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Wireless sensors networks (WSN) become more popular in recent years. For their efficient use, it is necessary to define energy-efficient routing protocols. In WSN, it sometimes arrives that some sensors have items that belong or not to them. The aim being to send each item to its real destination. This problem is known as the permutation routing problem. Some sensors may fail during the routing process. These particular cases must not prevent the operation of routing as a whole. Thus, it becomes important to ensure the fault-tolerance of the routing mechanism. In this paper we propose an energy-efficient and fault-tolerant permutation routing protocol for WSN. The proposed PEFTOSPRO protocol is realized in four stages. Firstly, we partition the sensors in cliques within which Cluster Heads (CH) are elected. Secondly, we achieve the hierarchical clustering of CH. Thirdly, we route items to their belonging cliques. And finally, we route each item to it real destination within all the cliques. We use the wake and sleep technique and the fault-tolerance in the data routing process in order to save the sensors' energy. Simulations show that the wake and sleep technique and the fault tolerance management during the data routing process help to save the sensors' energy, and by doing so, prolong the lifetime of the WSN. PEFTOSPRO is a suited protocol to solve the permutation routing problem in a multi-hop environment for wireless sensor networks, because it ensures that sensors efficiently use their energy and takes into account the fault tolerance. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) applications have massively increased in the last few years. Future‐generation IoTs are going to be massive in number, heterogeneous in nature, and extremely demanding in terms of computation and communication requirements. Therefore, effective techniques are needed to optimize the use of scarce communication resources. This work proposes a novel multi‐objective framework to investigate the channel assignment problem in cognitive radio network (CRN)‐based IoT applications. The multi‐objective optimization framework comprises three objective functions. The first objective aims to maximize the overall system throughput fairness, the second objective aims to maximize the overall system residual energy, and the third objective aims to increase the user satisfaction level by maximizing the overall priority index. The problem of channel assignment is modeled and solved using Mixed Integer Linear Programming (MILP) and ε‐constraint technique. This technique generates a set of Pareto efficient solutions, and an optimal solution among these solutions is selected using a fuzzy logic decision mechanism. Moreover, power level selection is incorporated by solving the problem multiple times for different power levels and obtaining a preferred solution for each power level. The simulations are conducted to unveil an interesting trade‐off between the conflicting system design objectives, that is, the overall throughput and the overall residual energy while incorporating the significance of primary user (PU) activity when preforming channel assignment in CRN. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio networks (CRNs) have emerged as a promising solution to address the spectrum scarcity problem by allowing unlicensed secondary users (SUs) to opportunistically access the underutilized licensed spectrum bands. However, efficient channel assignment in CRNs, especially with user mobility and power control considerations, remains a challenging problem. Traditional channel assignment algorithms often struggle to adapt to changing network conditions, resulting in suboptimal performance. To address these challenges, this research presents a novel Taylor Tuna Optimizer‐based hybrid deep Q‐network (TayTO‐based HDQNet) for channel assignment in CRNs with user mobility and power control considerations. The proposed TayTO‐based HDQNet combines the Elman neural network architecture with the reinforcement learning framework of deep Q‐networks, tuned using the Taylor Tuna Optimizer. This hybrid approach enables intelligent channel assignment decisions while considering power consumption constraints, leading to efficient communication in CRNs. To train the HDQNet agent, a comprehensive dataset is generated through simulations of various CRN scenarios, incorporating channel availability, interference levels, and power consumption for different channel assignment decisions. The HDQNet agent undergoes iterative training using this dataset to develop an optimal channel assignment policy. Experimental results demonstrate the effectiveness of the proposed TayTO‐based HDQNet approach done by MATLAB, achieving a high success rate of 98.47% and surpassing the reliability scores of previous studies. This highlights the improved performance and reliability of the proposed approach for channel assignment in CRNs. [ABSTRACT FROM AUTHOR]

