Hybrid-field channel estimation for extremely large-scale massive MIMO (XL-MIMO) system is discussed in this paper. By exploiting the structural characteristics of far-field path components in the angle domain and the sparsity of near-field path components in the polar domain, a channel estimation algorithm combining support detection and orthogonal matching pursuit (SD-OMP) is proposed. Specifically, the supports of the far-field path components are firstly detected according to components structure characteristics of the angle domain in the XL-MIMO system and then can be used to obtain the far-field path components. Next, effect on the XL-MIMO system induced by far-field path components can be removed firstly and the OMP algorithm is employed to obtain the near-field path components by exploiting its polar domain sparsity. Finally, the hybrid-field channel is recovered by superposing the given far-field path components and near-field path components. Experiment results show that the proposed algorithm can accurately recover channel with relatively low pilot overhead and computation complexity comparing with some classical channel estimation algorithms.

Aiming at high network energy consumption and data delay induced by mobile sink in wireless sensor networks (WSNs), this paper proposes a cluster-based energy optimization algorithm called Cluster-Based Energy Optimization with Mobile Sink (CEOMS). CEOMS algorithm constructs the energy density function of network nodes firstly and then assigns sensor nodes with higher remaining energy as cluster heads according to energy density function. Meanwhile, the directivity motion performance function of mobile sink is constructed to enhance the probability of remote sensor nodes being assigned as cluster heads. Secondly, based on Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) architecture, the energy density function and the motion performance function are introduced into the cluster head selection process to avoid random assignment of cluster head. Finally, an adaptive adjustment function is designed to improve the adaptability of cluster head selection by percentage of network nodes death and the density of all surviving nodes of the entire network. The simulation results show that the proposed CEOMS algorithm improves the cluster head selection self-adaptability, extends the network life, reduces the data delay, and balances the network load.

In wireless sensor networks (WSNs), conventional clustering routing algorithms often lead to uneven distribution of cluster heads (CHs), resulting in imbalanced and high energy consumption. Addressing these challenges, this study introduces a novel algorithm, termed the energy distance function-based improved K-Means algorithm (EDFIKM). This algorithm synthesizes the principles of the low energy adaptive clustering hierarchy (LEACH) with the K-Means clustering methodology. Key to this approach is the formulation of an adaptive weighted energy distance function that accounts for nodes’ residual energy, their proximity to cluster centers, and dynamic weights for energy and distance factors. This function strategically mitigates the randomness in CH selection. Incorporating this function into the K-Means clustering process achieves a more uniform distribution of CHs and an equitable allocation of nodes across clusters. Importantly, this contributes to a balanced energy load and a reduction in overall network energy consumption. Simulation studies validate that the EDFIKM algorithm efficiently lowers network energy demands and extends the network lifespan, without adding to the time complexity.

In wireless sensor networks (WSNs), finding the best transmission route from each node to the mobile sink is not a simple task because of the dynamic characteristics of sink node and cluster transmission. In order to balance energy consumption and reduce collisions in wireless transmissions, this paper proposes a Dynamic Spanning Tree with Mobile Sink (DSTMS) routing algorithm. Firstly, based on the Low Energy Adaptive Clustering Hierarchy Protocol (LEACH), a multi-layer transmission framework with dynamic minimum spanning tree (MST) is constructed to optimize data transmission route. In this framework, the selected rendezvous points, according to the motion parameters of mobile sink, build the dynamic rendezvous layer to constraint the hierarchical transmission of DSTMS. In addition, considering some factors of transmission energy consumption and residual energy distribution,the weight factor based on energy efficient function is introduced to evaluate the cost of transmission path within the MST. Moreover, the proposed algorithm not only reduces network energy consumption caused by frequent location updates of mobile sink, but also reduces data transmission congestion by constructing dynamic hierarchical transmission strategy. The simulation results show that the DSTMS algorithm can effectively prolong the network life, balance the network load and reduce collisions in wireless transmissions.

Current range-free node localization method experiences low localization accuracy in sparse anchors wireless sensor networks (WSN). To this end, a novel range-free node localization model based on minimize maximum error criterion is proposed in this letter. Moreover, an iterative localization algorithm called MinMax-DV-hop is proposed to tackle the nonconvex and non-differentiable objective function of proposed model too. Specifically, auxiliary variable is introduced to recast the minimum-maximum optimization into one with a linear object function, convex constraints and nonconvex constraints. And then, nonconvex constraints are tightened to linear inequality constraints by using its first-order Taylor expansions, thus a successive convex approximation method can be designed to iteratively solve the optimization problem finally. Simulations show that the MinMax-DV-hop experiences higher localization accuracy in sparse anchors WSN than those of the comparisons algorithm.

In sensor networks with anisotropic properties including irregular layout sensors, poor network connectivity and so on, employing traditional localization methods may lead to poor localization accuracy. Constructing a novel localization model suitable for anisotropic properties may effectively address the above problems. Generally speaking, anisotropic properties can lead to an increase in the hop count between anchor nodes and unknown nodes. While according to range-free localization principles, a higher hop count between anchor and unknown nodes typically results in poorer localization accuracy of unknown nodes. Inspired by this observation, we propose a weighted range-free localization model where the confidence level of distance between an anchor node and an unknown node is set to be inversely proportional to the hop count. Furthermore, within the proposed weighted model, we also introduce a rational distance estimation related to types of anchor pairs. Despite the model’s inherent non-convexity and non-differentiability, it can be reformulated into an optimization model with convex objective functions and non-convex constraints through matrix transformations. To resolve these non-convex constraints, we employ a Sequential Convex Approximation (SCA) algorithm that tightens non-convex constraint to its first Taylor expansion version, leading to an iterative solver of original optimization problem. Simulation results validate that our proposed algorithm substantially improves stability and accuracy in estimating range-free node locations.