Journal and conference rankings are based on CORE (The Computing Research and Education Association of Australasia).
Journal: A+: top 7%, A: top 17%;
Conferences: A+: top 4%, A: top 14%
Authors with * are the students in our lab.
Brake Signal-based Driver’s Location Tracking in Usage-based Auto Insurance Programs
Ankur Sarker, Haiying Shen, Chenxi Qiu, and 2 more authors
IEEE Internet of Things Journal [Impact factor = 9.936], 2023
[A] User Customizable and Robust Geo-Indistinguishability for Location Privacy
Primal Pappachan, Chenxi Qiu, Anna Squicciarini, and 1 more author
In Proceedings of 26th International Conference on Extending Database Technology (EDBT), 2023
[A+] Content Sharing Design for Social Welfare in Networked Disclosure Game
Feiran Jia, Chenxi Qiu, Sarah Rajtmajer, and 1 more author
In Proceedings of The 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023
[A+] CORGI: An interactive framework for Customizable and Robust Location Obfuscation
Primal Pappachan, Vishnu Manjunath, Chenxi Qiu, and 2 more authors
In Proceedings of 39th IEEE International Conference on Data Engineering (Demo), 2023
Location obfuscation functions generated by existing systems for ensuring location privacy tend to be monolithic and do not allow end users to customize their obfuscation range. In this demo, we present a new framework called CustOmizable Robust Geo-Indistinguishability. The demonstration platform is a web application built on top of a real-world dataset (Gowalla) and simulates users’ current location, target location, and customization preferences. The user-friendly interface of the demo platform allows demo participants to customize the location obfuscation function easily. They can also examine the interaction between the 3 key dimensions of location obfuscation: privacy, utility, and customizability, visualized on a map for two approaches: CORGI and baseline.
[A] Distributed Data-Sharing Consensus in Cooperative Perception of Autonomous Vehicles
Chenxi Qiu, *Sourabh Yadav, Anna Squicciarini, and 4 more authors
In Proceedings of The 42nd IEEE International Conference on Distributed Computing Systems (ICDCS), 2022
[A] TrafficAdaptor: An Adaptive Obfuscation Strategy for Vehicle Location Privacy Against Vehicle Traffic Flow Aware Attacks
Chenxi Qiu, Li Yan, Anna Squicciarini, and 3 more authors
In Proceedings of The 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL), 2022
[A+] Scheduling Inter-Datacenter Video Flows for Cost Efficiency
Haiying Shen, and Chenxi Qiu
IEEE Transactions on Services Computing [Impact factor = 8.216], May 2021
As video streaming applications are deployed on the cloud, cloud providers are charged by ISPs for inter-datacenter transfers under the dominant percentile-based charging models. In order to minimize the payment costs, existing works aim to keep the traffic on each link under the charging volume. However, these methods cannot fully utilize each link’s available bandwidth capacity. As a solution, we propose an economical and deadline-driven video flow scheduling system, called EcoFlow. Considering that different video flows have different transmission deadlines, EcoFlow transmits videos in the order of their deadline tightness and postpones the deliveries of later-deadline videos to later time slots. The flows that are expected to miss their deadlines are divided into subflows to be rerouted to other under-utilized links. We also propose setting each link’s initial charging volume to reduce the scheduling latency at the beginning of the charging period and discuss how to deal with issues such as the prediction errors of link available bandwidth and the lack of charging volume’s prior knowledge. Furthermore, we designed implementation strategies for using EcoFlow in both centralized and distributed situations. Experimental results demonstrate that EcoFlow achieves lower bandwidth costs and higher video flow transmission rates when compared to existing methods.
CatCharger: Deploying In-motion Wireless Chargers in a Metropolitan Road Network via Categorization and Clustering of Vehicle Traffic
Li Yan, Haiying Shen, Juanjuan Zhao, and 5 more authors
IEEE Internet of Things Journal [Impact factor = 9.936], May 2021
Secure Analysis of Optical Steganography With Spectral Signature Measurement
Ben Wu, Ying Tang, Chenxi Qiu, and 3 more authors
IEEE Photonics Technology Letters, May 2021
Wideband Anti-Jamming Based on Free Space Optical Communication and Photonic Signal Processing
Ben Wu, Yang Qi, Chenxi Qiu, and 1 more author
Sensors, May 2021
We propose and demonstrate an anti-jamming system to defend against wideband jamming attack. Free space optical communication is deployed to provide a reference for jamming cancellation. The mixed signal is processed and separated with photonic signal processing method to achieve large bandwidth. As an analog signal processing method, the cancellation system introduces zero latency. The radio frequency signals are modulated on optical carriers to achieve wideband and unanimous frequency response. With wideband and zero latency, the system meets the key requirements of high speed and real-time communications in transportation systems.
Distributed machine learning for energy trading in electric distribution system of the future
Ning Wang, Jie Li, Shen-Shyang Ho, and 1 more author
The Electricity Journal, May 2021
Special Issue: Machine Learning Applications To Power System Planning And Operation
Machine Learning (ML) has seen a great potential to solve many power system problems along with its transition into Smart Grid. Specifically, electric distribution systems have witnessed a rapid integration of distributed energy resources (DERs), including photovoltaic (PV) panels, electric vehicles (EV), and smart appliances, etc. Electricity consumers, equipped with such DERs and advanced metering/sensing/computing devices, are becoming self-interested prosumers who can behave more actively for their electric energy consumption. In this paper, the potential of distributed ML in solving the energy trading problem among prosumers of a future electric distribution system - building DC grid cell, is explored, while considering the limited computation, communication, and data privacy issues of the edge entities. A fully distributed energy trading framework based on ML is proposed to optimize the load and price prediction accuracy and energy trading efficiency. Computation resource allocation, communication schemes, ML task scheduling, as well as user sensitive data preserving issues in the distributed ML framework are addressed with consideration of all the economic and physical constraints of the electric distribution systems.