Many types of objects are crowdedly distributed on the surface of remote sensing scenes. Global semantics with object mixture and small training samples potentially cause difficulties in the classification of remote sensing scenes. The description of them often requires crucial parts of the scenes and corresponding discriminative features, especially as the convolutional neural network (CNN) goes deeper. In this paper, we propose a novel Location-Aware Multi-code Generator network (LAM-GAN) that incorporates multiple latent codes as the input to a generator network. This network is designed to train from scratch and recover most details of the input (real) image in a principled way. Meanwhile, multiple latent codes are reversely updated using K cluster centres located by the subsequently proposed part co-location module. By doing so, the global features of the real-fake image pair and part-level features are stacked and fed to a joint part classification network for discriminative classification. This approach makes it easier to induce the semantic concepts in a remote sensing scene. With this formulation, our approach generates an internal compact representation of the scene and enables weakly supervised part co-localization. The proposed method provides a unified framework for not only generating high-quality fake images but also facilitating the remote sensing scene classification task. We evaluated LAM-GAN on several benchmark datasets, and the experiment results demonstrate that the proposed method is more effective than previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Router nodes placement (RNP) is an important issue in the design and implementation of wireless mesh networks (WMN). This is known as an P-hard problem, which cannot be solved using conventional algorithms. Consequently, approximate optimization strategies are commonly used to solve this problem. With heavy node density and wide-area WMNs, solving the RNP problem using approximation algorithms often faces many difficulties, therefore, a more effective solution is necessary. This motivated us to conduct this work. We propose a new method for solving the RNP problem using reinforcement learning (RL). The RNP problem is modeled as an RL model with environment, agent, action, and reward are equivalent to the network system, routers, coordinate adjustment, and connectivity of the RNP problem, respectively. To the best of our knowledge, this is the first study that applies RL to solve the RNP problem. The experimental results showed that the proposed method increased the network connectivity by up to 22.73% compared to the most recent methods. [ABSTRACT FROM AUTHOR]

This paper proposes a rolling bearing fault diagnosis method based on the enhanced channel attention network. The vibration signals are collected via a wireless sensor node, which are input to the neural network, and channel attention block is used to strengthen the assignment of important features, so that the attention of network is paid onto the critical fault information. Furthermore, channel attention block and residual convolution block are combined to form an enhanced channel attention network to extract the detail features. Experiment results show that the model can achieve 100% recognition accuracy for rolling bearings in various working conditions. It is also shown that the proposed new learning algorithm can provide a higher diagnosis accuracy than those state-of-the-arts in a strong disturbance environment, which reflects better robustness and generalisation ability. [ABSTRACT FROM AUTHOR]

• A study about the performance of the MLS, Spline and IDW methods in hydromechanical transfer data problems is realized. • An adaptation of the compact support size based on a minimum number of target points is proposed. • The results show that the compact support size adaptation is very advantageous for the MLS in hydromechanical analyses. • IDW method shows a competitive although it is not conservatively. The investigation of the combined effect of diverse physical phenomena in engineering problems through multiphysics numerical analyses is a trend in many challenging engineering problems. To increase the efficiency of the numerical procedures, multiphysics analyses can be carried out under uncoupled or pseudo-coupled approaches, which frequently require the transfer of data between two non-matching spatial discretizations. Hydromechanical analyses are often executed through pseudo-coupling schemes once each phenomenon requires a different level of discretization or even different numerical approaches. Using appropriate interpolation methods that guarantee the accuracy and conservation of the results is challenging. Methods based on mesh-free interpolation have been broadly used since they show good accuracy and conservation and are topology independent. This work studies the performance and influence of three mesh-free interpolation methods on the response in pseudo-coupled hydromechanical simulations with different meshes. The Moving Least Squares reconstruction, the Spline Interpolation method, and the Inverse Distance Weighting method are assessed regarding accuracy and conservation. An auto-search of the compact support radius scheme based on a minimum number of source points inside the compact support is proposed. The numerical results show the high dependency of the transfer quality on the compact support size. The proposed support size adaptation is shown to be advantageous for the moving least squares method. The inverse distance average method is a promising method for hydromechanical problems. [ABSTRACT FROM AUTHOR]

Fog computing is today considered a promising candidate to improve the user experience in dynamic on-demand computing services. However, its ubiquitous application would require support for this service in wireless multi-hop mesh systems, where the use of conventional IP-based solutions is challenging. As a complementary solution, in this paper, we consider a Named-Data Networking (NDN) approach to enable fog computing services in autonomous dynamic mesh formations. In particular, we jointly implement two critical mechanisms required to extend the NDN-based fog computing architecture to wireless mesh systems. These are (i) dynamic face management systems and (ii) a learning-based route discovery strategy. The former makes it possible to solve NDN issues related to an inability to operate over a broadcast medium. Also, it improves the data-link layer reliability by enabling unicast communications between mesh nodes. The learning-based forwarding strategy, on the other hand, efficiently reduces the amount of overhead needed to find routes in the dynamically changing mesh networks. Our numerical results show that, for static wireless meshes, our proposal makes it possible to fully benefit from the computing resources sporadically available up to several hops away from the consumer. Additionally, we investigate the impacts of various traffic types and NDN caching capabilities, revealing that the latter result in much better system performance while the popularity of the compute service contributes to additional performance gains. [ABSTRACT FROM AUTHOR]

