A two-queue model for optimising the value of information in energy-harvesting sensor networks

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A Multi-dimensional Real World Spectrum Occupancy Data Measurement and Analysis for Spectrum Inference in Cognitive Radio Network

Spectrum Inference in contrast to Spectrum Sensing is an active technique for dynamically inferring radio spectrum state in Cognitive Radio Networks. Efficient spectrum inference demands real world multi-dimensional spectral data with distinct features. Spectrum bands exhibit varying noise floors; an effective band wise noise thresholding guarantees an accurate occupancy data. In this work, we have done an extensive real world spectrum occupancy data measurement in frequency range 0.7 GHz to 3 GHz for tele density wise varying locations at Pune, Solapur and Kalaburagi with time diversity ranging from 2 to 7 days. We have applied maximum noise (Max Noise), m-dB and probability of false alarm (PFA) noise thresholding for spectrum occupancy calculations in all bands and across all locations. Overall occupancy across these locations is 37.89 %, 18.90 % and 13.69 % respectively. We have studied signal to noise ratio (SNR), channel vacancy length durations (CVLD) and service congestion rates (SCR) as characteristic features of measured multi-dimensional spectrum data. The results reveal strong time, spectral and spatial correlations of these features across all locations. These features can be used for a multi-dimensional spectrum inference in cognitive radio based on machine learning

Analysis of discard of whole blood and its components with suggested possible strategies to reduce it

Background: Advances in medical technology demand more and more provision of safe blood for effective management of patients. To tackle with the demand and supply of blood and blood components, more stringent criteria should be applied for blood donations and for proper utilization of blood. The present study was designed to analyze the various reasons for the discard of whole blood and blood components. It also intended to suggest various possible strategies for optimum utilization of blood and reduction of wastage.Methods: In this retrospective study we analyzed various causes of discard of blood and blood components from January 2013 to June 2015 (30 months) using various records available in the blood bank of Jawaharlal Nehru Medical college and Acharya Vinoba Bhave Rural Hospital, Sawangi, Wardha, Maharashtra, India.Results: A total of 14,026 blood bags were collected during the study period of 30 months, out of which 9,785 were whole blood while components were prepared from remaining blood bags. A total of 3,944 Packed Red Cells, 2,137 Platelet Concentrate and 3,944 Fresh Frozen Plasma were prepared. Average discard rate was found to be 22.45% while discard rate for Whole blood, Packed Red Cells, Platelet Concentrate and Fresh Frozen Plasma were 07.70%, 06.74%, 61.11% and 14.24% respectively.Conclusions: Platelets were the most commonly discarded blood component due to short shelf life and non utilization in time as demand cannot be predicted. In our study the main reason for discarding whole blood and Packed Red Cells was sero-positivity for various Transfusion Transmitted Infections while non utilization after issue, breakage/leakage were the main reasons for Fresh Frozen Plasma discard. The self audit of blood transfusion service provides insight into current blood transfusion practices prevalent in the hospital.

Optimal control of wireless sensor networks: a mean-field approach

Wireless sensor networks (WSN) consist of a large number of sensor nodes that monitor the real-world environments and have wide-ranging applications such as military surveillance, forest fire monitoring etc. One of the key objectives in these WSNs is to deliver the information with higher quality (accuracy, timeliness, importance) defined by the nature of the application. Such WSNs with a large number of nodes often present important computational challenges which makes it more difficult to analyse the network. We consider a WSN with a large number of nodes N and model it as a very large Markov chain. Each node can take on J different states which denote the value of information present at the node. We aim to find optimal strategies which keep the value of information as high as possible with regard to minimal information exchange. As the number of nodes in the network increases, the number of states increases as J^N, and as such, solving such a system with dynamic programming becomes practically impossible due to the curse of dimensionality. Applying some existing limit results, we, therefore, formulate the equivalent mean-field model which heavily reduces the computational effort needed to find the optimal control. We discuss the computational efforts that are needed and present the two-state model in full detail. Finally, we present numerical results for the system under consideration and discuss the nature of optimal control in the transient and steady state

Optimal data collection in wireless sensor networks with correlated energy harvesting

We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision-making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate

Note to Coin Exchanger Using PLC

Now days, coins are required at lot of places and we have to suffer a lot for the change in various public places in daily life. Coins are used more instead of note in various places like bus station, railway station, malls, parks, even in rural areas where nowadays also coin telephone system is used. For such applications coins are used extremely, so we thought to develop an exchanger machine which will give us coins instead of notes. As there are lots of techniques to detect the Indian currency note, these are texture based, pattern based, checking by the watermarking, checking the micro lettering, color based recognition technique. The most preferable technique along all these is color based recognition. It is constructed by counting the number of pixels of each color. Color sensor is used which senses the RGB (Red, Green, Blue) colors and the result is given to the controller which will manipulate the coin container through relays and motors, the user simply press the keypad for which type of change he wants whether one rupee coins or five rupee or mixed and hence in the output we get coins as user requirement

Footstep Power Generation using Piezo Ceramic

People move all the time. Wouldn’t it be great to harness that movement and help to power our cities with the movement of people living in them? Piezoelectric harvesting is one of the most reliable and energy efficient method. The crystalline structure of piezoelectric material provides the ability to transform mechanical strain energy into electrical energy. The power generated by piezo is D.C signal with A.C ripples, which is not used directly for battery charging so hence we use rectifier and filter to get pure D.C signal. Further boost converter circuit is used to step up the D.C signal and through battery charger circuit, battery is charged. This charge can be used to drive the a.c loads by converting D.C signal to A.C with help of inverter circuit