Joint routing, channel allocation and power control for real‐life wireless sensor networks

Abstract

The energy consumption of wireless sensor networks (WSNs) is a critical issue, because replacement of their batteries can be difficult or even impossible. It is well known that the radio is the most energy demanding part of the node. Therefore, efficient communications are imperative to increase the network lifetime. However, many issues arise in practical WSNs implementations because of the uncertainty and dynamics of the environment and the complexity constraints of the nodes. Routing protocol for low‐power and lossy networks (RPL) is one of the most widely implemented routing strategies because it deals with many of these practical issues. Unfortunately, this protocol does not adapt the nodes' transmission power nor the nodes' transmission channel. Nowadays, commercial motes have multi‐power and multi‐channel capabilities, and these can be used to reduce the network consumption and avoid the high collision probability that may arise in convergecast networks. In this paper, we enhance RPL to obtain a joint routing, transmission power control and channel allocation solution for real‐life WSNs. Two different strategies (minimum aggregated power and maximum probability delivery ratio) are designed and implemented in a WSN testbed with commercial motes. They are compared with the original RPL and also with other standard routing protocols, such as the routing strategy adopted in ZigBee. The experimental results show that the network performance is improved both in terms of reliability and energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.

Weak node protection to maximize the lifetime of wireless sensor networks

“…Consider that node i is the weak node. Then, for any node p ∈ S i , its original upstream node r p is changed to q, i.e., r p = q, where q is determined in (16). There may be more than one node q that meets the criterions.…”

“…A full search strategy is adopted to ensure acceptable network performance and a balance of loads between nodes. We change the upstream node for all source nodes in S i whose target node q can be found using (16). Let C old denote the feature value of the network before path adjustment.…”

Distributed fast channel allocation in cognitive wireless sensor networks

“…Daniela et al [28] present the implementation of a CA algorithm in a field-programmable gate array, which assigns channels to sensor nodes based on the interference and noise levels experimented in the network. Barcelo et al [29] enhance Routing protocol for low-power and lossy networks to obtain a joint routing, transmission power control and channel allocation solution for real-life WSNs while Jinbao et al [30] develop a polynomial time heuristic channel optimal assignment algorithm jointing routing, scheduling, CA and power control. However, these network multi-targets optimisations may increase algorithm complexity and shows no significant performance.…”

“…Barcelo et al . [29] enhance Routing protocol for low‐power and lossy networks to obtain a joint routing, transmission power control and channel allocation solution for real‐life WSNs while Jinbao et al . [30] develop a polynomial time heuristic channel optimal assignment algorithm jointing routing, scheduling, CA and power control.…”

A reliable nonfeedback transmission mechanism for asymmetric channels based on machine learning

“…In addition, ZigBee nodes have limited energy because they are powered by batteries. In order to make them work normally for a long time, various energy‐saving measures are taken to reduce energy consumption of sensor nodes and extend the service life of the network 12 . Sleep mechanism 13 is a very important energy‐saving measure, which is usually used for idle state with low communication frequency between network nodes.…”