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This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the $ {E_D} $ ED. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics. [ABSTRACT FROM AUTHOR]

In situations where a regular network may not be appropriate or is unable to function correctly, data collection and real-time transmission are commonly done using Wireless Sensor Networks (WSNs). In order to accomplish this type of operation, the network's durability and performance are extremely vital. One of the key elements that directly affect a network's lifespan and performance is the routing protocol. This study proposes a Geographic Forwarding Energy Efficient Routing Protocol (GF-EERP), an enhanced version of the Geographic Energy Aware Routing (GEAR) protocol that helps to extend the network's lifespan and performance. The introduction of node categorization, a unique technique for choosing the region head, use of multi-hop communication method and removal of dead nodes serve as the foundation of the GF-EERP. In this study, the proposed protocol's performance is also compared to other protocols already in use, like Geographic Adaptive Fidelity (GAF), Geographic Energy Aware Routing (GEAR), Greedy Perimeter Stateless Routing (GPSR) and Multihop-Geographic Energy Aware Routing (M-GEAR), using a variety of performance metrics including Network Delay, Data Delivery Ratio and Network Throughput. The simulation outcomes prove that the performance of GF-EERP, with its distinct node categorization and region head selection mechanism is superior to the existing protocols. [ABSTRACT FROM AUTHOR]

This research suggests a new flexible method for the optimal operation of multi-carrier energy systems based on optimization techniques for different weather conditions and seasons. The results apply to different modes (a hot summer day, a cold winter day and a normal day). A new cost function from the operator's point of view is suggested considering the constraints on consumable gas supply and electrical energy in the cold and hot seasons respectively. Furthermore, a novel algorithm is suggested that gives the economical values of energy carriers, final cost, and the percentage of the direct electrical energy supply and outside the hub. In this method, load data, related prices, constraints, and penalties are applied to the suggested model, and afterward hub optimization assists in finding the optimal operation and costs. MATLAB software is used to check the results and the simulation outputs confirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Hybridisation of networks is a trend that will continue to evolve for more efficient communication solutions. Hybrid power line communication (PLC) and wireless networks deployed for the Internet of Things (IoT) applications have improved the efficiency of low-power and lossy networks (LLNs). State-of-the-art solutions on hybrid networks are mainly application specific and does not provide a mathematical model to evaluate the network performance. Furthermore, the parallel PLC and wireless networks result in more power consumption due to the additional circuitry required to support and switch between two networks. There is a need for a generalized study on hybrid network architectures. A mathematical model must be developed to evaluate the performance of hybrid networks. Unlike parallel PLC and wireless networks, some intermediate solutions must be proposed that can offer the benefits of wired and wireless networks without increasing power consumption and circuit complexity. The paper presents a generalized study on hybrid network architectures. A hybrid network has been proposed that can offer the benefits of wired and wireless networks in the best possible way. A novel Markov-based stochastic model has been developed to evaluate the reliability of hybrid network designs. The analytical results obtained from Continuous Time Markov Chains (CTMC) have been validated by simulation using MATLAB. Spectral and energy efficiency have been evaluated for the hybrid designs along with a trade-off analysis. The proposed hybrid network (with 50% jump nodes being hybridized) is 33.53% more reliable for data transmission than parallel wired and wireless networks after 100 hours of operation. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of resource allocation in wireless networks, given the increasing usage of mobile devices and sensors. To achieve energy efficiency, we propose a novel method called Hybrid Random Forest Ensemble Support Vector Machine (RFESVM) with Chaotic Cloud Quantum Bat Algorithm (CCQBA). The proposed method identifies the best power allocation for each user in Device-to-Device (D2D) communication, which can significantly improve coverage and lower data rates and latency. To overcome the shortcomings of existing methods, we use the chaotic cloud quantum bat algorithm that combines randomness, ergodicity of chaotic mapping, and stability inclination to enhance the convergence speed. To address the issue of class imbalance in the dataset, we combine SVM with RF and establish an ensemble technique called RFESVM. Our proposed method achieves higher energy efficiency compared to other techniques, demonstrating the effectiveness of optimal power allocation in D2D communication. Overall, the proposed method can significantly improve the systems' energy efficiency while ensuring high network performance in wireless networks. [ABSTRACT FROM AUTHOR]

This paper proposes an Indirect Field Oriented Controlled Dual Stator Multiphase Induction Motor (IFOC-DSMIM) as a traction unit in an Electric Vehicle (EV) for the enhancement of its fuel economy and average loading capacity. The performance of the drive is studied by loading it under standard drive cycles that include free running, acceleration, and braking conditions using the MATLAB/Simulink environment. Its performance analysis is carried out to obtain efficiency curves under the three modes of excitation using a steady-state equivalent circuit approach. Studies on load sharing between the two stators, speed tracking and torque-producing capability of the drive are executed on its dynamic model and the simulation results are depicted. The average energy efficiency of the vehicle with the proposed DSMIM drive system is at least 10% higher than a Single Stator Multiphase Induction Motor (SSMIM) drive under different load percentages. Furthermore, under 75% of rated load conditions the energy efficiency is 20% higher. Thus, the proposed DSMIM configuration promises to provide a sizeable energy saving in the EV system, reducing the energy cost and increasing the driving range. [ABSTRACT FROM AUTHOR]

With the proliferation of the internet of things (IoT) and device-to-device (D2D) communications enabled by the boom of mobile computing technology, secure high-speed communication has now become indispensable in our daily life. This is especially true when potentially private data are being continually sensed by and communicated among mobile devices as they exist in a world of interconnected inanimate objects, which is also one of the main themes of the upcoming 5G revolution. As the amount of data to be secured for high-speed communications is vast, there is a need to ensure that the block ciphers used for encryption are deployed without incurring significant computational cost. In this paper, we present fast implementations of recent industry standard block ciphers in typical embedded platforms, consisting of multi-core CPU (ARM A15 and A7) and GPU (Mali T628). We implemented the conventional block cipher (AES) and lightweight block ciphers (CLEFIA, SIMON, SPECK and PRESENT) optimized for fast computation. We also analyze the energy efficiency of these block ciphers computation in CPU and GPU, as low power consumption is crucial for the embedded system. Our experimental results show that the embedded GPU is not only able to compute block ciphers faster than conventional CPU but also consumes significantly less power. [ABSTRACT FROM AUTHOR]

Increasing data capacity requirements in cellular networks have raised the demand of low-cost and energy-efficient small cells to reduce the overall operating cost of the access network. This paper reports on the development of an energy-efficient and low-cost power amplifier design for TD-LTE outdoor pico base station. The proposed design of the power amplifier uses double-stage balanced configuration by exploiting the latest available process technologies on low-power devices and avoids commonly used a high-power LDMOS transistor. We examined the conceptual design of the power amplifier through ADS and SystemVue simulations and subsequently validated through practical implementation. The contribution of this paper is that the proposed design of pico base station operates at 12 V DC input supply rather than conventional −48 V which avoids the requirement of −48 V to 28 V DC–DC converter. As a result, the overall power efficiency of the proposed power amplifier for outdoor pico base station is increased by 2.7% in addition to the reduction of cost by 40%. Due to the light weight and small size, IP-65 compliant outdoor pico BS with proposed power amplifier can be contained in a single box. [ABSTRACT FROM AUTHOR]