In this paper, we propose the design, operation, and implementation of an Internet of Things based hybrid structural health monitoring. This innovative system leverages the capabilities of both fog and cloud layers in computing and monitoring. The system architecture consists of leaf nodes deployed on a target structure. These nodes, synchronously, collect acceleration signals from accelerometers attached directly to the structure and transmit the data to an on-site central node via short-range communication protocol. At the fog layer, the central node, applies damage detection algorithms on the collected data. If a damage is detected, it forwards the acceleration signals to a cloud-based monitoring server using cellular internet connectivity where more complex algorithms are used to identify and locate the damage. We provide detailed information about the design of the different system nodes, the implementation of damage detection algorithms, and the architecture of the monitoring server. To evaluate the effectiveness of the proposed system, several practical experiments were conducted. The results demonstrate that the hybrid system presented in this paper provides an efficient, reliable and cost-effective approach to damage detection and identification in civil infrastructures.

This paper presents a new method for the Optimal Micro-Phasor Measurement Units placement (OPP) for distribution grids. A general integer linear programming framework has been defined by dividing the distribution grid into two parts, non-branching bus series and overlapped buses. An objective function is defined for each part associated with observability constraints. Also, a region matrix is used to describe a grid configuration instead of defining a much larger grid connectivity matrix. Based on the region matrix definition, search space reduction technique is used for the NBS region which result a reduction of the number of decision variables and the number of constraints is reduced. The main objective goal of the optimization problem is to find the optimal number of units and the current channels used by each unit for the distribution grid. To evaluate the efficiency of installing the PMUs, which use different numbers of current channels, a cost model for the PMU is defined and used for the optimization problem. The cost model of the unit is divided into development and running costs as a function of the current channels number. The proposed method is tested for IEEE 33 and 69 bus distribution grid and compared with the conventional OPP. Three cases are studied in the comparison considering redial, Micro Grid (G) configuration, and current channels limitations. The proposed method shows the ability to find the optimal number of current channels with the same number of unties of Conventical Method (CM).

Opportunistic spectrum access (OSA) and self-interference cancellation (SIC) are two emerging solutions for enhancing spectrum utilization, which are expected to impact the design of 5G networks. In this paper, we consider the problem of composing an opportunistic LTE-A network using a set of existing base stations (BSs) with heterogeneous SIC capabilities and costs. The objective is to design the cheapest network that can support the probabilistic rate demands of multiple users. Towards achieving this goal, we propose novel stochastic joint channel and BS allocation schemes that account for uncertainty in channel availability. First, we develop two static (proactive) joint allocation models. We refer to these models as Het-SMKP1 and Hom-SMKP. In these models, the allocation is done once such that user demands are probabilistically met. In Het-SMKP1, a user can request different probabilistic rates for different small cells, whereas in Hom-SMKP each user requests the same probabilistic rate for the entire network. Second, we propose an adaptive (proactive and reactive), two-stage allocation model for heterogeneous rate demands, which we refer to as Het-SMKP2. The adaptive model allows for correcting the initial resource allocation once the channel availability uncertainties are partially resolved. We numerically evaluate the performance of the static and adaptive allocation schemes under various system parameters. Despite its computational complexity, the adaptive scheme improves the probability of rate demand satisfaction considerably compared to the static scheme.

Two promising solutions have been recently proposed to address the massive growth in mobile traffic and wireless devices: LTE-U and in-band full-duplex (FD) wireless. LTE-U extends the benefits of LTE-A to the unlicensed 5 GHz band, used mainly by Wi-Fi users. However, the uncertainty in Wi-Fi user activities makes provisioning QoS guarantees to LTE-U users challenging. On the other hand, FD wireless can double spectrum efficiency by enabling simultaneous transmission and reception over the same frequency band. Our objective in this paper is to exploit excess capacity of deployed Wi-Fi networks (operating in the 5 GHz band) to orchestrate a `robust' virtual LTE-U network from a hybrid set of half-duplex (HD) and FD Wi-Fi access points (APs). Although the orchestrated LTE-U network does not support deterministic QoS guarantees, it is designed to provide prespecified probabilistic QoS guarantees (hence, it is robust). Towards achieving our goal, we develop novel stochastic resource allocation formulations that optimally orchestrate a virtual LTE-U network from a hybrid set of HD/FD APs with the minimum cost. We first consider the single small-cell problem and propose a stochastic formulation, which we refer to as CCLTEU<sub>single</sub>. Then, we study the multi-cell stochastic allocation problem and develop another formulation, which we refer to as CCLTEU<sub>multi</sub>. Our formulations adopt a `chance-constrained stochastic programming' approach. We derive the deterministic equivalent programs of CCLTEU_single and CCLTEU_multi and evaluate them numerically under various system parameters.

In this paper, we present an Internet of Things framework for structural health monitoring. The proposed system detects the delimitation and debonding in composite concrete structures: more specifically, develop an IoT-Based Non-Destructive Test (NDT) to detect debonding between Carbon Fiber sheets and Concrete slabs. An Inter Digital Capacitance Sensor is designed and fabricated to function as the primary detection element. The sensor is embedded within an IoT node that manages the measurement process, captures the measurement location automatically, and performs essential data filtering operations with an attached graphical interface that allows basic control and early-access to the measured data. The IoT node integrates within the larger framework via wireless WiFi connection throughout which the data is transferred and control functions are administrated. The paper presents the system design details including mathematical modeling for the capacitance sensing element, finite element simulation results, and practical setup measured data.