Asynchronous Federated Learning (AFL) is acclaimed for accelerating collaborative training on heterogeneous systems by eliminating the wait for stragglers. While current solutions focus on improving convergence amidst update delays, they neglect how delayed aggregation fosters free-riding attacks, allowing malicious clients to easily extract the global model without contribution. This behavior results in significant fairness issues and performance degradation. To address this challenge, we propose OPTION, the first online pricing strategy tailored to mitigate free-riding in AFL. OPTION establishes an economic model in which access to model updates is purchased using credits earned from verified contributions. Specifically, OPTION values each model update according to its marginal performance gain and training cost, and subsequently necessitates a download fee from each client based on the Hotelling model to prevent zero-cost acquisition. Moreover, OPTION rewards clients for successful updates under non-arbitrage constraints, effectively balancing individual utility and task budget. To maximize the average model performance while satisfying these conditions, OPTION leverages the Lyapunov drift framework and a probabilistic sampling-based algorithm to optimize the pricing parameters. Extensive experimental results on three real-world datasets demonstrate that OPTION effectively mitigates freeriding attacks in AFL, increases the number of valid updates by at least 23.97%, and achieves a model accuracy improvement of at least 3.01% compared to state-of-the-art baselines.

3D Range-Gated Imaging technology is widely used for detection in complex environments (such as autonomous driving scenarios) due to its excellent anti-interference capabilities. However, its application faces the dual challenges of a lack of specialized datasets and the limited performance of traditional RGB models in low signal-to-noise ratio environments, which hinders the transfer and generalization of deep learning methods. To address these difficulties, this paper proposes a 3D imaging method based on multimodal feature fusion. Specifically, the model adopts a dual Vision Transformer (ViT) encoder, single-decoder architecture. On one hand, it performs pre-trained ViT encoding on geometrically re-projected RGB images. On the other hand, it applies an isomorphic ViT encoding to the range-gated images. Through layer-wise semantic recombination, it achieves efficient cross-modal feature fusion, not only does it enhance the robustness and accuracy of depth estimation, but it can also be easily deployed on edge devices. To overcome the problem of overfitting to LiDAR ground truth data, a spatially constrained window cropping data augmentation strategy is designed, significantly increasing the diversity of training samples and the model's generalization ability. To address the input resolution limitations of Transformers, an optimization scheme combining dynamic patch-based training and progressive up-sampling is further proposed, balancing high-resolution feature representation with efficient training. Experimental results show that the proposed method reduces the depth estimation RMSE on a public test set by more than 12% compared to mainstream baseline models, with particularly outstanding performance in low-texture and long-distance scenes. This research provides a systematic technical solution for cross-modal 3D perception and offers theoretical and engineering references for designing 3D imaging models for complex environments.

Microservice architecture provides innovative solutions for delay-sensitive applications and is widely used in Mobile Edge Computing (MEC). Deploying microservices and implementing request routing within large-scale networks are confronted with numerous challenges, primarily due to intricate dependencies between microservices, frequent data communications between microservices, and the dynamic fluctuations of user request traffic. Given that the user requests are time-varying, the orchestration scheme must enable automatic scaling of microservice instances to ensure service quality. Yet, existing research mainly focuses on static microservice instance deployment, inadequately achieving the intelligent autoscaling of microservices and addressing the time-varying nature of user requests. To address these challenges in dynamic MEC networks, this paper introduces a joint optimization strategy for microservice autoscaling and routing, which adapts to the dynamic fluctuations of user request traffic. Initially, we utilize open Jackson queuing network theory to construct the model, analyzing service request queuing, communication, and processing delays along routing paths, and formulate the problem aiming to minimize deployment costs while ensuring service processing delays are not beyond the delay range that the users can accept. To address the problem, we propose a multi-stage, fine-grained dynamic scaling and routing algorithm. Extensive simulation results indicate that our approach substantially reduces network costs while maintaining network delays within a reasonable range, compared to the other state-of-the-art methods.

