A Connected Carbased Parking Location Service System The number of vehicles is increasing rapidly, which results in finding a free parking spot as a prominent problem. Due to the increased number of cars, drivers have to hunt around the streets and parking facilities searching for free parking spots. Consequently, there are concerns about traffic congestion as well as time consumption, fuel wasting, and air quality degradation due to the emission of the fine dust at residential areas and shopping districts. To alleviate the problems, we propose a connected carbased onstreet parking location service system, in which the system consists of the parking space detection device (DD), the cloud server, and mobile application. Each of the cars in the system is equipped with a DD that acts as a mobile sensing unit (MSU). The proposed system is based on the concept of crowdsourcing; thus, each MSU is responsible for detecting the free parking location and transmitting the parkingrelated information (MSU id, MSU location, the width of detected free parking location) to the server through a wireless network. Later, a user (i.e., driver) can select a free parking location and navigate to its feasible parking location via a mobile application. However, when the system is unable to detect any free onstreet parking location, it suggests users about offstreet parking facilities near the useru0027s intended destination.

A CapacityAware User Recruitment Framework for FogBased Mobile CrowdSensing Platforms Mobile CrowdSensing and Fog Computing are fundamental Internet of Things technologies tailored for smart cities. The former enables userxe2x80x99s devices to collect and share data in urban environments. The latter shifts the computation close to end users, lightening the work that their devices have to perform to communicate sensed data in the Cloud. In a fogbased MCS campaign a large number of devices with heterogeneous resources executes sensing tasks generally distributed by remote servers. A careful selection of some of these usersxe2x80x99 devices for sensing operations can bring benefits to the whole platform in terms of computational costs and energy saving. In this paper, we propose a novel usersxe2x80x99 recruitment model based on distance, computational capacity, and residual battery of devices. The selection process is carried out in a scenario where devices of the MCS campaign periodically share their battery and Central Processing Unit status to fog nodes through their shortrange communication interfaces. Based on this information, fog nodes select devices suitable for performing specific tasks. To verify the effectiveness of the proposed model, we compare our solution with a selection model based only on distances, using an MCS simulator suitably modified for fogbased scenarios as testbed. Results show that our model is able to achieve a more accurate task resolution and a more effective recruitment selection, detecting those devices that can perform sensing operations better than others, thus, guaranteeing an overall average saving of computational and energy resources.