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Contemporary electromagnetic computer‐aided design tools augmented with parametric and optimization capabilities are extensively used in industry for design, validation, and prototyping to reduce time‐to‐market, resources, cost, and risks associated with the development of new radio frequency (RF) circuits and systems. Unfortunately, this technology still remains largely underutilized in undergraduate systems engineering education. This paper describes an approach for stimulating the analysis and design learning experience of undergraduate systems engineering students by engaging them in the cooperative, experiential, simulation‐assisted teaching, and research activities utilizing project‐based pedagogy. A case study is chosen in the realm of microwave‐assisted material processing, which is not typically covered in undergraduate engineering curricula. The objective is to further increase students' interest in electromagnetic and RF systems by providing a well‐rounded learning experience to break the monotony often encountered in the heavily theoretical and abstract topics involved with a minimum of complex analytical formulations, reinforce fundamental principles and mathematical analyses offered in the class, foster students' motivation and enthusiasm, reflect on today's technological advancement in industry, and stimulate early participation of undergraduate students in open‐ended research problems. [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) refers to the rapidly growing network of connected objects and people that are able to collect and exchange data using embedded sensors. To guarantee the connectivity among these objects and people, fault tolerance routing has to be significantly considered. In this paper, we propose a bio-inspired particle multi-swarm optimization (PMSO) routing algorithm to construct, recover, and select $k$ -disjoint paths that tolerates the failure while satisfying the quality of service parameters. Multi-swarm strategy enables determining the optimal directions in selecting the multipath routing while exchanging messages from all positions in the network. The validity of the proposed algorithm is assessed and results demonstrate high-quality solutions compared with the canonical particle swarm optimization (CPSO). Our results indicate the superiority of the multi-swarm and fully PMSO with constriction coefficient, which record an average improvement over CPSO equal to 88.45% in terms of sensors’ count, and 89.15% and 86.51% under the ring and mesh topologies, respectively. [ABSTRACT FROM PUBLISHER]

Modern multimedia sensor networks impose strict constraints on both the delay and energy consumption when time-critical data must be reported to the sink within a limited bandwidth without any loss. Failure to transmit an event to the sink occurs for many reasons, including inherence limitations of sensors, power consumption, and reliability. We propose a mathematical model for a novel quality-of-service (QoS) routing-determination method. The proposed scheme enables determining the optimal path to provide appropriate shared radio satisfying the QoS for a wide range of real-time intensive media. The mathematical model is based on the Lagrangian relaxation method, to control adaptive switching of hop-by-hop QoS routing protocols. The embedded criteria for each objective function are used to decide which path from source to sink will be selected. Simulation results show that, compared with existing routing protocols, the approach proposed in this paper significantly improves the packet received ratio, energy consumption, and average end-to-end delay of the sensor node. [ABSTRACT FROM PUBLISHER]

Multimedia sensor networks for real-time applications have strict constraints on delay, packet loss, and energy consumption requirements. For example, video streaming in a disaster-management scenario requires careful handling to ensure that the endto- end delay is within the acceptable range and the video is received properly without any distortion. The failure to transmit a video streameffectively occurs for many reasons, including sensor function limitations, excessive power consumption, and a lack of routing reliability. We propose a novel mathematical model for quality of service (QoS) route determination that enables a sensor to determine the optimal path for minimising resource use while satisfying the required QoS constraints. The proposedmathematical model uses the Lagrangian relaxation mixed integer programming technique to define critical parameters and appropriate objective functions for controlling the adaptive QoS constrained route discovery process. Performance trade-offs between QoS requirements and energy efficiency were simulated using the LINGOmathematical programming language. The proposed approach significantly improves the network lifetime, while reducing energy consumption and decreasing average end-to-end delays within the sensor network via optimised resource sharing in intermediate nodes compared with existing routing algorithms. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with Wireless Sensor Networks (WSNs) typically involves multihop relaying combined with sophisticated signal processing to serve as an information provider for several applications such as smart grids, industrial, and search-and-rescue operations. These applications entail deploying many sensors in environments that are often random which motivated the study of beamforming using random geometric topologies. This paper introduces a new algorithm for the synthesis of several geometries of Collaborative Beamforming (CB) of virtual sensor antenna arrays with maximum mainlobe and minimum sidelobe levels (SLL) as well as null control using Canonical Swarm Optimization (CPSO) algorithm. The optimal beampattern is achieved by optimizing the current excitation weights for uniform and non-uniform interelement spacings based on the network connectivity of the virtual antenna arrays using a node selection scheme. As compared to conventional beamforming, convex optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the proposed CPSO achieves significant reduction in SLL, control of nulls, and increased gain in mainlobe directed towards the desired base station when the node selection technique is implemented with CB. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is dramatically growing in support of the recent revolutionary cloud‐based survivability applications. It has to meet the performance expectations for these applications in real‐time while optimizing the available cloud resources. In this paper, we propose a cooperative resource scheduling in energy‐constrained applications for a reliable and fault‐tolerant performance. We present a task scheduling algorithm based on robust canonical particle swarm optimization (CPSO) and fully informed particle swarm optimization (FIPS) algorithm to solve the problem of resource allocation in both homogeneous and heterogeneous cloud‐based IoT applications. Our objective is to satisfy the quality of service in terms of throughput and delay by performing optimal task scheduling while considering the different experienced data traffic categories. Our results show that throughput and delay can be significantly improved while using the FIPS approach in comparison to the CPSO optimization algorithm. [ABSTRACT FROM AUTHOR]