In participatory crowdsensing, data sparsity in underrepresented areas and limited user engagement remain a persistent challenge. We present SpatioSense, a modular participatory crowdsensing platform that fully automates heterogeneous campaign prototyping, while addressing the challenge of data sparsity via hybrid task allocation models. We further leverage SpatioSense's modular architecture to enable rapid adaptation to diverse campaigns, as well as the integration of a novel context-aware task allocation mechanism, in which spatio-temporal and natural language processing models are used to match sensing tasks to participants. In this paper, we present the SpatioSense platform with its novel design features and adaptive context-aware task allocation algorithm, and our preliminary results.

Traditional graph-based ranking models that treat locations as isolated entities often fail to capture the complex spatio-temporal dependencies inherent in pickup and delivery platforms. Regardless of the quality of learning models adopted to predict future demand, their results can be further enhanced by encoding the spatio-temporal graph-based structure of the road network. We define a spatio-temporal graph-based measure for POI ranking, namely ZoneRank, that encodes spatial relationships, mobility flows, and temporal transitions in such platforms. Furthermore, we implement a multi-purpose ridesharing simulator to evaluate the effectiveness of ZoneRank in the context of idle vehicle repositioning.

Mobile Crowdsensing (MCS) has recently taken up an important role in sensor data collection paradigms because of its reduced costs and flexibility. It allows crowdsourcers to recruit a number of mobile users to execute sensing tasks in an area without deploying physical sensors. However, MCS algorithms and policies are very different depending on the application and testing them in the real world is impractical, due to the difficulties in recruiting large crowds of volunteers. For this purpose, the research is mostly oriented to simulations, however, to date, there is no simulation platform that focuses enough on different aspects of MCS, often disregarding some in favor of others. In this paper we focus on TACSim, an extensible simulator and we propose an additional mobility module that fills the gap of accurate road network representation. Participants navigate a real road network offering an improved realism and yielding more accurate results. We also propose a caching system that helps in reducing the processing time of simulations and demonstrate its effectiveness through extensive benchmarks.

In crowdsensing platforms, algorithms and models for task allocation play a critical role in shaping user behaviors, engagement levels, the quality of the collected data, and the performance of the platform as a whole. Regardless of the sensing model, task allocation mechanisms are difficult to evaluate and benchmark. In contrast to evaluating deployments of crowd-sensing platforms with real crowds, they are often evaluated via simulators that are incapable of modeling the complexities of human behavior, specifically in terms of their commitment to the platform and quality of sensing, but their strength is the ability to rapidly experiment with multiple algorithms. In this paper, we abstract the general characteristics of participant behaviors in crowdsensing, and implement these characteristics within the TACSim simulation framework. Further exemplifying the extendability power of that simulation framework, and the benefits it can offer the crowdsensing community.

A prevalent problem in current e-hailing and ride sharing platforms is deadheading; i.e., drivers roaming around already busy streets in search for rides, which increases congestion in urban areas; leading to negative impact on the environment, as well as the driver and customer experience. In this paper, we define a repositioning and routing algorithm for idle vehicles in urban settings that can be coupled with existing demand forecasting models. We analyze and evaluate our algorithms using benchmarks with real ride traces from NYC and San Francisco, that highlight their strength in reducing deadheading while improving drivers’ average income and not sacrificing customer pickup delays. Moreover, in areas with sparse demand, they achieve reductions in ride pickup delays and deadheading up to 40%.

While many countries are developing appropriate actions towards a greener future and moving towards adopting sustainable mobility activities, the real-time management and planning of innovative transportation facilities and services in urban environments still require the development of advanced mobile data management infrastructures. Novel green mobility solutions, such as electric, hybrid, solar and hydrogen vehicles, as well as public and gig-based transportation resources are very likely to reduce the carbon footprint. However, their successful implementation still needs efficient spatio-temporal data management resources and applications to provide a clear picture and demonstrate their effectiveness. This paper discusses the major data management challenges, open issues, and application opportunities closely related to urban green mobility. Additionally, it reports on recent successful experiences and challenging research questions. Furthermore, it highlights the global benefits one can expect when developing green mobility and emphasizes how mobile data infrastructures and services will play a crucial role in achieving these goals.

SIGCSE is packed with teaching insights and inspiration. However, we get these insights and inspiration from hearing our colleagues talk about their teaching. Why not watch them teach? This session does exactly that! Six exceptional educators will present innovative content just as they would to their students. The moderator, Colleen Lewis, will describe their pedagogical moves and how they connect to education research. The goal of the session is to inspire SIGCSE attendees by highlighting innovative instruction by exceptional educators. Attendees can adopt the content and/or pedagogical moves from each microteaching example.

In participatory Mobile CrowdSensing, tasks are allocated to participants via some allocation mechanism, which are challenging in terms of their evaluation due to the lack of general-purpose, modular, and extendable simulators. Thus, forcing researchers to either launch their own testbeds or develop single-purpose simulators.In this paper, we present our design and implementation of an extendable simulator, namely TACSim, for the evaluation of task allocation mechanisms in a participatory MCS setting over realistic urban environments. TACSim is designed to accommodate realistic urban road networks, as well as spatio-temporal traces of sensing tasks and participant mobility. It includes built-in autonomous task allocation mechanisms, and can be extended by researchers to accommodate their own algorithms with minimal effort. We discuss the components and architecture of the simulator, and present a use-case of integrating existing autonomous task allocation mechanisms that further exemplifies the usability and extendability of the simulator.

