Exposing engineering students during their education to real-world problems and giving them the chance to apply what they learn in the classroom is a vital element of engineering education. The Embedded Systems course at Princess Sumaya University for Technology (PSUT) is one of the main courses that bridge the gap between theoretical electrical engineering education and the real-world. This paper presents the experience of applying project-based learning to enhance teaching the Embedded Systems course at PSUT. The feedback from students illustrated the effectiveness of this method in enhancing the understanding and the ability of students in applying embedded systems design concepts to solve real-world engineering problems.
Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively.
In computer networking, the messages are split into smaller frames which are then transmitted over the network due to mainly having limited buffer size. Frames' retransmissions, as a result of frames being corrupted or dropped, need to be avoided as much as possible due to possible network congestion. In this paper, we propose a new buffering model to describe convolutional codes with adaptive behavior that aims to mitigate frames' retransmissions caused by frames' corruption. As technology evolves, new efficient decoding algorithms are emerging and have come out. Hence, we consider, in this paper, working on decoders that are faster than previously proposed. Moreover, in our buffering model, we can reduce the number of frames being dropped due to insufficient buffer size through developing a closed-form expression which concerns about the buffer occupancy level that needs to be operated in network elements that employ decoders with adaptive behavior.
Keywords-Expected occupancy level; decoders with variablebehavior; Pareto distribution; new arriving process.
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