Wireless Sensor Networks:A case study for Energy Efficient Environmental Monitoring

Energy efficiency is a key issue for wireless sensor networks, since sensors nodes can often be powered by non-renewable batteries. In this paper, we examine four MAC protocols in terms of energy consumption, throughput and energy efficiency. A forest fire detection application has been simulated using the well-known ns-2 in order to fully evaluate these protocols

Energy managed reporting for wireless sensor networks

In this paper, we propose a technique to extend the network lifetime of a wireless sensor network, whereby each sensor node decides its individual network involvement based on its own energy resources and the information contained in each packet. The information content is ascertained through a system of rules describing prospective events in the sensed environment, and how important such events are. While the packets deemed most important are propagated by all sensor nodes, low importance packets are handled by only the nodes with high energy reserves. Results obtained from simulations depicting a wireless sensor network used to monitor pump temperature in an industrial environment have shown that a considerable increase in the network lifetime and network connectivity can be obtained. The results also show that when coupled with a form of energy harvesting, our technique can enable perpetual network operatio

Energy Harvesting and Management for Wireless Autonomous Sensors

Wireless autonomous sensors that harvest ambient energy are attractive solutions, due to their convenience and economic benefits. A number of wireless autonomous sensor platforms which consume less than 100?W under duty-cycled operation are available. Energy harvesting technology (including photovoltaics, vibration harvesters, and thermoelectrics) can be used to power autonomous sensors. A developed system is presented that uses a photovoltaic module to efficiently charge a supercapacitor, which in turn provides energy to a microcontroller-based autonomous sensing platform. The embedded software on the node is structured around a framework in which equal precedent is given to each aspect of the sensor node through the inclusion of distinct software stacks for energy management and sensor processing. This promotes structured and modular design, allowing for efficient code reuse and encourages the standardisation of interchangeable protocols

Rule Managed Reporting in Energy Controlled Wireless Sensor Networks

This paper proposes a technique to extend the network lifetime of a wireless sensor network, whereby each sensor node decides its network involvement, based on energy resources and the information in each message (ascertained through a system of rules). Results obtained from the simulation of an industrial monitoring scenario have shown that a considerable increase in the lifetime and connectivity can be obtained

Resource Aware Sensor Nodes in Wireless Sensor Networks

Wireless sensor networks are continuing to receive considerable research interest due, in part, to the range of possible applications. One of the greatest challenges facing researchers is in overcoming the limited network lifetime inherent in the small locally powered sensor nodes. In this paper, we propose IDEALS, a system to manage a wireless sensor network using a combination of information management, energy harvesting and energy monitoring, which we label resource awareness. Through this, IDEALS is able to extend the network lifetime for important messages, by controlling the degradation of the network to maximise information throughput

Adaptive sampling in context-aware systems: a machine learning approach

As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification

A Structured Hardware/Software Architecture for Embedded Sensor Nodes

Owing to the limited requirement for sensor processing in early networked sensor nodes, embedded software was generally built around the communication stack. Modern sensor nodes have evolved to contain significant on-board functionality in addition to communications, including sensor processing, energy management, actuation and locationing. The embedded software for this functionality, however, is often implemented in the application layer of the communications stack, resulting in an unstructured, top-heavy and complex stack. In this paper, we propose an embedded system architecture to formally specify multiple interfaces on a sensor node. This architecture differs from existing solutions by providing a sensor node with multiple stacks (each stack implements a separate node function), all linked by a shared application layer. This establishes a structured platform for the formal design, specification and implementation of modern sensor and wireless sensor nodes. We describe a practical prototype of an intelligent sensing, energy-aware, sensor node that has been developed using this architecture, implementing stacks for communications, sensing and energy management. The structure and operation of the intelligent sensing and energy management stacks are described in detail. The proposed architecture promotes structured and modular design, allowing for efficient code reuse and being suitable for future generations of sensor nodes featuring interchangeable components

Graded junction termination extensions for electronic devices

A graded junction termination extension in a silicon carbide (SiC) semiconductor device and method of its fabrication using ion implementation techniques is provided for high power devices. The properties of silicon carbide (SiC) make this wide band gap semiconductor a promising material for high power devices. This potential is demonstrated in various devices such as p-n diodes, Schottky diodes, bipolar junction transistors, thyristors, etc. These devices require adequate and affordable termination techniques to reduce leakage current and increase breakdown voltage in order to maximize power handling capabilities. The graded junction termination extension disclosed is effective, self-aligned, and simplifies the implementation process

Pancreatectomy is underused in NSW regions with low institutional surgical volumes: A population data linkage study

© 2017 AMPCo Pty Ltd. Produced with Elsevier B.V. All rights reserved. Objective: To examine differences in the proportions of people diagnosed with pancreatic cancer who underwent pancreatectomy, post-operative outcomes and 5-year survival in different New South Wales administrative health regions of residence. Design, setting and participants: Retrospective analysis of NSW data on pancreatic cancer incidence and surgery, 2005e2013. Main outcome measures: The proportion of newly diagnosed patients with pancreatic cancer who were resected in each region; 90-day post-operative mortality; one-year post-operative survival; 5-year post-diagnosis survival. Results: 14% of people diagnosed with pancreatic cancer during 2010e2013 (431 of 3064) underwent pancreatectomy, an average of 108 resections per year. After adjusting for age, sex and comorbidities, the proportion that underwent resection varied significantly between regions, ranging between 8% and 21% (P<0.001). Higher resection rates were not associated with higher post-operative 90-day mortality or lower one-year survival (unadjusted and risk-adjusted analyses). Higher resection rates were associated with higher 5-year post-diagnosis survival: the mean survival in regions with resection rates below 10% was 3.4%, compared with 7.2% in regions with rates greater than 15% (unadjusted and adjusted survival analyses; P<0.001). There was a positive association between regional resection rate and the pancreatectomy volume of hospitals during 2005e2009. An additional 32 people would be resected annually if resection rates in low rate regions were increased to the 80th percentile regional resection rate (18%). Conclusion: There is significant geographic variation in the proportion of people with pancreatic cancer undergoing pancreatectomy, and the 5-year survival rate is higher in regions where this proportion is higher

AdaMD: Adaptive Mapping and DVFS for Energy-efficient Heterogeneous Multi-cores

Modern heterogeneous multi-core systems, containing various types of cores, are increasingly dealing with concurrent execution of dynamic application workloads. Moreover, the performance constraints of each application vary, and applications enter/exit the system at any time. Existing approaches are not efficient in such dynamic scenarios, especially if applications are unknown, as they require extensive offline application analysis and do not consider the runtime execution scenarios (application arrival/completion, and workload and performance variations) for runtime management. To address this, we present AdaMD, an adaptive mapping and dynamic voltage and frequency scaling (DVFS) approach for improving energy consumption and performance. The key feature of the proposed approach is the elimination of dependency on offline profiled results while making runtime decisions. This is achieved through a performance prediction model having a maximum error of 7.9% lower than the previously reported model and a mapping approach that allocates processing cores to applications while respecting performance constraints. Furthermore, AdaMD adapts to runtime execution scenarios efficiently by monitoring the application status, and performance/workload variations to adjust the previous DVFS settings and thread-to-core mappings. The proposed approach is experimentally validated on the Odroid-XU3, with various combinations of diverse multi-threaded applications from PARSEC and SPLASH benchmarks. Results show energy savings of up to 28% compared to the recently proposed approach while meeting performance constraints