فهرست مطالب شایسته طباطبائی

One of the vital activities in the economic and environmental sector of the country is fish farming, which requires precise management for optimal efficiency and profitability. The distribution and timing of feeding are crucial aspects influencing the productivity and profitability of fish farming. In this regard, an intelligent system has been designed to automatically distribute the fish feed and provide an ideal schedule for fish nutrition. This system utilizes advanced technologies such as programmable control systems and precise distribution mechanisms to improve issues related to manual fish feeding. By the Arduino output components, the system injects feed into the aquaculture environment using mechanisms like servo motors. Additionally, the system has the capability to adjust the feeding schedule based on the nutritional needs of the fish, enhancing efficiency and minimizing the need for human intervention. This system assists fish farm managers in automating and efficiently managing the feeding operations, ultimately leading to improvements in the quality and quantity of their fish production.

The main components of the fish feed distribution and scheduling system consist of three essential elements: the Arduino Uno board, serving as a programmable platform; a servo motor with a single output shaft capable of positioning itself at a specific angle by receiving encoded signals; and a feeder, functioning as a storage and dispensing container for fish feed in the aquaculture system. The system operates by the Arduino board, utilizing pre-stored code, and making decisions based on predetermined time intervals between feeding sessions. These decisions include the timing and quantity of the feed, planned according to the fish type. In this system, at the specified time, the Arduino board sends a command to activate the servo motor. Upon activation, the gears of the servo motor rotate in four different positions, determined by the set time for the feeding session, causing the feeder connected to the motor to rotate. Consequently, the feed is uniformly distributed across the pond's surface.

In the realm of fish farming, one of the main challenges is the efficient and effective management of fish nutrition. Traditional methods of nutritional management, reliant on human labor, result in human errors and uneven food distribution, jeopardizing the health and growth of fish. These methods require time-consuming planning and can lead to issues such as water pollution, disease outbreaks, and increased costs due to overfeeding or underfeeding. To address these challenges, this study proposes an intelligent feeding and scheduling system using the Arduino microcontroller. This automated system offers balanced food distribution, precise timing and frequency of feeding, and control over the amount of consumed food. By setting accurate feeding times based on fish type and preventing food wastage, the system enhances the performance of fish feeding. Leveraging its strengths, including cost-effectiveness, high precision in feeding, optimal management of feed rations, and easy accessibility, this system is practically applicable in fish farms and ponds.

Based on the results of the present study, the intelligent fish-feeding device can bring significant improvements in nutritional efficiency, growth, and health of fish and enhances the performance of fish-feeding systems, contributing to the improvement of fish farming conditions and increasing profitability in the aquaculture industry.

Internet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging information through headers is one way to improve energy consumption. This paper introduces a new clustering-based routing protocol called KHCMSBA. The proposed protocol biologically uses fast and efficient search features inspired by the Krill Herd optimization algorithm based on krill feeding behavior to cluster the sensor nodes. The proposed protocol also uses a mobile well to prevent the hot spot problem. The clustering process at the base station is performed by a centralized control algorithm that is aware of the energy levels and position of the sensor nodes. Unlike protocols in other research, KHCMSBA considers a realistic energy model in the grid that is tested in the Opnet simulator and the results are compared with AFSRP (Artifical Fish Swarm Routing ProtocolThe simulation results show better performance of the proposed method in terms of energy consumption by 12.71%, throughput rate by 14.22%, end-to-end delay by 76.07%, signal-to-noise ratio by 82.82%. 46% compared to the AFSRP protocol

Information transmitters in wireless sensor networks have limited storage and energy. One of the most critical issues in the design of these networks is the optimal use of energy since it is almost impossible to charge or replace batteries in sensor nodes. In order to solve energy limitations in sensor networks, the use of clustering algorithms can play an effective role. In fact, these algorithms help to balance the network load with proper clustering and selection of optimal cluster heads, which will reduce energy consumption and subsequently increase the network's lifespan. Accordingly, in this article, in order to select the best nodes as cluster heads, a new method based on the fruit fly algorithm and fuzzy logic is proposed. In the proposed protocol, fuzzy logic is used to calculate the odor intensity parameter in the fruit fly algorithm. Candidate nodes for clustering use the three parameters of the distance to the sink, the amount of remaining battery energy, and the distance to the center of the cluster as fuzzy logic input (to calculate the smell intensity). By simulating the proposed method and comparing it with the well-known AFSRP protocol, it can be seen that the proposed protocol has a much better performance in terms of energy consumption, data transmission delay, media access delay, and signal-to-noise ratio than AFSRP.

