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 ▲]

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 ▲]