Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 4 Articles
With the emergence of the Internet of Things (IoT), a large number of physical\nobjects in daily life have been aggressively connected to the Internet. As the number of\nobjects connected to networks increases, the security systems face a critical challenge due\nto the global connectivity and accessibility of the IoT. However, it is difficult to adapt\ntraditional security systems to the objects in the IoT, because of their limited computing\npower and memory size. In light of this, we present a lightweight security system that uses\na novel malicious pattern-matching engine. We limit the memory usage of the proposed\nsystem in order to make it work on resource-constrained devices. To mitigate performance\ndegradation due to limitations of computation power and memory, we propose two novel\ntechniques, auxiliary shifting and early decision. Through both techniques, we can efficiently\nreduce the number of matching operations on resource-constrained systems. Experiments\nand performance analyses show that our proposed system achieves a maximum speedup of\n2.14 with an IoT object and provides scalable performance for a large number of patterns....
Nowadays extensive volumes of pesticides are employed for agricultural and\nenvironmental practices, but they have negative effects on human health. The levels of\npesticides are necessarily restricted by international regulatory agencies, thus rapid,\ncost-effective and in-field analysis of pesticides is an important issue. In the present work,\nwe propose a butyrylcholinesterase (BChE)-based biosensor embedded in a flow system for\norganophosphorus pesticide detection. The BChE was immobilized by cross-linking on a\nscreen-printed electrode modified with Prussian Blue Nanoparticles. The detection of\nparaoxon (an organophosphorus pesticide) was carried out evaluating its inhibitory effect on\nBChE, and quantifying the enzymatic hydrolysis of butyrylthiocholine before and after the\nexposure of the biosensor to paraoxon, by measuring the thiocholine product at a working\nvoltage of +200 mV. The operating conditions of the flow system were optimized. A flow\nrate of 0.25 mL/min was exploited for inhibition steps, while a 0.12 mL/min flow rate was\nused for substrate measurement. A substrate concentration of 5 mM and an incubation time\nof 10 min allowed a detection limit of 1 ppb of paraoxon (corresponding to 10% inhibition). The stability of the probe in working conditions was investigated for at least eight\nmeasurements, and the storage stability was evaluated up to 60 days at room temperature in\ndry condition. The analytical system was then challenged in drinking, river and lake water\nsamples. Matrix effect was minimized by using a dilution step (1:4 v/v) in flow analysis. This\nbiosensor, embedded in a flow system, showed the possibility to detect paraoxon at ppb level\nusing an automatable and cost-effective bioanalytical system....
The development of applications as well as the services for mobile systems faces a varied range of devices with very heterogeneous\ncapabilities whose response times are difficult to predict. The research described in this work aims to respond to this issue by\ndeveloping a computational model that formalizes the problem and that defines adjusting computing methods. The described\nproposal combines imprecise computing strategies with cloud computing paradigms in order to provide flexible implementation\nframeworks for embedded or mobile devices. As a result, the imprecise computation scheduling method on the workload of the\nembedded system is the solution to move computing to the cloud according to the priority and response time of the tasks to be\nexecuted and hereby be able to meet productivity and quality of desired services. A technique to estimate network delays and\nto schedule more accurately tasks is illustrated in this paper. An application example in which this technique is experimented in\nrunning contexts with heterogeneous work loading for checking the validity of the proposed model is described....
This article discusses the experiences from the development and deployment\nof two image-based environmental monitoring sensor applications using an embedded\nwireless sensor network. Our system uses low-power image sensors and the Tenet general\npurpose sensing system for tiered embedded wireless sensor networks. It leverages Tenet�s\nbuilt-in support for reliable delivery of high rate sensing data, scalability and its flexible\nscripting language, which enables mote-side image compression and the ease of deployment.\nOur first deployment of a pitfall trap monitoring application at the James San Jacinto\nMountain Reserve provided us with insights and lessons learned into the deployment of and\ncompression schemes for these embedded wireless imaging systems. Our three month-long\ndeployment of a bird nest monitoring application resulted in over 100,000 images collected\nfrom a 19-camera node network deployed over an area of 0.05 square miles, despite highly\nvariable environmental conditions. Our biologists found the on-line, near-real-time access to\nimages to be useful for obtaining data on answering their biological questions....
Loading....