Ross Wilkins

Edge Mining the Internet of Things

This paper examines the benefits of edge mining -data mining that takes place on the wireless, battery-powered, and smart sensing devices that sit at the edge points of the Internet of Things. Through local data reduction and transformation, edge mining can quantifiably reduce the number of packets that must be sent, reducing energy usage, and remote storage requirements. In addition, edge mining has the potential to reduce the risk in personal privacy through embedding of information requirements at the sensing point, limiting inappropriate use. The benefits of edge mining are examined with respect to three specific algorithms: linear Spanish inquisition protocol (L-SIP), ClassAct, and bare necessities (BN), which are all instantiations of general SIP. In general, the benefits provided by edge mining are related to the predictability of data streams and availability of precise information requirements; results show that L-SIP typically reduces packet transmission by around 95% (20-fold), BN reduces packet transmission by 99.98% (5000-fold), and ClassAct reduces packet transmission by 99.6% (250-fold). Although energy reduction is not as radical because of other overheads, minimization of these overheads can lead up to a 10-fold battery life extension for L-SIP, for example. These results demonstrate the importance of edge mining to the feasibility of many IoT applications.

Bare necessities—Knowledge-driven WSN design

The viability of wireless sensor applications often hinges on minimising power consumption whilst maximising the informational output. Although many low-level platform-oriented energy saving mechanisms have been developed, considerable savings are possible at application level. This work presents an approach to pushing the calculation of application-level state closer to the information source. The context in which this approach is evaluated is a residential building monitoring application. Combined with the Spanish Inquisition Protocol (SIP), this is shown, based on deployment data, to reduce the average transmission period for temperature data from once every 5 minutes to an average of once every 38 days for an allowed error threshold of 10% on any component of the application-level state. For combined sensing of temperature, relative humidity and CO2, the average transmission period drops to 13 days. This transmission reduction should considerably extend network life while having minimal effect on the usefulness of the information gathered. Most importantly, the underlying approach generalises to a wide variety of applications.

Energy Profiling in Practical Sensor Networks: Identifying Hidden Consumers

Reducing energy consumption of wireless sensor nodes extends battery life and/or enables the use of energy harvesting and thus makes feasible many applications that might otherwise be impossible, too costly or require constant maintenance. However, theoretical approaches proposed to date that minimize Wireless Sensor Network energy needs generally lead to less than expected savings in practice. We examine the experiences of tuning the energy profile for two near-production wireless sensor systems and demonstrate the need for: microbenchmark-based energy consumption profiling; examining start-up costs; and monitoring the nodes during long-term deployments. The tuning exercise resulted in reductions in energy consumption of: 93% for a multihop Telos-based system (average power 0.029 mW); 94.7% for a single hop Ti-8051-based system during startup; and 39% for a Ti-8051 system post start-up. This paper shows that reducing the energy consumption of a node requires a whole system view, not just measurement of a typical sensing cycle. We give both generic lessons and specific application examples that provide guidance for practical WSN design and deployment.

Proof of concept of wireless TERS monitoring

Summary Temporary earth retaining structures help prevent collapse during construction excavation. To ensure that these structures are operating within design specifications, load forces on supports must be monitored. Current monitoring approaches are expensive, sparse, off-line, and thus difficult to integrate into predictive models. This work aims to show that wirelessly connected battery powered sensors are feasible, practical, and have similar accuracy to existing sensor systems. We present the design and validation of ReStructure, an end-to-end prototype wireless sensor network for collection, communication, and aggregation of strain data. ReStructure was validated through a 6-month deployment on a real-life excavation site with all but one node producing valid and accurate strain measurements at higher frequency than existing ones. These results and the lessons learnt provide the basis for future widespread wireless temporary earth retaining structure monitoring that increase measurement density and integrate closely with predictive models to provide timely alerts of damage or potential failure.

ReStructure: A Wireless Sensor Network for Monitoring Temporary Earth Retaining Systems

Temporary earth retaining structures help prevent collapse during construction excavation. To ensure that these structures are operating within design specifications, load forces on supports must be monitored. Current approaches are expensive, often manual, and difficult to integrate into predictive models. We developed a wireless strain gauge suitable for harsh construction environments along with a data collection back-end that could be integrated into on-line predictive models. This system has been used to monitor an underground train station construction site in Singapore for 5 months. Key challenges are to ensure valid measurements, reliable wireless communication, sufficiently long battery life, and protection against weather and incidental damage. This paper describes the system design, experiences with its deployment and lessons for future developments.