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In this paper, we address the issue of energy saving in wireless communication networks; a special interest is given to heterogeneous networks (HetNets) which are supposed to be the key feature of the future communication networks, namely, the 5G cellular networks . In a HetNet, different communication technologies coexist; with the use of vertical handovers, the users could switch between the different operating ones. Our work aims to make the used protocols for vertical handover collaborative, in such a way to reduce the overall network energy consumption. Our study is based on Media Independent Handover (MIH) protocol . We bring modifications into this later for energy saving purposes in order to create a green collaborative MIH protocol. The proposed enhancements on MIH protocol allow gathering detailed information about network state and using them as statistics and indicators to manage network density according to traffic load and users location. Our experiments are performed using NS2.29 with National Institute of Standards and Technology (NIST) add-on which implements the MIH protocol (802.21). By comparing the results while using our proposed contribution and the conventional MIH protocol, we can see that the modifications done allow a significant improvement in terms of energy saving while maintaining reliability. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. It enables not only remote real-time detection of situations, but also a timely reaction to reduce the damage made by harmful situations in targeted buildings. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then build a multi-output prediction model of indoor parameters using a random forest learning algorithm, and based on a longitudinal real dataset of one year. Our prediction model considers outdoor conditions to predict the indoor ones. Hence, it helps to detect discomfort situations in real-time when comparing predicted variables to real ones. Furthermore, when detecting an indoor thermal discomfort, we provide a new genetic-based algorithm to find the most suitable values of indoor parameters, enabling the improvement of the indoor occupants' thermal comfort. Numerical results show the efficiency of our prediction scheme, reaching an accuracy of 96%, as well as our genetic-based scheme in optimizing the indoor thermal parameters by 85%. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) is of great concern in cognitive radio networks since the throughput and energy consumption of secondary users (SUs) vary with the sensing time. However, the conditions of the detection probability and false alarm probability should be respected to better protect primary users (PUs) and to improve the sensing performance of SUs. Additionally, the PUs' minimum averaged power provision should also be regarded as a key problem of interactive linking to SUs. Therefore, an integrated design between the PU and SUs is desired for the coordination of the whole cognitive radio system, especially regarding the satisfaction of EE and performance metrics. This study formulates sensing constraints in a unified way and calculates the minimum SNR of SUs, based on which the essential PU power provision is computed. Furthermore, EE is proved as a decreasing function with the PU's active ratio, where the maximum EE is obtained corresponding to the minimum QoS requirements of the sensing process. Hence, a bisection-based method is proposed to maximize EE, which is considered as a concave function of SUs' sensing time and has only one unique optimum. EE's optimization was analyzed under different fusion rules for diverse SNR conditions. The optimum was also studied with sensing performance targets for various cases of PU power provision. [ABSTRACT FROM AUTHOR]