A new harmony search based allocation algorithm for location dependent tasks in crowdsensing

Amin, Ramin Ghanbari; Li, Juan; Zhu, Yanmin

doi:10.1109/pic.2016.7949567

“…A few studies in MCS that handle platform-centric task allocation are [13][14][15][16][17][18][19][20]. He et al proposed a local ratio based algorithm (LRBA) [13] to deal with location dependent task allocation.…”

Section: Related Workmentioning

confidence: 99%

“…Prevalent task allocation architectures are fixated on implementing a platform-centric task allocation approach [13][14][15][16][17][18][19][20]. In these systems, their primary goal is veered towards maximizing profits obtained by the platform, while reducing the rewards granted to the users.…”

Section: Introductionmentioning

confidence: 99%

Provisioning Load Balancing in Time-Sensitive Task Allocation for Mobile Crowdsensing

Meitei,

Marchang

2023

J Netw Syst Manage

0

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Task allocation is the mechanism which enables the allotment of sensing tasks to participating users in a Mobile Crowdsensing (MCS) environment. While conventional task allocation techniques focus on maximizing profit for either the platform or the user, our proposed task allocation scheme, called Load Balanced Task Allocation (LBTA) is geared towards user-oriented task allocation in order to mainly address altruistic MCS campaigns in which participants voluntarily contribute towards a common goal such as in citizen science-based projects. This paper deals with the problem of task allocation using a load balanced approach while trying to maximize the allocation of tasks at the same time. The proposed LBTA algorithm has been compared with a known algorithm and their relative performances have been analysed. Simulation results demonstrate that the proposed algorithm exhibits favourable performance for different crowdsensing scenarios.

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“…A few studies in MCS that handle platform-centric task allocation are [13][14][15][16][17][18][19][20]. He et al proposed a local ratio based algorithm (LRBA) [13] to deal with location dependent task allocation.…”

Section: Related Workmentioning

confidence: 99%

“…Prevalent task allocation architectures are fixated on implementing a platform-centric task allocation approach [13][14][15][16][17][18][19][20]. In these systems, their primary goal is veered towards maximizing profits obtained by the platform, while reducing the rewards granted to the users.…”

Section: Introductionmentioning

confidence: 99%

Provisioning Load Balancing in Time-Sensitive Task Allocation for Mobile Crowdsensing

Meitei

¹

,

Marchang

²

2022

Preprint

0

View full text Add to dashboard Cite

Task allocation is the mechanism which enables the allotment of sensing tasks to participating users in a Mobile Crowdsensing (MCS) environment. While conventional task allocation techniques focus on maximizing profit for either the platform or the user, our proposed task allocation scheme, called Load Balanced Task Allocation (LBTA) is geared towards user-oriented task allocation in order to mainly address altruistic MCS campaigns in which participants voluntarily contribute towards a common goal such as in citizen science-based projects. This paper deals with the problem of task allocation using a load balanced approach while trying to maximize the allocation of tasks at the same time. The proposed LBTA algorithm has been compared with a known algorithm and their relative performances have been analysed. Simulation results demonstrate that the proposed algorithm exhibits favourable performance for different crowdsensing scenarios.

show abstract

“…The problem of assignment of tasks was analyzed unilaterally from the perspective of platforms, and the benefits of contributors were overlooked. With time budget constraints of contributors, Amin et al [22] proposed a meta-heuristic allocation algorithm based on Harmony Search, achieving the goal of maximizing the benefits of crowdsourcing platform.…”

Section: Related Workmentioning

confidence: 99%

How Mobile Contributors Will Interact With Each Other in Mobile Crowdsourcing With Word of Mouth Mode

Zeng

¹

,

Wang

²

,

Wu

³

2019

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Mobile crowdsourcing is a promising paradigm for collecting sensing data by leveraging contributions of numerous mobile smart phones. It works efficiently with Word of Mouth Mode (WoM), especially for sensing tasks with time and location constraints, since the sensing task can be spread quickly among mobile contributors in the WoM mode. In this paper, we first investigate the behaviors of contributors, categorize all contributors into four types according to their different behaviors, and propose an inviting algorithm for contributors to recruit cooperators through social closeness. Then, we design a reward mechanism for crowdsourcing platform to evaluate the budget and pay the reward to contributors, meanwhile stimulate contributors to make the maximum contribution. Furthermore, considering two different scenarios, we model the interactions among contributors as two Stackelberg games, and backward induction approach is used to analyze each game. We propose an algorithm to compute the best response of every contributor, and we theoretically prove that this proposed algorithm may converge a unique Stackelberg equilibrium. The proposed approach can be applied to task formulation and task budget evaluations for crowdsourcing platforms. INDEX TERMS Mobile crowdsourcing, word of mouth, game theory, Stackelberg game.

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A new harmony search based allocation algorithm for location dependent tasks in crowdsensing

Cited by 7 publications

References 24 publications

Provisioning Load Balancing in Time-Sensitive Task Allocation for Mobile Crowdsensing

Provisioning Load Balancing in Time-Sensitive Task Allocation for Mobile Crowdsensing

Provisioning Load Balancing in Time-Sensitive Task Allocation for Mobile Crowdsensing

How Mobile Contributors Will Interact With Each Other in Mobile Crowdsourcing With Word of Mouth Mode

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