Torwards context-aware mobile crowdsensing in vehicular social networks
04 May 2015-pp 749-752
TL;DR: A novel application-oriented service collaboration (ASCM) model is introduced which can automatically match multiple users with multiple mobile crowd sensing tasks in VSNs in an efficient manner and a context information management model is proposed that aims to enable the mobile community sensing applications to autonomously match appropriate service and information with different users (requesters and participants) in crowdsensing.
Abstract: Driving is an integral part of our everyday lives, and the average driving time of people globally is increasing to 84 minutes everyday, which is a time when people are uniquely vulnerable. A number of research works have identified that mobile crowd sensing in vehicular social networks (VSNs) can be effectively used for many purposes and bring huge economic benefits, e.g., safety improvement and traffic management. This paper presents our effort that toward context-aware mobile crowd sensing in VSNs. First, we introduce a novel application-oriented service collaboration (ASCM) model which can automatically match multiple users with multiple mobile crowd sensing tasks in VSNs in an efficient manner. After that, for users' dynamic contexts of VSNs, we proposes a context information management model, that aims to enable the mobile crowd sensing applications to autonomously match appropriate service and information with different users (requesters and participants) in crowdsensing.
Citations
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TL;DR: An extensive survey of the literature on mobile crowdsourcing research is provided, highlighting the aspects of particular concerns in terms of implementation needs during the development, architectures, and key considerations for their development and presents a taxonomy based on the key issues in mobile crowds sourcing.
Abstract: Crowdsourcing using mobile devices, known as mobile crowdsourcing, is a powerful approach incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. The problems that can be tackled include the use of geographically distributed tasks, and mobile sensing using the collective wisdom of the crowd. However, the implementation of mobile crowdsourcing applications has been found to be challenging to users due to the nature of dynamic sensing, crowd engagement with data distribution, and a process of data verification. In this paper, we provide an extensive survey of the literature on mobile crowdsourcing research, highlighting the aspects of particular concerns in terms of implementation needs during the development, architectures, and key considerations for their development. We present a taxonomy based on the key issues in mobile crowdsourcing and discuss the different approaches applied to these issues. We also provide a critical analysis of some challenges and suggest directions for future work. In particular, with the future Internet-of-Things in view, we generalize the notion of mobile crowdsourcing to thing crowdsourcing, where crowdsourcing can be issued from smart Internet-connected things that need to harness the human resources to solve problems.
61 citations
TL;DR: This work proposes HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives and explores the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition.
Abstract: The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than using WiAR dataset.
55 citations
06 Jul 2020
TL;DR: A new deep learning based framework to tackle the problem that a group of UAVs energy-efficiently and cooperatively collect data from low-level sensors, while charging the battery from multiple randomly deployed charging stations is proposed.
Abstract: Different from using human-centric mobile devices like smartphones, unmanned aerial vehicles (UAVs) can be utilized to form a new UAV crowdsensing paradigm, where UAVs are equipped with build-in high-precision sensors, to provide data collection services especially for emergency situations like earthquakes or flooding. In this paper, we aim to propose a new deep learning based framework to tackle the problem that a group of UAVs energy-efficiently and cooperatively collect data from low-level sensors, while charging the battery from multiple randomly deployed charging stations. Specifically, we propose a new deep model called "j-PPO+ConvNTM" which contains a novel spatiotemporal module "Convolution Neural Turing Machine" (ConvNTM) to better model long-sequence spatiotemporal data, and a deep reinforcement learning (DRL) model called "j-PPO", where it has the capability to make continuous (i.e., route planing) and discrete (i.e., either to collect data or go for charging) action decisions simultaneously for all UAVs. Finally, we perform extensive simulation to show its illustrative movement trajectories, hyperparameter tuning, ablation study, and compare with four other baselines.
40 citations
Cites background from "Torwards context-aware mobile crowd..."
..., iPhone, iPad and iWatch) [1], [2], [3], UAV crowdsensing has the benefits to provide ubiquitous sensing services for certain extreme situations like earthquakes and flooding [4], [5], [6]....
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21 Oct 2015
TL;DR: SAfeDJ, a smartphone-based situation-aware music recommendation system, is proposed, which is designed to turn driving into a safe and enjoyable experience and helps drivers to diminish fatigue and negative emotion.
