The energy required to power an autonomous field device can be drawn from its environment by means of energy harvesting. Outdoors, one abundant and ubiquitous source of energy is solar irradiation, which can be reliably converted into electricity by solar cells. In principle, this is also true indoors if artificial light sources produce sufficient optical irradiation. Different solar cell materials can play off their individual strengths in these different use cases. Since ambient optical irradiation usually is an intermittent phenomenon, a solar powered field device has to include an energy storage solution for bridging gaps that occur in energy harvesting. In this study, different solar cell materials and energy storage solutions are discussed and evaluated quantitatively according to data sheet information. According to calculations for an assumed continuous power consumption of an autonomous field device of 1 mW for a period of 10 years, a solar panel of 4 × 4 cm² in the outdoor case and 20 × 20 cm² in the indoor case can deliver the required energy. Along with an energy storage system consisting of a 100 F supercapacitor and a primary backup cell, solar energy harvesting for autonomous field devices seems technically feasible.

Over the last couple of decades, clustering-based protocols are believed to be the best for heterogeneous wireless sensor networks (WSNs) because they work on the principle of divide and conquer. In this study, the authors propose and evaluate two new clustering-based protocols for heterogeneous WSNs, which are called single-hop energy-efficient clustering protocol (S-EECP) and multi-hop energy-efficient clustering protocol (M-EECP). In S-EECP, the cluster heads (CHs) are elected by a weighted probability based on the ratio between residual energy of each node and average energy of the network. The nodes with high initial energy and residual energy will have more chances to be elected as CHs than nodes with low energy whereas in M-EECP, the elected CHs communicate the data packets to the base station via multi-hop communication approach. To analyse the lifetime of the network, the authors assume three types of sensor nodes equipped with different battery energy. Finally, simulation results indicate that the authors protocols prolong network lifetime, and achieve load balance among the CHs better than the existing clustering protocols.

To achieve high localisation accuracy and precision is a fundamental requirement of passive radio-frequency identification (RFID) location systems. Current passive RFID location systems mostly deploy a dense RFID tag distribution to give initial localisation accuracy and use advanced localisation algorithms to extract the reliable features for enhancing localisation precision. High quality features are capable of significantly improving precision of passive RFID localisation systems, but it is hardly achieved in practice because of the environmental noise interference and the variety of tag behaviours. A localisation approach by using moving direction estimation to improve the quality of extracted features is proposed for enhancing localisation precision of passive RFID location systems. This approach relies on a general distribution of false-reading occurring probability function derived from experimental measurements. A feature adjustment scheme is introduced to correct these features regarding the estimation of moving direction of object. The experimental results demonstrate that the proposed localisation approach with feature improvement achieves higher precision for the state-of-the art deterministic localisation algorithm (Han et al. 2007) in passive RFID location systems.

This study investigates the performance of level-crossing sampling (LCS) for efficient sensing of the temporally sparse random signals assuming that the sampling levels can be chosen arbitrarily. In particular, the authors investigate a non-uniform LCS for sensing temporally sparse signals with sampling levels that are optimised to minimise the reconstruction error based on prior knowledge of signal's probability density function (pdf). Performance results showing the tradeoff between reconstruction error and average number of samples are presented. To define the sampling levels independent from the pdf of the signal, a tractable scheme is proposed. The performance and sampling efficiency of LCS for three schemes of defining the sampling levels when the signal range is known are evaluated.

ZigBee/IEEE 802.15.4 is one of the most used standards for low-power applications. However, full function devices must be always active to route data in a mesh network. The objective of this work is to implement a sleeping technique at the application layer that enables sleep mode for all nodes of a ZigBee network. A time synchronisation mechanism to deal with the clock drift of the sensor nodes was developed. The technique also enables the recovery of lost messages. A large network is organised into smaller groups to reduce latency and packet collisions. The active interval of each node is dynamically adapted to the network operation to optimise the energy consumption. The proposed technique was applied to a real testbed and the increase in energy efficiency was evaluated. The results demonstrated energy savings of about 95% for networks containing up to 20 nodes per group and wake up periods longer than 2 min.