Binary neural networks (BNNs) have been applied in limited resources and mobile devices because of their extreme model compression ability. However, manually designing suitable architectures is challenging given the specialized structure of binarized operations. Neural architecture search (NAS) provides a promising approach for designing high-performance BNN architectures. In practice, various situations require networks with different parameter sizes and performance levels. Therefore, this article proposes a multiobjective evolutionary NAS algorithm for BNNs based on a two-stage training strategy (MO-TS-BNAS) to solve these problems. First, the ApproxSign function is used to approximate the gradient error in the training of BNNs. To avoid the small model trap problem, two auxiliary objectives are introduced in nondominated sorting to retain larger models with similar errors. Then, a two-stage training strategy with flexible use of auxiliary objectives is proposed, forming the selection mechanism in environmental selection. The path dropout method is used in the second stage to prevent hypernetwork overfitting. In addition, the mini-batch gradient descent strategy is improved to speed up individual architecture evaluation and reduce time cost in the search process. Finally, the full precision baseline search space is binarized for general experimental comparison. Our MO-TS-BNAS algorithm balances the two different objective functions of the model size and error. A large number of experiments are carried out on the CIFAR10 and ImageNet datasets, and the results show the effectiveness of the proposed method.

As the foundation of next-generation wireless networks, distributed learning (DL) is expected to be integrated into 6 G communication networks, profoundly advancing the transformation of intelligent connectivity. However, network-induced delays critically degrade the performance of DL algorithms in practical deployments. Beyond the communication limitation, many emerging applications involve antagonistic interactions among agents, introducing additional complexity. To overcome these challenges, we propose a decentralized distributed cooperation–competition learning (DCCL) algorithm based on neuro-fuzzy networks, designed for latency communication networks. The algorithm innovatively employs the signed graph to naturally encode the coupling coopetition relationships among agents, and incorporates the delay model into its design. It demonstrates superior adaptability for DL problems with bimodal coalitional adversarial interactions in latency-prone mission-critical services. Moreover, we extend the neuro-fuzzy network into a distributed version, and the resulting distributed neuro-fuzzy model inherently preserves the interpretability characteristic and superior learning capability. Based on structural balance theory and discrete Lyapunov stability theory, we rigorously prove the convergence of the DCCL algorithm and derive an explicit sufficient condition in the form of a maximum allowable latency tolerance. The proposed algorithm benefits privacy protection by transmitting only model parameters. Experiments are conducted to validate the performance of the DCCL algorithm on several datasets for regression and classification. Furthermore, we discuss the limitations of the DCCL algorithm, providing a balanced perspective for future research.

We consider decentralized federated learning (DFL) in mobile computing networks (MCNs), where dynamically changing neighborhood sets among devices arise from mobility and environmental perturbations. The time-varying topology coupled with inherent system heterogeneity poses significant challenges to achieve stable and efficient convergence in DFL. However, existing studies rarely consider both dynamic connectivity and statistical heterogeneity. To close this gap, this paper proposes a novel DFL framework enhanced with topology learning (DFL-TL) to mitigate the spatio-temporal volatility induced by MCNs, where each mobile device faces coupled constraints on its temporal windows for local updates and spatial scopes for model interaction. We introduce a new bounded neighborhood heterogeneity to jointly measure and constrain both the inter-device heterogeneity and the spectral properties of the topologies. Additionally, we formulate a mixed-integer nonlinear programming (MINLP) problem to jointly optimize learning costs and neighborhood heterogeneity. Through problem decomposition, DFL-TL efficiently identifies optimal resource allocations and adaptive mixing matrices, thereby enabling the selection of optimal training time windows while reducing the adverse effects of dynamic topologies in heterogeneous networks. Furthermore, we establish the iteration complexity of DFL-TL under non-convex settings and show that solving the proposed MINLP formulation leads to a tighter convergence bound. Extensive experiments demonstrate that DFL-TL achieves a faster convergence performance and reduces the wall-clock training time compared to the state-of-the-art baselines.