Ambient backscatter communication (ABC) enables low-cost and energy-efficient connectivity for Internet of Things (IoT) devices by leveraging ambient radio-frequency (RF) signals. However, the passive nature and open wireless medium of ABC systems make them vulnerable to detection by unauthorized receivers (wardens). To mitigate this risk, covert communication, which conceals transmissions by embedding them within noise, offers a promising security enhancement for ABC systems. This paper proposes a jammer-assisted reflection coefficient optimization framework to enhance the covertness and reliability of ABC systems with an endogenous warden and an external jammer. Specifically, we consider two distinct jamming patterns: uniformly distributed and truncated exponentially distributed artificial noise power. We derive closed-form expressions for both the outage probability of the backscatter link and the minimum detection error rate at the warden under these jamming patterns. Based on these expressions, we determine the optimal reflection coefficients that maximize the effective covert rate while satisfying a predefined covertness constraint. Additionally, we introduce the concept of jamming cost to evaluate the efficiency and applicability of different jamming patterns in terms of the required jamming power to achieve a desired level of covertness. Numerical results validate the effectiveness of the proposed optimization framework and reveal that while uniform jamming provides stronger covertness and lower jamming cost, truncated exponential jamming achieves a lower outage probability. These findings provide key insights for designing secure and efficient ABC systems across diverse IoT deployment scenarios.

Efficient resource allocation is pivotal for achieving high performance in emerging computer systems, where multiple users and tasks compete for shared resources. This challenge spans various domains, including data centers, multicore processors, cloud computing and edge computing, each requiring nuanced allocation strategies to balance competing demands. Traditional approaches often assume concave utility (performance) functions for users, simplifying optimization but failing to capture the complexities of real-world scenarios where non-concave utility functions prevail. Numerous works in the literature apply the greedy algorithm to nonconcave utility functions, resulting in suboptimal solution due to the short-sighted behaviors. To improve this gap, we propose a novel multiphase resource allocation framework that accurately reflects the non-linear dynamics of these systems. To tackle the NP-complete nature of this problem, we formulate a customized resource allocation Markov Decision Process (MDP) that integrates the characteristics of multi-phase utility functions into a nuanced design of the key MDP components, such as state representations, reward signals, and actions. We explore two reinforcement learning (RL)-based methods, specifically Dueling Deep Q-Network (Dueling DQN) and Proximal Policy Optimization (PPO), to optimize resource allocation over time. Our RL-based strategies outperform the conventional greedy algorithm by approximately 37% in standard environments and up to 73% in specialized environments, highlighting their effectiveness in handling the resource allocation problem with non-concave utility functions and achieving scalable, real-time solutions.

The industrial wireless field network plays a critical role in industrial internet of things(IIoT) for various categories of industrial applications. However the channel resource utilization under such a kind of network faces the significant challenges due to the natural time-variance of channel state and its communication quality. As such we propose a transmission capacity exploiting scheme to maximize the channel data rate and its transmission capacity as well for each communication channel. Based on it we present a channel bandwidth resource utilization optimization scheme to achieve the desired network transmission performance objectives. Particularly an OFDMA multi-hop multi-channel based joint resource scheduling scheme is proposed to optimize network system transmission throughput with respect to the designed utility function for each type of user traffic flows over the network. We proposed computationally efficient polynomial-time scheduling algorithms to deal with both QoS and Non-QoS traffic flows with the consideration of both multi-user diversity, QoS performance demand and proportional fairness requirement as well. The experimental results also validate the superior performance of the proposed scheme in industrial wireless field networks.

With the rapid growth of the on-demand economy, logistics companies and merchants increasingly struggle to meet customer demands in dynamic and uncertain conditions. This paper studies the coordinated delivery of parcels by trucks and drones under such demands, proposing a Dynamic Task Scheduling Algorithm based on Hybrid Genetic Tabu algorithm (DTSAGT) for route optimization. Simulating dynamic customer demands with a Poisson distribution and statistical methods, the algorithm addresses timeliness issues due to variations in customer needs. It optimizes drone path planning and task allocation considering drone endurance and payload limits to minimize total delivery time. The algorithm includes three steps: initial solution construction, iterative optimization, and dynamic operations. Experimental results show that DTSAGT reduces the total service time by 15.05%, 34.13%, and 35.71% on average compared to the baseline algorithms. This paper’s contribution is the combination of hybrid genetic and tabu search algorithms applied to dynamic task scheduling in truck-drone delivery, enhancing logistics efficiency.

In the medical field, managing high-dimensional massive medical imaging data and performing reliable medical analysis from it is a critical challenge, especially in resource-limited environments such as remote medical facilities and mobile devices. This necessitates effective dataset compression techniques to reduce storage, transmission, and computational cost. However, existing coreset selection methods are primarily designed for natural image datasets, and exhibit doubtful effectiveness when applied to medical image datasets due to challenges such as intra-class variation and inter-class similarity. In this paper, we propose a novel coreset selection strategy termed as Evolution-aware VAriance (EVA), which captures the evolutionary process of model training through a dual-window approach and reflects the fluctuation of sample importance more precisely through variance measurement. Extensive experiments on medical image datasets demonstrate the effectiveness of our strategy over previous SOTA methods, especially at high compression rates. EVA achieves 98.27% accuracy with only 10% training data, compared to 97.20% for the full training set. None of the compared baseline methods can exceed Random at 5% selection rate, while EVA outperforms Random by 5.61%, showcasing its potential for efficient medical image analysis.

In this article, we consider incentive mechanism designs in asynchronous federated learning (FL) systems. With the consideration of unique characteristics inherent in asynchronous FL, such as dynamic participating and multiminded IoT nodes such as mobile users (MUs), requirements of model training (i.e., training accuracy and convergence time), and limited uplink bandwidth, we formulate considered system as an online incentive mechanism design problem, where each MU is not only a buyer for communication resource but also a seller for computation service. To address the challenges involved in the design, we first derive the relationship between the number of participants and the global training accuracy in asynchronous FL. Then, based on that, we propose a novel mechanism, called the online incentive mechanism for asynchronous FL (OIMAF). To the best of our knowledge, this is the first work to design incentive mechanisms for asynchronous FL. Furthermore, in order to obtain a more robust mechanism, an improved online mechanism, called the two-shot-based online incentive mechanism (TOIM), is proposed by using OIMAF as a building block. Theoretical analyses show that our proposed online incentive mechanisms can guarantee individual rationality, truthfulness, a sound performance, and solution feasibilities. We further conduct comprehensive simulations to validate the effectiveness of our proposed mechanisms.

The integration of Ambient Backscatter Communication (ABC) with covert communication is expected to support emerging Internet of Things (IoT) applications (e.g., Radio Frequency (RF)-powered networks) due to the need for low-cost connectivity and confidential transmission. In general, the purpose of covert communication is to hide the existence of the RF-powered wireless link to ensure the information security of the ABC link. However, the ABC link may have a high rate requirement, thus inevitably increasing the risk of information leakage. Hence, this paper considers jammer-aided endogenous covert communication, where an RF tag sends information covertly to an ABC receiver and exploits the jammer's Artificial Noise (AN) under the supervision of a warden-like legacy receiver. To obtain the maximum data rate of the backscatter link without being detected, we derive the minimum detection error rate of the warden and the outage probability of the backscatter link under random channel fading and the jammer's AN, respectively. Then, we optimize the tag's reflection coefficient to maximize its effective covert rate under the covert constraint based on the warden's mean detection error rate. Since the optimal reflection coefficient cannot be solved directly, monotonicity analyses of the objective and the constraint with respect to the reflection coefficient are adopted to achieve an efficient solution. Numerical results demonstrate the effectiveness of the optimized reflection coefficient for the jammer-aided system.

