ABSTRACT In recent years, robot path planning has become a critical aspect of autonomous navigation, especially in dynamic and complex environments where robots must operate efficiently and safely. One of the primary challenges in this domain is achieving high convergence efficiency while avoiding local optimal solutions, which can hinder the robot's ability to find the best possible path. Additionally, ensuring that the robot follows a path with minimal turns and reduced path length is essential for enhancing operational efficiency and reducing energy consumption. These challenges become even more pronounced in high‐dimensional optimization tasks where the search space is vast and difficult to navigate. In this article, a multi‐strategy fusion enhanced dung beetle optimization algorithm (MIDBO) is introduced to tackle key challenges in robot path planning, such as slow convergence and the problem of local optima, and so on, in which MIDBO incorporates several key innovations to enhance performance and robustness. First, the Tent chaotic strategy is used to diversify initial solutions during population initialization, thereby mitigating the risk of local optima and improving global search capability. Second, a penalty term is integrated into the fitness function to penalize excessive turning angles, aiming to reduce the frequency and magnitude of turns. This modification results in smoother and more efficient paths with reduced lengths. Third, the inertia weight is adaptively updated by a sine‐based mechanism, which dynamically balances exploration and exploitation, accelerates convergence, and enhances algorithm stability. To further improve efficiency for path planning, the MIDBO integrates a Levy flight strategy and a local search mechanism to boost the search capability during the stealing phase, contributing to smoother and more practical paths planned for the robot. A series of thorough and reproducible experiments are performed using benchmark test functions to evaluate the performance of MIDBO in comparison to several leading metaheuristic algorithms. The results demonstrate that MIDBO achieves superior outcomes in path planning tasks with optimal and mean path lengths of 42.1068 and 44.4755, respectively, which significantly outperforms other algorithms including IPSO (47.6244, 55.9375), original DBO (47.6244, 55.9375), and ISSA (47.6244, 55.9375). MIDBO also markedly reduces the number of turns by achieving best and average values of 10 and 13.4, respectively, compared with IPSO (11, 16.1), original DBO (12, 15.3), and ISSA (12, 16.4). Besides, the consistent performance of MIDBO is confirmed via stability analysis based on the mean square error of path lengths and turn counts across 10 independent trials. For the high‐dimensional optimization tasks, MIDBO achieves 8 and 7 functions about top rankings on 50‐ and 100‐dimensional functions, and specifically MIDBO outperforms DBO, IPSO, and ISSA on 13, 18, and 11 functions, respectively. Therefore, the findings validate MIDBO is a competitive solution of path planning for mobile robot navigation with complex requirements.

With the rapid growth of mobile communication technologies and smart devices, demand for location-based services has surged. Fingerprint-Based positioning using wireless signals is now a leading method for indoor localization. However, wireless signals from access points (APs) fluctuate due to time-varying noise, causing fingerprint signals sampled at the same location to differ over time, sometimes significantly. This variability not only hinders accurate fingerprint map construction but also disrupts precise matching between the target point’s fingerprint and the map, leading to inconsistent positioning results. To address this, we propose RSLoc, an indoor localization system based on a KAN convolutional network. First, APs with weak or erratic signals are excluded, and a refined fingerprint map is generated. We then develop a deep network model, FFEM, based on KAN convolution. The model is trained using a sample library derived from fingerprint data, optimized through entropy regularization and pruning. The FFEM model generates a mapping of the target point, calculates its similarity to the fingerprint map, and selects appropriate neighboring points for effective localization. Experiments using different mobile devices show that RSLoc improves average positioning accuracy by 33%-44% compared to methods like LORI and FML. Moreover, removing invalid APs and constructing the sample library reduce errors, enhancing accuracy by over 48% and 29%, respectively.

Power iteration has been applied to compute the eigenvectors of the similarity matrix in spectral clustering tasks . However, these power iteration based clustering methods usually suffer from the following two problems: (1) the power iteration usually converges very slowly; (2) the singular value decomposition method adopted to obtain the eigenvectors of the similarity matrix is time-consuming. To solve these problems, we propose a novel clustering method named Ada ptive P ower I teration C lustering (AdaPIC). Specifically, AdaPIC employs a sequence of rank-one matrices to approximate the normalized similarity matrix. Then, the first K + 1 eigenvectors can be computed in parallel, and the stopping condition of power iteration can be automatically yielded based on the target clustering error. We performed extensive experiments on public datasets to demonstrate the effectiveness of the proposed AdaPIC method, comparing with leading baseline methods . The experimental results indicate that the proposed AdaPIC algorithm has a competitive advantage in running time. The running time taken by spectral clustering baseline methods is usually more than 2.52 times of that taken by AdaPIC. For clustering accuracy, AdaPIC outperforms classic PIC by 97% on average, over all experimental datasets . Moreover, AdaPIC achieves comparable clustering accuracy with other 3 baseline methods, and achieves 6%–15% better clustering accuracy than the remaining 6 state-of-the-art baseline methods.

