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Cellular networks are optimized by targeting multiple objectives. Usually, the different objectives are not coherent: minimizing the transmit power; the number of base station (BS) sleep-mode switchings, ie, ACTIVE/SLEEP state transitions; and the activity of the BSs and guaranteeing the quality-of-service (QoS) of users. Hence, suitable trade-offs have to be managed by network planners to provide an efficient solution to the challenge of booming mobile data. In this paper, we propose a multiobjective optimization framework aimed at minimizing the power consumption and the number of BS sleep-mode switchings in cellular networks, by jointly considering QoS requirements. These requirements are expressed in terms of a required bit rate for each mobile terminal. The framework deals with network management, such as the number of BSs that should be switched on, considering common diurnal patterns of the traffic demand. The optimization technique proposed in this paper is mixed-integer quadratic programming, which solves the joint power allocation and user association problem while also considering optimized bandwidth allocation schemes. The trade-off between the conflicting objectives, as well as the performance analysis in terms of the throughput and energy consumption of the network, is shown for different traffic load cases. The proposed optimization can obtain up to 60% energy savings during off-peak hours, guaranteeing QoS target requirements. By optimizing the network configuration, a 70% reduction in BS switch on/off operations can be reached in a day with 3% more energy expense.

Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.

Abstract-This paper describes an innovative monitoring technology for detecting ground-level ozone pollution, b ased on the deployment of a network of wireless devices connected to a collection of plants, used as bio-sensors. Such devices retrieve and transmit the electrical activity signals experienced in plants, used to monitor environmental conditions. The distributed plants as sensors infrastructure communicates wirelessly with a weather station, equipped with meteorological sensors as well as data logging and both wireless and wired communication capabilities. A back end server collects and analyze real-time data from the station and delivers environmental and ozone pollution information to users through a web portal. In order to perform a classification on the level of ozone exposure, a correlation-based approach has been used. The system is implemented in pilot phase with a sensing infrastructure of four plants and started operation in November 2014. As the data collection volume increases, more accurate classification techniques can be performed.