Related papers: Providing Long-Term Participation Incentive in Par…

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users'…

Crowd sensing is a new paradigm that leverages pervasive sensor-equipped mobile devices to provide sensing services like forensic analysis, documenting public spaces, and collaboratively constructing statistical models. Extensive user…

Mobile crowdsensing (MCS) has been intensively explored recently due to its flexible and pervasive sensing ability. Although many incentive mechanisms have been built to attract extensive user participation, Most of these mechanisms focus…

Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of…

As mobile devices have been ubiquitous, participatory sensing emerges as a powerful tool to solve many contemporary real life problems. Here, we contemplate the participatory sensing in online double auction environment by considering the…

Participatory Sensing is an emerging computing paradigm that enables the distributed collection of data by self-selected participants. It allows the increasing number of mobile phone users to share local knowledge acquired by their…

Crowd sensing is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices to collect data. To achieve good quality for crowd sensing, incentive mechanisms are indispensable to attract more participants. Most of existing…

In this paper, we study the incentive mechanism design for real-time data aggregation, which holds a large spectrum of crowdsensing applications. Despite extensive studies on static incentive mechanisms, none of these are applicable to…

Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design…

In the past decade, the usage of mobile devices has gone far beyond simple activities like calling and texting. Today, smartphones contain multiple embedded sensors and are able to collect useful sensing data about the user and infer the…

Crowdsourcing has become an important tool to collect data for various artificial intelligence applications and auction can be an effective way to allocate work and determine reward in a crowdsourcing platform. In this paper, we focus on…

Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect and upload sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive…

Active sensing refers to the process of choosing or tuning a set of sensors in order to track an underlying system in an efficient and accurate way. In a wireless environment, among the several kinds of features extracted by traditional…

Participatory sensing (PS) is a novel and promising sensing network paradigm for achieving a flexible and scalable sensing coverage with a low deploying cost, by encouraging mobile users to participate and contribute their smartphones as…

We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and learns with partial information in a dynamic environment. We…

The problem of synthesizing an optimal sensor selection policy is pertinent to a variety of engineering applications ranging from event detection to autonomous navigation. We consider such a synthesis problem over an infinite time horizon…

This paper considers an electric vehicle charging scheduling setting where vehicle users can reserve charging time in advance at a charging station. In this setting, users are allowed to explicitly express their preferences over different…

The performance of state-of-the-art machine learning models often deteriorates when testing on demographics that are under-represented in the training dataset. This problem has predominately been studied in a supervised learning setting…

Understanding citizens' values in participatory systems is crucial for citizen-centric policy-making. We envision a hybrid participatory system where participants make choices and provide motivations for those choices, and AI agents…

Agents (specially humans) with smart devices are stemming with astounding rapidity and that may play a big role in information and communication technology apart from being used only as a mere calling devices. Inculcating the power of smart…