[A+] Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing via Geo-Indistinguishability
Chenxi Qiu, Anna Squicciarini, *Ce Pang, and 2 more authors
IEEE Transactions on Mobile Computing [Impact factor = 6.075], May 2020
Nowadays, vehicles have been increasingly adopted in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location to servers to facilitate the service utilities. Traditional location privacy protection mechanisms cannot be applied to vehicle-based SC since they assume workers mobility on a 2-dimensional plane without considering the network-constrained mobility features of vehicles. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) over road networks. Our objective is to design a location obfuscation strategy to minimize the quality loss due to obfuscation with geo-indistinguishability satisfied. Considering the computational complexity of VLP, by resorting to discretization, we first approximate VLP to a linear programming problem that can be solved by well-developed approaches. To further improve the time efficiency, we conduct constraint reduction for VLP by exploiting key features of geo-indistinguishability in road networks and problem decomposition based on VLPs constraint structure. Finally, we carry out both trace-driven simulation and real-world experiments, where our experimental results demonstrate the superiority of our approach over a known state-of-the-art location obfuscation strategy in terms of both QoS and privacy.
[A] Time-Efficient Geo-Obfuscation to Protect Worker Location Privacy over Road Networks in Spatial Crowdsourcing
Chenxi Qiu, Anna Squicciarini, Zhuozhao Li, and 2 more authors
In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, May 2020
To promote cost-effective task assignment in Spatial Crowdsourcing (SC), workers are required to report their location to servers, which raises serious privacy concerns. As a solution, geo-obfuscation has been widely used to protect the location privacy of SC workers, where workers are allowed to report perturbed location instead of the true location. Yet, most existing geo-obfuscation methods consider workers’ mobility on a 2 dimensional (2D) plane, wherein workers can move in arbitrary directions. Unfortunately, 2D-based geo-obfuscation is likely to generate high traveling cost for task assignment over roads, as it cannot accurately estimate the traveling costs distortion caused by location obfuscation. In this paper, we tackle the SC worker location privacy problem over road networks. Considering the network-constrained mobility features of workers, we describe workers? mobility by a weighted directed graph, which considers the dynamic traffic condition and road network topology. Based on the graph model, we design a geo-obfuscation (GO) function for workers to maximize the workers? overall location privacy without compromising the task assignment efficiency. We formulate the problem of deriving the optimal GO function as a linear programming (LP) problem. By using the angular block structure of the LP’s constraint matrix, we apply Dantzig-Wolfe decomposition to improve the time-efficiency of the GO function generation. Our experimental results in the real-trace driven simulation and the real-world experiment demonstrate the effectiveness of our approach in terms of both privacy and task assignment efficiency.
[A+] Connectivity Maintenance for Next-Generation Decentralized Vehicle Platoon Networks
Ankur Sarker, Chenxi Qiu, and Haiying Shen
IEEE/ACM Transactions on Networking, May 2020
Always keeping a certain distance between vehicles in a platoon system is important for collision avoidance. Centralized platoon systems let the leader vehicle determine and notify the velocities of all the vehicles in the platoon. Unfortunately, such a centralized method generates high packet drop rate and communication delay due to the leader vehicle’s limited communication capability. Therefore, we propose a decentralized platoon network, in which each vehicle determines its velocity by only communicating with the vehicles in a short range. However, the multiple simultaneous transmissions between different pairs of vehicles may interfere with each other. By leveraging a typical feature of a platoon, we devise a channel allocation algorithm, called the Fast and Lightweight Autonomous channel selection algorithm (FLA), in which each vehicle determines its channel simply based on its distance to the leader vehicle. We also devise a strategy, in which a succeeding vehicle uses its stored common velocity profile when it is disconnected from its preceding vehicle and then adjusts its velocity once the connection is built. We conduct experiments on NS-3 and Matlab to evaluate the performance of our proposed methods and implement a real-world prototype by equipping vehicles with Android mobile devices. The experimental results demonstrate the superior performance of our decentralized platoon network over the previous centralized platoon networks.
[A+] Brake Data-Based Location Tracking in Usage-Based Automotive Insurance Programs
Ankur Sarker, Chenxi Qiu, Haiying Shen, and 2 more authors
In Proceedings of 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), May 2020
Many usage-based automotive insurance programs do not directly use any GPS-based location tracking devices. Instead, CAN-bus data, such as brake signal data, can be collected by these programs to evaluate drivers’ driving habits and vehicle usage policies. In this paper, we demonstrate that by using a temporal sequence of applied brake signals collected from a vehicle, attackers can still possibly infer the vehicle’s route over the period, even though brake signal data does not reveal any specific location information. Our route inference is basically composed of three steps: At first, we categorize brake signal subsequences into four different driving maneuvers (i.e., stopping from a certain speed, reducing speed to adjust with the traffic flow, and taking left and right turns). Second, we estimate the number of intersections traversed by the vehicle using the applied brake signals and their corresponding maneuvers. Particularly, we also estimate the overall speed profile based on the magnitude and interval of different brake signals. From the estimated speed profile, we infer the distances, traveling time, and traffic signs corresponding to the candidate edges. Finally, we design a graph-based route-selection algorithm to find a list of (paths) routes from the regional map using the predicted driving maneuvers and the speed profile. We use a score function based on three factors (i.e., distance, traveling time, and traffic signs) to identify a candidate edge. We evaluate our approach using over 450km of transportation data, which has been collected from 24 individuals. The experimental results demonstrate that, by resorting to our solution, 89% of the original drivers’ trajectory can be successfully recovered from their brake data regardless of driver and vehicle models.
