WiFi-Fingerprint is extensively utilized for indoor localization with the advent of the high-density wireless networks deployment and research on ubiquitous intelligence. Nevertheless, establishing an elaborate radio map for localization is a highly time-consuming task. Aiming to alleviate this problem and enhance WiFi-based indoor localization accuracy, this paper has done the following contributions. First, we present gaussian process regression (GPR) models to predict the spatial distribution of signal strength in the uncalibrated domain with limited known labeled fingerprints in reference points (RPs). As a result, deployment effort for radio mapping can be greatly reduced. Second, in order to acquire fingerprints data with higher accuracy, the compound kernels for received signal strength (RSS) prediction models are presented. Finally, the definite position is determined with the weighted Similarity K-Nearest Neighbor (Similarity-WKNN) localization algorithm when new observation RSS is collected. The experiments show that compared with the original reference fingerprints localization system, the proposed localization system explicitly reduces the localization error. The results further demonstrate that our method can augment the fingerprints and improve the accuracy of fingerprint-based indoor localization without extra manual calibration or adding dedicated infrastructure.

The rise of the Internet of Things has spurred the growth of wireless applications, particularly Wi-Fi-based indoor localization, which is gaining prominence owing to its cost-effectiveness. Nevertheless, the accuracy of Wi-Fi-based indoor localization is hindered by signal instability. To address this limitation, we introduce an interval random analysis approach for uncertain Wi-Fi-based indoor localization. Specifically, this approach employs an interval random parameter lognormal shadowing model for radio map enhancement and adaptive Bayesian comprehensive learning (IRPLS-ABCL) particle swarm optimization (PSO) for location estimation accuracy enhancement. The process comprises two stages: offline training and online localization. During the offline phase, we establish the interval random parameter lognormal shadowing model, considering the parameters as interval random variables, rather than precise values, in a sparse reference point scenario. In the online phase, we use a double-panel fingerprint homogeneity model to assess fingerprint similarity and apply the adaptive Bayesian comprehensive learning PSO algorithm to enhance localization precision. The experimental results show that the proposed algorithm can achieve the best performance in terms of localization accuracy based on the predicted average received signal strength (RSS), reaching 1.89 m.

Wireless Sensor Networks (WSNs), as an important part of the Internet of Things (IoT), have developed rapidly in recent years. WSNs comprises of spatially distributed sensor nodes to collect the information from the environment. The energy of nodes is usually limited, so the speed of energy consumption determines the lifespan of the network. This paper proposed an energy-efficient adaptive clustering routing algorithm named MPDE&VSSLS-SIACR to minimize the energy consumption of WSNs and prolong the network lifespan. Here, SIACR is a swarm-intelligence-based adaptive clustering routing protocol, while MPDE&VSSLS is a novel Memetic Algorithm that belongs to the swarm intelligence algorithm. The SIACR protocol classifies WSN nodes into three categories: cluster heads, cluster members, and free nodes, and employs the MPDE&VSSLS to optimize clustering, with the objectives of reducing energy consumption and balancing the residual energy among nodes. In the comparison experiments, the algorithm presented in this study was compared to the LEACH, LEACH-C, and PSO-SIACR algorithms for two network topologies with three data fusion rates of 0, 0.5, and 1, respectively. The experimental results revealed that MPDE&VSSLS-SIACR outperformed the other algorithms in terms of network lifetime and residual energy. Meanwhile, it is demonstrated that the proposed algorithm is highly adaptable to different topologies and different data fusion rates. Furthermore, the PSO-SIACR algorithm performed similarly to the MPDE&VSSLS-SIACR algorithm, indicating that the SIACR is an effective framework for clustering routing optimization using swarm intelligence algorithms.

With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL’s superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.

To develop a high performance and widely applicable particle swarm optimization (PSO) algorithm, a heterogeneous differential evolution particle swarm optimization (HeDE-PSO) is proposed in this study. HeDE-PSO adopts two differential evolution (DE) mutants to construct different characteristics of learning exemplars for PSO, one DE mutant is for enhancing exploration and the other is for enhance exploitation. To further improve search accuracy in the late stage of optimization, the BFGS (Broyden–Fletcher–Goldfarb–Shanno) local search is employed. To assess the performance of HeDE-PSO, it is tested on the CEC2017 test suite and the industrial refrigeration system design problem. The test results are compared with seven recent PSO algorithms, JADE (adaptive differential evolution with optional external archive) and four meta-heuristics. The comparison results show that with two DE mutants to construct learning exemplars, HeDE-PSO can balance exploration and exploitation and obtains strong adaptability on different kinds of optimization problems. On 10-dimensional functions and 30-dimensional functions, HeDE-PSO is only outperformed by the most competitive PSO algorithm on seven and six functions, respectively. HeDE-PSO obtains the best performance on sixteen 10-dimensional functions and seventeen-30 dimensional functions. Moreover, HeDE-PSO outperforms other compared PSO algorithms on the industrial refrigeration system design problem.