Vehicular Ad Hoc Networks (VANETs) are considered as a promising scheme to actively guarantee vehicle safety, and broadcast is a key technology for warning message dissemination in VANETs. This paper proposes a novel Position-based Multi-hop Broadcast (PMB) protocol for VANETs in view of some shortcomings of existing broadcast protocols for VANETs, such as ignoring the differences of transmission range among different nodes (vehicles), and disseminating warning messages only with the help of nodes in the one-way lane, PMB calculates waiting time to select the rebroadcast nodes based on additional coverage area of adjacent nodes considering the transmission ranges of nodes together with the inter-vehicle spacing, to guarantee less nodes used to rebroadcast warning packets. Besides, it guarantees the reliability of warning message dissemination by adopting the alternative answering mechanism named implicit ACK and explicit ACK adaptively and rebroadcast packets based on nodes in the two-way lane. The simulation results show that PMB outperforms existing broadcast protocols for warning message dissemination in VANETs in terms of suppression of broadcast redundancy, real-time performance and reliability even if all nodes have different transmission ranges.
Particle set sampling and weighting are both at the core of particle filter‐based object tracking methods. Aiming to optimally represent the objectʹs motion state, a large amount of particles ‐ in the classical particle method ‐ is a prerequisite. The high‐cost calculation of these particles significantly slows down the convergence of the algorithm. To this problem, a prior approach which originated from the process of video compressing and uncompressing is introduced to optimize the phase of particle sampling, making the collected particles centre on and cover the object region in the current image. This advantage dramatically reduces the number of particles required by the regularized particle sampling method, solving the problem of the high computational cost for tracking objects, while the performance of the algorithm is stable
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