Abstract

In recent years, high utility itemsets (HUIs) mining from the transactional databases becomes one of the most emerging research topic in the field of data mining due to its wide range of applications in online e-commerce data analysis, identifying interesting patterns in biomedical data and for cross marketing solutions in retail business. It aims to discover the itemsets with high utilities efficiently by considering item quantities in a transaction and profit values of each item. However, it produces a tremendous number of HUIs, which imposes further burden in analysis of the extracted patterns and also degrades the performance of mining methods. Mining the set of closed + high utility itemsets (CHUIs) solves this issue as it is a loss-less and condensed representation of all HUIs. In this paper, we aim to present a new algorithm for finding CHUIs from a transactional database, called the CHUM (Closed + High Utility itemset Miner), which is scalable and efficient. The proposed mining algorithm adopts a tricky aimed vertical representation of the database in order to speed up the execution time in generating itemset closures and compute their utility information without accessing the database. The proposed method makes use of the item co-occurrences strategy in order to further reduce the number of intersections needed to be performed. Several experiments are conducted on various sparse and dense datasets and the simulation results clearly show the scalability and superior performance of our algorithm as compared to those for the existing state-of-the-art CHUD (Closed + High Utility itemset Discovery) algorithm.

Abstract

User authentication is one of the most important security services required forthe resource-constrained wire-less sensor networks (WSNs). In user authentication, for critical applications of WSNs, a legitimate user is allowed to query and collect the real-time data at any time from a sensor node of the network as and when he/she demands for it. In order to get the real-time information from the nodes,the user needs to be first authenticated by the nodes as well as the gateway node (GWN) of WSN so that illegal accesst o nodes do not happen in the network. Recently, Jiang et al.proposed an efficient two-factor user authentication scheme with unlinkability property in WSNs Jiang (2014). In this paper, we analyze Jiang et al.’s scheme. Unfortunately, we point out that Jiang et al.’s scheme has still several draw-backs such as (1) it fails to protect privileged insider attack,(2) inefficient registration phase for the sensor nodes,(3) it fails to provide proper authentication in login and authentication phase, (4) it fails to update properly the new changed password of a user in the password update phase,(5) it lacks of supporting dynamic sensor node addition after initial deployment of nodes in the network, and (6) itlacks the formal security verification. In order to withstand these pitfalls found in Jiang et al.’s scheme, we aim to pro-pose a three-factor user authentication scheme for WSNs.Our scheme preserves the original merits of Jiang et al.’sscheme. Our scheme is efficient as compared to Jiang et al.’s

Abstract

The Session Initiation Protocol (SIP) is a signaling communications protocol, which has been chosen for controlling multimedia communication in 3G mobile networks. The proposed authentication in SIP is HTTP digest based authentication. Recently, Tu et al. presented an improvement of Zhang et al.’s smart card-based authenticated key agreement protocol for SIP. Their scheme efficiently resists password guessing attack. However, in this paper, we analyze the security of Tu et al.’s scheme and demonstrate their scheme is still vulnerable to user’s impersonation attack, server spoofing attack and man-in-the middle attack. We aim to propose an efficient improvement on Tu et al.’s scheme to overcome the weaknesses of their scheme, while retaining the original merits of their scheme. Through the rigorous informal and formal security analysis, we show that our scheme is secure against various known attacks including the attacks found in Tu et al.’sscheme. Furthermore, we simulate our scheme for the for-mal security analysis using the widely-accepted AVISPA(Automated Validation of Internet Security Protocols and Applications) tool and show that our scheme is secure against passive and active attacks including the replay and man-in-the-middle attacks. Additionally, the proposed scheme is comparable in terms of the communication and computational overheads with Tu et al.’s scheme and other related existing schemes.

Abstract

In tropical countries such as India, increasing the roof albedo helps to reduce the heat ingress through the roof. This further reduces air-conditioning energy consumption in conditioned buildings and increases comfort in unconditioned buildings. In order to help users determine the benefits of high albedo roofs under varying conditions, a simple calculator has been developed. Parameters such as location, building type, roof area, and surface properties of the roof are taken as inputs. Annual EnergyPlus simulations are performed for the given parameters and the results are displayed in both graphical and tabular formats. It also calculates the simple payback by comparing a given base case roof albedo with the proposed roof albedo. The calculator can perform comfort simulations for unconditioned buildings and simulates measures including a radiant barrier system and under deck roof insulation. The calculator also runs a parametric simulation between insulation thickness and roof albedo to find an optimum roof insulation thickness based on incremental internal rate of return. This paper presents the features of the cool roof calculator and the type of analysis that can be performed using the cached results.

