Gaifang Xin

The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments.

The popularity of next-generation networks has brought ubiquitous location-based demands. This article proposes a node deployment-aided intelligent optimization estimation (NDIOE) for target positioning in terms of node deployment, wireless ranging, calibration errors, and the algorithm itself. Since the optimized deployment of anchor nodes is the basis of high-accuracy positioning, we derive the characteristic matrix closely related to the upper bound of positioning error and map the signal domain to the location domain for a preliminary result solved by unconstrained optimization estimation. On this basis, the preliminary locations as the initial search values can be further refined for more accurate locations with the use of improved local particle swarm optimization in a narrowed searching area, whose adaptive fitness function, inertia weight factor, and mutation operation are designed to avoid falling into the local optimal. Aside from that, considering the high computational cost of solving extreme values through node traversal, we theoretically derive the theoretical minimum expression of any point based on Cramer–Rao lower bound (CRLB) as a benchmark, whose theoretical minimum value is $0.2^{2}$ in the fully connected network. Compared with the existing related algorithms, the proposed NDIOE algorithm is effectively validated under simulation and experimental platforms, as well as in complex environments with obstacles.

The demands for high localization accuracy in wireless sensor networks (WSNs) have led to the development of various innovative algorithms. This article proposes a distributed joint robust filter (DJRF) algorithm for addressing fluctuant localization errors caused by the measurement noises and ill-conditioned matrix solution. Firstly, the distributed solution is established after considering the non-Gaussian noises, drifted anchor node coordinates as well as unknown point-to-point distances. Secondly, since the outliers may seriously degrade the localization performance, the robustness filter integrated with virtual observation is used to estimate the point-to-point distances. Thirdly, the variable separation method that can weaken the influence of ill-conditioned matrices is proposed for mobile target localization without generating large errors. The numerical results show that the proposed DJRF combines the merits of distributed solution and robustness filter, which effectively deal with various uncertainties under an acceptable amount of computation. Furthermore, the DJRF experimental result has a 0.26 m average error under non-Gaussian noises and increases to a 0.33 m average error in a more actual scenario. The DJRF algorithm uses outlier elimination and a robust filter to suppress its divergent trend, which can further verify the superiority compared with relevant localization algorithms.

Improper use of high beams can cause glare interference to oncoming drivers or pedestrian. In this paper, an adaptive high beam controlling system is designed to avoid glare interference. The adaptive high beams are illuminated by group light emitting diode (LED) particles as the light source. The luminous intensity distribution of single LED particle is determined along with the lighting zones of group LED particles. The oncoming and same direction vehicles are detected by positioning their headlights and tail lights via the received images from the on-board camera. The detected vehicles are mapped to the corresponding LED illumination zones. The adaptive high beam controlling system turns off the LED particles in corresponding with vehicle areas and keeps the remaining LED particles on. The novelty of our work is that an adaptive high beam controlling system is developed under the integration of controlled LED particles, an image processor, and a lamp controller. The benefit of proposed adaptive high beams is to avoid glare interference for improving night driving safety.