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

Design and comparative analysis of single-path and epidemic approaches to information and energy management in wireless sensor networks

Intelligent energy management is a key challenge in Wireless Sensor Networks. The choice of an appropriate routing algorithm constitutes a critical factor, especially in unstructured networks where, due to their dynamic nature, a reactive routing protocol is necessary. Such networks often favour packet flooding to fulfil this need. One such algorithm is IDEALS, a technique proposed in the literature, which balances energy consumed with information delivered. This paper evaluates the use of a single-path solution with IDEALS to increase efficiency. Simulation results comparing the two approaches show that the single-path algorithm outperforms flooding in terms of energy consumption for any network size. Furthermore the benefit of IDEALS is preserved as its combination with the single-path algorithm maximises information throughput

Efficient inter-group competition and the provision of public goods

We propose an intergroup competition scheme (ICS) to solve the free-riding problem in the public goods game. Our solution only requires knowledge of the group contributions, is budget balanced and with the right parameters a dominant strategy. The main innovations of our design are that the prize to the winning group is paid by the losing group and that the size of the transfer depends on the difference in contribution by the two groups. With the right parameters, this scheme changes the dominant strategy from none to full contribution. We tested different parameterizations for the ICS. The experiments show dramatic gains in efficiency in all the ICS treatments. Moreover, versions of the ICS in which intergroup competition should not change the zero contribution Nash equilibrium also produce remarkable gains in efficiency and no decline in contributions over time.public goods, intergroup competition, team production, voluntary contributions mechanism, economic experiments

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

A New Macroeconomic Time Series: Business Profitability in Twentieth-Century Australia

Macroeconomic time series, business profitability, Australia

Ultra low-power photovoltaic MPPT technique for indoor and outdoor wireless sensor nodes

Photovoltaic (PV) energy harvesting is commonly used to power wireless sensor nodes. To optimise harvesting efficiency, maximum power point tracking (MPPT) techniques are often used. Recently-reported techniques focus solely on outdoor applications, being too power-hungry for use under indoor lighting. Additionally, some techniques have required light sensors (or pilot cells) to control their operating point. This paper describes an ultra low-power MPPT technique which is based on a novel system design and sample-and-hold arrangement, which enables MPPT across the range of light intensities found indoors and outdoors and is capable of cold-starting. The proposed sample-and-hold based technique has been validated through a prototype system. Its performance compares favourably against state-of-the-art systems, and does not require an additional pilot cell or photodiode. This represents an important contribution, in particular for sensors which may be exposed to different types of lighting (such as body-worn or mobile sensors)

Energy-Efficient Data Acquisition in Wireless Sensor Networks through Spatial Correlation

The application of Wireless Sensor Networks (WSNs) is restrained by their often-limited lifetime. A sensor node's lifetime is fundamentally linked to the volume of data that it senses, processes and reports. Spatial correlation between sensor nodes is an inherent phenomenon to WSNs, induced by redundant nodes which report duplicated information. In this paper, we report on the design of a distributed sampling scheme referred to as the 'Virtual Sampling Scheme' (VSS). This scheme is formed from two components: an algorithm for forming virtual clusters, and a distributed sampling method. VSS primarily utilizes redundancy of sensor nodes to get only a subset to sense the environment at any one time. Sensor nodes that are not sensing the environment are in a low-power sleep state, thus conserving energy. Furthermore, VSS balances the energy consumption amongst nodes by using a round robin method

A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring

In recent years, the field of computational sustainability has striven to apply artificial intelligence techniques to solve ecological and environmental problems. In ecology, a key issue for the safeguarding of our planet is the monitoring of biodiversity. Automated acoustic recognition of species aims to provide a cost-effective method for biodiversity monitoring. This is particularly appealing for detecting endangered animals with a distinctive call, such as the New Forest cicada. To this end, we pursue a crowdsourcing approach, whereby the millions of visitors to the New Forest, where this insect was historically found, will help to monitor its presence by means of a smartphone app that can detect its mating call. Existing research in the field of acoustic insect detection has typically focused upon the classification of recordings collected from fixed field microphones. Such approaches segment a lengthy audio recording into individual segments of insect activity, which are independently classified using cepstral coefficients extracted from the recording as features. This paper reports on a contrasting approach, whereby we use crowdsourcing to collect recordings via a smartphone app, and present an immediate feedback to the users as to whether an insect has been found. Our classification approach does not remove silent parts of the recording via segmentation, but instead uses the temporal patterns throughout each recording to classify the insects present. We show that our approach can successfully discriminate between the call of the New Forest cicada and similar insects found in the New Forest, and is robust to common types of environment noise. A large scale trial deployment of our smartphone app collected over 6000 reports of insect activity from over 1000 users. Despite the cicada not having been rediscovered in the New Forest, the effectiveness of this approach was confirmed for both the detection algorithm, which successfully identified the same cicada through the app in countries where the same species is still present, and of the crowdsourcing methodology, which collected a vast number of recordings and involved thousands of contributors.</p

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