Distributed optimisation framework for in-network data processing

In an information network consisting of different types of communication devices equipped with various types of sensors, it is inevitable that a huge amount of data will be generated. Considering the practical network constraints such as bandwidth and energy limitations, storing, processing and transmitting this very large volume of data is very challenging, if not impossible. However, In-Network Processing (INP) has opened a new door to possible solutions for optimising the utilisation of network resources. INP methods primarily aim to aggregate (e.g., compression, fusion and averaging) data from different sources with the objective of reducing the data volume for further transfer, thus, reducing energy consumption, and increasing the network lifetime. However, processing data often results in an imprecise outcome such as irrelevancy, incompleteness, etc. Therefore, besides characterising the Quality of Information (QoI) in these systems, which is important, it is also crucial to consider the effect of further data processing on the measured QoI associated with each specific piece of information. Typically, the greater the degree of data aggregation, the higher the computation energy cost that is incurred. However, as the volume of data is reduced after aggregation, less energy is needed for subsequent data transmission and reception. Furthermore, aggregation of data can cause deterioration of QoI. Therefore, there is a trade-off among the QoI requirement and energy consumption by computation and communication. We define the optimal data reduction rate parameter as the degree to which data can be efficiently reduced while guaranteeing the required QoI for the end user. Using wireless sensor networks for illustration, we concentrate on designing a distributed framework to facilitate controlling of INP process at each node while satisfying the end user’s QoI requirements. We formulate the INP problem as a non-linear optimisation problem with the objective of minimising the total energy consumption through the network subject to a given QoI requirement for the end user. The proposed problem is intrinsically a non-convex, and, in general, hard to solve. Given the non-convexity and hardness of the problem, we propose a novel approach that can reduce the computation complexity of the problem. Specifically, we prove that under the assumption of uniform parameters’ settings, the complexity of the proposed problem can be reduced significantly, which may be feasible for each node with limited energy supply to carry out the problem computation. Moreover, we propose an optimal solution by transforming the original problem to an equivalent one. Using the theory of duality optimisation, we prove that under a set of reasonable cost and topology assumptions, the optimal solution can be efficiently, obtained despite the non-convexity of the problem. Furthermore, we propose an effective and efficient distributed, iterative algorithm that can converge to the optimal solution. We evaluate our proposed complexity reduction framework under different parameter settings, and show that the problem with N variables can be reduced to the problem with logN variables presenting a significant reduction in the complexity of the problem. The validity and performance of the proposed distributed optimisation framework has been evaluated through extensive simulation. We show that the proposed distributed algorithm can converge to the optimal solution very fast. The behaviour of the proposed framework has been examined under different parameters’ setting, and checked against the optimal solution obtained via an exhaustive search algorithm. The results show the quick and efficient convergences for the proposed algorithm.Open Acces

Measurement of surface dose in external radiotherapy of brain frontal lobe: A study on patient and phantom

Introduction: Radiotherapy plays an important role in the treatment of most malignant and also benign primary CNS tumors. Radiotherapy affects both tumor cells and normal cells. The aim of this study was to determine the absorbed dose of radiation by the skin surfacing organs at risk (lens, parotid, thyroid and submandibular gland) during radiotherapy of tumors at the frontal lobe of the brain via direct measurement in patients and phantom. Materials and methods: There are two conventional techniques for external radiotherapy of patients with tumors of the right frontal lobe. These techniques were used to estimate the amount of surface dose absorption by organs at risk (lens of eye, parotid, thyroid, submandibular glands). Technique 1 was performed for 10 patients who referred to Reza Radiation Oncology Center. Technique 1 is the radiation of frontal field with an extension to the lateral field portion. Technique I and 2, which included radiation of one frontal field along with a bilateral field were performed for a phantom model with an anthropomorphic head and neck constructed from natural human bone with paraffin wax and NaCl as surrounding soft tissues. The surface dose for each organ was measured using TLD chips. Results: The measured radiation dose within all organs was within the safe limits in phantom and most patients, however, the surface dose were higher in patients in compare to phantom. In some patients, the right lens was not protected and therefore there would be some risk for cataracts. The findings provide estimation for probable complications along with frontal lobe radiotherapy. Conclusion: Our findings showed that regarding these two techniques, technique I impose less harm to normal tissue, since the right lobe of brain is only irradiated. Besides, the higher values of surface dose in patients compared to phantom might be due to larger tumor field in patients, because the tumor dimensions in phantom was considered 4 cm, while in some patients it was larger. © 2015, Singapore Medical Association. All rights reserved

Exploration of Effective Factors on Microfinance in Iran

Microfinance programs emerged in mid 1980s. So, microfinance can be labeled as a new phenomenon. The reason for emergence and rapid development of microfinance programs can be weakness of former development strategies, especially those of financial development. In this research by using a three-dimension Delphi query, the important factors affecting microfinance at Iran have been explored. In the resources side, deposits are composed of Qard al-Hasan, donation, subrogation and saving. In the uses side, the loans are divided into consumption (general consumptions and emergencies) and investing purposes. The institutions active in microfinance are divided into two groups of banks (commercial banks and financial holdings) and financial institutions (for profit and non-profit). The group lending and solidarity responsibility were underlined at legal-jurisprudence. At the application aspects, emphasis on usage of electronic systems and separate report of loans by their types and contracts is noticeable

QoI-aware tradeoff between communication and computation in wireless ad-hoc networks

Data aggregation techniques exploit spatial and temporal correlations among data and aggregate data into a smaller volume as a means to optimize usage of limited network resources including energy. There is a trade-off among the Quality of Information (QoI) requirement and energy consumption for computation and communication. We formulate the energy-efficient data aggregation problem as a non-linear optimization problem to optimize the trade-off and control the degree of information reduction at each node subject to given QoI requirement. Using the theory of duality optimization, we prove that under a set of reasonable cost assumptions, the optimal solution can be obtained despite non-convexity of the problem. Moreover, we propose a distributed, iterative algorithm that will converge to the optimal solution. Extensive numerical results are presented to confirm the validity of the proposed solution approach