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Cooperative routing is one of the most widely used technologies for improving the energy efficiency and energy balance of wireless multi-hop networks. However, the end-to-end energy cost and network lifetime are greatly restricted if the cooperative transmission model is not designed properly. The main aim of this paper is to explore a two-stage cooperative routing scheme to further improve the energy efficiency and prolong the network lifetime. A two-stage cooperative (TSC) transmission model is firstly designed in which the core helper is introduced to determine the helper set for cooperation. Then, the two-stage link cost is formulated where x, the weight of residual energy, is introduced to be adjusted for different design goals. By selecting the optimal helper set, the two-stage link cost of each link can be optimized. Finally, based on the designed TSC transmission model and the optimized two-stage link cost, a distributed two-stage cooperative routing (TSCR) scheme is further proposed to minimize the end-to-end cooperative routing cost. Simulation results evaluate the effect of x on the different performance metrics. When x equals 0, TSCR can achieve the shortest end-to-end transmission delay and highest energy efficiency, while a larger x can achieve a longer network lifetime. Furthermore, simulation results also show that the proposed TSCR scheme can effectively improve both the energy efficiency and network lifetime compared with the existing schemes.

Recently, there has been an increasing interest in exploiting interference cancelation to support multiple adjacent concurrent transmissions instead of avoiding interference through scheduling. In line with these efforts, this paper propose an interference coordinated routing (ICR) scheme for wireless multi-hop networks to achieve more transmission concurrence, and thus lower the end-to-end delay. The proposed ICR scheme firstly constructs an initial path by the interference-aware routing algorithm, which captures the end-to-end latency and spatial resource cost as the routing metrics. Then, to analyze the feasibility of concurrent transmission for a given link set, we consider the interference coordination and formulate the concurrent transmission of multiple links as a linear programming (LP) problem. The solution to the LP problem indicates the power allocation. Finally, a distributed guard zone based selection (GBS) algorithm is further proposed to iteratively explore the maximum feasible link set for each time slot. The selected links are simultaneously active for packet transmission with the allocated power in the current time slot, and the remaining links will be put off to the next. Simulation results confirm that ICR reduces the end-to-end delay by 9.16% to 73.82%, and promotes better transmission concurrence compared with the existing schemes.

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Recently, Convolutional Neural Networks (CNNs) have shown tremendous potential in the visual tracking community. It is well-known that the receptive field is a critical factor for CNN affecting performance. However, standard CNNs based tracking methods design the receptive fields of artificial neurons in each layer that have the same size. We identify the main bottleneck of affecting the tracking accuracy as regular receptive fields. To settle the problem, we propose an Auto-Selecting Receptive Field Network (ASRF) to select receptive field information and effective clues dynamically. In particular, a Selective Receptive Field Block (SRFB) is designed to adaptively adjust receptive field size for each neuron according to multiple scales of input information. Additionally, we develop a Multi-Scale Receptive Field module (MSRF) that marks a further step in selecting effective clues from different scale receptive fields. The proposed ASRF method performs favorably against state-of-the-art trackers on five benchmarks, including OTB-2013, OTB-2015, UAV-123, VOT-2015, and VOT-2017 while running beyond real-time tracking speed. INDEX TERMS Visual tracking, deep learning, Siamese network, receptive field.