This paper investigates an unmanned aerial vehicle (UAV) aided over-the-air computation (AirComp) system, where the UAV is deployed as a flying base station to swiftly compute functional values of the data distributed at multiple ground sensors via AirComp within multiple time slots. Subject to the individual transmit power constraints of each ground sensor, we aim to minimize the computational mean-squared error (MSE) of AirComp, by optimizing the UAV's trajectory over multiple slots, the ground sensors' transmit coefficients, and the UAV's de-noising factors per slot. Due to the complicated variable coupling, the resultant AirComp design problem is non-convex. As such, we decompose the original AirComp design problem into two low-dimensional subproblems, one for obtaining multiple groups of ground sensors to determine the UAV's trajectory over time, and the other for optimizing the ground sensors' transmit coefficients and the UAV's receive de-noising factors for AirComp. For the first subproblem, we use the K-means algorithm to group ground sensors, and then the UAV's hovering point at each time slot is determined based on each group of ground sensors. For the second subproblem, we recast it as a convex problem and then employ the Lagrange duality method to obtain the optimal solution. Numerical results show that the proposed scheme achieves a significant computational MSE performance gain over the alternative benchmark schemes.

Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing and local caching) to ground users. How to dynamically determine a UAV swarm's cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question but unaddressed in the literature. Regarding a single UAV's path planning design, we manage to substantially simplify the traditional dynamic program and propose an optimal algorithm of low computation complexity. After coordinating a large number K of UAVs, this simplified dynamic optimization problem becomes intractable and we alternatively present a fast iterative cooperation algorithm with provable approximation ratio$1-(1-\frac{1}{K})^{K}$in the worst case. To relax UAVs' battery capacity limit for sustainable service provisioning, we further allow UAVs to travel to charging stations in the mean time and thus jointly design UAVs' path planning over users' locations and charging stations. We successfully transform the problem to an integer linear program by creating novel directed acyclic graph of the UAV-state transition diagram, and propose an iterative algorithm with constant approximation ratio.

Recent years have witnessed much research effort on automatically predicting game results (win predictions), which has great potential in esports live streaming and game commentator AI systems. The exsiting works on win prediction largely overlook the interpretability and the predication accuracies are hardly satisfactory. To address this issue, we collected a large-scale dataset that contains real-time game records with rich input features of the popular game Honor of Kings. For interpretable and more accurate predictions, we proposed a Hero Featured Network (HFN) by learning from real Honor of Kings combat data and heros’ mutual attributes and interactions. For each time stage of the game, the prediction accuracy can archive 84.7%. The results show that HFN model can not only provide accurate real-time win predictions but also attribute the ultimate prediction results to the contributions of heros’ mutual attributes and interactions for interpretability.

Recent years have witnessed increasingly more exercises and uses of deep unfolding network (DUN) in image compressed sensing (CS) due to its high performance and interpretability. However, the existing DUN does not make full use of more flexible regularization methods. Besides, the intermediate information generated during the iterations of the DUN, which is crucial for the quality improvement of image reconstruction, has been largely overlooked in the existing methods. To alleviate this problem, we propose a novel DUN for image CS with regularization by denoising which casts half quadratic splitting (HQS) algorithm into the neural network. Further, we design an information collection strategy to leverage the useful information generated during the iterations. The information is provided to the image denoiser of the proposed network, which could enhance the image processing ability of the denoiser. The extensive experiments demonstrate that the proposed method is more efficient and achieves state-of-the-art reconstruction quality.

Trajectory planning for a logistic delivery using an unmanned aerial vehicle (UAV) involves a typical traveling salesman problem (TSP), in which the turning of the UAV to avoid obstacles can cause significant energy consumption. The obstacles in the airspace and the angle constraints of the UAV must also be considered in the delivery. To address the low precision of UAV trajectory searches, and the serious impact of flight angles on UAV energy consumption, we propose a UAV trajectory planning strategy called bi-ant-colony optimization (BACO). BACO consists of two phases: path planning and track planning. By applying the guidance layer ant colony optimization (GuLACO) algorithm, the path planning phase eliminates the problem of ant colony deadlock that arises in multi-target point environments, and reopens the ant tabu table to search for a guidance path. Following this, the track planning phase employs the general layer ant colony optimization (GeLACO) algorithm to build the guidance path in segments. Furthermore, the precision of the flight heading for the UAV is optimized by adjusting the flight step in an adaptive manner, and obtaining fine-grained UAV flight tracks to control the turning angle of the logistics UAV. Our simulation results show that compared with the use of the greedy algorithm and the classical ACO algorithm, UAV trajectory planning using BACO can not only obtain shorter flight paths that take into account obstacle avoidance, but can also reduce the energy consumption of the UAV by finely controlling the amplitudes of the flight angles to ensure the safety and energy efficiency of UAV while in flight.

