Research on Wireless Sensor Network Positioning Based on Genetic Algorithm

For smart city wireless sensing network construction needs, a network positioning algorithm based on genetic algorithm is proposed. The genetic algorithm uses a real number encoding, and the positioning model is constructed by analyzing the communication constraint between unknown nodes and a small amount of anchor nodes and constructs the positioning model, and the model is solved. The results show that when the ranging error is 50%, the positioning error is only increased by approximately 15% compared to the nonranging error. In a more harsh environment, if the ranging error is equal to the node wireless range, the ranging error is 100%, and the positioning error and the positioning ratio are not significantly changed. The scheme obtained by this algorithm can be well approarded with an ideal limit. In the case where the sensor node is given, the algorithm can obtain the maximum coverage.

Scalable Distributed DL Training: Batching Communication and Computation

Scalability of distributed deep learning (DL) training with parameter server architecture is often communication constrained in large clusters. There are recent efforts that use a layer by layer strategy to overlap gradient communication with backward computation so as to reduce the impact of communication constraint on the scalability. However, the approaches cannot be effectively applied to the overlap between parameter communication and forward computation. In this paper, we propose and design iBatch, a novel communication approach that batches parameter communication and forward computation to overlap them with each other. We formulate the batching decision as an optimization problem and solve it based on greedy algorithm to derive communication and computation batches. We implement iBatch in the open-source DL framework BigDL and perform evaluations with various DL workloads. Experimental results show that iBatch improves the scalability of a cluster of 72 nodes by up to 73% over the default PS and 41% over the layer by layer strategy.

Multiple Quadrotors Formation Flying Control Design and Experimental Verification

The formation control problem in multi-quadrotor systems is studied in this paper. Each quadrotor has limited access to its neighbors’ information due to communication constraint. First, the dynamics model of the quadrotor is linearized using Newton–Euler method. The distributed formation control law is then designed and the stability analysis is provided. An experimental platform is built with three Parrot Bebop drones and an indoor motion capture system. Numerical and experimental results are provided to show the effectiveness of the proposed algorithms.

Control Method for Sampled-Data Systems with Multiple Channels Based on Deadband-Triggered Scheme

A new deadband-triggered scheme is proposed to investigate the control problems for sampled-data systems with multiple transmitting channels. Sampled-data systems simultaneously contain continuous-time and discrete-time signals, which make the systems hybrid. In the sampled-data systems with multiple channels, the every state signals are transmitting at different channels. The deadband communication constraint is adopted to reduce the usage of communication resources. When the difference between the previous value and the most present value is lager than a given threshold of deadband, then the node of channels transmits the most present value. Furthermore, by use of Lyapunov functional method and input delay approach, the new stability analysis and stabilization conditions for the sampled-data with multiple channels on the basis of deadband-triggered scheme are proposed. Numerical simulations and experiments show the validity and usefulness of the derived conditions. The proposed deadband-triggered scheme is beneficial to further reduce the load of the communication data.