Abstract

Increasing roof reflectance reduces absorption of solar radiation, roof surface temperature and heat ingress into the building. This leads to an increase in indoor comfort and reduction in room air temperature that in turn results in energy saving by reducing AC demand. To assess this impact of cool roofs on buildings in rural and peri-urban areas of India, a study was performed on two un-conditioned school buildings in Hyderabad (ZPH School) and Nagpur (VNHM School). Two classrooms of same size, function, and occupancy from both the school buildings were monitored for a period of ten weeks starting from March 2014 till May 2014. The data from the experiments and their analyses showed promising results. The average reduction and peak reduction in indoor air, roof underdeck and roof overdeck surface temperatures are 2.1 ◦C, 5.0 ◦C and 12.3 ◦C and 4.3 ◦C, 10.0 ◦C and 26.3 ◦C respectively for the room with white roof as opposed to grey roof in ZPH School and 1.5 ◦C, 4.0 ◦C and 9.5 ◦C, and 3.3 ◦C, 4.2 ◦C, and 25.2 ◦C respectively in VNHM School. The results from the experiment prove that white roof has a significant effect in reducing the indoor air temperatures in buildings due to reduction in outdoor and indoor roof surface temperature

Abstract

Selecting a high albedo (solar reflectance) waterproofing layer on the top of a roof helps lower the roof’s surface temperature and reduce the air conditioning energy consumption in the top floor of a building. The annual energy savings depend on factors including weather, internal loads, and building operation schedule. To demonstrate the energy saving potential of high albedo roofs, an apparatus consisting of two nearly identical test chambers (A and B) has been built in four Indian climates: Chennai (hot & humid), Bangalore (temperate), Jhagadia (Hot & dry) and Delhi (composite). Each chamber has well-insulated walls to mimic the core of an office building. Both chambers have the same construction, equipment, and operating schedule, differing only in roof surface. The reinforced cement concrete roof of Chamber A is surfaced with a low-albedo cement layer, while that of Chamber B is surfaced with a high-albedo water proof membrane (change in solar reflectance of 0.28). The experiment will be carried out for one year to explore seasonal variations in energy savings. Initial results in the month of July (post summer) shows that savings from high albedo roof ranges from 0.04 kWh/m2/day in temperate climates, to 0.08 kWh/m2/day in hot & dry climate.

Abstract

Land use land cover (LULC) information at temporal scale has become necessary in the analysis of environmental progression, resource management and planning. Uncontrolled expansion of human activities are playing an important role in global land use land cover change affecting ecology, climate change, soil erosion and hydrological regime. Visualizing and quantifying future changes of land use is also essential in high forested regions such as Uttara Kannada to ensure appropriate management practices and policies for maintaining the ecosystem services provided by forests. The landscape transition has accounted across five forest jurisdictions from 2004 to 2013 and visaulised further by non-agent based modeling for 2026. Cellular automata (CA), Markov chain model approaches has evolved as a promising tools of visualisation helps in complicated decision making process to understand the neighboring effects of land use activities. The Haliyal division has lost major cover from 2004 (55.48%) to 2013 (45.29%) due to increase in population pressure, unplanned developmental activities and conversion of forest area into

Abstract

There have been various ways in which the indoor space of a building has been defined. In most of the cases the models have specific purpose on which they focus such as facility management, visualisation or navigation. The focus of our work is to define semantics of a model which can incorporate different aspects of the space within a building without losing any information provided by the data model. In this paper we have suggested a model which defines indoor space in terms of semantic and syntactic features. Each feature belongs to a particular class and based on the class, has a set of properties associated with it. The purpose is to capture properties like geometry, topology and semantic information like name, function and capacity of the space from a real world data model. The features which define the space are determined using the geometric information and the classes are assigned based on the relationships like connectivity, openings and function of the space. The ontology of the classes of the feature set defined will be discussed in the paper.

Abstract

LANDSAT images are used to identify organic contaminants in water bodies, but, there is no enough evidence in present literature that LANDSAT is also good in identifying a mixture of organic and mineral contaminants such as agricultural waste. The focus of this paper is to evaluate the effectiveness of LANDSAT imagery to identify organic and mineral contamination (OMC) and to identify spread extent variations of pollution over the season/year in the Nagarjuna Sagar (NS) reservoir using only satellite images. A new band combination is proposed in order to detect OMC, because existing formulae based on band ratio proved to be inadequate in detecting the contamination in NS. Difference in reflectance values of Red and Green channel of an image helps clearly distinguish clear water from OMC water. This procedure was applied over LANDSAT data of the calendar years 2008, 2014 and 2015 to understand the contamination spread pattern through the reservoir. Results show that contamination is following a similar pattern over these calendar years. In January contamination starts at inlets and by May contamination spreads to almost 90% of the reservoir when the total area of water spread is also reduced by half. Contamination spread is low during the monsoonal period of June to September due to heavy inflow and heavy outflow of waters from NS reservoir. Post monsoon NS is contaminated again because of heavy inflow of runoffs from neighboring land use and limited water outflow. This contamination spread pattern matches the agricultural seasons and fertilizer application pattern in this region, indicating that agricultural use of fertilizers could be one of the primary causes of contamination for this waterbody.

Abstract

This paper demonstrates the application of Autotune, a methodology aimed at automatically producing calibrated building energy models using measured data, in two case studies. In the first case, a building model is de-tuned by deliberately injecting faults into more than 60 parameters. This model was then calibrated using Autotune and its accuracy with respect to the original model was evaluated in terms of the industry-standard normalized mean bias error and coefficient of variation of root mean squared error metrics set forth in ASHRAE Guideline 14. In addition to whole-building energy consumption, outputs including lighting, plug load profiles, HVAC energy consumption, zone temperatures, and other variables were analyzed. In the second case, Autotune calibration is compared directly to experts’ manual calibration of an emulated-occupancy, full-size residential building with comparable calibration results in much less time. The paper concludes with a discussion of the key strengths and weaknesses of auto-calibration approaches.