A Geo-Obfuscation Approach to Protect Worker Location Privacy in Spatial Crowdsourcing Systems
*Ce Pang, Chenxi Qiu, and Ning Wang
In Proceedings of IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW), May 2019
[A] Rating Mechanisms for Sustainability of Crowdsourcing Platforms
Chenxi Qiu, Anna Squicciarini, and Sarah Rajtmajer
In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, May 2019
Crowdsourcing leverages the diverse skill sets of large collections of individual contributors to solve problems and execute projects, where contributors may vary significantly in experience, expertise, and interest in completing tasks. Hence, to ensure the satisfaction of its task requesters, most existing crowdsourcing platforms focus primarily on supervisingcontributors’ behavior. This lopsided approach to supervision negatively impacts contributor engagement and platform sustainability.In this paper, we introduce rating mechanisms to evaluate requesters’ behavior, such that the health and sustainability of crowdsourcing platform can be improved. We build a game theoretical model to systematically account for the different goals of requesters, contributors, and platform, and their interactions. On the basis of this model, we focus on a specific application, in which we aim to design a rating policy that incentivizes requesters to engage lessexperienced contributors. Considering the hardness of the problem, we develop a time efficient heuristic algorithm with theoretical bound analysis. Finally, we conduct a user study in Amazon Mechanical Turk (MTurk) to validate the central hypothesis of the model. We provide a simulation based on 3 million task records extracted from MTurk demonstrating that our rating policy can appreciably motivate requesters to hire less-experienced contributors.
[A+] Incentivizing Distributive Fairness for Crowdsourcing Workers
Chenxi Qiu, Anna Squicciarini, and Benjamin Hanrahan
In a crowd market such as Amazon Mechanical Turk, the remuneration of Human Intelligence Tasks is determined by the requester, for which they are not given many cues to ascertain how to "fairly” pay their workers. Furthermore, the current methods for setting a price are mostly binary – in that, the worker either gets paid or not – as opposed to paying workers a "fair” wage based on the quality and utility of work completed. Instead, the price should better reflect the historical performance of the market and the requirements of the task. In this paper, we introduce a game theoretical model that takes into account a more balanced set of market parameters, and propose a pricing policy and a rating policy to incentivize requesters to offer "fair” compensation for crowdsourcing workers. We present our findings from applying and developing thismodel on real data gathered from workers on Amazon Mechanical Turk and simulations that we ran to validate our assumptions. Our simulation results also demonstrate that our policies motivate requesters to pay their workers more "fairly” compared with the payment set by the current market.
Dynamic Demand Prediction and Allocation in Cloud Service Brokerage [Impact factor = 5.697]
Chenxi Qiu, and Haiying Shen
IEEE Transactions on Cloud Computing, May 2019
To maximize its own profit, cloud service brokerage (CSB) aims to distribute tenant demands to reserved servers such that the total reservation cost is minimized with the tenants’ service level agreement (SLA) being satisfied. The demand allocation problem for CSB is non-trivial to solve due to uncertainty of tenants’ behavior. To avoid possible violations among demands, existing schemes allocate additional padding resources on the predicted demands, which leads to under-utilization of reserved resources. Accordingly, we propose a Probabilistic Demand Allocation (PDA) system to address the demand allocation problem for CSB. In PDA, we not only predict tenants’ demands based on their historical records, but also estimate the probability distribution of prediction errors. As over- and under-estimation are equally likely to happen with our prediction method, when allocating demands to a single server, their errors are possibly offset. Hence, it is unnecessary to allocate additional resource to each demand for violation prevention. Given the predication results, we formulate the demand allocation problem by probabilistic optimization, of which the objective is to minimize the overall cost from reserved servers while satisfying tenants’ SLA with high probability. Both simulation and real-world experimental results demonstrate the superiority of PDA in reducing servers’ reservation cost.
Towards Green Cloud Computing: Demand Allocation and Pricing Policies for Cloud Service Brokerage
Chenxi Qiu, Haiying Shen, and Liuhua Chen
IEEE Transactions on Big Data, May 2019
Towards Green Wireless Networking: Fading-Resistant Time Constraint Broadcasts Using Cooperative Communication
Chenxi Qiu, Haiying Shen, and Lei Yu
IEEE Transactions on Network Science and Engineering, Jul 2019
Cooperative broadcast, in which receivers are allowed to combine received packets from different senders to combat transmission errors, has gained increasing attention. Previous studies showed that broadcast optimization solutions are sufficient in non-fading environments but may suffer a low delivery ratio under wireless channel fading. Although some previous works analyze the tradeoff between energy and delay in cooperative broadcast, no work has investigated the tradeoff in a fading environment. Thus, in this paper, we study this tradeoff with the consideration of different fading models (i.e., Rayleigh fading, Rician fading, and Weibull fading models). We formulate this problem as a Fading-resistant Delay-constrained Minimum Energy Cooperative Broadcast (FDMECB) problem and prove the problem to be NP-hard under the Rayleigh fading, Rician fading, and Weibull fading models. We then identify an approximation algorithm for theoretical interests and propose a time efficient heuristic algorithm for practical use. Furthermore, we propose a dynamic programming (DP) based algorithm, which can achieve global optimization of FDMECB given the ordering of nodes to be informed. Our experimental results show that both FREEB and DP algorithms outperform a previous non-fading resistant algorithm, and also, DP nearly achieves the optimal.
Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing Via Geo-Indistinguishability
Chenxi Qiu, and Anna Cinzia Squicciarini
In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Jan 2019
Cloud-Based Collision-Aware Energy-Minimization Vehicle Velocity Optimization
Chenxi Qiu, and Haiying Shen
In Proceedings of IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Jan 2018
[A+] CrowdEval: A Cost-Efficient Strategy to Evaluate Crowdsourced Worker’s Reliability
Chenxi Qiu, Anna Squicciarini, Dev Rishi Khare, and 2 more authors
In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, Jan 2018
Crowdsourcing platforms depend on the quality of work provided by a distributed workforce. Yet, it is challenging to dependably measure the reliability of these workers, particularly in the face of strategic or malicious behavior. In this paper, we present a dynamic and efficient solution to keep tracking workers’ reliability. In particular, we use oth gold standard evaluation and peer consistency evaluation to measure each worker performance, and adjust the proportion of the two types of evaluation according to the estimated distribution of workers’ behavior (e.g., being reliable or malicious). Through experiments over real Amazon Mechanical Turk traces, we find that our approach has a significant gain in terms of accuracy and cost compared to state-of-the-art algorithms.
[A+] Combating Behavioral Deviance via User Behavior Control
Chenxi Qiu, Anna Squicciarini, Christopher Griffin, and 1 more author
In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, Jan 2018
Compared to traditional behavioral deviance, online deviant behavior (like cyberbullying) is more likely to spread over online social communities since it is not restricted by time and space, and can occur more frequently and intensely. To control risks associated with the spread of deviant and anti-normative behavior, it is essential to understand online users’ reaction when they interact with other users. In this paper, we model online users’ behavior interaction as an evolutionary game on a graph and analyze users’ behavior dynamics under different network conditions. Based on this theoretical framework, we then investigate behavior control strategies that aim to eliminate behavioral deviance. Finally, we use a real world dataset from a social network to verify the accuracy of our model’s hypothesis.We also and test the performance of our behavior control strategy through simulations based on both real and synthetically generated data. The experimental results demonstrate that our behavior control methods can effectively eliminate the impact of bullying behavior even when the proportion of bullying messages is higher than 60%.
[A+] An Energy-Efficient and Distributed Cooperation Mechanism for k
-Coverage Hole Detection and Healing in WSNs
Chenxi Qiu, Haiying Shen, and Kang Chen
IEEE Transactions on Mobile Computing [Impact factor = 6.075], Jan 2018
Present approaches to achieve k-coverage for Wireless Sensor Networks still rely on centralized techniques. In this paper, we devise a distributed method for this problem, namely Distributed VOronoi based Cooperation scheme (DVOC), where nodes cooperate in hole detection and recovery. In previous Voronoi based schemes, each node only monitors its own critical points. Such methods are inefficient for k-coverage because the critical points are far away from their generating nodes in k-order Voronoi diagram, causing high cost for transmission and computing. As a solution, DVOC enables nodes to monitor others’ critical points around themselves by building local Voronoi diagrams (LVDs). Further, DVOC constrains the movement of every node to avoid generating new holes. If a node cannot reach its destination due to the constraint, its hole healing responsibility will fall to other cooperating nodes. The experimental results from the real world testbed demonstrate that DVOC outperforms the previous schemes.
[A+] Employing Opportunistic Charging for Electric Taxicabs to Reduce Idle Time
Li Yan, Haiying Shen, Zhuozhao Li, and 5 more authors
Proceedings of ACM Interact. Mob. Wearable Ubiquitous Technol., Mar 2018
For electric taxicabs, the idle time spent on cruising for passengers, seeking chargers, and charging is wasteful. Previous works can only save cruising time through better routing, or charger seeking and charging time through proper charger deployment, but not for both. With the advancement of wireless charging techniques, efficient opportunistic charging of electric vehicles at their parked positions becomes possible. This enables a taxicab to get charged while waiting for the next passenger. In this paper, we present an opportunistic wireless charger deployment scheme in a city, which both maximizes the taxicabs’ opportunity of picking up passengers at the chargers and supports the taxicabs’ continuous operability on roads, while minimizing the total deployment cost. We studied a metropolitan-scale taxicab dataset on several factors important for deploying wireless chargers and determining the numbers of the chargers in the regions: the number of passengers, the functionalities of buildings, and the frequency of passenger appearance in a region, and taxicab traffic flows in a city. Then, we formulate a multi-objective optimization problem and find the solution. Our trace-driven experiments demonstrate the superior performance of our scheme over other representative methods in terms of reducing idle time and supporting the operability of the taxicabs.
[A] Fading-Resistant Link Scheduling in Wireless Networks
Chenxi Qiu, and Haiying Shen
In Proceedings of 46th International Conference on Parallel Processing (ICPP), Aug 2017
In this paper, we study the link scheduling problem considering the fluctuating fading effect in transmissions. We extend the previous deterministic physical interference model to the Rayleigh-fading model that uses the stochastic propagation to address fading effects. Based on this model, we formulate a problem called Fading-Resistant Link Scheduling (Fading-R-LS) problem, which aims to maximize the throughput of all links in a single time slot. We prove that this problem is NP-hard. Based on the geometric structure of Fading-R-LS, we then propose two centralized schemes with O(g(L)) and O(1) performance guarantee, respectively, where g(L) is the number of magnitudes of transmission link lengths. Our experimental results show that the superior performance of our proposed schemes compared to previous schemes.