The coupling of multiple constraints can pose difficulties in solving constrained multi-objective optimization problems (CMOPs). Existing constrained multi-objective evolutionary algorithms (CMOEAs) often overlook this issue by considering all constraints together. This article proposes MTOTC, a novel multi-tasking optimization algorithm that addresses this challenge through a task clone technique. MTOTC clones the target CMOP with q constraints into q+1 copies, resulting in a total of q+2 tasks. Each cloned task is handled using an independent population that considers a unique constraint-handling sequence, effectively decoupling the constraints in q+1 different ways. Additionally, the algorithm incorporates online information sharing between the target task and cloned tasks, enabling the utilization of valuable search history as much as possible. Experimental results on four recently developed complex CMOP benchmark suites and a series of real-world CMOPs demonstrate the superior performance of MTOTC compared to seven state-of-the-art CMOEAs.

The hardly dominated boundary (HDB) is commonly observed in multi-objective optimization problems (HDBMOPs). However, there are only a few benchmark problems related to HDB-MOPs in the evolutionary computation community, which is insufficient to validate the performance of multi-objective evolutionary algorithms (MOEAs). In this paper, we first introduce a new set of HDB-MOPs characterized by various shapes of Pareto fronts and scalable HDB sizes. We then systematically analyze the capabilities of several representative existing MOEAs in handling HDB-MOPs and reveal their strengths and weaknesses in solving this type of problem. Finally, based on this insightful analysis, we propose an indicator-based MOEA with an adaptive reference point to effectively address HDB-MOPs. The source codes of the proposed benchmark problems and the IMOEAARP algorithm are available from https://github.com/CIAMGroup/ EvolutionaryAlgorithm Codes/tree/main/IMOEA-ARP.

Federated learning (FL) is a distributed learning paradigm that leverages local updates and parameter sharing to address privacy concerns in Industrial Internet of Things (IIoT) environments. The presence of statistical heterogeneity among IIoT devices poses significant challenges for FL, impacting convergence and model performance. Although previous studies have attempted to mitigate this issue, a fundamental solution remains elusive. To address this, we propose a novel approach called federated multidiscriminators multigenerators generative adversarial network (FedMDMG-GAN), which employs a distributed generative adversarial network (GAN). IIoT devices concurrently train local generators and discriminators to generate data with global information. The server then aggregates parameters and redistributes them to the devices to refine local GANs. In addition, we introduce a proximal term for global aggregation to enhance convergence. Theoretical analysis suggests that the FedMDMG-GAN algorithm can asymptotically converge to a stable point. Qualitative assessments demonstrate that our method can generate images closely resembling real data with comprehensive global information. Quantitative results indicate that FedMDMG-GAN outperforms vanilla FL and state-of-the-art methods.

Transforming a constrained optimization problem (COP) into a multi/many-objective optimization problem (MOP/MaOP) represents a practical approach for solving COPs. This article introduces an adaptive multi/many-objective transformation technique, termed adaptive many-objective transformation technique (AMaOTCO), designed to effectively address COPs. The transformed many-objective optimization problem (MaOP) defines an objective using a convex combination of the objective function (or constraint violation function) and an auxiliary function. This auxiliary function is constructed through a convex combination of the objective function and a weighted constraint violation function. The adaptive tuning of all combination coefficients is based on population information. This adaptive tuning ensures an intelligent balance between minimizing various constraint violations and managing the tradeoff between objective function minimization and constraint violation reduction. The effectiveness of the proposed AMaOTCO is demonstrated through comparisons with state-of-the-art constrained evolutionary algorithms (CEAs) on a set of real-world COPs.

Surrogate-assisted evolutionary algorithms for addressing expensive optimization problems with both continuous and categorical variables (EOPCCVs) are still in the early stages of development. This study makes significant advancements by leveraging the mixed-variable nature of EOPCCVs in two crucial ways. First, it introduces a novel hybrid approach combining differential evolution and upper confidence bound sampling (DEUCB), designed to explore the mixed search space effectively. Second, a specialized value distance metric (VDM) is proposed, integrating continuous and categorical variables, to enhance the accuracy of the radial basis function (RBF) model approximation. Finally, we present a customized evolutionary expensive optimization algorithm (CEEO), which seamlessly incorporates DEUCB and RBF-VDM into the widely utilized global and local surrogate-assisted evolutionary optimization framework. Experimental results, compared against state-of-the-art counterparts on three distinct sets of benchmark problems and a convolutional neural network hyperparameter optimization task, consistently affirm the efficacy of the proposed CEEO in addressing EOPCCVs. The source code for the proposed CEEO algorithm is available athttps://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/CEEO_Code.