Load-balanced flow scheduling for big data centers in clouds, in which a large amount of data needs to be transferred frequently among thousands of interconnected servers, is a key and challenging issue. The OpenFlow is a promising solution to balance data flows in data center networks through its programmatic traffic controller. Existing OpenFlow based scheduling schemes, however, statically set up routes only at the initialization stage of data transmissions, which suffers from dynamical flow distribution and changing network states in data centers and often results in poor system performance. In this paper, we propose a novel dynamical load-balanced scheduling (DLBS) approach for maximizing the network throughput while balancing workload dynamically. We first formulate the DLBS problem, and then develop a set of efficient heuristic scheduling algorithms for the two typical OpenFlow network models, which balance data flows time slot by time slot. Experimental results demonstrate that our DLBS approach significantly outperforms other representative load-balanced scheduling algorithms Round Robin and LOBUS; and the higher imbalance degree data flows in data centers exhibit, the more improvement our DLBS approach will bring to the data centers.

Software defined networking (SDN) provides flexible management for datacenter networks with the flow-level control. Such the fine-grained management, however, consumes large amount of bandwidth between data and control planes, which results in the bottleneck in the scalability of SDN-based datacenters. "The elephant and mouse phenomenon" suggests that there are only very few elephant flows that carry the majority of bytes in datacenters so that it can improve management efficiency to detect and reroute elephant flows while leaving mice flows in data plane leveraging wildcard flow table in OpenFlow. Unfortunately, existing mechanisms for elephant flow detection suffer from high bandwidth consumption and long detection time. In this paper, we propose an efficient sampling and classification approach (ESCA) with the two-phase elephant flow detection. In the first phase, ESCA improves sampling efficiency by estimating the arrival interval of elephant flows and filtering out redundant samples using a filtering flow table. In the second phase, ESCA classifies samples with a new supervised classification algorithm based on correlation among data flows. The mathematical analysis proofs our ESCA outperforms related schemes. Extensive experiment results on real public datacenter traces further demonstrate that our ESCA can provide accurate detection with less sampled packets and shorter detection time.

Cognitive radio significantly mitigates the spectrum scarcity for various applications built on wireless communication. Current techniques on mobile cognitive ad hoc networks (MCADNs), however, cannot be directly applied to time-critical applications due to channel interference, node mobility as well as unexpected primary user activities. In multichannel multiflow MCADNs, it becomes even worse because multiple links potentially interfere with each other. In this paper, we propose a delay-minimized routing (DMR) protocol for multichannel multiflow MCADNs. First, we formulate the DMR problem with the objective of delay minimization. Next, we propose a delay prediction model based on a conflict probability. Finally, we design the minimized path delay as a routing metric, and propose a heuristic joint routing and channel assignment algorithm to solve the DMR problem. Our DMR can find out the path with a minimal end-to-end (e2e) delay for time-critical data transmission. NS2-based simulation results demonstrate that our DMR protocol significantly outperforms related proposals in terms of average e2e delay, throughput, and packet loss rate.

The stability of links in mobile cognitive networks (MCNets) is significantly affected by primary user activities and node mobility, which makes topology control and stable routing more challenging than that in traditional wireless networks. In multi-channel multi-hop MCNets, it will become worse. In this paper, we propose a primary user activity prediction model to reveal channel utilization patterns of primary users. Next, we put forward a novel routing metric Primary user activity Prediction based Stability Metric (PPSM) to quantitatively capture the affect of primary user activities and node mobility. Finally, we propose and implement a Primary user activity Prediction based Joint Topology Control and Stable Routing (PP-JTCSR) protocol for maximizing network throughput based on our primary user activity prediction model, which can find out the most stable and the shortest path between a source and a destination. NS2-based simulation results demonstrate that our PP-JTCSR protocol can generate stable topology through predicting link and path duration quantitatively, and outperforms related proposals in terms of path stability and average throughput.

Summary Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Internet‐based delay‐sensitive applications. It is an important but unsolved issue, however, to efficiently schedule tasks in network processors with multicore and multithread for improving the system throughput as much as possible. Profiling can gather runtime environment information and guide the compiler to optimize programs through scheduling tasks based on the runtime context. This paper proposes a profiling‐based task scheduling approach, targeting on improving the throughput of multicore network processor (Intel IXP) systems in the balanced pipeline way. In this work, we investigate a profiling‐based task scheduling framework, a task scheduling algorithm, and a set of performance models. Our task allocation scheme maps tasks onto the pipeline architecture and multiple threads of network processors in parallel, which incorporates the profiling context and global thread refinement. We evaluate our task scheduling algorithm by implementing representative network applications on the Intel IXP network processor. Experimental results demonstrate that our algorithm is able to schedule tasks in a balanced pipeline fashion and achieve the high throughput and data transmission rate. Copyright © 2012 John Wiley & Sons, Ltd.

Tor is a popular anonymity-preserving network, consisting of routers run by volunteers all around the world. It protects Internet users' privacy by relaying their network traffic through a series of routers, thus concealing the linkage between the sender and the recipient. Despite the advantage of Tor's anonymizing capabilities, it also brings extra latency, which discourages more users from joining the network.