Power Distribution Scheduling for Electric Vehicles in Wireless Power Transfer Systems
Chenxi Qiu, Ankur Sarker, and Haiying Shen
In Proceedings of 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Jun 2017
Electric vehicles (EVs) will become a component of the future generation intelligent transportation system. Because of EVs’ limited battery power, the wireless power transfer (WPT) system has drawn much attention in recent years. The WPT system charges EVs in motion when they pass the charging lanes installed in roads without requiring physical contact between utility power supply and vehicle battery. A charging lane has limited power that can be transferred to EVs on the charging lane. A challenge here is how to allocate the limited power to the EVs so that they have sufficient power to arrive at the next charging lane or their destinations (when there are no charging lanes ahead). In this paper, we study this power distribution scheduling problem. We provide solutions to handle this challenge and also achieve each of the following goals as much as possible: i) balancing the state of charge (SOC) of the EVs, ii) balancing the amount of stored power of the EVs, and iii) minimizing the total power charged. This paper is the first work that handles such a power distribution scheduling problem in WPT systems. Our extensive experiments on MatLab and Simulation for Urban MObility (SUMO) show the effectiveness of our scheduling solutions in achieving the different goals compared with other scheduling methods including first-come-first-serve and equal share.
[A] Dynamic Contract Design for Heterogenous Workers in Crowdsourcing for Quality Control
Chenxi Qiu, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, and 1 more author
In Proceedings of IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Jun 2017
Crowdsourcing sites heavily rely on paid workers to ensure completion of tasks. Yet, designing a pricing strategies able to incentivize users’ quality and retention is non trivial. Existing payment strategies either simply set a fixed payment per task without considering changes in workers’ behaviors, or rule out poor quality responses and workers based on coarse criteria. Hence, task requesters may be investing significantly in work that is inaccurate or even misleading. In this paper, we design a dynamic contract to incentivize high-quality work. Our proposed approach offers a theoretically proven algorithm to calculate the contract for each worker in a cost-efficient manner. In contrast to existing work, our contract design is not only adaptive to changes in workers’ behavior, but also adjusts pricing policy in the presence of malicious behavior. Both theoretical and experimental analysis over real Amazon review traces show that our contract design can achieve a near optimal solution. Furthermore, experimental results demonstrate that our contract design 1) can promote high-quality work and prevent malicious behavior, and 2) outperforms the intuitive strategy of excluding all malicious workers in terms of the requester’s utility.
[A+] CatCharger: Deploying wireless charging lanes in a metropolitan road network through categorization and clustering of vehicle traffic
Li Yan, Haiying Shen, Juanjuan Zhao, and 3 more authors
In Proceedings of IEEE Conference on Computer Communications, May 2017
The future generation of transportation system will be featured by electrified public transportation. To fulfill metropolitan transit demands, electric vehicles (EVs) must be continuously operable without recharging downtime. Wireless Power Transfer (WPT) techniques for in-motion EV charging is a solution. It however brings up a challenge: how to deploy charging lanes in a metropolitan road network to minimize the deployment cost while enabling EVs’ continuous operability. In this paper, we propose CatCharger, which is the first work that handles this challenge. From a metropolitan-scale dataset collected from multiple sources of vehicles, we observe the diversity of vehicle passing speed and daily visit frequency (called traffic attributes) at intersections (i.e., landmarks), which are important factors for charging lane deployment. To select landmarks for deployment, we first group landmarks with similar traffic attribute values using the entropy minimization clustering method, and choose better candidate landmarks from each group suitable for deployment. To determine the deployment locations from the candidate landmarks, we infer the expected vehicle residual energy at each landmark using a Kernel Density Estimator fed by the vehicles’ mobility, and formulate and solve an optimization problem to minimize the total deployment cost while ensuring a certain level of expected residual energy of EVs at each landmark. Our trace-driven experiments demonstrate the superior performance of CatCharger over other methods.
Quick and Autonomous Platoon Maintenance in Vehicle Dynamics For Distributed Vehicle Platoon Networks
Ankur Sarker, Chenxi Qiu, and Haiying Shen
In Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, May 2017
Platoon systems, as a type of adaptive cruise control systems, will play a significant role to improve travel experience and roadway safety. The stability of a platoon system is crucial so that each vehicle maintains a safety distance from its proceeding vehicle and can take necessary actions to avoid collisions. However, current centralized platoon maintenance method cannot meet this requirement. We suggest to use a decentralized platoon maintenance method, in which each vehicle communicates with its neighbor vehicles and self-determines its own velocity. However, a vehicle needs to know its distance from its preceding vehicle to determine its velocity, which is unavailable in vehicle communication disconnection caused by vehicle dynamics (i.e., node joins and departures). Thus, a formidable challenge is: how to recover the platoon quickly in vehicle dynamics even when the distance information is unavailable? To handle this challenge, we first profile a succeeding vehicle’s velocity to minimize the time to recover the connectivity hole with its preceding vehicle and find that the profiles are almost the same at the beginning regardless of its current velocity and distance to its preceding vehicle. Accordingly, we devise a strategy, in which a succeeding vehicle uses its stored common velocity profile when it is disconnected from its preceding vehicle and then adjusts its velocity once the connection is built. Experimental results from simulation show the efficiency and effectiveness of our decentralized platoon maintenance method.
Towards Green Transportation: Fast Vehicle Velocity Optimization for Fuel Efficiency
Chenxi Qiu, Haiying Shen, Ankur Sarker, and 4 more authors
In Proceedings of IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Dec 2016
To minimize the fuel consumption for driving, several methods have been proposed to calculate vehicles’ optimal velocity profiles on a remote cloud. Considering the traffic dynamism, each vehicle needs to keep updating the velocity profile, which requires low latency for information uploading and profile calculation. However, these proposed methods cannot satisfy this requirement due to (1) high queuing delay for information uploading caused by a large number of vehicles, and (2) the neglect of the traffic light and high computation delay for velocity profile. For (1), considering the driving features of close vehicles on a road, e.g., similar velocity and interdistances, we propose to group vehicles within a certain range and let the leader vehicle in each group to upload the group information to the cloud, which then derives the velocity of each vehicle in the group. For (2), we propose spatial-temporal DP (ST-DP) that additionally considers the traffic lights. We innovatively find that the DP process makes it well suited to run on Spark (a fast parallel cluster computing framework) and then present how to run ST-DP on Spark. Finally, we demonstrate the superiority of our method using both trace-driven simulation (NS-2.33 simulator and MATLAB) and real-world experiments.