Recently ride-sharing platforms have struggled with a decreased supply of drivers, which has negatively impacted their passengers, by subjecting them to long delays and extremely high surge prices. An approach for mitigating these problems is for service providers to facilitate and coordinate carpooling via the recommendation of individually curated paths, not necessarily the shortest, for drivers towards completing their chosen rides. In this paper, we redesign the Weight Evolving Temporal graph structure to efficiently encode large dynamic road networks with temporal ride availability. Leveraging that graph structure, we efficiently define a polynomial-time optimal route recommendation algorithm that increases carpooling opportunities, taking into consideration the spatio-temporal constraints of both drivers and rides in such a highly-dynamic setting. Finally, we use simulations to demonstrate the effectiveness of these route recommendations, on both the driver and passenger experience.

With the increased popularity of mobile crowdsensing, personal trajectory information has become easily attainable, compromising the privacy of participants. The focus of this work is to obfuscate the participants’ trajectory information during the task recommendation process within crowdsensing. In this paper, we present a cooperative peer-to-peer crowdsensing model, in which peers assist each other in obfuscating their trajectory information. We define a cooperative recommendation mechanism, coupled with an efficient trip segmentation algorithm, which together can preserve the privacy of participants, without sacrificing the performance of task recommendation in the system. Finally, since privacy achieved via dummy-based obfuscation cannot be theoretically guaranteed, we evaluate the privacy and efficiency of the proposed mechanism via simulations.

In Mobile Crowd Sensing (MCS) platforms, users are typically human participants who willingly take time out of their daily schedules to complete sensing tasks. Albeit the unreliable nature of human's behavior, existing task allocation mechanisms proposed within MCS platforms typically assume that participants will accept the tasks allocated to them and complete them successfully, which in turn affects the realized quality of task completion. In this paper, we define a novel participation reliability metric, which forgives erratic misbehavior but doesn't forget if it's repeated. Moreover, to incentivize participants to be more reliable, we integrate the defined reliability metric into an online multi-task allocation mechanism, associated with a rational payment model. Finally, we theoretically analyze the proposed components and evaluate their performance on synthesized mobility traces.

With the recent developments in Mobile CrowdSensing, an interesting model of temporal graphs has emerged, in which node weights evolve over time, according to the availability of spatio-temporal tasks on the mobility field. The analysis and understanding of these types of graphs, namely Weight Evolving Temporal (WET) graphs, is critical for optimizing task allocation in such crowdsensing platforms. In this paper, we formally define WET graphs and their corresponding routing problem, in which the objective of the routing is to maximize the reward collected from vertices visited amid the graph traversal. By modeling a WET graph as a time-ordered graph, we define efficient and optimal routing algorithms, and theoretically analyze them. Moreover, we present a novel node centrality measure, namely Coverage Centrality, that captures the popularity of various nodes of the WET graph, and which we incorporate in an online crowdsensing task allocation mechanism to increase task coverage. Finally, we evaluate the efficacy of this novel centrality measure on different types of graphs, when compared to other centrality measures, and evaluate its effect on task coverage in online mobile crowdsensing platforms.

With the recent trend in Mobile Crowd Sensing (MCS), i.e., using the power of crowds to assist in completing spatio-temporal sensory tasks, the pool of resources suitable for sensor systems has expanded to include already roaming devices. In this work, we present a model of MCS, in which agents share their journey information and allow the platform to guide them through their journey, completing spatio-temporal tasks on their way, in return for monetary rewards. In this paper, we formulate the task allocation problem as a routes coordination problem for participating agents. We define an optimal routing algorithm for a single agent, with an objective to maximize the rewards collected from performing tasks, which is used to define a 1/2-approximation algorithm to coordinate the routes of multiple agents. The algorithm is accompanied with an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthful participation is an ex-post Nash equilibrium strategy, in an optimal setting. Finally, we analyze the defined mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provide predictability guarantees in such settings, in which neither application scheduling nor cloud provider resources cane managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of thence problem, and we evaluate its efficiency using simulations on various application workloads, and network models.

Existing mobile devices roaming around the mobility field should be considered as useful resources in geo-temporal request satisfaction. We refer to the capability of an application to access a physical device at particular geographical locations and times as Geo-Presence, and we presume that mobile agents participating in geo-presence-capable applications should be rational, competitive, and willing to deviate from their routes if given the right incentive. In this paper, we define the Hitchhiking problem, which is that of finding the optimal assignment of requests with specific spatio-temporal characteristics to competitive mobile agents subject to spatio-temporal constraints. We design a mechanism that takes into consideration the rationality of the agents for request satisfaction, with an objective to maximize the total profit of the system. We analytically prove the mechanism to be convergent with a profit comparable to that of a 1/2-approximation greedy algorithm, and evaluate its consideration of rationality experimentally.