Typically, the electric power companies employ a group of power meter readers to collect data on the customers energy consumption. This task is usually carried out manually, which can lead to high cost and errors, causing financial losses. Some approaches have tried to minimize these problems, using strategies such as discovering the minimal route or relying on vehicles to perform the readings. However, errors in the manual readings can occur and vehicles suffer from congestion and high fuel and maintenance costs. In this work, we go further and propose an architecture to the Automatic Meter Reading (AMR) system using Unmanned Aerial Vehicles (UAV). The main challenge of the solution is to design a robust and lightweight protocol that is capable of dealing with wireless communication collisions. Therefore, the main contribution of this work is the design of a new protocol to ensure wireless communication from UAV to the power meters. We validated and evaluated the architecture in an urban scenario, with results showing a decrease of time and distance when compared to other approaches. We also evaluated the system proposed with Linear Flight Plan, the Ant Colony Optimization and Guided Local Search metaheuristic. Our mechanism attains an improvement of 98% in reducing the message collisions and reducing the energy consumption of the power meters.

Unlike giant clouds, small cloud providers (SCPs) focus attention on offering personalized services to better fit business needs of regional customers. However, SCPs exploit such a business niche with no massive computing infrastructure. To ensure well provisioned resources, capacity planning and resource management are pivotal tasks. To this end, this paper describes a regression-based resource estimation method for selecting and provisioning virtual machines (vms) with minimal configurations to meet tenants' web application workloads in SCP. From hundreds of http workload tests performed with dozens of vm configurations on the SCP's virtualization platform, a non-linear regression model was obtained to estimate not only CPU demands, but also amount of RAM required to handle workloads in different scales. A resource utilization improvement was verified from a case study of a tenant's web application monitored for a month in an SCP. According to the observed workload, estimated configurations have indicated that resources were overprovisioned for the tenant's vm, so that the underutilized residual resources could provision, at least, a new vm for all the monitoring time.

Congestion is a major problem in large cities. One of the main causes of congestion is the sudden increase of vehicle traffic during peak hours. Current solutions are based on perceiving road traffic conditions and re-routing vehicles to avoid the congested area. However, they do not consider the impact of these changes on near future traffic patterns. Hence, these approaches are unable to provide a long-term solution to the congestion problem, since when suggesting alternative routes they create new bottlenecks at roads closer to the congested one, thus just transferring the problem from one point to another. With this issue in mind, we propose an intelligent traffic system called CHIMERA, which improves the overall spatial utilization of a road network and also reduces the average vehicle travel costs by avoiding vehicles from getting stuck in traffic. Simulation results show that our proposal is more efficient in forecasting congestion and is able to re-route vehicles appropriately, performing a proper load balance of vehicular traffic.

Many Vehicular Ad-hoc Networks (VANETs) applications require that each vehicle knows precisely its current position in real time. The Global Positioning System (GPS) is technology most widely used to determine the positioning of vehicles in VANETs. However, the GPS has several drawbacks, one of them is the lack of accuracy of the measurement of impact is the most unacceptable disadvantage. In this work, we propose a roadside unit- based localization scheme to improve the accuracy level of the vehicles' position for VANETs. In this way, each one of roadside units fix the relative position error and informs all vehicles that are within of the coverage area. The proposed solution requires few roadside units, which represents a low-cost of deployment, and it was able to reduce GPS error in this critical area from 7.21 m to 0.74 m.

A number of vehicular networking applications require continuous knowledge of the location of vehicles and tracking of the routes they follow, including, e.g., real-time traffic monitoring, e-tolling, and liability attribution in case of accidents. Locating and tracking vehicles has however strong implications in terms of security and user privacy. On the one hand, there should be a mean for an authority to verify the correctness of positioning information announced by a vehicle, so as to identify potentially misbehaving cars. On the other, public disclosure of identity and position of drivers should be avoided, so as not to jeopardize user privacy. In this paper, we address such issues by introducing A-VIP, a secure, privacy-preserving framework for continuous tracking of vehicles. A-VIP leverages anonymous position beacons from vehicles, and the cooperation of nearby cars collecting and reporting the beacons they hear. Such information allows a location authority to verify the positions announced by vehicles, or to infer the actual ones if needed, without resorting to computationally expensive asymmetric cryptography. We assess the effectiveness of A-VIP via realistic simulation and experimental testbeds.

Knowledge of the location of vehicles and tracking of the routes they follow are a requirement for a number of applications. However, public disclosure of the identity and position of drivers jeopardizes user privacy, and securing the tracking through asymmetric cryptography may have an exceedingly high computational cost. In this paper, we address all of the issues above by introducing A-VIP, a lightweight privacy-preserving framework for tracking of vehicles. A-VIP leverages anonymous position beacons from vehicles, and the cooperation of nearby cars collecting and reporting the beacons they hear. Such information allows an authority to verify the locations announced by vehicles, or to infer the actual ones if needed. We assess the effectiveness of A-VIP through testbed implementation results.