuration Adaptation and Bandwidth Allocation for Edge-based Real-time Video Analytics

Spatial crowdsourcing (SC) is an emerging paradigm of crowdsourcing, which commits workers to move to some particular locations to perform spatio-temporal-relevant tasks (e.g., sensing and activity organization). Task allocation or worker selection is a significant problem that may impact the quality of completion of SC tasks. Based on a conceptual model and generic framework of SC task allocation, this paper first gives a review of the current state of research in this field, including single task allocation, multiple task allocation, low-cost task allocation, and quality-enhanced task allocation. We further investigate the future trends and open issues of SC task allocation, including skill-based task allocation, group recommendation and collaboration, task composition and decomposition, and privacy-preserving task allocation. Finally, we discuss the practical issues on real-world deployment as well as the challenges for large-scale user study in SC task allocation.

Task Allocation in Spatial Crowdsourcing: Current State and Future Directions

This paper surveys the literature of opportunistic offloading. Opportunistic offloading refers to offloading traffic originally transmitted through the cellular network to opportunistic network, or offloading computing tasks originally executed locally to nearby devices with idle computing resources through opportunistic network. This research direction is recently emerged, and the relevant research covers the period from 2009 to date, with an explosive trend over the last four years. We provide a comprehensive review of the research field from a multi-dimensional view based on application goal, realizing approach, offloading direction, etc. In addition, we pinpoint the major classifications of opportunistic offloading, so as to form a hierarchical or graded classification of the existing works. Specifically, we divide opportunistic offloading into two main categories based on application goal: traffic offloading or computation offloading. Each category is further divided into two smaller categories: with and without offloading node selection, which bridges between subscriber node and the cellular network, or plays the role of computing task executor for other nodes. We elaborate, compare, and analyze the literatures in each classification from the perspectives of required information, objective, etc. We present a complete introductory guide to the researches relevant to opportunistic offloading. After summarizing the development of the research direction and offloading strategies of the current state-of-the-art, we further point out the important future research problems and directions.

A Survey of Opportunistic Offloading

Intelligent route planning on large road networks with efficiency and privacy

Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart phones to conduct complex sensing tasks. In this paper, we focus on the makespan sensitive task assignment problems for the crowdsensing in mobile social networks, where the mobility model is predicable, and the time of sending tasks and recycling results is non-negligible. To solve the problems, we propose an Average makespan sensitive Online Task Assignment (AOTA) algorithm and a Largest makespan sensitive Online Task Assignment (LOTA) algorithm. In AOTA and LOTA, the online task assignments are viewed as multiple rounds of virtual offline task assignments. Moreover, a greedy strategy of small-task-first-assignment and earliest-idle-user-receive-task is adopted for each round of virtual offline task assignment in AOTA, while the greedy strategy of large-task-first-assignment and earliest-idle-user-receive-task is adopted for the virtual offline task assignments in LOTA. Based on the two greedy strategies, both AOTA and LOTA can achieve nearly optimal online decision performances. We prove this and give the competitive ratios of the two algorithms. In addition, we also demonstrate the significant performance of the two algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.

https://ieeexplore.ieee.org/ielaam/7755/7963940/7588135-aam.pdf

Online Task Assignment for Crowdsensing in Predictable Mobile Social Networks

Data disseminations based on Roadside Access Points (RAPs) in vehicular ad-hoc networks attract lots of attentions and have a promising prospect. In this paper, we focus on a roadside data dissemination, including three basic elements: RAP Service Provider (RSP), mobile vehicles and requesters. The RSP has deployed many RAPs at different locations in a city. A requester wants to rent some RAPs, which can disseminate their data to vehicles with some probabilities. Then, it tries to select the minimal number of RAPs to finish the data dissemination, in order to save the expenses. Meanwhile, the selected RAPs need to ensure that the probability of each vehicle receiving data successfully is no less than a threshold. We prove that this RAP selection problem is NP-hard, since it’s a meaningful extension of the classic Set Cover problem. To solve this problem, we propose a greedy algorithm and give its approximation ratio. Moreover, we conduct extensive simulations on real world data to prove its good performance.

Nearly Optimal Probabilistic Coverage for Roadside Data Dissemination in Urban VANETs

Advertisement disseminations based on Roadside Access Points (RAPs) in vehicular ad-hoc networks (VANETs) attract lots of attentions and have a promising prospect. In this paper, we focus on a roadside advertisement dissemination, including three basic elements: RAP Service Provider (RSP), mobile vehicles and shops. The RSP has deployed many RAPs at different locations in a city. A shop wants to rent some RAPs, which can disseminate advertisements to vehicles with some probabilites. Then, it tries to select the minimal number of RAPs to finish the advertisement dissemination, in order to save the expenses. Meanwhile, the selected RAPs need to ensure that each vehicle's probability of receiving advertisement successfully is not less than a threshold. We prove that this RAP selection problem is NP-hard. In order to solve this problem, we propose a greedy approximation algorithm, and give the corresponding approximation ratio. Further, we conduct extensive simulations on real world data sets to prove the good performance of this algorithm.

Ruhong Cheng

Papers

Online Task Assignment for Crowdsensing in Predictable Mobile Social Networks

Nearly Optimal Probabilistic Coverage for Roadside Data Dissemination in Urban VANETs

Nearly Optimal Probabilistic Coverage for Roadside Advertisement Dissemination in Urban VANETs