Federated learning (FL) encounters two major obstacles, namely, the heterogeneity among Internet of Things (IoT) devices hampers both personalized and generalized performance, and the limited communication resources impede the learning efficiency. This article proposes a decentralized FL approach based on prototype representation learning (called DeProFL) and time-varying communication topology. The heterogeneity among devices can arise from varying data distributions and model architectures, leading to local or global gradient drift. To mitigate the effects of heterogeneity, we introduce prototype learning to establish consistent representations of samples and models across devices. Rather than directly propagating the large model weights, which require significant transmission volumes, we propagate prototype representations among local devices to reduce transmission. Given the limited communication resources of IoT and the dynamic wireless environments, we propose a time-varying decentralized FL approach to address these practical constraints. During each global iteration, every device shares its prototype only with adjacent devices for the collaborative learning, aiming to improve system stability and reduce network bandwidth usage. We have theoretically analyzed and verified the convergence of the DeProFL under nonconvex conditions. Extensive experiments have been applied to the benchmark data sets, and the results show that DeProFL outperforms state-of-the-art methods.

We consider a federated semi-supervised learning (FSSL) scenario in heterogeneous Internet of Things (IoT) healthcare systems where only a tiny fraction of IoT healthcare devices possess labels. The challenge is that most IoT devices need label information and exhibit heterogeneous data distributions. This article proposes a unified FSSL framework (FSSL-HD) tailored for IoT healthcare systems with heterogeneous distributions. The FSSL-HD framework introduces a novel objective function for unlabeled healthcare devices to enhance generalization performance and reduce intermodel discrepancies. A straightforward loss-based aggregation mechanism is designed to reinforce distillation from labeled healthcare devices to supervise unlabeled samples. Multiple iterations for labeled devices and dynamically adjusted confidence thresholds are proposed to improve the model performance. Our theoretical analysis of the generalization error in FSSL suggests directions for enhancing performance by improving the self-training capabilities of unlabeled devices and reinforcing the distillation and transfer of supervision signals from labeled devices. Our empirical experiments on the federated benchmark data sets and the medical image data sets show that FSSL-HD surpasses the performance of state-of-the-art methods. The code is available athttps://github.com/baoshengli96/FSSL-HD.

Binary Neural Networks (BNNs) using 1-bit weights and activations are emerging as a promising approach for mobile devices and edge computing platforms. Concurrently, traditional Neural Architecture Search (NAS) has gained widespread usage in automatically designing network architectures. However, the computation involved in binary NAS is more complex than in NAS due to the substantial information loss incurred by binary modules, and different binary spaces are required for different tasks. To address these challenges, a universal binary neural architecture search (UBNAS) algorithm is proposed. In this paper, the ApproxSign function is used to reduce the gradient error and accelerate the convergence in binary network searching and training. Moreover, UBNAS adopts a novel search space consisting of operations appropriate for the binary methods. To improve the original space operation module, we explore the effect of diverse structures for various modules and ultimately obtain a universal binary network structure. Additionally, the channel sampling ratio is adjusted to balance the advantages of different operations and an early stopping strategy is implemented to significantly reduce the computational burden associated with searching. We perform extensive experiments on CIFAR10, and ImageNet datasets and the results demonstrate the effectiveness of the proposed method.

Multimodal multiobjective optimization problems (MMOPs) possess multiple Pareto optimal sets (PSs) corresponding to the identical Pareto optimal front (PF). To handle MMOPs, we propose a bi-objective evolutionary algorithm (BOEA), which transforms an MMOP into a bi-objective optimization problem. This problem is constructed by the penalty boundary intersection technique and a diversity indicator to obtain multiple PSs. The first objective reflects the population convergence and factors in the population diversity in the objective space, while the other objective concentrates more on the population diversity in the decision space. Furthermore, an environmental selection strategy is designed to choose the offspring solutions, which consists of nondominated sorting based on the transformed optimization problem and hierarchical clustering for selecting promising solutions. Experiments on 34 MMOPs demonstrate that BOEA performs better than selected state-of-the-art representatives, including 22 MMOPs from CEC2019 and 12 imbalanced MMOPs. In addition, the effectiveness of BOEA is further validated by six feature selection problems in real-world applications.