In wireless mesh networks (WMNs) the traffic-aware reliable gateway selection is the key for enhancing the quality of service (QoS) to improve the network performance. In this paper, we have proposed the novel traffic aware reliable gateway selection (TARGS) scheme to improve the QoS in WMNs. In the TARGS scheme, the aggregated traffic at a particular Mesh Router (MR) is calculated using two QoS parameters that are connecting degree and interface queue length. Based on the aggregated traffic, the candidate internet gateways (CIGs) are selected in the region where a high amount of traffic is generated. Internet gateways (IGWs) are chosen based on the reliability value, which is obtained for each CIG using the path tracing method. Simulation results show that the proposed scheme has outperformed two existing schemes in the literature and obtained lower average latency and traffic load and higher throughput than the existing schemes. [ABSTRACT FROM AUTHOR]

In the realm of industrial wireless mesh networks, an efficient routing protocol is highly demanded to play a crucial role in ensuring that packets are efficiently directed along shorter and congestion-free routes toward gateways. Field-based routing has emerged as a promising solution to tackle these network challenges. This routing approach draws inspiration from physics and employs a differential equation to model its behavior in finding efficient routes. Given the fundamental significance of boundary conditions in physics, where they play an essential role in shaping the solutions to the equation, exploring the impact of boundary conditions on field-based routing behavior within network domains becomes highly significant. However, despite their influence, the impact of boundary conditions has remained unexplored in existing studies on field-based routing. In this context, our work explores the boundary condition problem and introduces new advanced fine-grained boundary conditions for field-based routing. We demonstrate the superior performance of our proposed scheme, highlighting the substantial role of boundary conditions in network behavior. Our work holds significant value in that it explores the boundary condition problem, an aspect largely overlooked in previous research, and provides a viable solution, underscoring its crucial importance in routing enhancement. [ABSTRACT FROM AUTHOR]

In simulations involving fluid-structure interactions, the partitioned coupling approach allows calculating the fluid flow and the structural displacement using different solvers, which are coupled by a structure-to-fluid algorithm. This partitioned scheme can result in non-conforming meshes at the solid-fluid interface, especially when the fluid and the structure meshes contain elements of different characteristic sizes. In this context, a mesh-interpolation technique is required to transmit the nodal information from one mesh to the other. This paper focuses on comparing radial basis functions and k-nearest neighbours interpolations on fluid-structure interaction test cases. Indeed, those are fast and flexible techniques requiring no topological information other than relative distances between nodes, allowing a straightforward transfer of nodal data between non-conforming meshes. This feature is particularly favourable in the Particle Finite Element Method (PFEM), where remeshing procedures result in significant changes of the finite element discretisation at the fluid-structure interface. Accordingly, our contribution compares the interpolations in a PFEM/FEM partitioned scheme, using 2D and 3D benchmark problems. [ABSTRACT FROM AUTHOR]

Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models. [ABSTRACT FROM AUTHOR]

Transparent conductive electrodes (TCEs) are indispensable components of various optoelectronic devices such as displays, touch screen panels, solar cells, and smart windows. To date, the fabrication processes for metal mesh‐based TCEs are either costly or having limited resolution and throughput. Here, a two‐step surface energy‐directed assembly (SEDA) process to efficiently fabricate high resolution silver meshes is introduced. The two‐step SEDA process turns from assembly on a functionalized substrate with hydrophilic mesh patterns into assembly on a functionalized substrate with stripe patterns. During the SEDA process, a three‐phase contact line pins on the hydrophilic pattern regions while recedes on the hydrophobic non‐pattern regions, ensuring that the assembly process can be achieved with excellent selectivity. The necessity of using the two‐step SEDA process rather than a one‐step SEDA process is demonstrated by both experimental results and theoretical analysis. Utilizing the two‐step SEDA process, silver meshes with a line width down to 2 µm are assembled on both rigid and flexible substrates. The thickness of the silver meshes can be tuned by varying the withdraw speed and the assembly times. The assembled silver meshes exhibit excellent optoelectronic properties (sheet resistance of 1.79 Ω/□, optical transmittance of ≈92%, and a FoM value of 2465) as well as excellent mechanical stability. The applications of the assembled silver meshes in touch screen panels and thermal heaters are demonstrated, implying the potential of using the two‐step SEDA process for the fabrication of TCEs for optoelectronic applications. [ABSTRACT FROM AUTHOR]

