ECRP: an energy-aware cluster-based routing protocol for wireless sensor networks

Energy conservation is the main issue in wireless sensor networks. Many existing clustering protocols have been proposed to balance the energy consumption and maximize the battery lifetime of sensor nodes. However, these protocols suffer from the excessive overhead due to repetitive clustering resulting in high-energy consumption. In this paper, we propose energy-aware cluster-based routing protocol (ECRP) in which not only the cluster head (CH) role rotates based on energy around all cluster members until the end of network functioning to avoid frequent re-clustering, but also it can adapt the network topology change. Further, ECRP introduces a multi-hop routing algorithm so that the energy consumption is minimized and balanced. As well, a fault-tolerant mechanism is proposed to cope up with the failure of CHs and relay nodes. We perform extensive simulations on the proposed protocol using different network scenarios. The simulation results demonstrate the superiority of ECRP compared with recent and relevant existing protocols in terms of main performance metrics.

     

Enhanced fair earliest due date first scheduling strategy for multimedia applications in LTE downlink framework

Guaranteeing a certain delay threshold for delay‐sensitive applications in long term evolution (LTE) cellular communication system is a very challenging mission. By implementing an optimal scheduling strategy, this mission will be achieved. In this article, a novel scheduler is introduced in order to meet a predefined level of service quality by guaranteeing a specific delay threshold for delay‐sensitive applications in LTE cellular systems. The proposed scheduler assigns the available resource blocks (RBs) to active user equipments (UEs) tacking into consideration several attributes. The expiration date of each packet, the channel quality, the average data rate previously achieved by each UE, and the number of dropped packets for each UE compared with the average number of packets totally dropped are all considered in the proposed scheduler working mechanism. Consequently, the proposed scheduling strategy reduces the number of packets dropped for multimedia applications, and at the same time maximizes the overall throughput of the network. Simulation results are provided to study and evaluate the performance of the proposed scheduling strategy. A comparative study is presented between the proposed strategy and the most recent scheduling techniques. The obtained results prove that the proposed scheduling strategy has considerably acceptable and appreciated results compared with the results of the state‐of‐the‐art scheduling techniques.     

Congestion‐aware multiaccess edge computing collaboration model for 5G

In 5G cloud computing, the most notable and considered design issues are the energy efficiency and delay. The majority of the recent studies were dedicated to optimizing the delay issue by leveraging the edge computing concept, while other studies directed its efforts towards realizing a green cloud by minimizing the energy consumption in the cloud. Active queue management‐based green cloud model (AGCM) as one of the recent green cloud models reduced the delay and energy consumption while maintaining a reliable throughput. Multiaccess edge computing (MEC) was established as a model for the edge computing concept and achieved remarkable enhancement to the delay issue. In this paper, we present a handoff scenario between the two cloud models, AGCM and MEC, to acquire the potential gain of such collaboration and investigate its impact on the cloud fundamental constraints; energy consumption, delay, and throughput. We examined our proposed model with simulation showing great enhancement for the delay, energy consumption, and throughput over either model when employed separately.     

A new thermal infrared and visible spectrum images-based pedestrian detection system

In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.

     

A sliding mode control and artificial neural network based MPPT for a direct grid‐connected photovoltaic source

A robust sliding mode controller for a grid‐connected photovoltaic source is proposed in this paper. The objective of the presented control scheme is to force both the output voltage of the photovoltaic PV source and the power factor at the inverter output to follow a certain trajectory reference. The main idea is to apply the robust sliding mode controller directly to the nonlinear state model of the system composed of the PV source and the inverter with its input and output filters. In order to operate the PV system at the maximum power point and to satisfy the environmental factors, such as solar irradiance and temperature, we included a rigorous maximum power point tracker based on an artificial neural network. Simulation results are presented to illustrate the performance of the proposed control scheme. In addition, we show that the grid current satisfies the harmonic limits of the IEEE standard for interconnecting distributed energy sources with electric power systems.     