Abstract Anomaly detection is one of the most important applications in video surveillance that involves the temporal localisation of anomaly events in unannotated video sequences. By learning the normal patterns to generate frames and calculating their reconstruction error relative to the ground truth, a frame can be recognised as being abnormal if the reconstruction error exceeds a threshold. Most existing works use a fixed threshold that computes over all the testing data to determine the anomalies. However, fixed threshold strategy cannot address the challenges brought by the dynamic environment, e.g. changes in illumination conditions. In this paper, a dynamic thresholding algorithm (DTA) is proposed, which is fully data‐driven and capable of automatically determining thresholds such that the developed anomaly detection system can flexibly adapt to different scenarios. The proposed DTA is independent of the backbone network and can be easily incorporated into most existing video anomaly detection models to help identify the appropriate thresholds. On both synthetic and real‐world datasets, the experimental results show that with the proposed DTA, the video anomaly detection methods achieve a better performance considering the changes in dynamic environment.

With the popularity and development of Internet of Things (IoT) technology, human activity recognition using IoT devices such as wearable sensors can be implemented for various applications. Due to the complexity of activity recognition, multiple homogeneous or heterogeneous sensors are used to obtain excessive information in most wearable activity recognition systems. However, the increased number of sensors and the way of multichannel signal data bring huge challenges to human activity recognition tasks. How to select suitable sensor channels to balance the computational complexity and recognition accuracy has become a major issue. In this article, we extend the sparse group Lasso mechanism to human activity recognition tasks and propose a hybrid attention-based multi-sensor pruning and feature selection deep neural network, called HAP-DNN. This architecture is able to further perform feature selection on the basis of sensor pruning. HAP-DNN consists of three detachable modules: 1) a feature compression & reconstruction module for sensor feature information fusion and restoration; 2) a feature weight calculation module for calculating sensor channel weights and feature weights; and 3) a learning module for classification, which can be regarded as a filter feature selection method. Four public activity recognition data sets are used to verify our proposed architecture, and the experimental results show that HAP-DNN achieves the best classification performance with the least number of retained feature channels.

Achieving high performance in many multi-server systems (e.g., web hosting center, cloud) requires finding a good assignment of worker threads to servers and also effectively allocating each server’s resources to its assigned threads. The assignment and allocation components of this problem have been studied extensively but largely separately in the literature. In this paper, we introduce theassign and allocate (AA)problem, which seeks to simultaneously find an assignment and allocation that maximizes the total utility of the threads. Assigning and allocating the threads together can result in substantially better overall utility than performing the steps separately, as is traditionally done. We model each thread by a utility function giving its performance as a function of its assigned resources. We first prove that the AA problem is NP-hard. We then present a$2 (\sqrt {2}-1) > 0.828$factor approximation algorithm for concave utility functions, which runs in$O(mn^{2} + n (\log mC)^{2})$time for$n$threads and$m$servers with$C$amount of resources each. We also give a faster algorithm with the same approximation ratio and$O(n (\log mC)^{2})$time complexity. We then extend the problem to two more general settings. First, we consider threads with nonconcave utility functions, and give a 1/2 factor approximation algorithm. Next, we give an algorithm for threads using multiple types of resources, and show the algorithm achieves good empirical performance. We conduct extensive experiments to test the performance of our algorithms on threads with both synthetic and realistic utility functions, and find that they achieve over 92% of the optimal utility on average. We also compare our algorithms with a number of practical heuristics, and find that our algorithms achieve up to 9 times higher total utility.

DNA microarray data contain valuable biological information, which makes it of great importance in disease analysis and cancer diagnosis. However, the classification of microarray data is still a challenging task because of the high dimension and small sample size, especially for multiclass data accompanied by class imbalance. In this paper, we propose a cooperative coevolutionary multiobjective genetic programming (CC-MOGP) for microarray data classification. It converts a multiclass problem into a set of tractable binary problems and coevolves the corresponding population. And a cooperative coevolutionary Pareto archived evolution strategy (CC-PAES) is employed to approximate the Pareto front. During this procedure, we propose a synergy test method to assist in guiding the coevolution between populations. Experimental results on 8 multiclass microarray data show that CC-MOGP can obtain competitive prediction accuracy compared with several state-of-art evolutionary computation and traditional methods.