With the development of Internet-of-Things (IoT) technology, industries such as smart agriculture, smart health, smart buildings, and smart cities are attracting attention. As a core wireless communication technology, Bluetooth Low Energy (BLE) is gaining a lot of interest as a highly reliable low-power communication technology. In particular, BLE enables a connectionless mesh network that propagates data in a flooding manner using advertising channels. In this paper, we aim to optimize the energy consumption of the network by minimizing the scanning time while preserving the reliability of the network. Maximizing network lifetime requires various optimizing algorithms, including exhaustive searching and gradient descent searching. However, they are involved with excessive computational complexity and high implementation costs. To reduce computational complexity of network optimization, we mathematically model the energy consumption of BLE networks and formulate maximizing network lifetime as an optimization problem. We first present an analytical approach to solve the optimization problem and show that finding the minima from the complicated objective function of the optimization problem does not guarantee a valid solution to the problem. As a low-complexity solution, we approximate the complicated objective function into a convex form and derive a closed-form expression of the suboptimal solution. Our simulation results show that the proposed suboptimal solution provides almost equivalent performance compared to the optimal solution in terms of network lifetime. With very low computational complexity, the proposed suboptimal solution can extensively reduce implementation costs. [ABSTRACT FROM AUTHOR]

Power-performance constraints have been the key driving force that motivated the microprocessor industry to bring unique design techniques in the past two decades. The rising demand for high-performance microprocessors increases the circuit complexity and data transfer rate, resulting in higher power consumption. This work proposes a set of energy recycling resonant pulsed flip-flops to reuse some of the dissipated energy using series inductor–capacitor (LC) resonance. Moreover, this work also presents wideband clocking architectures that use series LC resonance and an inductor tuning technique. By employing pulsed resonance, the switching power dissipated is recycled back. The inductor tuning technique aids in reducing the skew, increasing the robustness of the clock networks. This new resonant clocking architecture saves over 43% power and 90% reduced skew in clock tree networks and saves 44% power and 90% reduced skew in clock mesh networks, clocking a range of 1–5 GHz frequency, compared to conventional primary–secondary flip-flop-based clock networks. Implementation of resonant clock architectures on standard clock network benchmarks depicts 66% power savings and 6.5 × reduced skew while using the proposed pulsed resonant flip-flop and saves 64% power and 12.7 × reduced skew while using the proposed resonant true single-phase clock (TSPC) flip-flop. [ABSTRACT FROM AUTHOR]

In this paper, an improved routing protocol for multipath network load balancing is proposed for defects in the traditional AOMDV (Ad hoc On-demand Multipath Distance Vector) protocol. This research work analyzes problems in traditional routing protocols and estimates the available path load according to network transmission in Wireless Mesh Networks (WMN). Moreover, we design a load distribution scheme according to a given load and improve multi-path load balancing by using the MCMR method. We also control path discovery and the number of paths while also establishing routing paths and probability balancing. Lastly, improvements are made to the AOMDV protocol and efficient data transmission is acheived. The performance results of the modified routing protocol show that the designed protocol can improve successful delivery rate and prolong network survival time. [ABSTRACT FROM AUTHOR]

Relief patterns represent a surface characteristic that can be seen as the 3D counterpart of the texture concept in 2D images. Such characteristic is well distinct from the 3D object shape but represents a good information source to recognize the object itself. The majority of state-of-the-art techniques for 2D images rely on convolution-based filtering so, the idea of extending such techniques to the mesh manifold domain is quite intriguing as much as challenging. In this paper, we propose a novel approach based on Graph Neural Networks for 3D mesh relief pattern classification. To this end, we designed a bi-level architecture that learns on data structures computed thanks to a mesh resampling algorithm that allows us to represent local surface patches uniformly, while keeping a consistent points order. The local mesh structures are represented by SpiderPatches , that aim to capture local features of the 3D mesh surface, providing a fine-grained rich representation of the relief patterns; global structures are instead captured by MeshGraphs , whose nodes are SpiderPatches, representing the mesh at a macroscopic level. We tested our architecture using SpiderPatches and MeshGraphs on the original meshes of the SHREC'17 and SHREC'20 relief patterns track datasets, showing superior performance to that reported in the literature using a comparable experimental setting. [Display omitted] • A new and original method for relief patterns classification on mesh manifold. • A bi-level graph attention network architecture. • A SpiderPatch surface structure for local mesh description. • A MeshGraph structure for mesh description at higher level of detail. [ABSTRACT FROM AUTHOR]

To achieve autonomous anti‐jamming routing for wireless mesh networks (WMNs) under the threat of unknown removable jammers, a Q‐learning anti‐jamming routing algorithm using the improved upper‐confidence‐bound (UCB) algorithm is proposed in this paper. Specifically, the WMNs anti‐jamming routing path finding problem is first modelled as a Markov decision process. Then, to address the slow‐convergence anti‐jamming strategy induced by the drawback that the preset parameters uniquely determine the length of the traditional Q‐learning exploration process, a discounted‐UCB‐tuned algorithm (Dis‐UCB1‐tuned) is adopted as the decision algorithm to replace the ε‐greedy or softmax algorithm in traditional Q‐learning. Finally, the simulation results demonstrate the superiority of the proposed anti‐jamming scheme over the UCB‐based Q learning(UCBQ) algorithm, Dyna‐Q algorithm, and the traditional Q‐learning algorithm. To elaborate, the proposed scheme can fast converge to a superior anti‐jamming routing without any prior knowledge of jamming environment, and its performance is also independent with the preset parameter. [ABSTRACT FROM AUTHOR]

