Publications

k-Level Truthful Incentivizing Mechanism and Generalized k-MAB Problem

Published in IEEE Transactions on Computers (TC), 2021

Multi-armed bandits problem has been widely utilized in economy-related areas. Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users’ cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users’ cost distributions. We further extend the problem to a more generalized k-MAB problem by removing the contextual information of difficulties. CUE-UCB algorithm is proposed to address the online advertisement problem for multi-platforms. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works in sharing economy, and up to 175% increment of utility for online advertising.

Recommended citation: Pengzhan Zhou, Xin Wei, Cong Wang, Yuanyuan Yang. " k-Level Truthful Incentivizing Mechanism and Generalized k-MAB Problem." IEEE Transactions on Computers (TC), 2021.

Design and Optimization of Solar-Powered Shared Electric Autonomous Vehicle System for Smart Cities

Published in IEEE Transactions on Mobile Computing (TMC), 2021

Smart transportation shall address utility waste, traffic congestion, and air pollution problems with least human intervention in future smart cities. To realize the sustainable operation of smart transportation, we leverage solar-harvesting charging stations and rooftops to power electric autonomous vehicles(AVs) solely via design. With a fixed budget, our framework first optimizes the locations of charging stations based on historical spatial-temporal solar energy distribution and usage patterns, achieving (2+epsilon) factor to the optimal. Then a stochastic algorithm is proposed to update the locations online to adapt to any shift in the distribution. Based on the deployment, a strategy is developed to assign energy requests in order to minimize their traveling distance to stations while not depleting their energy storage. Equipped with extra harvesting capability, we also optimize route planning to achieve a reasonable balance between energy consumed and harvested en-route. As a promising application, utility optimization of shared electric AVs is discussed, and (2k+1)-approx algorithm is proposed to manage k vehicles simultaneously. Our extensive simulations demonstrate the algorithm can approach the optimal solution within 10-15% approximation error, improve the operating range of vehicles by up to 2-3 times, and improve the utility by more than 50% compared to other competitive strategies.

Recommended citation: Pengzhan Zhou, Cong Wang, Yuanyuan Yang. " Design and Optimization of Solar-Powered Shared Electric Autonomous Vehicle System for Smart Cities." IEEE Transactions on Mobile Computing (TMC), 2021.

Design of Self-sustainable Wireless Sensor Networks with Energy Harvesting and Wireless Charging

Published in ACM Transactions on Sensor Networks (TOSN), 2021

Energy provisioning plays a key role in the sustainable operations of Wireless Sensor Networks (WSNs). Recent efforts deploy multi-source energy harvesting sensors to utilize ambient energy. Meanwhile, wireless charging is a reliable energy source not affected by spatial-temporal ambient dynamics. This article integrates multiple energy provisioning strategies and adaptive adjustment to accomplish self-sustainability under complex weather conditions. We design and optimize a three-tier framework with the first two tiers focusing on the planning problems of sensors with various types and distributed energy storage powered by environmental energy. Then we schedule the Mobile Chargers (MC) between different charging activities and propose an efficient, 4-factor approximation algorithm. Finally, we adaptively adjust the algorithms to capture real-time energy profiles and jointly optimize those correlated modules. Our extensive simulations demonstrate significant improvement of network lifetime (3X), increase of harvested energy (15%), reduction of network cost (30%), and the charging capability of MC by 100%.

Recommended citation: Pengzhan Zhou, Cong Wang, Yuanyuan Yang. " Design of Self-sustainable Wireless Sensor Networks with Energy Harvesting and Wireless Charging." ACM Transactions on Sensor Networks (TOSN), 17, no. 4 (2021): 1-38.

E-Sharing: Data-driven Online Optimization of Parking Location Placement for Dockless Electric Bike Sharing

Published in IEEE International Conference on Distributed Computing Systems (ICDCS), 2020

The rise of dockless electric bike sharing becomes a new urban lifestyle recently. More than just the first-and-last mile, it offers a new modality of green transportation. However, in addition to the traditional re-balance and overcrowding problems, it also brings new challenges to urban management and maintenance. Due to the safety risks of batteries, customers are regulated to park at designated locations, which potentially causes dissatisfaction and customer loss. Meanwhile, service providers should charge those scattering low-energy batteries in time. To address these issues, we propose E-sharing, a two-tier optimization framework that leverages data-driven online algorithms to plan parking locations and maintenance. First, we balance the user dissatisfaction and the number of parking locations by minimizing their sum. To account for real-time dynamics while not losing track of the historical optimality, we propose an online algorithm based on its near-optimal offline solution. Second, we develop an incentive mechanism to motivate users to aggregate low-battery bikes together, saving the cost of bike charging. Our experiment based on the public dataset demonstrates that the online algorithm can minimize the cost from the conflicting objectives and incentive mechanism further reduces the maintenance cost by 47%.

Recommended citation: Pengzhan Zhou, Cong Wang, Yuanyuan Yang, Xin Wei. " E-Sharing: Data-driven Online Optimization of Parking Location Placement for Dockless Electric Bike Sharing." IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 474-484, 2020.

