Many companies, such as Aramex, FedEx, and SMSA, offer product delivery services. However, the delivery process through these companies is costly and/or requires the customer’s physical attendance at the company to get the sent shipments. There is a persistent need to improve the delivery process in Saudi Arabia to reduce the effort, cost, and time that the customer spends to get the shipped products. This paper presents a new delivery approach in Saudi Arabia by developing an Android-based MobApp that allows the customers to use their mobile devices to send and receive shipped products at their doorstep by submitting online requests through the developed MobApp. The proposed MobApp, named as Door-to-Door (D2D) product delivery MobApp, guarantees fast and costless service among its competitors. The MobApp will provide its customers a reliable delivery process. It aims to provide a domestic delivery chain with whomever to wherever within Saudi Arabia. In addition, the proposed delivery MobApp allows the customers to create, update and track the delivery orders. Moreover, the proposed D2D delivery MobApp is easy to install and use, it provides a friendly GUI and has a powerful steady performance.

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The queue length and the load rate should be monitored to overcome the problem of router congestion due to the increase in network utilization and achieve a high-speed transmission. Previous active queue management methods manage the queued packets in the router buffer to maintain high network performance. However, these methods depend on monitoring indicators that do not cover all the congestion signs, leading to packet loss and delay. Accordingly, all the congestion signs should be wrapped into these indicators and managed by an algorithm that randomly drops packets to avoid global synchronization, loss, and delay. In this paper, a fuzzy comprehensive random early detection (FCRED) is proposed to deal with the gap in network monitoring and congestion control at the router buffer. FCRED is built by using three indicators, which monitor the router's arrival, departure, and queue length. Accordingly, a fuzzy inference process is developed to manage these indicators and calculate the dropping probability (Dp). Simulation results show that FCRED improves loss and packet dropping under various network statuses compared with RED, BLUE, and ERED. In terms of loss, FCRED achieves zero loss at high congested status. For dropping, FCRED achieves an optimal rate of 0.47 with an arrival rate of 0.95. For the throughput and delay, FCRED achieves the best results. Accordingly, the proposed FCRED method achieves zero loss and reduces packet dropping from 0.28 to 0.21, a 25% reduction compared with the best performance of these methods. Compared with recent fuzzy-based methods, the proposed FCRED achieves comparable results and outperforms them by dropping more packets to avoid loss, which in such case is necessary dropping.

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Active Queue Management (AQM) methods control the router's buffer to maintain high network performance and control congestion at the router buffer. Random Early Detection (RED) method is the most well-known and the most utilized AQM. RED suffers from a high dropping rate, which motivates the later AQM methods to use more complex processes, which reach the limits of using fuzzy systems as a processing technique. Yet, high computational cost affects the router's performance specifically and the network as a whole, with so-called processing delay. In this paper, a linear version of RED (LRED) is presented to reduce the computational cost of the original RED and maintain the network performance in terms of throughput, delay, dropping, and loss. LRED is built based on two distinctive features, simplifying the congestion indicator calculation and reducing the operations in calculating the dropping probability. The experimental results showed that the proposed method reduces the delay and the processing time while maintaining the throughput and loss of the RED method. Povzetek: Opisan je razvoj nove metode za upravljanje predpomnilnika na omrežju.