Wireless Sensor Networks for Process Monitoring: The Rise of Remote Control (Editorial)

Wireless sensor networks (WSNs), which are capable of monitoring or controlling the systems to which they are coupled, have seen increased usage in industrial applications over recent years. A WSN consists of multiple ‘nodes’: small, autonomous devices which are inherently resource constrained and must operate for extended periods of time from limited local energy reserves. Nodes typically contain sensors, a microcontroller, radio transceiver, and power supply. The node’s sensors monitor the system to which they are coupled; for example, a node mounted on an electric motor could measure its vibration signature

Supercapacitor leakage in energy-harvesting sensor nodes: fact or fiction?

As interest in energy-harvesting sensor nodes continues to grow, the use of supercapacitors as energy stores or buffers is gaining popularity. The reasons for their use are numerous, and include their high power density, simple interfacing requirements, simpler measurement of state-of-charge, and a greater number of charging cycles than secondary batteries. However, supercapacitor energy densities are orders of magnitude lower. Furthermore, they have been reported to exhibit significant leakage, and this has been shown to increase exponentially with terminal voltage (and hence stored energy). This observation has resulted in a number of algorithms, designs and methods being proposed for effective operation of supercapacitor-based energy-harvesting sensor nodes. In this paper, it is argued that traditional ‘leakage’ is not as significant as has commonly been suggested. Instead, what is observed as leakage is in fact predominantly due to internal charge redistribution. As a result, it is suggested that different approaches are required in order to effectively utilize supercapacitors in energy-harvesting sensor nodes

Energy-driven computing for energy-harvesting embedded systems

There has been increasing interest over the last decade in the powering of embedded systems from ‘harvested’ energy, and this has been further fuelled by the promise and vision of IoT. Energy harvesting systems present numerous challenges, although some of these are also posed by their battery-powered counterparts: e.g. ultra-low power consumption. However, a significant challenge not witnessed in battery-powered systems is a requirement to manage the combination of a highly unpredictable and variable (spatially and temporally) power supply with a highly dynamic (across many orders of magnitude) and often event-driven system power consumption. This problem is typically rectified through the addition of energy storage (e.g. a supercapacitor) to provide energy buffering to smooth out the dynamics of supply and consumption. This has the significant advantage of making the system ‘look like’ a battery-powered system, yet usually adds volume, mass and cost to the resultant system – something that is counterproductive in future flexible, wearable and implantable IoT systems. Such systems can, alternatively, include only a very small amount (or even zero) energy-storage. Now, instead of the system’s operation being dictated solely by the application, operation starts to become ‘energy-driven’, with execution being highly intertwined with power and energy availability. In this presentation, I will first introduce the landscape of energy-harvesting computing systems, and articulate how energy-driven computing presents a different class of computing to conventional approaches. A significant issue in the successful operation of these systems is their ability to operate from an intermittent, constrained and variable supply, and I will show how transient operation and power-neutrality can be used to achieve the vision for these systems, and hence enable the proliferation of tiny self-powered systems that will underpin much of the IoT

Water quality monitoring, control and management (WQMCM) framework using collaborative wireless sensor networks

Improving water quality is of global concern, with agricultural practices being the major contributors to reduced water quality. The reuse of nutrient-rich drainage water can be a valuable strategy to gain economic-environmental benefits. However, currently the tools and techniques to allow this do not exist. Therefore, we have proposed a framework, WQMCM, which utilises increasingly common local farm-scale networks across a catchment, adding provision for collaborative information sharing. Using this framework, individual sub-networks can learn their environment and predict the impact of catchment events on their locality, allowing dynamic decision making for local irrigation strategies. Since resource constraints of network nodes (e.g. power consumption, computing power etc.) require a simplified predictive model for discharges, therefore low-dimensional model parameters are derived from the existing National Resource Conservation Method (NRCS), utilising real-time field values. Evaluation of the predictive models, developed using M5 decision trees, demonstrates accuracy of 84-94% compared with the traditional NRCS curve number model. The discharge volume and response time model was tested to perform with 6% relative root mean square error (RRMSE), even for a small training set of around 100 samples; however the discharge response time model required a minimum of 300 training samples to show reasonable performance with 16% RRMS

Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption

An Instrumented Crutch for Monitoring Patients' Weight Distribution during Orthopaedic Rehabilitation

This paper discusses an instrumented forearm crutch that has been developed to monitor a patient’s weight bearing over the full period of their recovery, and that can potentially be used in a home environment. The crutch measures the applied weight, crutch tilt, and hand position on the grip. Data are transmitted wirelessly to a remote computer, where they are processed and visualized in LabVIEW. The results obtained from a successful pilot study highlight both the need for such an instrumented crutch and its ability to measure the weight being applied through a patient’s lower limb

Photovoltaic sample-and-hold circuit enabling MPPT indoors for low-power systems

Photovoltaic (PV) energy harvesting is commonly used to power autonomous devices, and maximum power point tracking (MPPT) is often used to optimize its efficiency. This paper describes an ultra low-power MPPT circuit with a novel sample-and-hold and cold-start arrangement, enabling MPPT across the range of light intensities found indoors, which has not been reported before. The circuit has been validated in practice and found to cold-start and operate from 100 lux (typical of dim indoor lighting) up to 5000 lux with a 55cm2 amorphous silicon PV module. It is more efficient than non-MPPT circuits, which are the state-of-the-art for indoor PV systems. The proposed circuit maximizes the active time of the PV module by carrying out samples only once per minute. The MPPT control arrangement draws a quiescent current draw of only 8uA, and does not require an additional light sensor as has been required by previously-reported low-power MPPT circuits

Accurate supercapacitor modeling for energy-harvesting wireless sensor nodes

Supercapacitors are often used in energy-harvesting wireless sensor nodes (EH-WSNs) to store harvested energy. Until now, research into the use of supercapacitors in EH-WSNs has considered them to be ideal or over-simplified, with non-ideal behavior attributed to substantial leakage currents. In this brief, we show that observations previously attributed to leakage are predominantly due to redistribution of charge inside the supercapacitor. We confirm this hypothesis through the development of a circuit-based model which accurately represents non-ideal behavior. The model correlates well with practical validations representing the operation of an EH-WSN, and allows behavior to be simulated over long periods

Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition

Driven by real-world applications such as fitness, wellbeing and healthcare, accelerometry-based activity recognition has been widely studied to provide context-awareness to future pervasive technologies. Accurate recognition and energy efficiency are key issues in enabling long-term and unobtrusive monitoring. While the majority of accelerometry-based activity recognition systems stream data to a central point for processing, some solutions process data locally on the sensor node to save energy. In this paper, we investigate the trade-offs between classification accuracy and energy efficiency by comparing on- and off-node schemes. An empirical energy model is presented and used to evaluate the energy efficiency of both systems, and a practical case study (monitoring the physical activities of office workers) is developed to evaluate the effect on classification accuracy. The results show a 40% energy saving can be obtained with a 13% reduction in classification accuracy, but this performance depends heavily on the wearer’s activity