With the advancement of wireless communication technology, MANET networks have attracted a lot of attention due to the improvement of flexibility and cost reduction. Mobile stations in a MANET are constantly moving, so a routing protocol needs to be implemented to cope with these changes. Designing such protocols usually brings special challenges and problems. One of these challenges is the possibility of congestion due to the high rate of sending information to the destination node and also the high energy consumption of the nodes. Congestion causes loss of information and waste of energy in nodes. Accordingly, in this article, in order to control congestion, a new method based on fuzzy logic is proposed. In the proposed protocol, fuzzy logic detects, announces and controls congestion by using three parameters of queue buffer length, node mobility speed and available bandwidth as inputs. By simulating the proposed method and comparing it with the CBP protocol, it can be seen that the proposed protocol has a much better performance than CBP for congestion control.

Agriculture consumes the majority of the country's water. The control of irrigation time and the use of suitable irrigation technology are extremely crucial due to the restricted quantity and quality of water resources in IRAN for plant development in desert regions. This paper's goal is to create and develop a sensor node-based control system that will provide an ideal intelligent drip irrigation system for agricultural products. Using the hardware components as a foundation, the software component of the project is developed and produced. The hardware component includes the control box, which is utilized to collect soil moisture data via soil moisture sensors to monitor the planting environment. The second component is a C++ application that is used to analyze soil moisture data for better management and to select the best drip irrigation plan. This technology was implemented and tested as a pilot in the Saravan Higher Education Complex Innovation Center in Sistan and Baluchistan province. The findings revealed that, in addition to lowering water consumption costs, timely irrigation management boosts agricultural product productivity.

In this paper, in order to increase the accuracy of target tracking, it tries to reduce the energy consumption of sensors with a new algorithm for tracking distributed targets called hunting search algorithm. The proposed method is compared with the DCRRP protocol and the NODIC protocol, which uses the OPNET simulator version 11.5 to test the performance of these algorithms. The simulation results show that the proposed algorithm performs better than the other two protocols in terms of energy consumption, healthy delivery rate and throughput rate.

Wireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the ways to improve energy consumption. This paper presents a new cluster-based routing protocol called SFLCFBA. The proposed protocol biologically uses fast and effective search features inspired by the Shuffled Frog Leaping algorithm, which acts based on the Frog food behavior to cluster sensor nodes. The proposed protocol also uses fuzzy logic to calculate the node fitness, based on the two criteria of distance to the sink and the remaining energy of the sensor node or power of battery level. IEEE 802.15.4 Protocol and NODIC Protocol with the proposed methodology and OPNET Simulator were simulation and the results in terms of energy consumption, end to end delay, signal to noise ratio, the success property data and throughput were compared with each other. The results of the simulation showed that the proposed method outperforms the IEEE 802.15.4 Protocol and NODIC Protocol due to the use of the criteria listed.

Wireless sensor networks (WSNs) consist of sensor nodes with limited energy. Energy efficiency is an important issue in WSNs as the sensor nodes are deployed in rugged and non-care areas and consume a lot of energy to send data to the central station or sink if they want to communicate directly with the sink. Recently, the IEEE 802.15.4 protocol is employed as a low-power, low-cost, and low rate communication standard for WSNs, which ensures real-time applications via Guaranteed Time Slot (GTS). Accordingly, in this paper, a new protocol energy efficient bat algorithm and a mobile sink are presented. It can choose the optimal route based on: (1) distance of a sensor node to sink, (2), bat loudness, and (3) energy level of its battery. Using the OPNET simulator version 11.5, the proposed method was simulated by bat and NODIC protocols, and IEEE 802.15.4 and the results were considered in terms of energy consumption, end to end delay, signal to noise ratio, the success probability of sending data to sink and rate of passing data. The results of the simulation showed the use of the above-mentioned parameters in the proposed method leads to the improvement of the network throughput against using the IEEE 802.15.4 protocol, Bat algorithm, and NODIC protocol.