On the path to exascale the landscape of computer device architectures and corresponding programming models has become much more diverse. While various low-level performance portable programming models are available, support at the application level lacks behind. To address this issue, we present the performance portable block-structured adaptive mesh refinement (AMR) framework P arthenon, derived from the well-tested and widely used A thena++ astrophysical magnetohydrodynamics code, but generalized to serve as the foundation for a variety of downstream multi-physics codes. P arthenon adopts the K okkos programming model, and provides various levels of abstractions from multidimensional variables, to packages defining and separating components, to launching of parallel compute kernels. P arthenon allocates all data in device memory to reduce data movement, supports the logical packing of variables and mesh blocks to reduce kernel launch overhead, and employs one-sided, asynchronous MPI calls to reduce communication overhead in multi-node simulations. Using a hydrodynamics miniapp, we demonstrate weak and strong scaling on various architectures including AMD and NVIDIA GPUs, Intel and AMD x86 CPUs, IBM Power9 CPUs, as well as Fujitsu A64FX CPUs. At the largest scale on Frontier (the first TOP500 exascale machine), the miniapp reaches a total of 1.7 × 1013 zone-cycles/s on 9216 nodes (73,728 logical GPUs) at ≈ 92 % weak scaling parallel efficiency (starting from a single node). In combination with being an open, collaborative project, this makes Parthenon an ideal framework to target exascale simulations in which the downstream developers can focus on their specific application rather than on the complexity of handling massively-parallel, device-accelerated AMR. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication has been regarded as a promising solution to alleviate the mobile traffic explosion problem for its capabilities of improving system data rate and resource utilization. A reconfigurable intelligent surface (RIS) aided mobile D2D communications framework is investigated, where the RIS is deployed to improve communication quality. As the transmission distance of D2D pairs changes, the mode selection for D2D pairs and the phase shift design for RIS is essential for mobile scenarios. Therefore, we formulate a joint optimization problem of mode selection, channel assignment, power allocation, and discrete phase shift selection to maximize the average sum data rate of D2D pairs. This problem is also constrained by the maximum transmit power and the minimum data rate requirements of users, where the latter is to guarantee the fairness of D2D pairs. We first reformulate the original sequential decision-making problem into a Markov game (MG) problem to solve the challenging optimization. Furthermore, a multi-agent deep reinforcement learning (MADRL) framework is proposed, in which multiple agents cooperatively determine the joint mode selection and resource allocation strategy. The proposed MADRL-based framework combines both the multi-pass deep Q-networks (MP-DQN) algorithm and the decaying DQN algorithm to solve the optimization problem. Specifically, we adopt the MP-DQN algorithm for D2D pairs to handle the hybrid discrete-continuous action space. Moreover, the decaying DQN algorithm is invoked by the RIS agent to select discrete phase shifts. Simulation results demonstrate that the proposed algorithm can converge under different cases. The proposed MADRL-based algorithm outperforms the combination algorithm of DQN and the deep deterministic policy gradient (DDPG) in terms of system performance. Moreover, it is also shown that the average sum data rate of D2D pairs can be significantly improved by deploying the RIS and further enhanced by increasing the number of reflecting elements (REs). [ABSTRACT FROM AUTHOR]

Employing the cognitive radio (CR) technology in IoT networks, referred to as CR-IoT, is considered a promising solution for providing the spectrum opportunities needed for massive IoT deployments. Jamming attacks can significantly increase energy consumption in CR-IoT networks by increasing the number of dropped packets, and hence the number of needed packet re-transmissions. Designing jamming-aware communication protocols that attempt to reduce the number of dropped packets in the network results in green CR-IoT networking. In this paper, we develop an energy-efficient, QoS-constrained, and jamming-aware routing scheme for green CR-IoT networks. The proposed routing protocol employs a probabilistic channel assignment scheme that provides reliable data delivery under both dynamic and static jamming attacks. The underlying channel assignment problem is mathematically formulated as a binary linear programming optimization problem with the goal of maximizing the end-to-end throughput using the least number of frequency channels while meeting a set of QoS requirements. This optimization is known to be NP-hard. The sequential fixing technique is adopted to provide a sub-optimal solution in polynomial time. Simulation results indicate that the proposed routing scheme provides a remarkable performance improvement over existing schemes. [ABSTRACT FROM AUTHOR]