Recent years have witnessed increasingly more uses of drone networks for providing wireless coverage to ground users. Each drone is constrained in its energy storage and wireless coverage, and it consumes most energy when flying to the top of the target area, leaving limited leftover energy for hovering at its deployed position and providing wireless coverage. The literature largely overlooks this sustainability issue of drones’ energy consumption during deployment, and we aim to minimize the maximum energy consumption among all drones after their deployment. This min-max drone deployment problem solving requires drones to cooperate with each other in deployment distance and altitude to evenly use up their energy, which is shown to be NP-hard. Thus, we propose a hybrid genetic algorithm to solve the min-max drone deployment problem. In our proposal, the integer code scheme is used to encode the sequence of drones’ deployment. The energy consumption determined by the horizontal and vertical flying distance is adopted as the fitness value. With the determined order of the drones sequence by coding process, we introduce a feasibility checking operator with binary search to archive the optimum. Experimental study shows that the algorithm has capability and superiority to find good solutions under different drones’ characteristics distribution and outperforms solutions from existing competitors by extensive simulations.

Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing, fast Internet connection, and local caching) to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing demands locally. It is necessary for different UAVs to cooperate with each other for servicing many users, and how to determine their cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question. This paper is the first to design and analyze cooperative path-planning algorithms of a UAV swarm for optimally servicing many spatial locations with dynamic user arrivals and waiting deadlines in the time horizon. For each UAV, it needs to decide whether to wait at the current location or chase a newly released demand in another location, under upper coordination with the other UAVs in the swarm. For each UAV's routing problem even without coordinating with the rest UAVs, it follows dynamic programming structure and is difficult to solve directly given many user demands. We manage to simplify and propose an optimal algorithm of fast computation time (only polynomial with respect to both the numbers of user locations and user demands) for returning the UAV's optimal path-planning. When a large number |K| of UAVs are coordinating, the dynamic programming simplification becomes intractable. Alternatively, we present an iterative cooperation algorithm with approximation ratio 1 - (1 - 1/|K| )|K|in the worst case, which is proved to obviously outperform the traditional idea of partitioning UAVs to serve different user/location clusters separately. Finally, we conduct simulation experiments to show that our algorithm's average performance is close to the optimum.

Unmanned Aerial Vehicle (UAV) networks have emerged as a promising technique to rapidly provide wireless coverage to a geographical area, where a flying UAV can be fast deployed to serve as cell site. Existing work on UAV-enabled wireless networks overlook the fast UAV deployment for wireless coverage, and such deployment problems have only been studied recently in sensor networks. Unlike sensors, UAVs should be deployed to the air and they are generally different in flying speed, operating altitude and wireless coverage radius. By considering such UAV heterogeneity to cover the whole target area, this paper studies two fast UAV deployment problems: one is to minimize the maximum deployment delay among all UAVs (min-max) for fairness consideration, and the other is to minimize the total deployment delay (min-sum) for efficiency consideration. We prove both min-max and min-sum problems are NP-complete in general. When dispatching UAVs from the same location, we present an optimal algorithm of low computational complexity O(n2) for the min-max problem. When UAVs are dispatched from different locations, we propose to preserve their location order during deployment and successfully design a fully polynomial time approximation scheme (FPTAS) of computation complexity O(n2log 1/ϵ) to arbitrarily approach the global optimum with relative error ϵ. The min-sum problem is more challenging. When UAVs are dispatched from the same initial location, we present an approximation algorithm of linear time. As for the general case, we further reformulate it as a dynamic program and propose a pseudo polynomial-time algorithm to solve it optimally.

