Learning about computing technology has become an increasingly important part of the school curriculum but it remains unclear how best to teach it to children. Here, we report on an empirical study that investigated how the process of making affects how children of different ages learn about computing technology. In one condition, they had to first make an electronic cube before conducting other activities and in the other they were given a ready made one to use. The results of the study show that for younger children, the making significantly improved their performance in a post-lesson test, whereas the older children performed equally well in both conditions. We discuss possible reasons for this, in terms of differences in creative appropriation. We also saw much spontaneous collaboration between the children that suggests making can encourage a collaborative relationship between children of different ages.

Carbon Monoxide (CO) induced by traffic pollution is highly dynamic and non-linear. In a pilot research, we collected some fine-grained 1Hz CO pollution data from a residential road and a busy motorway in Hyderabad, India, in preparation of the deployment of a larger scale, longer term wireless sensor monitoring system. Power conservation is an important issue as the sensor nodes are battery operated. We studied the characteristics of the collected data and designed an adaptive sampling algorithm, Bi-Scale temporal sampler, which adapts the sampling frequency to the statistics collected in real time. This design has incorporated practical engineering considerations including minimising electronic noise, sensor warm-up time and data characteristics. Results show that Bi-Scale sampler achieves better energy saving and statistical deviation ratio for our requirements than burst sampling and eSENSE sampling strategies, which are techniques popularly used in environmental monitoring applications.

Air pollution data exhibit characteristics like long range correlations and multi-fractal scaling that can be exploited to implement an energy efficient, adaptive spatial sampling technique for pollution sensor nodes. In this work, we present a) results from de-trended fluctuation analysis to prove the presence of non-linear dynamics in real pollution datasets gathered from trials carried out in Cyprus, b) a novel Multi-scale Nearest Neighbors based Adaptive Spatial Sampling (MNNASS) technique that determines the predictability and in turn the directional influences between data from different sensor nodes, and c) performance analysis of the algorithm in terms of energy savings and measurement accuracy.

A pollution data collection exercise was conducted in Hyderabad, India in February, 2012. Fifteen bespoke Carbon Monoxide (CO) monitors were deployed across a small section of a busy highway to collect data for an urban environmental engineering study targeting traffic-generated pollution. The monitors were used to record CO concentrations and temperature; however, in spite of the fact that the monitors were calibrated in advance of deployment, interpretation of the data collected has proved to be challenging. This paper reports the findings of the experiment and proposes a bias-adjustment separation technique that provides a consensual baseline for all the monitors. The result is that spatial variation in the distribution of CO can be studied at snapshots of time. Moreover, the cross-correlations between sensors can be reliability extracted after the bias adjustment.

Small-size, light-weight, flexible and programmable, yet low-cost sensor node is one of the keys to enable Wireless Sensor Network (WSN) research and a wide range of user applications. In this paper, the Bracelet computer is presented. The system is designed to be flexible and wearable that it supports a range of daughter boards of user's choice, and provides different ways for the boards to be connected to the main processor board. Despite a range of features, the monetary cost of the system is kept low. An application of the system - which uses the same wireless board as the Bracelet computer for localising a running athlete - is presented in this paper to demonstrate the applicability of the system. The localisation results show that the system achieves an average positional error of 28.945cm.

We present the design of a novel adaptive sampling technique called Exponential Double Smoothing-based Adaptive Sampling (EDSAS), in which the temporal data correlations provide an indication of the prevailing environmental conditions and are used to adapt the sensing rate of a sensor node. EDSAS uses irregular data series prediction to reduce sampling rate in combination with change detection to maintain data fidelity. The prediction method employs Wright's extension to Holt's method of Exponential Double Sampling (EDS) coupled with a change detection mechanism based on exponentially weighted moving averages (EWMA). The main advantages of EDSAS are that it does not require heavy computation, incurs low memory and communication overhead and the prediction model can be implemented with ease on resource constrained sensor nodes. EDSAS has been evaluated by using real urban road traffic Carbon Monoxide (CO) pollution datasets and has been compared and shown to give better results for performance metrics like sampling fraction and miss ratio. We have also undertaken analysis of the pollution data based on the information received and shown that EDSAS scores over other published technique called e-Sense in capturing the underlying characteristics of the real data.