When deep neural networks are used to process remote sensing data, complex network structures, and parameters limit their real-time processing deployment in satellites. Binary Neural Networks (BNNs) can overcome this obstacle by reducing network complexity. However, the binarization process naturally leads to information loss, reducing the performance of the original neural networks. In this article, three modules are introduced to improve the performance of BNNs. First, we propose an improved channel shuffle (CS) module to enhance communication between different channels in feature maps, in which the grouped pointwise convolution is used to reduce the original calculation scale. Second, the squeeze and excitation (SE) module called the attention mechanism is inserted before each layer of the binary convolution operation to recognize the importance of different feature channels. Third, the adaptive linear scaling factor (ALSF) module is proposed to make the weights of the convolution kernel more suitable for the structure of BNNs, which further improves the performance of our network. With these three modules, our CSA-Net reaches or even outperforms the other state-of-the-art (SOTA) methods with a lower computational cost. It can achieve 65.35% Top-1 accuracy on the ImageNet, 90.11% accuracy on the CIFAR10, 89.58% accuracy on the WHU-RS19, and 90.86% accuracy on the UC-Merced, respectively. Meanwhile, compared with other mainstream remote sensing classification models, the number of our parameters is reduced by two or even three orders of magnitude, and the results confirm the effectiveness of the proposed method.

During the past two decades, many evolutionary algorithms have been proposed to solve nonlinear equation systems (NESs). However, the benchmark test sets have not received enough attention. Some features of NESs (e.g., high dimension, large search range, the connectivity of the feasible region) are rarely considered in the original benchmark test sets, which results in that they cannot represent the real-world problems well. Thus, a general toolkit is proposed to generate artificial test problems and 18 test instances with diverse characteristics are constructed in this article, which is the first attempt to design NESs. The experimental results indicate that the current algorithms perform poorly on this new benchmark test set. Furthermore, we develop a transformation method that transforms a NES into a new single-objective optimization problem and design a two-phase method to solve this transformed multimodal optimization problem. Compared to other algorithms, the proposed method has a superior or at least competitive performance.

An expensive multimodal optimization problem (EMMOP) is that the computation of the objective function is time consuming and it has multiple global optima. This article proposes a decomposition differential evolution (DE) based on the radial basis function (RBF) for EMMOPs, called D/REM. It mainly consists of two phases: the promising subregions detection (PSD) and the local search phase (LSP). In PSD, a population update strategy is designed and the mean-shift clustering is employed to predict the promising subregions of EMMOP. In LSP, a local RBF surrogate model is constructed for each promising subregion and each local RBF surrogate model tracks a global optimum of EMMOP. In this way, an EMMOP is decomposed into many expensive global optimization subproblems. To handle these subproblems, a popular DE variant, JADE, acts as the search engine to deal with these subproblems. A large number of numerical experiments unambiguously validate that D/REM can solve EMMOPs effectively and efficiently.

LoRa has become one of the most promising networking technologies for Internet-of-Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, causing long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rates, they will drain the batteries much earlier and the network may be broken early. Such an energy unfairness can be mitigated by deploying more gateways. However, with more gateways, more end devices may choose small spreading factors to reach closer gateways, increasing the collision probability. In this paper, we propose a networking solution for LoRa networks, EF-LoRa, that can achieve energy fairness among end devices by carefully allocating network resources, including frequency channels, spreading factors and transmission power. We develop a LoRa network model to study the energy consumption of the end devices, considering the unique features of LoRa networks such as LoRaWAN MAC protocol and the capacity limitation of a gateway. We formulate the energy fairness allocation as an optimization problem, and propose a greedy allocation algorithm to achieve max-min fairness of energy efficiency. Simulation results show that EF-LoRa can improve the energy fairness of the state-of-the-art works by 177.8%.