[A] CrowdSelect: Increasing Accuracy of Crowdsourcing Tasks through Behavior Prediction and User Selection
Chenxi Qiu, Anna C. Squicciarini, Barbara Carminati, and 2 more authors
In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, Dec 2016
Crowdsourcing allows many people to complete tasks of various difficulty with minimal recruitment and administration costs. However, the lack of participant accountability may entice people to complete as many tasks as possible without fully engaging in them, jeopardizing the quality of responses. In this paper, we present a dynamic and time efficient solution to the task assignment problem in crowdsourcing platforms. Our proposed approach, CrowdSelect, offers a theoretically proven algorithm to assign workers to tasks in a cost efficient manner, while ensuring high accuracy of the overall task. In contrast to existing works, our approach makes minimal assumptions on the probability of error for workers, and completely removes the assumptions that such probability is known apriori and that it remains consistent over time. Through experiments over real Amazon Mechanical Turk traces and synthetic data, we find that CrowdSelect has a significant gain in term of accuracy compared to state-of-the-art algorithms, and can provide a 17.5% gain in answers’ accuracy compared to previous methods, even when there are over 50% malicious workers.
A Decentralized Network with Fast and Lightweight Autonomous Channel Selection in Vehicle Platoons for Collision Avoidance
Ankur Sarker, Chenxi Qiu, and Haiying Shen
In Proceedings of IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Oct 2016
Always keeping a certain distance between vehicles in a platoon is important for collision avoidance. Centralized platoon systems let the leader vehicle determine and notify the velocities of all the vehicles in the platoon. Unfortunately, such a centralized method generates high packet drop rate and communication delay due to the leader vehicle’s limited communication capability. Therefore, we propose a decentralized platoon network, in which each vehicle determines its own velocity by only communicating with the vehicles in a short range. However, the multiple simultaneous transmissions between different pairs of vehicles may interfere with each other. Directly applying current channel allocation methods for interference avoidance leads to high communication cost and delay in vehicle joins and departures (i.e., vehicle dynamics). As a result, a challenge is how to reduce the communication delay and cost for channel allocation in decentralized platoon networks? To handle this challenge, by leveraging a typical feature of a platoon, we devise a channel allocation algorithm, called the Fast and Lightweight Autonomous channel selection algorithm (FLA), in which each vehicle determines its own channel simply based on its distance to the leader vehicle. We conduct experiments on NS-3 and Matlab to evaluate the performance of our proposed methods. The experimental results demonstrate the superior performance of our decentralized platoon network over the previous centralized platoon networks and of FLA over previous channel allocation methods in platoons.
[A] An Efficient Wireless Power Transfer System to Balance the State of Charge of Electric Vehicles
Ankur Sarker, Chenxi Qiu, Haiying Shen, and 6 more authors
In Proceedings of 45th International Conference on Parallel Processing (ICPP), Aug 2016
As an alternate form in the road transportation system, electric vehicle (EV) can help reduce the fossil-fuel consumption. However, the usage of EVs is constrained by the limited capacity of battery. Wireless Power Transfer (WPT) can increase the driving range of EVs by charging EVs in motion when they drive through a wireless charging lane embedded in a road. The amount of power that can be supplied by a charging lane at a time is limited. A problem here is when a large number of EVs pass a charging lane, how to efficiently distribute the power among different penetrations levels of EVs? However, there has been no previous research devoted to tackling this challenge. To handle this challenge, we propose a system to balance the State of Charge (called BSoC) among the EVs. It consists of three components: i) fog-based power distribution architecture, ii) power scheduling model, and iii) efficient vehicle-to-fog communication protocol. The fog computing center collects information from EVs and schedules the power distribution. We use fog closer to vehicles rather than cloud in order to reduce the communication latency. The power scheduling model schedules the power allocated to each EV. In order to avoid network congestion between EVs and the fog, we let vehicles choose their own communication channel to communicate with local controllers. Finally, we evaluate our system using extensive simulation studies in Network Simulator-3, MatLab, and Simulation for Urban MObility tools, and the experimental results confirm the efficiency of our system.
[A] Link Scheduling in Cooperative Communication with SINR-Based Interference
Chenxi Qiu, and Haiying Shen
In Proceedings of 25th International Conference on Computer Communication and Networks (ICCCN), Aug 2016
Though intensive research efforts have been devoted to the study of the link scheduling problem in wireless networks, no previous work has discussed this problem for cooperative communication networks, in which receivers are allowed to combine messages from different senders to combat transmission errors. In this paper, we study the link scheduling problem in wireless cooperative communication networks, in which receivers are allowed to combine copies of a message to combat fading. We formulate two problems named cooperative link scheduling problem (CLS) and one-shot cooperative link scheduling problem (OCLS). The first problem aims to find a schedule of links that uses the minimum number of time slots to inform all the receivers. The second problem aims to find a set of links that can inform the maximum number of receivers in one time slot. As a solution, we propose an algorithm for both CLS and OCLS with g(K) approximation ratio, where g(K) is so called diversity of key links. In addition, we propose a greedy algorithm with O(1) approximation ratio for OCLS when the number of links for each receiver is upper bounded by a constant. Simulation results indicate that our cooperative link scheduling approaches outperform non-cooperative ones.