Design and optimization of electric autonomous vehicles with renewable energy source for smart cities

Published in IEEE International Conference on Computer Communications (INFOCOM), 2020

Electric autonomous vehicles provide a promising solution to the traffic congestion and air pollution problems in future smart cities. Considering intensive energy consumption, charging becomes of paramount importance to sustain the operation of these systems. Motivated by the innovations in renewable energy harvesting, we leverage solar energy to power autonomous vehicles via charging stations and solar-harvesting rooftops, and design a framework that optimizes the operation of these systems from end to end. With a fixed budget, our framework first optimizes the locations of charging stations based on historical spatial-temporal solar energy distribution and usage patterns, achieving (2 + ε) factor to the optimal. Then a stochastic algorithm is proposed to update the locations online to adapt to any shift in the distribution. Based on the deployment, a strategy is developed to assign energy requests in order to minimize their traveling distance to stations while not depleting their energy storage. Equipped with extra harvesting capability, we also optimize route planning to achieve a reasonable balance between energy consumed and harvested en-route. Our extensive simulations demonstrate the algorithm can approach the optimal solution within 10-15% approximation error, and improve the operating range of vehicles by up to 2-3 times compared to other competitive strategies.

Recommended citation: Pengzhan Zhou, Cong Wang, Yuanyuan Yang. "Design and Optimization of Electric Autonomous Vehicles with Renewable Energy Source for Smart Cities." IEEE International Conference on Computer Communications (INFOCOM), pp. 1399-1408, 2020.

Self-sustainable Sensor Networks with Multi-source Energy Harvesting and Wireless Charging

Published in IEEE International Conference on Computer Communications (INFOCOM), 2019

Energy supply remains to be a major bottleneck in Wireless Sensor Networks (WSNs). A self-sustainable network operates without battery replacement. Recent efforts employ multi-source energy harvesting to power sensors with ambient energy. Meanwhile, wireless charging is considered in WSNs as a reliable energy source. It motivates us to integrate both fields of research to build a self-sustainable network and guarantee operation under any weather condition. We propose a three-step solution to optimize this new framework. We first solve the Sensor Composition Problem (SCP) to derive the percentage of different types of sensors. Then we enable self-sustainability by bringing energy harvesting storage to the field for charging the Mobile Charger (MC). Next, we propose a 3-factor approximation algorithm to schedule sensor charging and energy replenishment of MC. Our extensive simulation results demonstrate significant improvement of network lifetime and reduction of network cost. The network lifetime can be extended at least three times compared with traditional approaches and the charging capability of MC increases at least 100%.

Recommended citation: Pengzhan Zhou, Cong Wang, Yuanyuan Yang. "Self-sustainable Sensor Networks with Multi-source Energy Harvesting and Wireless Charging." IEEE International Conference on Computer Communications (INFOCOM), pp. 1828-1836, 2019.

Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy

Published in International Joint Conferences on Artificial Intelligence (IJCAI), 2019

Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget feasible incentive mechanism to learn users’ cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this general problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users’ cost distributions. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works.

Recommended citation: Pengzhan Zhou, Xin Wei, Cong Wang, Yuanyuan Yang. " Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy." International Joint Conferences on Artificial Intelligence (IJCAI), 2019.

Static and Mobile Target k-Coverage in Wireless Rechargeable Sensor Networks

Published in IEEE Transactions on Mobile Computing (TMC), 2018

Energy remains a major hurdle in running computation-intensive tasks on wireless sensors. Recent efforts have been made to employ a Mobile Charger (MC) to deliver wireless power to sensors, which provides a promising solution to the energy problem. Most of previous works in this area aim at maintaining perpetual network operation at the expense of high operating cost of MC. In the meanwhile, it is observed that due to low cost of wireless sensors, they are usually deployed at high density so there is abundant redundancy in their coverage in the network. For such networks, it is possible to take advantage of the redundancy to reduce the energy cost. In this paper, we relax the strictness of perpetual operation by allowing some sensors to temporarily run out of energy while still maintaining target k-coverage in the network at lower cost of MC. We first establish a theoretical model to analyze the performance improvements under this new strategy. Then we organize sensors into load-balanced clusters for target monitoring by a distributed algorithm. Next, we propose a charging algorithm named lambda-GTSP Charging Algorithm to determine the optimal number of sensors to be charged in each cluster to maintain k-coverage in the network and derive the route for MC to charge them. We further generalize the algorithm to encompass mobile targets as well. Our extensive simulation results demonstrate significant improvements of network scalability and cost saving that MC can extend charging capability over 2-3 times with a reduction of 40% of moving cost without sacrificing the network performance.

Recommended citation: Pengzhan Zhou, Cong Wang, Yuanyuan Yang. "Static and Mobile Target k-Coverage in Wireless Rechargeable Sensor Networks." IEEE Transactions on Mobile Computing (TMC). 18, no. 10 (2018): 2430-2445.