Flying ad hoc networks (FANETs) consist of unmanned aerial vehicles (UAVs) with energy limitations which have the capability of sending recorded live video stream to supervise their surroundings completely and intelligently. Although significant efforts have been made by previous researchers to increase the quality of received video stream as a main mission of a UAV, challenges like energy consumption, effective use of bandwidth, effective clustering among UAVs and their intelligent communication with ground stations especially at the same time have not been noticed in the past research studies simultaneously. Therefore, in the proposed method, for the first time, a low complex AHP-TOPSIS hybrid algorithm has been used for effective clustering in FANETs. Cluster heads (CHs), in addition to imaging, receive the recorded videos frames by other UAVs through Wi-Fi and send them to the ground station through 5G connection. Using AHP-TOPSIS algorithm, the ground controller intelligently specifies which UAVs should be CH in regular intervals. Therefore, because of UAVs’ swarm reduction and, at the same time, effective use of bandwidth, traffic and delay in transferring live video frames are reduced which leads to achieving high video quality in ground station and, at the same time, reduction UAV energy consumption. The results of numerous simulations in OMNET +  + under different conditions show that the parameters of video quality percentage, UAV average energy consumption and the number of necessary cluster head have been significantly improved when two famous mobility models including Paparazzi and Random Waypoint are considered comparing other methods.

An enhanced AHP-TOPSIS-based clustering algorithm for high-quality live video streaming in flying ad hoc networks

Providing required level of service quality in cloud computing is one of the most significant cloud computing challenges because of software and hardware complexities, different features of tasks and computing resources and also, lack of appropriate distribution of tasks in cloud computing environments. The recent research in this field show that lack of smart prioritization and ordering of tasks in scheduling (as an NP-hard problem) has been very effective and resulted in lack of load balancing, response time increase, total execution time increase and also, average resource use decrease. In line with this, the proposed method of this research called LATOC considered first the key criteria of an input task like required processing unit, data length of task and execution time. Then, it addressed task prioritization in separate queues using the technique for order preference by similarity to ideal solution (TOPSIS) and analytic hierarchy process (AHP) in figure of a hybrid intelligent algorithm (AHP-TOPSIS). Each ordered task in separate priority queues was placed based on its priority level, and then, to assign each task from each priority queue to virtual machines, optimized particle swarm optimization was used. Many simulations based on various scenarios in Cloudsim simulator show that smart assignment of prioritized tasks by LATOC resulted in improvement of important cloud computing parameters such as total execution time and average resource use comparing similar methods.

LATOC: an enhanced load balancing algorithm based on hybrid AHP-TOPSIS and OPSO algorithms in cloud computing

HATMOG: an enhanced hybrid task assignment algorithm based on AHP-TOPSIS and multi-objective genetic in cloud computing

Software-defined networks (SDNs) are an emerging technology to improve the scalability and network performance of data-intensive applications with high-volume traffic. Load balancing among controllers with several switch migrations is a major obstacle in a multi-controller SDN, degrading the overall system’s performance. To resolve the above issue, this paper proposes OptiGSM, an optimum greedy-based switch migration approach for load balancing among the controllers. OptiGSM starts the switch migration process from an overloaded controller to an underloaded controller if the mean-square deviation of a load of each controller from the average load of all controllers is greater than a pre-defined threshold. A controller is overloaded if its load is greater than the average load of all controllers and vice-versa. We introduce an algorithm for load balancing and prove that it achieves load balancing using the minimum number of switch migrations. Next, we analyze the time and space complexities of the algorithm. Furthermore, we evaluate OptiGSM using simulation and experimental studies. Numerical results indicate that OptiGSM outperforms conventional approaches in terms of the number of switch migrations, response time, throughput, and delay.

OptiGSM: Greedy-Based Load Balancing with Minimum Switch Migrations in Software-Defined Networks

Property service quality (PSQ) evaluation is the key means of property management. Traditional PSQ evaluation is usually based on the rational hypothesis of the evaluators. The evaluators’ behaviour with bounded rationality is more in line with the characteristics of modern PSQ evaluation practice. At this point, evaluation information is usually incomplete information and heterogeneous data. The existing LINMAP methods have the ability to process incomplete information and heterogeneous information, but cannot adapt to evaluation information processing under bounded rational behaviour. As one of the major achievements of behavioural economics, prospect theory can explain people’s bounded behaviour when making decisions. The purpose of this paper was to develop a linear programming technique for multidimensional analysis of preference (LINMAP) model based on prospect theory for modern PSQ evaluation. First, a data preprocessing method was designed by referring to the TOPSIS method. Then, the value function was used to construct the dominance matrix, and the LINMAP model based on prospect theory was constructed. Finally, all alternatives were ranked in order according to overall performance value, which was solved by the LINMAP model. The feasibility and effectiveness of the proposed method were demonstrated by example analysis. Based on the evaluation results of PSQ of public projects, the important items and excellent property service projects and their reasons were determined, and some suggestions were proposed to improve PSQ.

A LINMAP Method Based on the Bounded Rationality of Evaluators for Property Service Quality Evaluation

Considering a software defined network, distributed controller architecture uses multiple controllers in which each controller manages a part of the network. The load imbalance problem in this architecture causes a large number of switch migrations resulting in a significant increase in switch migration cost and average network response time along with a decrease in throughput. Although recent studies have addressed these issues, access to optimal response time had been achieved with high cost of switch migration and sometimes with reduction of throughput using their methods. Therefore, the load balance in the present study is managed by a variable threshold based on the controllers’ workload. In other words, migration is done by selecting optimal switch and controller so that the switch will be selected with the lowest traffic generation rate which could return the source controller to its steady state. Using the suggested method, a destination controller is selected based on some important parameters such as CPU utilization, rate of incoming packets and the number of hops between switch and controller. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) algorithm is used to select the best controller based on the above-mentioned criteria and the AHP (analytic hierarchy process) algorithm is employed for determining the ratio of each criterion. The proposed method considerably outperforms other methods by achieving about 6 and 78% improvement in throughput and the number of switch migration in our implementation, respectively.

An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks

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An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks