This paper proposes a generalized approach combining two-way ranging (TWR) and passive ranging methods, called active-passive two-way ranging (AP-TWR). The proposed approach offers a generalized solution for a wide range of anchor configurations in positioning systems. The possibility to define active-passive and passive-only anchor roles allows scaling the system to improve the root-mean-square-error (RMSE) of the ranging estimations and the air time occupancy. Practical experiments show that with the proposed method consisting of 5 active-passive anchors and a single passive anchor, the RMSE is improved by 7.4% and the air time occupancy by 12.5% as compared to the single-sided TWR method with a 6 anchor configuration. Moreover, simulation results show that a maximum theoretical RMSE improvement of 31.7% can be achieved with the proposed setup.

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In the context of green communication and energy-efficiency in wireless communication, this paper investigates distributed estimation algorithms in an energy-constrained wireless sensor network and proposes an energy-efficient distributed leader selection algorithm. The existing state-of-the-art diffusion algorithm and the recently introduced distributed leader selection algorithm are investigated. To evaluate the energy consumption of the algorithms, their respective algorithmic complexity, and number of operations and information exchanges are derived and compared. The obtained values are used as a basis to estimate the execution time and energy consumption of the algorithms. We propose and introduce the energy-efficient distributed leader selection algorithm which retains the performance of the existing leader selection algorithm while reducing the complexity and energy consumption. For the simulations, the algorithms are mapped to widely used wireless sensor network hardware architectures (MSP430 and RSL10). The numerical results show that the proposed algorithm is able to decrease the energy consumption of the network by 32%-53% and can extend the network lifetime by 14%-46% as compared with the diffusion and the distributed leader selection algorithms.