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; and 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 isbilateral. However, the status is broken by the zero-determinant (ZD) strategy, where the ZD player canunilaterallycontrol 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.

Abstract Trajectory clustering, which aims at discovering groups of similar trajectories, has long been considered as a corner stone task for revealing movement patterns as well as facilitating higher level applications such as location prediction and activity recognition. Although a plethora of trajectory clustering techniques have been proposed, they often rely on spatio‐temporal similarity measures that are not space and time invariant. As a result, they cannot detect trajectory clusters where the within‐cluster similarity occurs in different regions and time periods. In this paper, we revisit the trajectory clustering problem by learning quality low‐dimensional representations of the trajectories. We first use a sliding window to extract a set of moving behaviour features that capture space‐ and time‐invariant characteristics of the trajectories. With the feature extraction module, we transform each trajectory into a feature sequence to describe object movements and further employ a sequence‐to‐sequence auto‐encoder to learn fixed‐length deep representations. The learnt representations robustly encode the movement characteristics of the objects and thus lead to space‐ and time‐invariant clusters. We evaluate the proposed method on both synthetic and real data and observe significant performance improvements over existing methods.

Predicting the next location a user tends to visit is an important task for applications like location-based advertising, traffic planning, and tour recommendation. We consider the next location prediction problem for semantic trajectory data, wherein each GPS record is attached with a text message that describes the user's activity. In semantic trajectories, the confluence of spatiotemporal transitions and textual messages indicates user intents at a fine granularity and has great potential in improving location prediction accuracies. Nevertheless, existing methods designed for GPS trajectories fall short in capturing latent user intents for such semantics-enriched trajectory data. We propose a method named semantics-enriched recurrent model (SERM). SERM jointly learns the embeddings of multiple factors (user, location, time, keyword) and the transition parameters of a recurrent neural network in a unified framework. Therefore, it effectively captures semantics-aware spatiotemporal transition regularities to improve location prediction accuracies. Our experiments on two real-life semantic trajectory datasets show that SERM achieves significant improvements over state-of-the-art methods.

Trajectory clustering, which aims at discovering groups of similar trajectories, has long been considered as a corner stone task for revealing movement patterns as well as facilitating higher-level applications like location prediction. While a plethora of trajectory clustering techniques have been proposed, they often rely on spatiotemporal similarity measures that are not space- and time-invariant. As a result, they cannot detect trajectory clusters where the within-cluster similarity occurs in different regions and time periods. In this paper, we revisit the trajectory clustering problem by learning quality low-dimensional representations of the trajectories. We first use a sliding window to extract a set of moving behavior features that capture space- and time-invariant characteristics of the trajectories. With the feature extraction module, we transform each trajectory into a feature sequence to describe object movements, and further employ a sequence to sequence autoencoder to learn fixed-length deep representations. The learnt representations robustly encode the movement characteristics of the objects and thus lead to space- and time-invariant clusters. We evaluate the proposed method on both synthetic and real data, and observe significant performance improvements over existing methods.

Personalized applications often provide their functionality by extracting sensitive data from users. Such a strategy brings potential threats to users' privacy because malicious applications may sell users' sensitive data to third-parties for economic interests. The state-of-the-art literature addresses the privacy issue mainly from a technical perspective. In this paper, we take a different angle in which the main players involving privacy leakage are studied from a connected perspective rather than an isolated one. More specifically, we propose the concept of application ecosystem, which consists of user, application, and adversary (malicious third-party) as entities. Our aim is to analyze the tension forces inside the application ecosystem and their impacts on the behavior of each player, which can serve as a theoretical basis for designing effective and efficient privacy preservation solutions from a management level. Another outstanding trait of our analysis is the adoption of quantum game theory to model the application ecosystem, which is suitable because the important property of entanglement in quantum games can be employed to well depict the inner tension forces among the user, application, and adversary. This makes us take an important step towards understanding the complexity of decision-making from rational individuals. To the best of our knowledge, we are the first to quantize the privacy leakage issue. The simulation results quantitatively demonstrate how the mutual restrictions among all players determine their strategies and hence the development of the application ecosystem.

