Using an unmanned aerial vehicle (UAV) to collect data from wireless sensor networks deployed in the field, one of the key tasks is to plan the path for the collection so as to minimize the energy consumption of the UAV. At present, most of the existing methods generally take the shortest flight distance as the optimal objective to plan the optimal path. They simply believe that the shortest path means the least energy consumption of the UAV and ignore the fact that changing direction (heading) can also consume the UAV’s energy in its flight. If the path can be planned based on the UAV’s energy consumption closer to the real situation, the energy consumption of the UAV can be really reduced and its working energy efficiency can be improved. Therefore, this paper proposes a path planning method for UAV-assisted data collection, which can plan an energy-efficient flight path. Firstly, by analyzing the experiment data, we, respectively, model the relationship between the angle of heading change and the energy consumption of the UAV and the relationship between the distance of straight flight and the energy consumption of the UAV. Then, an energy consumption estimation model based on distance and the angle of heading change (ECEMBDA) is put up. By using this model, we can estimate or predict the energy consumption of a UAV to fly from one point (or node) to another (including the start point). Finally, the greedy algorithm is used to plan the path for UAV-assisted data collection according to the above estimated energy consumption. Through simulation and experiments, we compare our proposed method with the conventional method based on pure distance index and greedy algorithm. The results show that this method can obtain data collection path with lower energy consumption and smoother path trajectory, which is more suitable for actual flight.

Wireless sensor networks as the base support for the Internet of things have been a large number of popularity and application. Such as intelligent agriculture, we have to use the sensor network to obtain the growing environment data of crops and others. However, the difficulty of power supply of wireless nodes has seriously hindered the application and development of Internet of things. In order to solve this problem, people use low-power sleep scheduling and other energy-saving methods on the nodes. Although these methods can prolong the working time of nodes, they will eventually become invalid because of the exhaustion of energy. The use of solar energy, wind energy, and wireless signals in the environment to obtain energy is another way to solve the energy problem of nodes. However, these methods are affected by weather, environment, and other factors, and they are unstable. Thus, the discontinuity work of the node is caused. In recent years, the development of wireless power transfer (WPT) has brought another solution to this problem. In this paper, a three-layer framework is proposed for mobile station data collection in rechargeable wireless sensor networks to keep the node running forever, named TLFW which includes the sensor layer, cluster head layer, and mobile station layer. And the framework can minimize the total energy consumption of the system. The simulation results show that the scheme can reduce the energy consumption of the entire system, compared with a Mobile Station in a Rechargeable Sensor Network (MSiRSN).

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be true in many actual WSNs—the wireless links among nodes are often subject to all kinds of disturbance, obstruction, etc., and may transmit with different success rates. This paper proposes a new routing strategy, named Adaptive Network Coding Routing (ANCR). ANCR firstly establishes a routing path with the traditional network coding routing (NCR), and then applies the neighborhood search algorithm to adaptively determine nodes’ coding opportunities based on the links’ transmission success rates, with the target of reducing the total number of transmission. The simulation results show that, in WSNs with different-success-rate links, ANCR can reduce the network delay by about 50%, and increase the network throughput by about 67%, compared with the traditional NCR.

A three-dimensional elastic-plastic finite element model was established in finite element software ANSYS to investigate the interfacial stress of carbon fiber patch bonded to transverse cracked steel plate reinforcement. The patch and adhesive were modeled as linear elastic. The steel plate was modeled as elastic-plastic. The load-deflection curve of the steel was obtained by bending test. The influence of crack depth, adhesive thickness, patch thickness and patch length on reinforcing efficiency was studied. The results show that the peel stress of the adhesive was distributed symmetrically along the center line, mainly concentrated on the crack center, near the crack and the patch ends. The peel stress at the crack center was always greater than the near crack. The shear stress is anti-symmetric along the center line, and the shear stress at the crack center is zero, mainly concentrated near the crack and plate end. The debonding may be initiated from the vicinity of the crack. Increasing the crack depth can significantly increase the peel stress and shear stress at the crack, but has little effect on the stresses at the end. Increasing the thickness of the adhesive can reduce the peel stress and shear stress at the crack center and near the crack, and the shear stress at the ends, but has little effect on the peel stress at the ends. Increasing the patch thickness can reduce the stress at the crack but increase the stress at the end. Increasing the patch length cannot reduce the stress at the crack, but significantly reduce the stress at the end.