Iterative Decoding for Redistributing Energy Consumption in Wireless Sensor Networks

Abstract

Abstract-In this paper, we propose a method for desirably redistributing a wireless sensor network's energy consumption from its sensor nodes (which may have scarce energy resources obtained through energy harvesting, for example) to its central node (which often has an abundant energy resource, such as the mains). At the cost of increasing the central node's decoding complexity, our method facilitates (1) a significant reduction in the number of times the sensor nodes are required to retransmit data owing to transmission errors and/or (2) a reduction of up to 3.99 dB in the sensor node's total transmit energy consumption. We show that our approach can reduce the overall energy consumption of transmitting sensor nodes by more than 20% in practice.

An Energy-Efficient Error Correction Scheme for IEEE 802.15.4 Wireless Sensor Networks

“…As detailed in [4], the proposed augmentation to the IEEE 802.15.4 PHY can be employed to convey the payloads of IEEE 802.15.4 data frames using a reduced transmission energy. However, the augmented PHY imposes additional interleaving and rate-1 encoding operations upon the sensor node transmitters.…”

“…Section II reviews the augmented IEEE 802.15.4 PHY that was proposed in [4]. In Section III, we detail a novel deterministic interleaver design that facilitates the practical implementation of the proposed PHY.…”

“…In [4], we proposed an augmentation to the physical layer (PHY) of the IEEE 802.15.4 standard for WSNs [5] to redistribute energy consumption as described above. This was achieved by employing a sophisticated forward error correction (FEC) decoder within the central node, which reduced the transmission energy required to achieve reliable communications.…”

Hardware development for pervasive healthcare systems: Current status and future directions

“…If required, the central facility can call for the data stored in the home computer and other relevant information over the communication layer for detail diagnosis of the vital signs. However, processing the physiological signals on a continuous basis in this type of infrastructure is an extremely challenging problem owing to the limited resources as mentioned in [18], [20] and [22]. To materialise such system, it is necessary to reduce the complexity of the required signal processing tasks ("light-weight signal processing") and associated architectural optimisation in such a way that each of the processing circuit consumes as small power as possible.…”

“…This reduces the burden on communication layer as well as consumes less power. [20] and [22] show that sensor nodes have limited energy resources and mainly depend on on-board energy (battery back-up) or harvested energy.…”