With the development of internet, there are billions of short texts generated each day. However, the accuracy of large scale short text classification is poor due to the data sparseness. Traditional methods used to use external dataset to enrich the representation of document and solve the data sparsity problem. But external dataset which matches the specific short texts is hard to find. In this paper, we propose a framework to solve the data sparsity problem without using external dataset. Our framework deal with large scale short text by making the most of semantic similarity of words which learned from the training short texts. First, we learn word distributed representation and measure the word semantic similarity from the training short texts. Then, we propose a method which enrich the document representation by using the word semantic similarity information. At last, we build classifiers based on the enriched representation. We evaluate our framework on both the benchmark dataset(Standford Sentiment Treebank) and the large scale Chinese news title dataset which collected by ourselves. For the benchmark dataset, using our framework can improve 3% classification accuracy. The result we tested on the large scale Chinese news title dataset shows that our framework achieve better result with the increase of the training set size.

Mobile opportunistic networks make use of a new networking paradigm that takes advantage of node mobility to distribute information. Studying their inherent properties of information dissemination can provide a straightforward explanation on the potentials of mobile opportunistic networks to support emerging applications such as mobile commerce, emergency services, and so on. In this paper, we investigate the inherent properties of information dissemination using the Lévy mobility model to characterize the movement pattern of the nodes. Because Lévy mobility can closely mimic human walk, the analysis model we adopt is practical. Our analyses are taken from the perspectives of small- and large-scales. From the perspective of small-scale, the distribution of the minimum time needed by the information to spread to a given region is investigated, from the perspective of large-scale, the bounds of the probability of the earliest time at which the information arrives in a region that is sufficiently farther away are obtained. We also provide the rate that such probability approaches zero as the distance to the region increases to infinity. Finally, our main results are validated by the numerical simulations.

In mobile ad-hoc cognitive radio networks (CRNs), end-to-end paths with available spectrum bands for secondary users may exist temporarily, or may never exist, due to the dynamism of the primary user activities. Traditional CRN routing algorithms, which typically ignore the intermittent connectivity of network topology, and traditional mobility-assisted routing algorithms, which generally overlook the spectrum availability, are obviously unsuitable. To tackle this challenge, we propose a Mobility-Assisted Routing algorithm with Spectrum Awareness (MARSA) to select relays based on not only the probability that a node meets the destination but also the chance at which there exists at least one available channel when they meet. To the best of our knowledge, this paper is the first to bring the idea of mobility-assisted routing to deal with the intermittently connected attribute of mobile ad-hoc CRNs, and the first to enhance the mobility-assisted routing by considering the temporal , spatial, and spectrum domains at the same time. Our simulation results demonstrate the superiority of MARSA over traditional algorithms in intermittently connected mobile CRNs.

Mobile P2P networking is an enabling technology for mobile devices to self-organize in an unstructured style and communicate in a peer-to-peer fashion. Due to user mobility and/or the unrestricted switching on/off of the mobile devices, links are intermittently connected and end-to-end paths may not exist, causing routing a very challenging problem. Moreover, the limited wireless spectrum and device resources together with the rapidly growing number of portable devices and amount of transmitted data make routing even harder. To tackle these challenges, the routing algorithms must be scalable, distributed, and light-weighted. Nevertheless, existing approaches usually cannot simultaneously satisfy all these three requirements. In this paper, we propose two opportunistic routing algorithms for intermittently connected mobile P2P networks, which exploit the spatial locality, spatial regularity, and activity heterogeneity of human mobility to select relays. The first algorithm employs a depth-search approach to diffuse the data towards the destination. The second one adopts a depth-width-search approach in a sense that it diffuses the data not only towards the destination but also to other directions determined by the actively moving nodes (activists) to find better relays. We perform both theoretical analysis as well as a comparison based simulation study. Our results obtained from both the synthetic data and the real world traces reveal that the proposed algorithms outperform the state-of-the-art in terms of delivery latency and delivery ratio.

An adaptive threshold-based call admission control (CAC) scheme used in wireless/mobile network for multi-class services is proposed. In the scheme, each class' CAC thresholds are solved through establishing a reward-penalty model which tries to maximize network's revenue in terms of each class's average new call arrival rate and average handoff call arrival rate, the reward or penalty when network accepts or rejects one class's call etc. To guarantee the real time running of CAC algorithm, an enhanced genetic algorithm is designed. Analyses show that the CAC thresholds indeed change adaptively with the average call arrival rate. The performance comparison between the proposed scheme and mobile IP reservation (MIR) scheme shows that with the increase of average call arrival rate, the average new call blocking probability (CBP) and the average handoff dropping probability (HDP) within 2000 simulation intervals of the proposed scheme are confined to lower levels, and they show approximatively periodical trends of first rise and then decline. While these two performance metrics of MIR always increase. At last, the analysis shows the proposed scheme outperforms MIR in terms of network's revenue