A two-phase constraint-handling technique is integrated into the evolutionary algorithms to solve constrained optimization problems (called TPDE) in this article. In phase one, denoted as the exploration phase, an exterior penalty function method with the dynamic penalty coefficients is developed to compare any two candidate solutions, which aims to push the population into the feasible region. To reduce the computational burden, in phase two, denoted as the exploitation phase, an interior penalty function method with the dynamic penalty coefficients is developed, which enhances the search ability by using the information of constraints in feasible solutions. During the optimization process, differential evolution is adopted as the search algorithm to produce the offspring population. Experiment results on four benchmark test suites, namely, IEEE CEC 2006, IEEE CEC 2010, IEEE CEC 2017, and IEEE CEC 2020, indicate that TPDE is competitive with other popular algorithms.

This article proposes a three-level radial basis function (TLRBF)-assisted optimization algorithm for expensive optimization. It consists of three search procedures at each iteration: 1) the global exploration search is to find a solution by optimizing a global RBF approximation function subject to a distance constraint in the whole search space; 2) the subregion search is to generate a solution by minimizing an RBF approximation function in a subregion determined by fuzzy clustering; and 3) the local exploitation search is to generate a solution by solving a local RBF approximation model in the neighborhood of the current best solution. Compared with some other state-of-the-art algorithms on five commonly used scalable benchmark problems, ten CEC2015 computationally expensive problems, and a real-world airfoil design optimization problem, our proposed algorithm performs well for expensive optimization.

It is very difficult to locate multiple global optimal solutions (GOSs) of multimodal optimization problems (MMOPs). To deal with this issue, a penalty-based multimodal optimization differential evolution (DE), called PMODE, is developed in this article. In PMODE, a penalty strategy with a dynamic penalty radius is constructed to solve MMOPs. An elite selection mechanism is designed to identify and select elite solutions. The neighboring areas of these elite solutions are penalized. PMODE uses a popular DE variant-JADE as its search engine. The proposed PMODE is compared with several other state-of-the-art multimodal optimization algorithms on 20 MMOPs used in the IEEE CEC2013 special session. The experimental results show that PMODE performs better than other state-of-the-art methods.

Low-power wireless networks (LPWNs) are of paramount importance for the pervasive deployment of Internet-of-Things (IoT). To deal with the lossy nature of LPWNs, opportunistic routing (OR) and multichannel communications (MC) have received significant research interests. In particular, coupling OR with MC has become an important way to further enhance the communication performance in LPWNs. However, as OR requires nodes in the same channel while MC separates nodes into different channels, the benefits of MC-OR combination are largely under-utilized. To address this problem, we investigate the challenging issue of establishing opportunistic routing in multichannel LPWNs. Different from the existing studies that separately assign channels and select forwarders, we propose a synergistic multichannel and opportunistic routing (SMOpp) approach, which jointly combines the benefits of both OR and MC by considering link correlation. SMOpp explicitly evaluates the routing opportunities of each channel/forwarder set and then employs a forwarder-initiated scheme to select the best combinations of senders, forwarders, and channels. The testbed evaluation shows that compared to the existing methods, SMOpp significantly improves the transmission efficiency for LPWNs.

Federated learning (FL) has been employed for numerous privacy-sensitive applications, where distributed devices collaboratively train a global model. In industrial Internet of things (IIoT) systems, training latency is the key performance metric as the automated manufacture usually requires timely processing. The existing works increase the number of effective devices to accelerate the training. However, devices in IIoT systems are usually deployed densely; increasing the number of clients can potentially cause serious interference and prolonged training latency. In this article, we propose a resource allocation scheme for FL, namely RaFed. We formulate the problem of reducing training latency as an optimization problem, which is proved to be NP-hard. We propose a heuristic algorithm to select appropriate devices for achieving a good tradeoff between the interference and convergence time. We conduct experiments using an RGB-D dataset in an IIoT system. The results show that RaFed significantly reduces the latency by 29.9%, compared to the state-of-the-art works.

The last few decades have witnessed an explosive growth of the Internet-of-Things (IoT) systems, which provide ubiquitous sensing and computing services. When adopted in industrial and manufacturing environments, IoT is referred to as the industrial IoT (IIoT), which has attracted increasing research attention. Energy efficiency is one of the most important research topics in green IIoT, as 1) the limited resource can significantly affect the lifetime of IIoT systems and 2) massive sensors, devices, machines keep consuming a considerable amount of energy, and increasing the carbon footprint. In this article, we present a comprehensive survey on energy-efficient communications and computation mechanisms in IIoT systems (such as smart grids). We categorize the existing works, review, discuss, and compare the works to explore their pros and cons. We also discuss the open issues and research challenges, considering the recent 5G communications and edge computing trends.

