Abstract
In an Internet of Things (IoT), the number of interconnected devices is huge and has been increasing drastically. Their generated data requires powerful aggregated computing resources and consumes enormous energy for processing and transmission. Having said that, most IoT devices are very limited and heterogeneous in computing capabilities, causing a big challenge for designing a commonly used interconnect that is both reliable and energy-efficient. Random Linear Network Coding (RLNC) schemes have proven its capability both theoretically and in practical deployment not only to increase throughput and reliability but also to reduce latency and energy consumption. However, it is unclear how different variations of RLNC, in particular, Fulcrum codes aimed for heterogeneous devices perform in heterogeneous IoT settings. In this paper, we conduct a measurement campaign, allowing for a fair comparison among the state of the art RLNC families, with regard to energy consumption, decoding probability, and goodput. The study provides insights and guidelines for applying RLNC schemes to data transmission in heterogeneous IoT networks.
Highlights
With the fast-dissemination of wireless smart devices, the interconnection between a large number of devices becomes more important
We observe that DSEP-fulcrum network coding (FNC) combined decoder saves about 32% of the consumed energy in the decoding process, and its processing speed is up to four times faster, as compared with S-Random Linear Network Coding (RLNC) Galois field (GF)(28) (see Fig. 7(b) and (d))
It is well known among the network coding community that sparse RLNC (S-RLNC) is more energy-efficient than the original RLNC at the trade-off of a reduced decoding probability
Summary
With the fast-dissemination of wireless smart devices, the interconnection between a large number of devices becomes more important. This paper focuses on investigating whether those advantages of FNC and DSEP-FNC can be translated into reduced computational complexity and energy consumption in high-end, off-the-shelf IoT devices, namely Odroid-XU4 and Odroid-C2 [21]. Some previous studies measured the consumed energy of RLNC and compared it with its variations such as S-RLNC and TSNC [22], [23], this paper is, to our knowledge, the first study to explore the energy consumption and performance aspects of the latest FNC and DSEP-FNC through IoT devices.
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