Unmanned Aerial Vehicle (UAV) networks have emerged as a promising technique to rapidly provide wireless coverage to a geographical area out of the reach or capacity of existing core networks, where a flying UAV can be fast deployed to serve as a base station. Existing work on UAV overlook the emergency deployment problem and only the recent research on sensor networks study the deployment problems the assumed in one-dimensional (1D) ground. However, UAVs should be deployed to the air (beyond one-dimension) by considering their different flying speeds during deployment and deployment altitudes, this paper studies this novel emergency UAV deployment to minimize the UAV deployment delay till covering the whole target area. When a number n of diverse UAVs are dispatched from the same location (e.g., the closest UAV station) to the target area, we present an optimal deployment algorithm by balancing UAVs' diverse flying speeds and coverage radii, and this algorithm has low computation complexity O(n2). When UAVs are generally dispatched from different locations, we first prove that the emergency UAV deployment problem is NP-complete. By preserving UAVs' location order, we then successfully design a fully polynomial time approximation scheme (FPTAS) of computation complexity O(n2log 1/ε) to arbitrarily approach the global optimum.

Barrier coverage with wireless sensors aims at detecting intruders who attempt to cross a specific area, where wireless sensors are distributed remotely at random. This paper considers limited-power sensors with adjustable ranges deployed along a linear domain to form a barrier to detect intruding incidents. We introduce three objectives to minimize: 1) total power consumption while satisfying full coverage; 2) the number of active sensors to improve the reliability; and 3) the active sensor nodes' maximum sensing range to maintain fairness. We refer to the problem as the tradeoff barrier coverage (TBC) problem. With the aim of obtaining a better tradeoff among the three objectives, we present a multiobjective optimization framework based on multiobjective evolutionary algorithm (MOEA)/D, which is called problem specific MOEA/D (PS-MOEA/D). Specifically, we define a 2-tuple encoding scheme and introduce a cover-shrink algorithm to produce feasible and relatively optimal solutions. Subsequently, we incorporate problem-specific knowledge into local search, which allows search procedures for neighboring subproblems collaborate each other. By considering the problem characteristics, we analyze the complexity and incorporate a strategy of computational resource allocation into our algorithm. We validate our approach by comparing with four competitors through several most-used metrics. The experimental results demonstrate that PS-MOEA/D is effective and outperforms the four competitors in all the cases, which indicates that our approach is promising in dealing with TBC.

Sparse signal reconstruction can be regarded as a problem of locating the nonzero entries of the signal. In presence of measurement noise, conventional methods such as l1norm relaxation methods and greedy algorithms, have shown their weakness in finding the nonzero entries accurately. In order to reduce the impact of noise and better locate the nonzero entries, in this paper, we propose a two-phase algorithm which works in a coarse-to-fine manner. In phase 1, a decomposition-based multiobjective evolutionary algorithm is applied to generate a group of robust solutions by optimizing l1norm of the solutions. To remove the interruption of noise, the statistical features with respect to each entry among these solutions are extracted and an initial set of nonzero entries are determined by clustering technique. In phase 2, a forward-based selection method is proposed to further update this set and locate the nonzero entries more precisely based on these features. At last, the magnitudes of the reconstructed signal are obtained by the method of least squares. We conduct the comparison of our proposed method with several state-of-the-art compressive sensing recover methods, the best result in phase 1 and the approach combining phases 1 and 2 without the statistical features. Experimental results on benchmark signals as well as randomly generated signals demonstrate that our proposed method outperforms the above methods, achieving higher recover precision and maintaining larger sparsity.

We propose a multi-crossover and adaptive island based population algorithm (MAIPA). This technique divides the entire population into subpopulations, or demes, each with a different crossover function, which can be switched according to the efficiency. In addition, MAIPA reverses the philosophy of conventional genetic algorithms. It gives priority to the autonomous improvement of the individuals (at the mutation phase), and introduces dynamism in the crossover probability. Each subpopulation begins with a very low value of crossover probability, and then varies with the change of the current generation number and the search performance on recent generations. This mechanism helps prevent premature convergence. In this research, the effectiveness of this technique is tested using three well-known routing problems, i.e., the traveling salesman problem (TSP), capacitated vehicle routing problem (CVRP), and vehicle routing problem with backhauls (VRPB). MAIPA proves to be better than a traditional island based genetic algorithm for all these three problems.