Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTask Allocation in Mobile Crowd Sensing: State of the Art and Future Opportunities
66
0
0.0
(
0
)
Added by
Jiangtao Wang
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Mobile Crowd Sensing (MCS) is the special case of crowdsourcing, which leverages the smartphones with various embedded sensors and users mobility to sense diverse phenomenon in a city. Task allocation is a fundamental research issue in MCS, which is crucial for the efficiency and effectiveness of MCS applications. In this article, we specifically focus on the task allocation in MCS systems. We first present the unique features of MCS allocation compared to generic crowdsourcing, and then provide a comprehensive review for diversifying problem formulation and allocation algorithms together with future research opportunities.
rate research
Read More
With the advent of seamless connection of human, machine, and smart things, there is an emerging trend to leverage the power of crowds (e.g., citizens, mobile devices, and smart things) to monitor what is happening in a city, understand how the city is evolving, and further take actions to enable better quality of life, which is referred to as Crowd-Powered Smart City (CPSC). In this article, we provide a literature review for CPSC and identify future research opportunities. Specifically, we first define the concepts with typical CPSC applications. Then, we present the main characteristics of CPSC and further highlight the research issues. In the end, we point out existing limitations which can inform and guide future research directions.
Data credibility is a crucial issue in mobile crowd sensing (MCS) and, more generally, people-centric Internet of Things (IoT). Prior work takes approaches such as incentive mechanism design and data mining to address this issue, while overlooking the power of crowds itself, which we exploit in this paper. In particular, we propose a cross validation approach which seeks a validating crowd to verify the data credibility of the original sensing crowd, and uses the verification result to reshape the original sensing dataset into a more credible posterior belief of the ground truth. Following this approach, we design a specific cross validation mechanism, which integrates four sampling techniques with a privacy-aware competency-adaptive push (PACAP) algorithm and is applicable to time-sensitive and quality-critical MCS applications. It does not require redesigning a new MCS system but rather functions as a lightweight plug-in, making it easier for practical adoption. Our results demonstrate that the proposed mechanism substantially improves data credibility in terms of both reinforcing obscure truths and scavenging hidden truths.
The health and various ways to improve healthcare systems are one of the most concerns of human in history. By the growth of mobile technology, different mobile applications in the field of the healthcare system are developed. These mobile applications instantly gather and analyze the data of their users to help them in the health area. This volume of data will be a critical problem. Big data in healthcare mobile applications have its challenges and opportunities for the users and developers. Does this amount of gathered data which is increasing day by day can help the human to design new tools in healthcare systems and improve health condition? In this chapter, we will discuss meticulously the challenges and opportunities of big data in the healthcare mobile applications.
Network Functions Virtualization (NFV) allows implantation of network functions to be independent of dedicated hardware devices. Any series of services can be represented by a service function chain which contains a set of virtualized network functions in a specified order. From the perspective of network performance optimization, the challenges of deploying service chain in network is twofold: 1) the location of placing virtualized network functions and resources allocation scheme; and 2) routing policy for traffic flow among different instances of network function. This article introduces service function chain related optimization problems, summarizes the optimization motivation and mainstream algorithm of virtualized network functions deployment and traffic routing. We hope it can help readers to learn about the current research progress and make further innovation in this field.
Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influenced propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