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications. [ABSTRACT FROM AUTHOR]

Peer-to-Peer (P2P) network is a logical layer distributed network built on the physical network, and nowadays the communication of mobile P2P (MP2P) network in various wireless environments is also developing rapidly. However, the peer in the MP2P network needs to take on both server and client roles, resulting in poor network performance and high energy consumption. Combining this feature, improving network performance and reducing system energy consumption becomes a hot issue. In order to address the problem of wasted energy consumption caused by unnecessary online behavior of partially idle peers on the server side when there are few peers on the requesting side of the network, the mechanism of spare service peers online and standard service peers sleep is applied. In the meantime, considering physical damage, peer feedback and peer impatience, impatient customers and feedback strategies are introduced. Then an M/M/c+d queueing model based on synchronous multiple vacation with fault repairable and spare servers, and variable service rates is built, which is subsequently used to simulate the performance of an MP2P network in various wireless environments, such as Wireless Mesh network (WMNs), wireless Local Area Network (WLAN), wireless Metropolitan Area network (WMAN), etc. The expressions for system performance indicators are obtained by employing quasi-birth-and-death process (QBD), matrix-geometric solution method and Gauss-Seidel iteration method. Through numerical experiments, the effects of system parameters on performance indicators of the MP2P system, such as online energy consumption, are studied. Moreover, through using Nash equilibrium and social optimal strategy, the value of the social maximum benefit under the social optimization is obtained, which provides a decision framework for the MP2P system to improve its network performance and reduce the online energy consumption. [ABSTRACT FROM AUTHOR]

Pilot contamination due to pilot reuse seriously mitigates the performance of the cell-free massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a joint pilot assignment scheme that employs user clustering and graph coloring (UC-GC) to reduce pilot contamination. The proposed method consists of two steps: firstly, we utilize AP selection to classify all users; secondly, we assign pilots to users with more severe pilot contamination using the graph coloring algorithm and then assign pilots to the remaining users. The numerical simulation results show that the proposed scheme outperforms existing pilot assignment schemes and significantly improves throughout with low complexity. [ABSTRACT FROM AUTHOR]

Cohesive elements are routinely used to simulate delamination between plies in composites. However, the allowable maximum size of these elements is limited, owing to the presence of a very small process zone ahead of the cohesive crack-tip, which needs to be discretised with multiple elements for obtaining an accurate failure response. This restricts their applicability beyond analysing delamination in coupon scale problems, due to the need for a very fine mesh and associated large problem size. In this paper, a novel hp cohesive element is developed to address this problem. This element is capable of simultaneous mesh refinement and elevation of interpolation order, locally and adaptively around the propagating cohesive crack tip. Additional degrees of freedom associated with the hp refinement are handled by extra nodes already supplied in the initial mesh file, that are selectively activated by the program as required. Several example problems are presented showing the ability of this method to improve the prediction of delamination onset and growth in extremely coarse meshes with a reduced computational time. [ABSTRACT FROM AUTHOR]

Summary: One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi‐objective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta‐heuristic algorithms: Moth‐Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi‐Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings. The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network. [ABSTRACT FROM AUTHOR]

Low-overhead, robust, and fast-convergent time synchronization is important for resource-constrained large-scale industrial wireless sensor networks (IWSNs). The consensus-based time synchronization method with strong robustness has been paid more attention in wireless sensor networks. However, high communication overhead and slow convergence speed are inherent drawbacks for consensus time synchronization due to inefficient frequent iterations. In this paper, a novel time synchronization algorithm for IWSNs with a mesh–star architecture is proposed, namely, fast and low-overhead time synchronization (FLTS). The proposed FLTS divides the synchronization phase into two layers: mesh layer and star layer. A few resourceful routing nodes in the upper mesh layer undertake the low-efficiency average iteration, and the massive low-power sensing nodes in the star layer synchronize with the mesh layer in a passive monitoring manner. Therefore, a faster convergence and lower communication overhead time synchronization is achieved. The theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm in comparison with the state-of-the-art algorithms, i.e., ATS, GTSP, and CCTS. [ABSTRACT FROM AUTHOR]

In general, the vast array of signaling strategies as it informs novel systems is a timely application of bio-inspired communication technologies that is well suited for current engineering challenges including improving trust and making communication technology more trustworthy. In the 1950s, the pioneers of modern Computer Science Alan Turing and John von Neumann not only developed their one-dimensional computing machine models but also discovered two-dimensional cellular automata, which explain distributed textural patterns and self-reproduction in nature [[1], [6]]. From the same source, biomimicry computing has evolved from mathematical descriptions to systems engineering models, up to executable, large-scale computer algorithms. Furthermore, modern computing technologies enable more powerful means to implement biomimicry algorithms, for example, biomorphic chips such as Akida Spiking Neural Network chip [[17]]. [Extracted from the article]

