RPL Cross-Layer Scheme for IEEE 802.15.4 IoT Devices With Adjustable Transmit Power

Abstract

We propose a novel cross-layer scheme to reduce energy consumption in wireless sensor networks composed of IEEE 802.15.4 IoT devices with adjustable transmit power. Our approach is based on the IETF's Routing Protocol for Low power and lossy networks (RPL). Nodes discover neighbors and keep fresh link statistics for each available transmit power level. Using the product of ETX and local transmit power level as a single metric, each node selects both the parent that minimizes the energy for packet transmission along the path to the root and the optimal local transmit power to be used. We have implemented our cross-layer scheme in NG-Contiki using the Z1 mote and two transmit power levels (55mW and 31mW). Simulations of a network of 15 motes show that (on average) 66% of nodes selected the low-power setting in a 25 m × 25 m area. As a result, we obtained an average reduction of 25% of the energy spent on transmission and reception of packets compared to the standard RPL settings where all nodes use the same transmit power level. In large scenarios (e.g., 150 m × 150 m and 40-100 motes), our approach provides better results in dense networks where reducing the transmit power of nodes does not translate into longer paths to the root nor degraded quality of service.

ACTOR: Adaptive Control of Transmission Power in RPL

“…While ATPC and many of its counterparts lack integration with RPL, the most prominent works that extend RPL to adapt transmission power control are PC-RPL [10] and XRPL [21]. We studied these two proposals in more detail as the selected benchmarks.…”

“…XRPL [21] is our second benchmark, which takes a cross-layer approach to jointly adjusting the transmission power and the routes. Their proposal integrates an OF called the Minimum Expected Transmission Power OF (METOF), which basically multiplies ETX with the transmission power.…”

“…ACTOR differentiates from the previous works [10,21,24] as follows: Primarily, AC-TOR aims to improve the throughput by mitigating the density of the networks and the hidden terminal problem. Consequently, the energy consumption is also reduced due to lower transmission power settings and fewer retransmissions of packets.…”

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

“…For this process, routing algorithm plays an important role 13 . The Internet Engineering Task Force developed a routing protocol called RPL, 25 which is a distance vector routing protocol for low‐power, lossy networks for IoT environment. It is based on the construction of routing graph, and in it, the routing process starts from leaf node to the end node.…”