Abstract: Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09p and 36.35p, respectively, compared to traditional smartphone-based music player under similar driving situations.
39 citations
TL;DR: A comprehensive reference framework is proposed to investigate context-aware mobile crowd sensing systems from three viewpoints of concepts, context-awareness, and functionalities, to thoroughly review the existing works, foster the dissemination of state-of-the-art research, and present future research directions.
26 citations
Cites background or methods from "Torwards context-aware mobile crowd..."
...The VSN project [42] also proposes a model for crowd sensing in the social network of vehicles, which can be useful in improving safety and managing traffic....
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...phone brand) CATA [53] Location, Time Activity, Gender, Age Battery level Built-in sensors D-CLOCK [62] Future location User task, User cost CAPR [55] Location User task, User cost VSN [42] Location, Delay User task Identities of neighbors and objects, Network overhead Battery consumption...
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...FCC [46] [47] Implemented Conference MAC [59] Deployed Magazine Matador [52] Implemented Conference MCSP [54] Designed Report TMS [60] Designed Conference Curios [58] [57] Implemented Journal CMC [37] Prototyped Conference Here-n [38] Designed Conference PAN360 [36] Deployed Journal CARROT [56] Designed patent FEV [41] Simulated Magazine CADE [61] Simulated Workshop CATA [53] Simulated Conference D-cloc [62] Simulated Conference CAPR [55] Simulated Journal VSN [42] Designed Conference AWI [51] Designed Conference PCAM [63] Deployed Workshop WSC [50] Prototyped Conference LBSC [64] Designed Conference NCC [45] Implemented Conference CSP [48] Implemented Journal C3P [49] Designed Symposium COM [65] [66] Designed Conference CANS [40] [39] Simulated Journal Beacon [43] Designed Workshop...
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2,691 citations
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...According to the statistics of World Health Organization (WHO), 1.2 million people die and 50 million people are injured or disabled on roads every year [2]....
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TL;DR: This article surveys existing mobile phone sensing algorithms, applications, and systems, and discusses the emerging sensing paradigms, and formulates an architectural framework for discussing a number of the open issues and challenges emerging in the new area ofMobile phone sensing research.
Abstract: Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.
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"Torwards context-aware mobile crowd..." refers background in this paper
...However, from the aspects of participatory sensing, unlike the social communities which usually formed by similar social entities, the crowdsensing teams may be formed by dissimilar social entities (i.e., participants with different skills/experiences/positions in VSNs) [10]....
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IBM1
TL;DR: The need for a unified architecture for mobile crowdsensing is argued and the requirements it must satisfy are envisioned.
Abstract: An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy.
1,833 citations
"Torwards context-aware mobile crowd..." refers background in this paper
...A number of research works have identified that crowdsensing in VSN can be effectively used for many purposes and bring huge economic benefits, e.g., safety improvement and traffic management [4, 7]....
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...Mobile crowdsensing involves participatory sensing or opportunistic sensing at the two ends [4]: participatory sensing requires the active involvement of individuals to contribute sensing data (i.e., reporting a road closure) related to some large-scale phenomenon, and opportunistic sensing is more…...
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25 Jun 2012
TL;DR: This paper designs and implements Medusa, a novel programming framework for crowd-sensing that provides high-level abstractions for specifying the steps required to complete a crowd-Sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud.
Abstract: The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.
351 citations
"Torwards context-aware mobile crowd..." refers methods or result in this paper
...For example, as shown in Table 1 [12], it demonstrates in the same conditions, the time latency of our ASCM approach is much lower than the alternative approach Medusa [15]....
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...Furthermore, referring [15], we develop two qualitatively prototype crowdsensing applications called Social Drive [16] and SAfeDJ [17, 18] based on the platform to demonstrate the expressivity of ASCM....
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Posted Content•
TL;DR: In this paper, the authors present the literary history of mobile crowd sensing and its unique issues and a reference framework for MCS systems is also proposed, further clarify the potential fusion of human and machine intelligence in MCS and discuss the future research trends as well as their efforts to MCS.
Abstract: The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.
339 citations