A two-phase evolutionary algorithm is developed to find multiple solutions of a nonlinear equations system. It transforms a nonlinear equations system into a multimodal optimization problem. In phase one of the proposed algorithm, a strategy combines a multiobjective optimization technique and a niching technique to maintain the population diversity. Phase two consists of a detection method and a local search method for encouraging the convergence. The detection method finds several promising subregions and the local search method locates the corresponding optimal solutions in each promising subregion. The experiments on a set of 30 nonlinear equation systems demonstrate that the proposed algorithm is better than other state-of-the-art algorithms.

LoRa has attracted much research attention due to its long communication range and low power consumption on end devices. In LoRa networks, the energy consumption on the end devices can be unfair, because some end devices have to use large spreading factors (leading to long transmission time) or large transmission power to reach a far-away gateway, and their energy consumption can be quite different. As a result, these end devices will run out of their batteries much faster, which may significantly reduce the network lifetime. The existing works have focused on the static resource allocation in LoRa networks to achieve energy fairness. However, due to the dynamic wireless environment, the static allocation can be inefficient in practice. In this paper, we develop AdapLoRa, a lifetime-aware dynamic network resource allocation system, to maximize the network lifetime of LoRa networks. AdapLoRa periodically adapts the resource allocation according to the link quality of end devices. A fine-grained network model is developed to capture the link quality variations and network interference. Finally, by considering the adaptation overhead (e.g., energy consumed by end devices to receive the configuration commands), we propose to gradually improve the network lifetime by periodically estimating network lifetime with different resource allocations. We implement AdapLoRa on a LoRa testbed, and the experimental results reveal that AdapLoRa improves the network lifetime by 23.7% compared with the state-of-the-art works.

Cascaded module topology is widely used in large scale photovoltaic system because of its easy expansion and small output harmonic. For traditional photovoltaic three-phase inverter topology, the power generation of each phase bridge can be unequal because of nonuniform solar illumination, partial shading, or unequal module temperatures, resulting in the interphase power imbalance. The DC-link capacitance is usually very large to decouple the fluctuating power, resulting in a large volume of the converter. In this paper, a novel modular three-phase converter topology applied to large scale photovoltaic systems is proposed, which can realize the automatic interphase power balance, and make the fluctuating power offset in a four-winding transformer, greatly reduce the size and cost of the converter and improve the power density of the PV system. The feasibility of the proposed topology is verified by simulation.

Multichannel communication is an important means to improve the reliability of low-power Internet-of-Things (IoT) networks. Typically, data transmissions in IoT networks are often required to be delivered before a given deadline, making deadline-driven channel allocation an essential task. The existing works on time-division multiple access often fail to establish channel schedules to meet the deadline requirement, as they often assume that transmissions can be successful within one transmission slot. Besides, the allocation and link estimation incur considerable overhead for the IoT nodes. In this article, we propose an edge-based channel allocation (ECA) for unreliable IoT networks. In ECA, we explicitly consider the impact of allocation sequences and employ a recurrent-neural-network-based channel estimation scheme. We utilize link quality and retransmission opportunities to maximize the packet delivery ratio before deadline. The allocation algorithms are executed on edge servers such that: 1) the channel allocation can be updated more frequently to deal with the wireless dynamics; 2) the allocation results can be obtained in real time; and 3) channel estimation can be more accurate. Extensive evaluation results show that ECA can significantly improve the reliability of deadline-driven IoT networks.