This paper proposes an enhanced version of Moth Flame Optimization (MFO) algorithm, called Enhanced Chaotic Lévy Opposition-based MFO (ECLO-MFO) for solving the mesh router nodes placement problem in wireless mesh network (WMN-MRNP). The proposed ECLO-MFO incorporates three strategies including the chaotic map concept, the Lévy flight strategy, and the Opposition-Based Learning (OBL) technique to enhance the optimization performance of MFO. Firstly, chaotic maps are used to increase the chaotic stochastic behavior of the MFO algorithm. Lévy flight distribution is adopted to increase the population diversity of MFO. Finally, OBL is introduced to improve the convergence speed of MFO and to explore the search space effectively. The effectiveness of the proposed ECLO-MFO is tested based on various scenarios under different settings, considering network connectivity and client coverage metrics. The results of simulation obtained using MATLAB 2020a demonstrate the accuracy and superiority of ECLO-MFO in determining the optimal positions of mesh routers when compared with the original MFO and ten other optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Harmony Search (HS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), Bat Algorithm (BA), Firefly optimization (FA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). [ABSTRACT FROM AUTHOR]

Wireless acoustic sensor networks (WASNs) and intelligent microsystems are crucial components of the Internet of Things (IoT) ecosystem. In various IoT applications, small, lightweight, and low-power microsystems are essential to enable autonomous edge computing and networked cooperative work. This study presents an innovative intelligent microsystem with wireless networking capabilities, sound sensing, and sound event recognition. The microsystem is designed with optimized sensing, energy supply, processing, and transceiver modules to achieve small size and low power consumption. Additionally, a low-computational sound event recognition algorithm based on a Convolutional Neural Network has been designed and integrated into the microsystem. Multiple microsystems are connected using low-power Bluetooth Mesh wireless networking technology to form a meshed WASN, which is easily accessible, flexible to expand, and straightforward to manage with smartphones. The microsystem is 7.36 cm3 in size and weighs 8 g without housing. The microsystem can accurately recognize sound events in both trained and untrained data tests, achieving an average accuracy of over 92.50% for alarm sounds above 70 dB and water flow sounds above 55 dB. The microsystems can communicate wirelessly with a direct range of 5 m. It can be applied in the field of home IoT and border security. [ABSTRACT FROM AUTHOR]

In Vehicular cloud computing (VCC), at the time of congestion, to deal with traffic, the vehicles' underutilized resources are shared; subsequently, these resources aren't constrained to computing power, storage, along with internet connectivity. Nevertheless, owing to the vehicular network characteristics, attaining the QoS requirements search together with the allocation of resources in the Vehicular Cloud (VC) has turned into a complicated task. An intelligent Square Shaped (SS)-Adaptive Neuro-Fuzzy Interference (SS-ANFIS) methodology for Resource Scheduling (RS) in addition to Mean-centered Penguins Search Optimization Algorithm (M-PeSOA) for Optimal Path Selection (OPS) in the VC is proposed here for efficient resource allocation. (a) Feature extraction, (b) Vehicles clustering, (c) OPS, (d) Resource information extraction, and (e) RS included in the proposed methodology. First, the vehicular network is initialized following that the vehicle features are extracted. Next, Cluster Heads (CHs) are generated regarding which vehicles are clustered; subsequently, the multi-paths are generated. After that, by employing the M-PeSOA, the OPS procedure is conducted; thus, the VC's resource information is extracted aimed at scheduling the resources efficiently. Lastly, by employing the SS-ANFIS, vehicles are scheduled in the optimal paths. The proposed resource allocation system's performance is assessed, and the experiential outcomes are analogized using the sumo tool and java platform. [ABSTRACT FROM AUTHOR]

The integration of cognitive radio (CR) into power domain non-orthogonal multiple access (NOMA) wireless systems has been envisioned as a promising paradigm for supporting massive energy-efficient beyond fifth-generation (B5G)-based IoT connectivity. This paper presents a novel two-dimensional orthogonal multiple access (OMA)-NOMA CR-based power minimization approach for battery-constrained B5G-based IoT networks. According to the proposed approach, the time and frequency domains are integrated together with NOMA for the purpose of serving a larger number of CR users. Our approach divides the available time of each idle frequency channel into a set of time slots, each can serve a group of CR users through power-domain multiplexing. Our approach consists of two phases: user-pairing with channel assignment and power-allocation. The user-pairing associated channel-assignment problem is mathematically presented as a binary linear-programming problem, which is shown to be a uni-modular optimization with polynomial-time complexity. Given the paired users and assigned channels, the power-minimization problem under a set of quality-of-service constraints is formulated as a non-convex optimization. Then, we implement an iterative method that transfers the problem into a convex optimization. Simulation results reveal that our two-dimensional OMA-NOMA CR-based approach provides significant performance gain over the conventional CR-based OMA-NOMA power-minimization in terms of the total consumed transmission power. [ABSTRACT FROM AUTHOR]

