Intelligent reflecting surface (IRS) is deemed a promising technique for future wireless and mobile technologies due to its ability to reconfigure the radio propagation environment. In this paper, we analyze the physical layer security (PLS) of a downlink non-orthogonal multiple access (NOMA) network through an IRS intended for assisting the wireless transmission, taking into account the external and internal eavesdropping scenarios. Considering the hardware limitations, a 1-bit coding scheme is utilized to investigate the secrecy performance of the IRS-aided NOMA (IRS-NOMA) network. Exact and asymptotic expressions in terms of the secrecy outage probability (SOP) and the effective secrecy throughput (EST) are presented, which are in consideration of the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC). Based on the analytical results, the secrecy diversity orders are related to the number of reflecting elements and EST ceilings are observed in the high SNR regime. Monte Carlo simulations are presented to corroborate the correctness of the analyses and illustrate that the IRS-NOMA scheme is superior in the secrecy outage performance to the IRS-aided orthogonal multiple access scheme. In addition, the SOP performance enhancement through the increased number of reflecting elements and the improvement of EST through proper target secrecy rate are also evidenced by the simulations.

The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.

Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement.

Large scale grids permit to share grid resources spread over different autonomous administrative sites in the internet. The rapid progress of grid systems opens the door for numerous companies to adopt this technology in their business development. This progress is characterized by the increasing openness and opportunity of resource-sharing across organizations in different domains. In the business context, these shared resources can be misused by some malicious users that can abuse the provided resources and make them behave maliciously to return wrong results and sabotage the jobs execution. The common technique used by most grid systems to deal with this problem is based on replication with voting. Nevertheless, these techniques rely on the assumption that the grid resources behave independently. They may be ineffective where a number of collusive resources collectively return the same wrong results of a job execution. In order to overcome this threat, we propose a reputation-based voting (RBV) approach, which investigates the trustworthiness of the grid resources through a reputation system, and then takes a decision about the results. In addition, the performance of our approach and other voting techniques, like m-first voting and credibility-based voting, are evaluated through simulation to perceive the effect of collusive grid resources on the correctness of the results. The obtained results show that our approach achieves a lower error-rate and better performance in terms of overhead.