1,721,088 research outputs found
Energy-driven computing for energy-harvesting embedded systems
No full textThere 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
New tricks with old sensors: Pervasive Technologies for Novel Applications
No full textSensors are interleaved into society, instrumenting considerable aspects of our lives without our comprehension. Sensors such as the MEMS accelerometer have transitioned from their original domains to applications that they were never conceived for: from games controllers to contextually rotating the screen on your smartphone. Further advances in technologies such as pervasive computing and networked embedded sensing are enabling new applications and smart devices which utilise sensors in new ways. In this presentation I will highlight new trends, applications and research in these fields, and show how "simple" sensors are being used in larger connected systems – from assistive technologies to distributed monitoring
Energy harvesting and transient computing: a paradigm shift for embedded systems?
No full textEmbedded systems powered from time-varying energy harvesting sources traditionally operate using the principles of energy-neutral computing: over a certain period of time, the energy that they consume equals the energy that they harvest. This has the significant advantage of making the system ‘look like’ a battery-powered system, yet typically results in large, complex and expensive power conversion circuitry and introduces numerous challenges including fast and reliable cold-start. In recent years, the concept of transient computing has emerged to challenge this traditional approach, whereby low-power embedded systems are enabled to operate as usual while energy is available but, after loss of supply, can quickly regain state and continue where they left off. This paper provides a summary of these different approaches
Transient and power-neutral computing: a paradigm shift for embedded systems?
No full textEmbedded systems powered from time-varying energy harvesting power sources, for example solar PV or mechanical vibration, have traditionally operated using the principles of energy-neutral computing. That is, over a sensible period of time (e.g. 24 hours), the energy consumed is equal to the energy that was harvested. This has the advantage of making the system ‘look like’ a battery-powered system, yet typically results in large, complex and expensive power conversion circuitry and introduces challenges such as fast and reliable cold-start. In recent years, the concept of transient computing has emerged to challenge this, whereby low-power embedded systems can be designed to operate and perform useful computation when energy is available, and carefully ‘hibernate’ when the power disappears such that it can continue where it left off when supply is regained. In this talk I will explain this shift towards transient computing and the different approaches that have been proposed, and the new challenges that are raised as a result. I will also discuss a complementary approach to the powering of transient systems, named power-neutral computing. Instead of equating energy consumption to energy supply, as is the case in energy-neutral systems, power-neutral systems attempt to match instantaneous power consumption to the instantaneous power supplied. This fine-grained control permits better use of available resources while overcoming the disadvantages of energy-neutral computing; furthermore, it can work alongside aforementioned transient computing techniques if supply disappears altogether
Wireless Sensor Networks for Process Monitoring: The Rise of Remote Control (Editorial)
No full textWireless 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
Energy- and information-managed wireless sensor networks: modelling and simulation
No full textWireless Sensor Networks (WSNs) allow the remote and distributed monitoring of parameters in their deployed environment. WSNs are receiving increasing research interest, due to their ability to enable a wide range of applications, and their potential to have a major impact on ubiquitous computing. Many research challenges are encountered in retaining a useful network lifetime under constraints imposed by the limited energy reserves that are inherent in the small, locally-powered sensor nodes. This research addresses some of these challenges through the development and evaluation of energy- and information-managed algorithms leading to increased network lifetime.The first contribution of this research is the development of an Information manageD Energy-aware ALgorithm for Sensor networks with Rule Managed Reporting (IDEALS/RMR). IDEALS/RMR is an application-independent, localised system to control and manage the degradation of a network through the positive discrimination of packets. This is achieved by the novel combination of energy management (through IDEALS) and information management (through RMR) which increases the network lifetime at the possible expense of often trivial data. IDEALS/RMR is particularly suited to applications where sensor nodes are small, energy constrained, embedded devices particularly those that feature energy harvesting) that are required to report data in an unassisted fashion.The second contribution of this research is the analysis of various environmental and physical aspects of WSNs, and the effect that they have on the operation of nodes and networks. These aspects include energy components (stores, sources and consumers), sensing devices, wireless communication, and timing; these aspects are independently modelled and, through simulation, their effect on the operation of the network is quantified.The third contribution of this research is the evaluation of IDEALS/RMR using a simulator that has been specifically developed to integrate both the proposed environmental and physical models, and a novel node architecture that facilitates structured software design. A scenario depicting the use of a WSN to monitor pump temperature in a water pumping station is simulated, and highlights the benefits of the developed algorithms
Listening to the forest and its curators: lessons learnt from a bioacoustic smartphone application deployment
No full textOur natural environment is complex and sensitive, and is home to a number of species on the verge of extinction. Surveying is one approach to their preservation, and can be supported by technology. This paper presents the deployment of a smartphone-based citizen science biodiversity application. Our findings from interviews with members of the biodiversity community revealed a tension between the technology and their established working practices. From our experience, we present a series of general guidelines for those designing citizen science apps
Full Citation
Moran, Stuart, Pantidi, Nadia, Rodden, Tom, Chamberlain, Alan, Griffiths, Chloe, Zilli, Davide, Merrett, Geoff V. and Rogers, Alex (2014) Listening to the forest and its curators: lessons learnt from a bioacoustic smartphone application deployment. In, ACM CHI Conference on Human Factors in Computing Systems, Toronto, CA, 26 Apr - 01 May 2014. (doi:10.1145/2556288.255702)
Improving Learning of Electronic Engineering Skills through e-Learning: a Case Study
No full textIn this paper, we report on changes made to a module that is taken by all first-year electronic engineering students, and which covers transferable and engineering skills. To improve students’ perception of and learning experience on the module, half of the taught material was migrated from a traditional ‘physical’ lecture format to specially recorded ‘online’ lectures. The changes were evaluated through 1) looking at how regularly students were accessing material, 2) online questionnaires, 3) focus groups, and 4) the standard module evaluation surveys. The changes were very well received by the students and, in a single year, the overall module rating improved from 60% (static over the preceding three years) to 75%. The changes showed evidence of giving students greater freedom in the way in which they learn, for example allowing them to watch lectures when it best suited their learning style (even if this was at night or on a weekend), pause the lecture while they looked up more information, and rewind sections of the lecture to watch difficult topics again. This has also proved beneficial to international students, as the majority found the online lectures easier to understand, and were able to pause the lecture while they translated unknown words
Water quality monitoring, control and management (WQMCM) framework using collaborative wireless sensor networks
No full textImproving 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
Collaborative catchment-scale water quality management using integrated wireless sensor networks
No full textThe challenge of improving water quality (WQ) is a growing global concern. Poor WQ is mainly attributed to poor water management and outdated agricultural activities. We propose that collaborative sensor networks spread across an entire catchment can allow cooperation among individual activities for integrated WQ monitoring and management. We show that sharing information on critical parameters among networks of water bodies and farms can enable identification and quantification of the contaminant sources, enabling better decision making for agricultural practices and thereby reducing contaminants fluxes
- …
