Abstract
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.
Highlights
In order to obtain first-hand data of wildlife’s living conditions, researchers have to keep watch in the wild, suffer from boredom and the rugged environment and get a limited amount of data which has low accuracy
Adaptive Network Coding Routing (ANCR) is better than network coding routing (NCR), which is mainly reflected in the decrease of the total number of transmission because ANCR can use high-quality links and create network coding opportunities
The simulation results show that compared with NCR, in the network having many nodes, ANCR can reduce the network delay by about 50% and improve the network throughput by 66.67%
Summary
In order to obtain first-hand data of wildlife’s living conditions, researchers have to keep watch in the wild, suffer from boredom and the rugged environment and get a limited amount of data which has low accuracy. ANCR [9] proposed in this paper chooses effective coding opportunities to minimize transmission count overall by taking into account various transmission success rates on different links and in two directions of a link In this way, the total number of transmission in a network will decrease, the network latency and energy consumption will reduce and the network throughput will be improved. ANCR is better than NCR, which is mainly reflected in the decrease of the total number of transmission because ANCR can use high-quality links and create network coding opportunities This leads to the shorter transmission time, the higher network throughput and the lower energy consumption of the network. The main contribution of this paper is that it proposes an ANCR model which can work efficiently in real networks with different transmission success rates on links and different transmission success rates in both directions of a link
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