[A+] Probabilistic demand allocation for cloud service brokerage
Chenxi Qiu, Haiying Shen, and Liuhua Chen
In Proceedings of IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, Apr 2016
Functioning as an intermediary between cloud tenants and providers, cloud service brokerages (CSBs) bring about great benefits to the cloud market. To maximize its own profit, a CSB is faced with a challenge: how to reserve servers and distribute tenant demands to the reserved servers such that the total reservation cost is minimized while the reserved servers can satisfy the tenant service level agreement (SLA)? Demand prediction and demand allocation are two steps to solve this problem. However, previous demand prediction methods cannot accurately predict tenant demands since they cannot accurately estimate prediction errors and also assume the existence of seasonal periods of demands. Previous demand allocation methods only aim to minimize the number of reserved servers rather than the server reservation cost, which is more challenging. To solve this challenge, we propose a Probabilistic Demand Allocation system (PDA). It predicts demands and more accurate prediction errors without the assumption of the existence of seasonal periods. It then formulates a nonlinear programming problem and has a decentralized method to find the problem solution. In addition to overcoming the shortcomings in previous methods, PDA is novel in that rather than separately conducting the prediction and demand allocation, it considers prediction errors in demand allocation in order to allocate demands with offsetting prediction errors (e.g., -1 and +1) to the same server, which helps find the problem solution. Both simulation and real-world experimental results demonstrate the superior performance of our system in reducing servers’ reservation cost.
A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC)
Kakan C. Dey, Li Yan, Xujie Wang, and 6 more authors
IEEE Transactions on Intelligent Transportation Systems, Feb 2016
Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.
Link Scheduling in Wireless Cooperative Communication Networks
Chenxi Qiu, and Haiying Shen
In Proceedings of IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems, Oct 2015
In this paper, we study the link scheduling problem in wireless cooperative communication networks, in which receivers are allowed to combine copies of a message from different senders to combat fading. We formulate a problem called cooperative link scheduling problem (CLS), which aims to find a schedule of links that uses the minimum number of time slots to inform all the receivers. As a solution, we propose an algorithm for CLS with g(K) approximation ratio, where g(K) is so called diversity of key links. Simulation results indicate that our cooperative link scheduling approaches outperform noncooperative ones.
[A] Energy-Efficient and Delay-Constrained Broadcast in Time-Varying Energy-Demand Graphs
Chenxi Qiu, Haiying Shen, and Lei Yu
In Proceedings of 44th International Conference on Parallel Processing, Sep 2015
In this paper, we study the minimum energy broadcast problem in time-varying graphs (TVGs), which are a very useful high level abstraction for studying highly dynamic wireless networks. To this end, we first incorporate a channel model, called energy-demand functions, to the current TVGs, namely time-varying energy-demand graphs (TVEGs). Based on this model, we formulate the problem: given a TVEG, what is the optimal schedule (i.e., Which nodes should forward a packet in what times and at what power levels) to minimize the energy consumption of the broadcast? We prove the problem to be NP-hard and o(log N) in approximable. It is a challenge to find a solution for this problem on continuous time. Fortunately, we prove that the problem on continuous time is equivalent to the problem on certain discrete time points, called discrete time set (DTS). Based on this property, we propose polynomial time solutions for this problem with different channel models, and evaluate the performance of these methods from real-life contact traces.
[A+] A Distributed Three-Hop Routing Protocol to Increase the Capacity of Hybrid Wireless Networks
Haiying Shen, Ze Li, and Chenxi Qiu
IEEE Transactions on Mobile Computing, Oct 2015
Hybrid wireless networks combining the advantages of both mobile ad-hoc networks and infrastructure wireless networks have been receiving increased attention due to their ultra-high performance. An efficient data routing protocol is important in such networks for high network capacity and scalability. However, most routing protocols for these networks simply combine the ad-hoc transmission mode with the cellular transmission mode, which inherits the drawbacks of ad-hoc transmission. This paper presents a Distributed Three-hop Routing protocol (DTR) for hybrid wireless networks. To take full advantage of the widespread base stations, DTR divides a message data stream into segments and transmits the segments in a distributed manner. It makes full spatial reuse of a system via its high speed ad-hoc interface and alleviates mobile gateway congestion via its cellular interface. Furthermore, sending segments to a number of base stations simultaneously increases throughput and makes full use of widespread base stations. In addition, DTR significantly reduces overhead due to short path lengths and the elimination of route discovery and maintenance. DTR also has a congestion control algorithm to avoid overloading base stations. Theoretical analysis and simulation results show the superiority of DTR in comparison with other routing protocols in terms of throughput capacity, scalability, and mobility resilience. The results also show the effectiveness of the congestion control algorithm in balancing the load between base stations.
[A+] CEDAR: A Low-Latency and Distributed Strategy for Packet Recovery in Wireless Networks
Chenxi Qiu, Haiying Shen, Sohraab Soltani, and 3 more authors
IEEE/ACM Transactions on Networking, Oct 2015
Underlying link-layer protocols of well-established wireless networks that use the conventional “store-and-forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and balancing en/decoding load among nodes. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in a packet’s route. To identify the intermediate nodes for the decoding, we mathematically calculate the average packet delay and formalize the problem as a nonlinear integer programming problem. By minimizing the delays, we derive three propositions that: 1) can identify the intermediate nodes that minimize the propagation and transmission delay of a packet; and 2) and 3) can identify the intermediate nodes that simultaneously minimize the queuing delay and maximize the fairness of en/decoding load of all the nodes. Guided by the propositions, we then propose a scalable and distributed scheme in CEDAR to choose the intermediate en/decoding nodes in a route to achieve its objective. The results from real-world testbed “NESTbed” and simulation with MATLAB prove that CEDAR is superior to schemes using hop-by-hop decoding and destination decoding not only in packet delay and throughput but also in energy-consumption and load distribution balance.
