Application of NSGA-II to Obtain the Charging Current-Time Tradeoff Curve in Battery Based Underwater Wireless Sensor Nodes

In this paper, a novel application of the Nondominated Sorting Genetic Algorithm II (NSGA II) is presented for obtaining the charging current–time tradeoff curve in battery based underwater wireless sensor nodes. The selection of the optimal charging current and times is a common optimization problem. A high charging current ensures a fast charging time. However, it increases the maximum power consumption and also the cost and complexity of the power supply sources. This research studies the tradeoff curve between charging currents and times in detail. The design exploration methodology is based on a two nested loop search strategy. The external loop determines the optimal design solutions which fulfill the designers’ requirements using parameters like the sensor node measurement period, power consumption, and battery voltages. The inner loop executes a local search within working ranges using an evolutionary multi-objective strategy. The experiments proposed are used to obtain the charging current–time tradeoff curve and to exhibit the accuracy of the optimal design solutions. The exploration methodology presented is compared with a bisection search strategy. From the results, it can be concluded that our approach is at least four times better in terms of computational effort than a bisection search strategy. In terms of power consumption, the presented methodology reduced the required power at least 3.3 dB in worst case scenarios tested.

Intelligent Reflecting Surface-Assisted Secure Multi-Input Single-Output Cognitive Radio Transmission

Intelligent reflecting surface (IRS) is a very promising technology for the development of beyond 5G or 6G wireless communications due to its low complexity, intelligence, and green energy-efficient properties. In this paper, we combined IRS with physical layer security (PLS) to solve the security issue of cognitive radio (CR) networks. Specifically, an IRS-assisted multi-input single-output (MISO) CR wiretap channel was studied. To maximize the secrecy rate of secondary users subject to a total power constraint (TPC) for the transmitter and interference power constraint (IPC) for a single antenna primary receiver (PR) in this channel, an alternating optimization (AO) algorithm is proposed to jointly optimize the transmit covariance R at transmitter and phase shift coefficient Q at IRS by fixing the other as constant. When Q is fixed, R is globally optimized by equivalently transforming the quasi-convex sub-problem to convex one. When R is fixed, bisection search in combination with minorization–maximization (MM) algorithm was applied to optimize Q from the non-convex fractional programming sub-problem. During each iteration of MM, another bisection search algorithm is proposed, which is able to find the global optimal closed-form solution of Q given the initial point from the previous iteration of MM. The convergence of the proposed algorithm is analyzed, and an extension of applying this algorithm to multi-antenna PR case is discussed. Simulations have shown that our proposed IRS-assisted design greatly enhances the secondary user’s secrecy rate compared to existing methods without IRS. Even when IPC is active, the secrecy rate returned by our algorithm increases with transmit power as if there is no IPC at all.

DRV Evaluation of 6T SRAM Cell Using Efficient Optimization Techniques

An optimization based method which uses bisection search algorithm has been proposed to evaluate the accurate value of Data Retention Voltage (DRV) of a 6T Static Random Access Memory (SRAM) cell using 45 nm technology in the presence of process parameter variations. Further, we incorporate an Artificial Neural Network (ANN) block in our proposed methodology to optimize the simulation run time. The highest values obtained from these two methods are declared as the DRV. We noted an increase in DRV with temperature (T) and process variations (PVs). The main advantage of the proposed technique is to reduce the DRV evaluation time and for our case, we observe improvement in evaluation time of DRV by ≈46, ≈27, and ≈8 times at 25°C for 3 σ, 4 σ, and 5 σ variations, respectively, using ANN block to without using ANN block.

An Input-to-State Stability-Based Load Restoration Approach for Isolated Power Systems

An isolated power system (IPS) is usually operated under a bad environment and is influenced by external disturbances. Advanced load restoration technology is an important way to enhance the survivability and reliability of IPS. This paper proposes a fast load restoration approach based on Input-to-State Stability (ISS) theory for IPS. The method can recover load after an outage happens in an IPS under severe perturbations. In the proposed restoration approach, both stability and security constraints are considered based on the ISS theory, which can guarantee the stable and secure operation of IPS during the restoration dynamic process. These constraints have good adaptability for topology transformation and operation status transition of IPS. A heuristic approach to efficiently solve the load restoration problem is proposed, where bisection search is used to check the feasibility of the loads to be restored. Case studies on a typical IPS are used to verify the proposed method.