Systematic Analysis and Modelling of Profit Maximization on Carsharing

in Journal of Advanced Transportation (2022)

Systematic Analysis and Modelling of Profit Maximization on Carsharing

Poster (2021, January)

The success of carsharing as a new and more sustainable way of travel is moving private car ownership towards a service use model. Competitivity is an essential aspect of this service and ways to increase ... [more ▼]

The success of carsharing as a new and more sustainable way of travel is moving private car ownership towards a service use model. Competitivity is an essential aspect of this service and ways to increase profit while offering the most appealing service are still getting explored. Among others, dynamic pricing strategies can be designed to increase profit by attracting more users, selling more rental hours or maximizing fleet utilization. In this paper, we propose an experimental method aimed at developing a model for maximizing service profit. Using agent-based modeling to generate realistic scenarios, we analyze pricing as a function of the potential demand (i.e. number of members) and supply (hours of booking supplied). The process of reaching the maximum profit consists of testing various combinations of pricing - demand and pricing – supply ranges in order to find the price that maximize the profit for every demand and supply level. Once the optimal prices are known, a polynomial fitting and an optimization method are used to generate a function linking all the maximal profit obtaining the advised price to offer for any specific supply levels. Results show how the profit only slightly depends on the variability of the potential demand, while it strongly depends on the amount of supply. It is then shown how it is possible to obtain a linear relation that maximizes the profit in function of the price offered once the supply is known. [less ▲]

The Impact of Human Mobility on Edge Data Center Deployment in Urban Environments

in IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019 (2019, December)

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better low-latency applications. To this end ... [more ▼]

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better low-latency applications. To this end, effective placement of EDCs in urban environments is key for proper load balance and to minimize outages. In this paper, we specifically tackle this problem. To fully understand how the computational demand of EDCs varies, it is fundamental to analyze the complex dynamics of cities. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments in order to minimize outages. To this end, we propose and compare two heuristics. In particular, we present the mobility-aware deployment algorithm (MDA) that outperforms approaches that do not consider citizens mobility. Simulations are conducted in Luxembourg City by extending the CrowdSenSim simulator and show that efficient EDCs placement significantly reduces outages. [less ▲]

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

in IEEE Communications Surveys and Tutorials (2019), 21(3, thirdquarter 2019), 2419-2465

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices ... [more ▼]

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas. [less ▲]

Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

in IEEE Access (2019), 7

Wearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the ... [more ▼]

Wearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the recently emerged Fog Computing (FC) paradigms unleash unprecedented opportunities to augment capabilities of wearables devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural trade-offs that exist between the paradigms and devise guidelines for proper power management of service-centric Internet of Things (IoT) applications. [less ▲]

Why Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collection Frameworks

in Pervasive and Mobile Computing (2018)

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data ... [more ▼]

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCF) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly. [less ▲]

Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications

in The 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (IEEE Mobile Cloud 2018) (2018, March)

Mobile crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. In MCS, citizens actively participate in the sensing process by contributing ... [more ▼]

Mobile crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. In MCS, citizens actively participate in the sensing process by contributing data with their smartphones, tablets, wearables and other mobile devices to a collector. As citizens sustain costs while contributing data, i.e., the energy spent from the batteries for sensing and reporting, devising energy efficient data collection frameworks (DCFs) is essential. In this work, we compare the energy efficiency of several DCFs through CrowdSenSim, which allows to perform large-scale simulation experiments in realistic urban environments. Specifically, the DCFs under analysis differ one with each other by the data reporting mechanism implemented and the signaling between users and the collector needed for sensing and reporting decisions. Results reveal that the key criterion differentiating DCFs' energy consumption is the data reporting mechanism. In principle, continuous reporting to the collector should be more energy consuming than probabilistic reporting. However, DCFs with continuous reporting that implement mechanisms to block sensing and data delivery after a certain amount of contribution are more effective in harvesting data from the crowd. [less ▲]

Cost analysis of Smart Lighting Solutions for Smart Cities

in IEEE International Conference on Communications (ICC), Paris, France, 2017 (2017, May)

Lighting is an essential community service, but current implementations are not energy efficient and impact on the energy budget of the municipalities for at least 40\%. In this paper, we propose ... [more ▼]

Lighting is an essential community service, but current implementations are not energy efficient and impact on the energy budget of the municipalities for at least 40\%. In this paper, we propose heuristics and devise a comparison methodology for new smart lighting solutions in next generation smart cities. The proposed smart lighting techniques make use of Internet of Things (IoT) augmented lamppost, which save energy by turning off or dimming the light according to the presence of citizens. Assessing costs and benefits in adopting the new smart lighting solutions is a pillar step for municipalities to foster real implementation. For evaluation purposes, we have developed a custom simulator which enables the deployment of lampposts in realistic urban environments. The citizens travel on foot along the streets and trigger activation of the lampposts according to the proposed heuristics. For the city of Luxembourg, the results highlight that replacing all existing lamps with LEDs and dimming light intensity according to the presence of users nearby the lampposts is convenient and provides an economical return already after the first year of deployment. [less ▲]

A Cost-Effective Distributed Framework for Data Collection in Cloud-based Mobile Crowd Sensing Architectures

in IEEE Transactions on Sustainable Computing (2017)

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices ... [more ▼]

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns. [less ▲]

Min_c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention

in Programming and Computer Software (2017), 43(3), 204-215

In this paper, we address energy-aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking ... [more ▼]

In this paper, we address energy-aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking into account types of applications and heterogeneous workloads that could include CPU-intensive, diskintensive, I/O-intensive, memory-intensive, network-intensive, and other applications. When jobs of one type are allocated to the same resource, they may create a bottleneck and resource contention either in CPU, memory, disk or network. It may result in degradation of the system performance and increasing energy consumption. We focus on energy characteristics of applications, and show that an intelligent allocation strategy can further improve energy consumption compared with traditional approaches. We propose heterogeneous job consolidation algorithms and validate them by conducting a performance evaluation study using the Cloud Sim toolkit under different scenarios and real data. We analyze several scheduling algorithms depending on the type and amount of information they require. [less ▲]