Robust H∞ filtering for 2D continuous systems with finite frequency specifications

This paper investigates the design problem of robust H_∞ filtering for uncertain two-dimensional (2D) continuous systems described by Roesser model with polytopic uncertainties and frequency domain specifications. Our aim is to design a new filter guaranteeing an H_∞ performance level in specific finite frequency (FF) domains. Using the well-known generalised Kalman Yakubovich Popov lemma and homogeneous polynomially parameter-dependent matrices of arbitrary degrees, sufficient conditions for the existence of H_∞ filters for different FF ranges are proposed and then unified in terms of solving a set of linear matrix inequalities. Illustrative examples are provided to show the usefulness and potential of the proposed results.     

Exploration of Advanced Electrode Materials for Approaching High‐Performance Nickel‐Based Superbatteries

The surging interest in high performance, low‐cost, and safe energy storage devices has spurred tremendous research efforts in the development of advanced electrode active materials. Herein, the in situ growth of zinc–iron layered double hydroxide (Zn–Fe LDH) on graphene aerogel (GA) substrates through a facile, one‐pot hydrothermal method is reported. The strong interaction and efficient electronic coupling between LDH and graphene substantially improve interfacial charge transport properties of the resulting nanocomposite and provide more available redox active sites for faradaic reactions. An LDH–GA||Ni(OH)₂ device is also fabricated that results in greatly enhanced specific capacity (187 mAh g^?1 at 0.1 A g^?1), outstanding specific energy (147 Wh kg^?1), excellent specific power (16.7 kW kg^?1), along with 88% capacity retention after >10 000 cycles. This approach is further extended to Ni–MH and Ni–Cd batteries to demonstrate the feasibility of compositing with graphene for boosting the energy storage performance of other well‐known Ni‐based batteries. In contrast to conventional Ni‐based batteries, the nearly flat voltage plateau followed by a sloping potential profile of the integrated supercapacitor–battery enables it to be discharged down to 0 V without being damaged. These findings provide new prospects for the design of high‐performance and affordable superbatteries based on earth‐abundant elements.     

Multiscale proper generalized decomposition based on the partition of unity

Solutions of partial differential equations could exhibit a multiscale behavior. Standard discretization techniques are constraints to mesh up to the finest scale to predict accurately the response of the system. The proposed methodology is based on the standard proper generalized decomposition rationale; thus, the PDE is transformed into a nonlinear system that iterates between microscale and macroscale states, where the time coordinate could be viewed as a 2D time, representing the microtime and macrotime scales. The macroscale effects are taken into account because of an FEM-based macrodiscretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the domain. The proposed methodology can be seen as an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors.     

Magnetically Guided Self‐Assembly and Coding of 3D Living Architectures

In nature, cells self‐assemble at the microscale into complex functional configurations. This mechanism is increasingly exploited to assemble biofidelic biological systems in vitro. However, precise coding of 3D multicellular living materials is challenging due to their architectural complexity and spatiotemporal heterogeneity. Therefore, there is an unmet need for an effective assembly method with deterministic control on the biomanufacturing of functional living systems, which can be used to model physiological and pathological behavior. Here, a universal system is presented for 3D assembly and coding of cells into complex living architectures. In this system, a gadolinium‐based nonionic paramagnetic agent is used in conjunction with magnetic fields to levitate and assemble cells. Thus, living materials are fabricated with controlled geometry and organization and imaged in situ in real time, preserving viability and functional properties. The developed method provides an innovative direction to monitor and guide the reconfigurability of living materials temporally and spatially in 3D, which can enable the study of transient biological mechanisms. This platform offers broad applications in numerous fields, such as 3D bioprinting and bottom‐up tissue engineering, as well as drug discovery, developmental biology, neuroscience, and cancer research.     

3D Printing of Living Responsive Materials and Devices

3D printing has been intensively explored to fabricate customized structures of responsive materials including hydrogels, liquid‐crystal elastomers, shape‐memory polymers, and aqueous droplets. Herein, a new method and material system capable of 3D‐printing hydrogel inks with programed bacterial cells as responsive components into large‐scale (3 cm), high‐resolution (30 μm) living materials, where the cells can communicate and process signals in a programmable manner, are reported. The design of 3D‐printed living materials is guided by quantitative models that account for the responses of programed cells in printed microstructures of hydrogels. Novel living devices are further demonstrated, enabled by 3D printing of programed cells, including logic gates, spatiotemporally responsive patterning, and wearable devices.