This paper deals with designing and implementing a battery management system (BMS) for autonomous devices, using batteries as the power source. BMSs are now available in packaged form as an integrated chip. However, these chips are often expensive and unavailable in Vietnam and the Czech markets. Besides, their ability to integrate custom functions, especially function wireless communication between BMS and workstation, is limited. In practice, monitoring the state of the battery during the device’s operation is very important to devise a suitable operating tactic. This work aims to design a system that can measure and estimate the parameters of several batteries in an unmanned autonomous device, and send the measured data to the workstation by using technology Internet of Things for monitoring and analysis during the device’s operation.
In this paper, we present a comparison of four wireless sensor network protocols: MultiHopRouter, TinyAODV(Ad-hoc On-demand Distance-Vector), GF (Greedy Forwarding), and GF-RSSI (Greedy Forward with Received Signal Strength Indication). Performance measurement was conducted on a wireless sensor network testbed for medical application research.Based on our measurements, GF-RSSI performs well in various operating conditions. Especially, it shows a high success rate of packet delivery and moderate energy consumption.
This paper combines a fixed scheduling scheme and a simple p-persistent approach, creating a new MAC scheduling scheme for wireless networks. In a fixed scheduling scheme, a predetermined scheduling vector is assigned to each wireless node before run time. When a node is ready, it calculates the transmission probability p new , using a successful transmission ratio, a previous transmission probability p old , and a queue utilization ratio. The transmission probability p new determines whether or not a node can transmit. Our simulation study shows that the new MAC scheduling scheme improves the network throughput and minimizes collision counts.
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