[A+] Energy-efficient cooperative broadcast in fading wireless networks
Chenxi Qiu, Haiying Shen, and Lei Yu
In IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, Apr 2014
Cooperative broadcast, in which receivers are allowed to combine received packet from different senders to combat transmission errors, has gained increasing attention. Previous studies showed that broadcast optimization solutions are sufficient in non-fading environments but may suffer a low delivery ratio under wireless channel fading. Though previous work analyzed the tradeoff between energy and delay in cooperative broadcast, no works investigated the tradeoff in a fading environment. Thus, in this paper, we study this tradeoff with the consideration of fading. We formulate this problem as a Fading-resistant Delay-constrained Minimum Energy Cooperative Broadcast (FDMECB) problem, and prove that it is NP-complete. We then propose an approximation algorithm for theoretical interests. We further propose a heuristic algorithm that makes approximately optimal local decision to achieve global optimization. Our experimental results show that our algorithms outperform a previous non-fading resistant algorithm.
[A+] A Delaunay-Based Coordinate-Free Mechanism for Full Coverage in Wireless Sensor Networks
Chenxi Qiu, and Haiying Shen
IEEE Transactions on Parallel and Distributed Systems, Apr 2014
Recently, many schemes have been proposed for detecting and healing coverage holes to achieve full coverage in wireless sensor networks (WSNs). However, none of these schemes aim to find the shortest node movement paths to heal the coverage holes, which could significantly reduce energy usage for node movement. Also, current hole healing schemes require accurate knowledge of sensor locations; obtaining this knowledge consumes high energy. In this paper, we propose a Delaunay-based coordinate-free mechanism (DECM) for full coverage. Based on rigorous mathematical analysis, DECM can detect coverage holes and find the locally shortest paths for healing holes in a distributed manner without requiring accurate node location information. Also, DECM incorporates a cooperative movement mechanism that can prevent generating new holes during node movements in healing holes. Simulation results and experimental results from the real-world GENI Orbit testbed show that DECM achieves superior performance in terms of the energy-efficiency, effectiveness of hole healing, energy consumption balance and lifetime compared to previous schemes.
[A+] Efficient Data Collection for Large-Scale Mobile Monitoring Applications
Haiying Shen, Ze Li, Lei Yu, and 1 more author
IEEE Transactions on Parallel and Distributed Systems, Jun 2014
Radio frequency identification (RFID) and wireless sensor networks (WSNs) have been popular in the industrial field, and both have undergone dramatic development. RFID and WSNs are well known for their abilities in identity identification and data transmission, respectively, and hence widely used in applications for environmental and health monitoring. Though the integration of a sensor and an RFID tag was proposed to gather both RFID tag and sensed information, few previous research efforts explore the integration of data transmission modes in the RFID and WSN systems to enhance the performance of the applications. In this paper, we propose a hybrid RFID and WSN system (HRW) that synergistically integrates the traditional RFID system and WSN system for efficient data collection. HRW has hybrid smart nodes that combine the function of RFID tags, the reduced function of RFID readers, and wireless sensors. Therefore, nodes can read each other’s sensed data in tags, and all data can be quickly transmitted to an RFID reader through the node that first reaches it. The RFID readers transmit the collected data to the back-end servers for data processing and management. We also propose methods to improve data transmission efficiency and to protect data privacy and avoid malicious data selective forwarding in data transmission. Comprehensive simulation and trace-driven experimental results show the high performance of HRW in terms of the cost of deployment, transmission delay and capability, and tag capacity requirement.
[A+] Low-latency multi-flow broadcasts in fading wireless networks
Chenxi Qiu, Lei Yu, Haiying Shen, and 1 more author
In Proceedings of IEEE INFOCOM, Apr 2013
Cooperative broadcast, in which a packet receiver cooperatively combines received weak signal power from different senders to decode the original packet, has gained increasing attention. However, existing approaches are developed based on the assumption that there is a single flow in the network; thus, they are not suitable for multi-flow broadcasting in which broadcasts are initiated by different nodes and consist of more than one packet at any point in time. In this paper, we aim to achieve low-latency multi-flow broadcast in wireless multihop networks with fading channels. We formulate this problem as a Minimum Slotted Delay Cooperative Broadcast (MSDCB) problem, and prove that it is NP-complete and o(logN) inapproximable. We then propose two heuristic algorithms named PCBHS and PCBH-M to solve MSDCB. Our experimental results show that our algorithms outperform previous methods.
[A+] CEDAR: An optimal and distributed strategy for packet recovery in wireless networks
Chenxi Qiu, Haiying Shen, Sohraab Soltani, and 3 more authors
In Proceedings of IEEE INFOCOM, Apr 2013
Underlying link-layer protocols of wireless networks use the conventional “store and forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and high throughput. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically-sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in packet’s route. To identify the intermediate nodes for the en/decoding for minimizing average packet latency, we mathematically analyze the average packet delay, using Finite State Markovian Channel model and priority queuing model, and then formalize the problem as a non-linear integer programming problem. Also, we propose a scalable and distributed scheme to solve this problem. The results from real-world testbed “NESTbed” and simulation with Matlab prove that CEDAR is superior to the schemes using hop-by-hop decoding and destination-decoding not only in packet delay but also in throughput. In addition, the simulation results show that CEDAR can achieve the optimal performance in most cases.