Accurate vehicle position is crucial to vehicular Ad-Hoc network (VANET) applications. The wildly used global navigation satellite systems (GNSS) have limited localization accuracy, especially when GNSS signals are blocked or contaminated with reflected signals. Cooperative localization based on multi-vehicle information fusion will be the core component in VANET in the near future. This paper strives to enhance the localization accuracy using the fusion of GNSS and relative range information in static scenes. The formulation following the Maximum-Likelihood strategy is a quadratic non-convex programming, which is NP-hard. An iterative localization algorithm is proposed, where an approximate quadratic programming with linear constraints is solved in each iteration. Additional inspection steps and correction step are designed to ensure the convergence and feasibility of the proposed algorithm. The theoretical analysis shows that the limit of the proposed algorithm must be a global optimal solution of the formulated problem and the mean squared error can reach the Cramér-Rao lower bound. In terms of localization accuracy, the proposed method outperforms the existing linearized weighted least-squares method and semidefinite relaxation method, while exhibiting moderate computational complexity.

The ambient radio frequency (RF) energy harvesting technology has recently been regarded as a potential solution for powering the wireless sensor networks (WSNs). However, the ultra low power density of ambient RF energy is the main impediment to its further application. In this paper, we propose a novel energy efficient cooperative communication scheme (EECCS), which combines energy beamforming communication and ambient backscatter communication, to overcome the energy problem of WSNs powered by ambient RF energy harvesting. Moreover, to further reduce the energy consumption of nodes, we present an optimal resource allocation problem for EECCS. It can be formulated as an signomial geometric programming (SGP), which is nonconvex and NP-hard. We develop a Sequential Convex Approximation (SCA) algorithm for finding a solution of this SGP problem, which transforms the SGP problem into a sequence of geometric programming (GP) problems that are convex. Simulation results indicate that the EECCS can improve the energy efficiency of the ambient RF powered WSNs and maximize the total amount of data received by sink.

Ambient radio frequency (RF) energy harvesting technology provides a hopeful solution to power the low-power electronic system. Nevertheless, the extremely low power density and randomness of ambient RF energy pose a significant obstacle to the application of ambient RF energy harvesting technology. Aiming at wireless transmission by ambient RF-powered sensors, this paper presents a cooperative communication technology by combining ambient backscattering and energy beamforming to significantly decrease the transmission energy consumption of each sensor. For wireless transmission via this cooperative communication technology, a novel optimization problem is formulated to maximize the long-term time-averaged sensed data size, which jointly takes into account sensing scheduling, transmission power allocation and the control of sensed data size. Moreover, we employ techniques of Lyapunov drift and Lyapunov optimization to propose an online optimal control algorithm, which achieves the optimal solution to the proposed problem without requiring a-priori knowledge of harvested energy and wireless channels. By exploiting mixed integer programming (MIP) and signomial geometric programming (SGP) that cause the proposed problem difficult to solve, a novel Sequential Convex Approximation algorithm is further introduced into the online optimal control algorithm. Simulation results indicate the proposed online algorithm offers a dramatic performance gain over alternative online algorithms.

This paper studies a wireless powered sensor network (WPSN) where the senor nodes harvest energy from the power beacons by wireless power transfer (WPT). In order to improve the energy transfer Efficiency of WPT, a WPT scheme with energy beamforming is presented to power the sensor network. Moreover, for the sake of maximizing energy efficiency (EE), we present a resource assignment optimization problem for this scheme, which is nonconvex because of the fractional form of the objective function. So we develop a low complexity algorithm to resolve this problem. Compared to other traditional algorithms (e.g., Dinkelbach 1) for solving this type of problem, whose objective function is of fractional form, it works with lower time complexity. Experimental results indicate the superiority of the proposed scheme.