Network simulation is a fundamental service for performance testing and protocol design in wireless networks. Due to the wireless dynamics, it is highly challenging to provide repeatable and reliable simulation results that are comparable to the empirical experimental results. To achieve repeatability for simulation, the existing works focus on reproducing the behaviors on individual links. However, as observed in recent works, individual link behaviors alone are far from enough to characterize the protocol-level performance. As a result, even if the link behaviors can be simulated very closely, these works often fail to simulate the protocol performance with high reliability. In this article, we propose a novel performance-aware simulation approach which can preserve not only the link-level behaviors but also the performance-level behaviors. We first combine the spatial-temporal link diversity to devise an accurate performance model. Based on the model, we then propose a Performance Aware Hidden Markov Model (PA-HMM), where the protocol performance is directly fed into the Markov state transitions. Compared to the existing works, PA-HMM is able to simulate both link-level behaviors and high-level protocol performance. We conduct extensive testbed and simulation experiments with broadcast and anycast protocols. The results show that 1) the proposed model is able to accurately characterize communication performance for both broadcast and anycast and 2) the protocol performance is closely simulated as compared to the empirical results and the PA-HMM based simulation is more repeatable compared to the existing works.

In this paper, we propose a computationally efficient variant of elitist covariance matrix evolution strategy for continuous local search in high dimensional space. It focuses on searching in a low-dimensional subspace expanded by a small number of promising search directions. This leads to the linear internal computational complexity of each iteration, which enables the algorithm to scale to high dimensional problems. We conduct comprehensive experiments to evaluate the parameter sensitivity and the algorithm’s performance. The experimental results validate that the proposed algorithm reduces the running time by a factor of ten, and it can be easily scaled up to n>1000 on a set of commonly used test functions.

LoRa has recently become one of the most promising networking technologies for the Internet of Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, which can cause long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rate, they will drain the batteries much earlier and the network may be broken. Such an energy unfairness can be mitigated by deploying more gateways, since it allows end devices to reach closer gateways with higher data rates. However, multiple gateways may not solve the energy unfairness problem efficiently due to the collision problem caused by the chirp spread spectrum modulation of LoRa networks. Spreading factors of LoRa links can determine both data rate and multiplexing of different transmissions. With more gateways, more end devices may choose low spreading factors and reach closer gateways, which increase the collision probability. In this paper, we propose a networking solution for LoRa networks named EF-LoRa that can achieve fair energy consumption among end devices by carefully allocating different network resources, including frequency channels, spreading factors and transmission power, to achieve fair energy consumption among end devices in LoRa networks. We develop a LoRa network model to study the energy consumption of all the end devices in a network by considering the unique features of LoRa networks, such as LoRaWAN MAC protocol, spreading factors, interference, and the capacity limitation of a LoRa gateway. We formulate the energy fairness problem as an optimization problem and finally propose a greedy resource allocation algorithm to achieve the max-min fairness of energy efficiency in the LoRa networks. Simulation results show that the proposed solution EF-LoRa can improve the energy fairness of legacy LoRa networks by 177.8%.

The openness of indoor WiFi networks makes it prone to spoofing attacks and password leakage/sharing, where undesired users may be able to access the network resources. The fundamental reason is that the actual communication range is much larger than the valid authentication range, leaving undesired users the same chance to access the network from invalid authentication ranges. The existing works exploit various features of wireless signals to identify legitimate/undesired users. Either frequent measurement/learning of signal features or hardware modifications are required. In this paper, we identify visible light (VL) as a good indicator to distinguish the positions of the accessing users due to its frequency diversity. Based on this observation, we propose VL-Auth, a novel Authentication framework based on VL for indoor WiFi networks, which uses the VL features as credentials to authenticate WiFi users. VL-Auth can be easily installed on commercial-off-the-shelf (COTS) devices and do not require frequent measurement/learning of the VL features. We implement VL-Auth with OpenWrt and Android devices. The evaluation results show that compared to the existing works, VL-Auth can accurately identify legitimate/undesired users and the connection delay is also reduced.

The emergence of narrowband Internet of Things (NB-IoT) has brought hope for the popularization and application of mobile Internet standards. Nowadays, NB-IoT technology has been introduced into the Third Generation Partnership Project (3GPP) standards, where low-overhead, low-data-transmission IoT devices can securely access the fifth generation (5G) core network through the 3GPP access network. However, according to the current 3GPP standard, these NB-IoT devices still employ the traditional authentication process of normal user equipment to implement mutual authentication between NB-IoT devices and 5G core networks, which brings a lot of communication and storage overhead, and it will be more serious when lots of NB-IoT devices are activated simultaneously. In this paper, we propose a fast mutual authentication and data transfer scheme for massive NB-IoT devices, which integrates the access authentication and secure data transmission process and achieves the authentications and data transmissions of a group of NB-IoT devices at the same time. Our scheme can not only greatly simplify the authentication process and alleviate the load of the networks but also ensure robust security protection including user anonymity and nonrepudiation. The performance analysis results show that the proposed scheme can withstand a variety of security attacks with ideal efficiency.

