Jianhui Huang

The way of task posting serves as the main pillar in achieving an efficient crowdsourcing market. Pioneer solutions on task posting can be categorized as retail task posting and batch task posting. Unlike retail task posting, which simply matches the most suitable worker to tasks, batch task posting considers the collaborations not only between workers and tasks but also among tasks, which brings high efficiency, low costs, and satisfactory task completion rates. However, the state of the arts on batch task posting leverage specific attributes to combine tasks as bundles for posting, leading to limited scalability. Hence, we propose a causal analysis framework for batch crowdsourcing to achieve an attribute-independent batch crowdsourcing solution that disentangles multi-factors to uncover the posting merits of tasks bundled at optimal prices, based on which an approximately optimal algorithm is further introduced to form reasonable bundles for posting. Since batch crowdsourcing may incur losses due to short-term profit fluctuation, a risk assessment method is proposed to encourage the requestor to act properly for loss mitigations. Our work explores the causal analysis and risk assessment in batch crowdsourcing for the first time, with the following highlights: 1) generality. It proposes a composite metric for gauging task bundles which avoids the issue of attribute dependence in the state of the arts, resulting in better universality;2) synergy. By collaboratively considering the “value” and “relative position” of variables, our work derives results reflecting causal relationships rather than naive correlations; 3) precision. We not only elucidate the probability of risk in batch crowdsourcing but also delineate the rate function governing its probability decay. This allows a requestor to know when and how fast to halt batch task posting.

The integration of directed acyclic graph (DAG)-based blockchain and Internet of Things (IoT) aims at improving the efficiency of data storage. However, if massive IoT data are not placed in an organized way, the search and usage of the data for upper-level applications can be burdensome, since they have to examine the data block by block, which also increases the difficulty of data verification, affecting consensus efficiency. To maintain the high throughput advantage of DAG-based blockchain applied in IoT and improve the data analysis efficiency, we propose a novel consensus mechanism named TidyBlock, including the transaction collation mechanism for block generation and the block selection mechanism for verification. The first mechanism can tidy up scattered transactions before they are packaged into blocks, while the second one can collate blocks to facilitate verification, realizing a two-layer collation of IoT data so as to increase analysis efficiency of upper-level IoT applications. Additionally, the second mechanism can provide a self-driven incentive for rational participants to follow the first one in case they are reluctant to do extra collation work. Theoretical analysis is provided to demonstrate the validity of our proposed algorithms by formal methods. Extensive simulations based on synthetic data verify the rationality and effectiveness of the proposed mechanisms.

Game theory is an effective analytical tool for crowdsourcing. Existing studies based on it share a commonality: the influence of players’ decisions is bilateral. However, the status is broken by the zero-determinant (ZD) strategy, where the ZD player can unilaterally control the opponent's expected payoff. Thereby, crowdsourcing games trigger conclusions that differ from traditional ones. By addressing three questions, this paper is the first work to analyze the turbulence in crowdsourcing caused by the inequality between the requestor and the worker in the ZD game. The first question reveals the potential for the requestor to exploit the worker; the second question quantifies the worker's tolerance towards exploitation, providing a basis for confrontation; the third question serves as the cornerstone for maintaining the crowdsourcing, regulating the requestor's exploitative behavior. To answer these questions, we extend ZD strategies from binary games to continuous ones, not only revealing the requestor's dominance but also enriching the theoretical system of ZD strategies and broadening their application. Furthermore, we introduce the worker's dissatisfaction degree, identifying the exponential trend and decay rate, revealing optimal timing and speed for the worker's effective confrontation and maximum exploitation for the requestor. Numerical simulations have validated the effectiveness of our analyses.

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences.

Query privacy has led to a serious concern for location-based services (LBSs). Existing mainstream schemes preserve query privacy by either introducing a third party or degrading the accuracy of LBS. To address these deficiencies, we propose a scheme named the content sealed bottle in this paper for the users to get accurate LBSs while the server learns zero knowledge on the query content.To realize this objective, the Euclidean distance based on homomorphic encryption is used to signal the difference between the query attribute vector of a user and each database attribute vector of the server; then a boolean circuit is designed to identify the exact match for the query attribute vector. Through optimizing the traditional sub-circuits, the total number of non-XOR gates, which is the main cost during the matching process, can be reduced by a factor of l compared to that of the traditional M-to-1 MIN sub-circuit, where l is the input bit length of the boolean circuit. Finally, the computationally private information retrieval technique is employed to privately retrieve the POI set. To the best of our knowledge, our content sealed bottle is the first scheme that can achieve cost-effective query privacy preservation without the involvement of a third party or sacrificing the accuracy of LBSs. Our security analysis and extensive experimental results demonstrate the effectiveness of the proposed scheme.

Pocket switched networks (PSNs) take advantage of human mobility to deliver data. Investigations on real-world trace data indicate that human mobility shows an obvious spatial regularity: a human being usually visits a few places at high frequencies. These most frequently visited places form the home of a node, which is exploited in this paper to design two HomE based Relay selectiOn (HERO) algorithms. Both algorithms input single data copy into the network at any time. In the basic HERO, only the first node encountered by the source and whose home overlaps a destination’s home is selected as a relay while the enhanced HERO keeps finding more optimal relay that visits the destination’s home with higher probability. The two proposed algorithms only require the relays to exchange the information of their home and/or the visiting frequencies to their home when two nodes meet. As a result, the information update is reduced and there is no global status information that needs to be maintained. This causes light loads on relays because of the low communication cost and storage requirements. Additionally, only simple operations are needed in the two proposed algorithms, resulting in little computation overhead at relays. At last, a theoretical analysis is performed on some key metrics and then the real-world based simulations indicate that the two HERO algorithms are efficient and effective through employing only one or a few relays.