FANET optimization: a destination path flow model

Closed-loop routing in flying ad hoc networks (FANET) arises as a result of the quick changes of communication links and topology. As such, causing link breakage during information dissemination. This paper proposed a destination path flow model to improve the communication link in FANET. The models utilized Smell Agent Optimization and Particle Swarm Optimization algorithms in managing link establishment between communicating nodes. The modeled scenario depicts the practical application of FANET in media and sports coverage where only one vendor is given the license for live coverage and must relay to other vendors. Three different scenarios using both optimization Algorithms were presented. From the result obtained, the SAO optimizes the bandwidth costs much better than PSO with a percentage improvement of 10.46%, 4.04% and 3.66% with respect to the 1st, 2nd and 3rd scenarios respectively. In the case of communication delay between the FANET nodes, the PSO has a much better communication delay over SAO with percentage improvement of 40.89%, 50.26% and 68.85% in the first, second and third scenarios respectively

Interface protocol design: a communication guide for indoor FANET

The present and the future routing protocols in relation to the high throughput requirement, adaptivity to fast-changing link topology and speed makes the choice of routing protocol for unmanned aerial vehicle communication important. Due to this fact, an efficient routing protocol is highly dependent on the nature of the communication link. A flexible solution that presents these features is the use of light fidelity as a communication medium. Therefore, this paper presents the design of an interface protocol for indoor Flying Ad-hoc Network specific routing protocol using light fidelity as a communication link. The interface protocol governs communication when UAV move in a swarm. The architecture, the state machine model is discussed in this paper. Results of the design are validated via simulation using the NS3 in terms of packet delivery ratio and throughput

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods

An independent framework for off-grid hybrid renewable energy design using Optimal Foraging Algorithm (OFA)

The rapidly increase in electrical energy demand from residential, commercial and industrial sectors is one of the major challenge in power system, especially in the current period of high oil prices, steadily reducing energy sources and increased concerns about environmental pollution. Renewable energy is considered as one of the solution to this increase in power demand. The conventional method of power system cannot meet the power demand for many reasons such as environmental effects, location of the consumer, price of fuel and others. This paper presents the design of an off-grid Hybrid Renewable Energy System (HRES) for electrification of a typical remote area. The designed hybrid system consists of three different configurations of PV/Battery, Wind/Battery and PV/Wind/Battery systems. The system components are modelled and the objective function is designed as a function of total annualized cost of the system subject to some constraints binding the decision variables. The total annual cost is formulated as a function of annual capital cost and annual maintenance cost of the system subject to some operational constraints. In order to determine the optimal number of the decision variables that would satisfy the load demand in the most cost effect manner, Optimal Foraging Optimization (OFA) algorithm was used. Finally, a simulation experiment shows that the total annual cost obtained by each algorithm for the PV/Battery system is $9,340.42 or N3,876,274.30,$9,446.77 or N3,920,409.55 and $10,076.34 or N4,181,681.1 for OFA, GA and PSO respectively. For the Wind/Battery configuration, the total annual cost obtained by OFA, GA and PSO are$17,508.20 or N7,265,903, $12,493.27 or N 5,184,707.05 and$16,535.93 or N6,862,410.95 respectively. Similarly, the PV/Wind/Battery configuration showed that the OFA, GA and PSO obtained an annualized cost of $15,926.07 or N6,609,319.05,$18,167.09 or N7,539,342.35 and $16,535.93 or N6,862,410,95 respectively. From the results obtained by OFA are compared with that of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Results showed that all the algorithm can efficiently size the hybrid system with OFA obtaining the most economical design. Therefore, for economically and efficiently electrification of a remote area in Abuja using an off-grid hybrid renewable energy system, GA optimization algorithm is recommended for wind/Battery system and OFA optimization algorithm is recommended for PV/Wind/Battery system

A Modified Real-Time Fault-Tolerant Task Allocation Scheme for Wireless Sensor Networks

In WSNs, the sensor nodes are at risk of failure and malicious attacks (selective forwarding). This may have a profound negative effect when you consider real-time WSNs, making them challenging to deploy. When there is a delay in tasks allocation execution processes in real-time WSNs because of sensor nodes failures, this will cause disastrous consequences if the systems are safety-critical, e.g. aircraft, nuclear power plant, forest fire detection, battlefield monitoring, thus the need to developed a real-time system that is fault-tolerable. This paper developed a modified real-time fault-tolerant task allocation scheme (mRFTAS) for WSNs (wireless sensor networks), using active replication techniques. mRFTAS and RFTAS performance were compared using time of execution of the task, network lifetime and reliability cost. The mRFTAS performance showed an improvement over that of RFTAS when it comes to reducing the time it takes for task execution by 45.56% and reliability cost of 7.99% while prolonging the network lifetime by 36.35%

A QOBL-SAO and its variant: An open source software for optimizing PV/wind/battery system and CEC2020 real world problems