Although the introduction of fuzzy theory into a portfolio selection model can help improve the model's practicality, it would increase the difficulty of solving the model. To tackle the issue, this paper proposes a novel mechanism based artificial bee colony algorithm (ABC) consisting of two new proposed learning strategies—direction learning and elite learning. The direction learning strategy has a great potential to guide the search toward the promising areas. The elite learning strategy can gradually pick up the convergence rate without loss of the population diversity. The cooperation of the two approaches forms a mechanism, complementing each other to improve the performance of the algorithms. The proposed mechanism, named LL-mechanism, is introduced into three ABC variants-ABC, gbest-guided ABC (GABC), and CABC, generating LL-ABC, LL-GABC, and LL-CABC, respectively. The experimental results demonstrate the superior performance of the LL-mechanism and LL-CABC outperforms other methods in terms of solution quality, convergence rate, robustness, and numerical stability. Finally, the proposed LL-CABC approach is employed to solve the portfolio selection with fuzzy security return. The experiments on two portfolio selection models illustrate that LL-CABC is effective and promising for a fuzzy portfolio selection.

The recent advances in microelectronics and communications have led to the development of large-scale Internet of Things (IoT) networks, where tremendous sensory data is generated and needs to be processed. To support realtime processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node (EN) deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of ENs. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of ENs and improves the throughput between IoT and ENs.

Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms.

Inspired by the fact that in modern society, team cooperation and the division of labor play important roles in accomplishing a task, this paper proposes a dual-population differential evolution (DPDE) with coevolution for constrained optimization problems (COPs). The COP is treated as a bi-objective optimization problem where the first objective is the actual cost or reward function to be optimized, while the second objective accounts for the degree of constraint violations. At each generation during the evolution process, the whole population is divided into two based on the solution's feasibility to treat the both objectives separately. Each subpopulation focuses on only optimizing the corresponding objective which leads to a clear division of work. Furthermore, DPDE makes use of an information-sharing strategy to exchange search information between the different subpopulations similar to the team cooperation. The comparison of the proposed method on a number of benchmark functions with selected state-of-the-art constraint-handling algorithms indicates that the proposed technique performs competitively and effectively.

A novel differential evolution with coevolution (CDE) for constrained optimization problems (COPs) is proposed in this paper. The proposed CDE treats a COP as a bi-objective optimization problem where the first objective remains the same objective function itself while the second objective accounts for the degree of constraint violations. It adopts a simple and straightforward idea to solve COPs by dividing the whole population into dual subpopulations based on their feasibility. Each subpopulation corresponds with only one objective alone. An information-sharing strategy is incorporated to exchange search information between the different subpopulations. The comparison of the proposed approach with selected state-of-the-art constraint handling techniques demonstrates on a number of benchmark problems that the proposed framework performs competitively and effectively.

Multimodal optimization is one of the most challenging tasks for optimization. It requires an algorithm to effectively locate multiple global and local optima, not just single optimum as in a single objective global optimization problem. To address this objective, this paper first investigates a cluster-based differential evolution (DE) for multimodal optimization problems. The clustering partition is used to divide the whole population into subpopulations so that different subpopulations can locate different optima. Furthermore, the self-adaptive parameter control is employed to enhance the search ability of DE. In this paper, the proposed multipopulation strategy and the self-adaptive parameter control technique are applied to two versions of DE, crowding DE (CDE) and species-based DE (SDE), which yield self-CCDE and self-CSDE, respectively. The new algorithms are tested on two different sets of benchmark functions and are compared with several state-of-the-art designs. The experiment results demonstrate the effectiveness and efficiency of the proposed multipopulation strategy and the self-adaptive parameter control technique. The proposed algorithms consistently rank top among all the competing state-of-the-art algorithms.

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.