Mesh antennas in orbit are periodically affected by solar radiation, earth reflection and space low temperature environment, and the temperature fluctuates in a wide range. Mesh antenna produce large thermal deformation or even obvious thermal disturbance under extreme temperature condition, which seriously deteriorates the surface accuracy and the tension distribution. To improve the shape stability of reflector surface and the rationality of tension distribution, a thermal design optimization method for mesh antenna considering the interaction between cable net and flexible truss is proposed. The equilibrium equation of mesh antenna system under space thermal loads is established based on finite element theory and force density equation. Due to the complexity of directly analyzing the influence of thermal loads on the entire mesh antenna, a research strategy of applying thermal loads step by step from flexible truss to cable network is adopted, and the force density increment equation of cable net under space thermal loads is derived. Then, the force density vector of the cable net is selected as the design variable, and the sum of squares of the thermal deformation of the reflector nodes is taken as the objective function, and the stability optimization model of the reflector in the whole temperature interval is established. Finally, a typical AstroMesh antenna under uniform temperature working conditions is used to illustrate the effectiveness and feasibility of the proposed method. Compared with the traditional optimization method, which can only ensure the better performance of a certain temperature point, the proposed method has better surface accuracy and thermal stability in the whole temperature interval. [ABSTRACT FROM AUTHOR]

Device‐to‐device (D2D) and full duplex (FD) technologies demonstrate immense potentials to improve the spectral efficiency of wireless networks. However, their synergistic combination to reap added benefits is proved to be technically challenging. This letter investigates the throughput‐maximization resource allocation problem for FD D2D communications underlaying cellular networks. The problem is decoupled into two subproblems, namely power allocation and channel assignment, which are solved by using a novel quadratic transform based scheme and the Kuhn–Munkres algorithm, respectively. Simulation results show that the proposed algorithm outperforms existing ones in terms of overall system throughput. [ABSTRACT FROM AUTHOR]

In this paper, a novel sleep‐mode base station (BS) selection scheme, namely forced handover, is proposed using the mobile stations as mobile relay stations in order to maximize number of sleeping BSs. Forced‐handover is a concept of forcing edge mobile stations to neighbour relay stations for both energy saving and SINR improvement of the network. The proposed energy saving scheme can be applied to not only micro sleep and macro sleep mode but also turning off the BSs. The performances of BS sleep mode schemes is investigated in various environments such as small cell network and different user distributions. [ABSTRACT FROM AUTHOR]

Network-on-Chip (NoC) is an emerging and efficient on-chip interconnection network. NoC is expected to be the communication backbone of next-generation Multi-processor System-on-Chip (MPSoC) architectures. Topology is a crucial design aspect of NoC, as it affects the performance of the interconnection network. This paper proposes a novel, scalable, hybrid Hexagonal Star (HS) topology for on-chip interconnection networks. Properties of the proposed topology have been explored and compared with those of Mesh, Torus and Honeycomb Mesh topologies. The performance of the Hexagonal Star topology has been evaluated and compared with that of Mesh topology for different scenarios. The comparative studies of topological properties have indicated that the proposed topology can be a potential choice for on-chip interconnection networks. The simulation results have shown that the proposed topology outperforms Mesh topology in terms of latency for low traffic loads. For different traffic patterns and traffic loads, HS topology has registered a reduction of packet latency ranging from 15% to 50% and from 9% to 23% for 18 nodes and 32 nodes, respectively, compared to Mesh topology. [ABSTRACT FROM AUTHOR]

The conventional pretension design of cable mesh usually takes the ring truss as a rigid body or assumes small deformation. This method needs to be more accurate in designing super large aperture antennas. A cable mesh design method considering the large deformation of the ring truss is proposed in this paper based on the flexible multibody dynamics (FMD) and the force density method (FDM). With this method, the significant range deviation of the boundary points' position of the ring truss under the tension of the external area of the cable mesh after the completion of the pretension design can be predicted with high accuracy. After the ring truss and cable mesh surface are assembled stably, there is only a minimal deviation between the actual mesh surface and the theoretical mesh surface of the antenna, and there is only a minimal deviation between the genuine mesh pretension and the design pretension. The above calculation method is verified by numerical simulation, and the simulation results show that the technique can be applied to the mesh pretension design of a super large aperture ring deployable antenna. [ABSTRACT FROM AUTHOR]