The Quasi oppositional smell agent optimization (QOBL-SAO) and its levy flight variant (LFQOBL-SAO) are two cutting-edge software tools for optimizing PV/wind/battery power systems. They can also be used to solve real-world CEC2020 optimization problems and are as good as top-performing software such as IUDE, MAgES and the iLSHAD ɛ. The QOBL-SAO exploits the random mode’s weakness and then adds a number to the initial population. The LFQOBL-SAO, on the other hand, improves the random mode’s weakness in order to solve this problem. The LFQOBL-SAO improves performance and search space by using levy flight instead of random code

Optimization of process parameters for enhanced mechanical properties of polypropylene ternary nanocomposites

Preparation of Polypropylene ternary nanocomposites (PPTN) was accomplished by blending multiwall carbon nanotube (MWCNT) in polypropylene/clay binary system using a melt intercalation method. The effects of MWCNT loadings (A), melting temperature (B) and mixing speed (C) were investigated and optimized using central composite design. The analysis of the fitted cubic model clearly indicated that A and B were the main factors influencing the tensile properties at a fixed value of C. However, the analysis of variance showed that the interactions between the process parameters, such as; AB, AC, AB2, A2B and ABC, were highly significant on both tensile strength and Young’s modulus enhancement, while no interaction is significant in all models considered for elongation. The established optimal conditions gave 0.17%, 165 °C, and 120 rpm for A, B and C, respectively. These conditions yielded a percentage increase of 57 and 63% for tensile strength and Young’s modulus respectively compared to the virgin Polypropylene used

An Independent Framework for Off-Grid Hybrid Renewable Energy Design Using Optimal Foraging Algorithm (OFA).

The rapidly increase in electrical energy demand from residential, commercial and industrial sectors is one of the major challenge in power system, especially in the current period of high oil prices, steadily reducing energy sources and increased concerns about environmental pollution. Renewable energy is considered as one of the solution to this increase in power demand. The conventional method of power system cannot meet the power demand for many reasons such as environmental effects, location of the consumer, price of fuel and others. This paper presents the design of an off-grid Hybrid Renewable Energy System (HRES) for electrification of a typical remote area. The designed hybrid system consists of three different configurations of PV/Battery, Wind/Battery and PV/Wind/Battery systems. The system components are modelled and the objective function is designed as a function of total annualized cost of the system subject to some constraints binding the decision variables. The total annual cost is formulated as a function of annual capital cost and annual maintenance cost of the system subject to some operational constraints. In order to determine the optimal number of the decision variables that would satisfy the load demand in the most cost effect manner, Optimal Foraging Optimization (OFA) algorithm was used. Finally, a simulation experiment shows that the total annual cost obtained by each algorithm for the PV/Battery system is $9,340.42 or N3,876,274.30,$9,446.77 or N3,920,409.55 and $10,076.34 or N4,181,681.1 for OFA, GA and PSO respectively. For the Wind/Battery configuration, the total annual cost obtained by OFA, GA and PSO are$17,508.20 or N7,265,903, $12,493.27 or N 5,184,707.05 and$16,535.93 or N6,862,410.95 respectively. Similarly, the PV/Wind/Battery configuration showed that the OFA, GA and PSO obtained an annualized cost of $15,926.07 or N6,609,319.05,$18,167.09 or N7,539,342.35 and $16,535.93 or N6,862,410,95 respectively. From the results obtained by OFA are compared with that of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Results showed that all the algorithm can efficiently size the hybrid system with OFA obtaining the most economical design. Therefore, for economically and efficiently electrification of a remote area in Abuja using an off-grid hybrid renewable energy system, GA optimization algorithm is recommended for wind/Battery system and OFA optimization algorithm is recommended for PV/Wind/Battery system. &nbsp

Tuning of a PID Controller Using Cultural Artificial Bee Colony Algorithm

This research is aimed at developing a cultural-algorithm based artificial bee colony algorithm (CABCA) for improved Proportional Integral Derivative (PID) controller parameters tuning. The normative and situational knowledge inherent in cultural algorithm were utilized to guide the step size as well as the direction of evolution of Artificial Bee Colony (ABC) at different configurations, in order to combat the disparity between exploration and exploitation associated with the standard ABC, which results to poor convergence and optimization efficiency. Consequently, four variants of CABCA (CABCA(Ns), CABCA(Sd), CABCA(Ns+Sd) and CABCA(Ns+Nd)) were accomplished in MATLAB R2015a using different configurations of cultural knowledge. A total of 20 standard applied mathematical optimization test functions (Ackley, Michalewicz, Quartic, Sphere etc) were employed to evaluate the performance of each CABCA variant. The results indicate that CABCA(Ns) performed best in 4 test functions (20%), CABCA(Ns+Nd) also in 4 functions (20%), while CABCA(Sd) and CABCA(Ns+Sd) performed best in 3 test cases (15%) and 2 test cases (10%) respectively. On the remaining 7 test functions (35%) of their results were similar. The CABCA(Ns) was chosen as the best performed variant based on the success ratio, which is the number of successful runs that found the solution. Hence, CABCA(Ns) was used to obtain the optimal parameters and of a PID controller which was employed in the speed control of a DC motor. The DC motor attained steady-state in 0.4178s with the CABCA-based PID controller as against 0.6778s and 2.2057s obtained using standard ABC and Ziegler-Nichols (Z-N) tuned PID controllers, respectively

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations.

This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization