ABSTRACT Background Early response systems and efficient resource allocation are vital for ensuring timely and reliable accident detection and prevention, especially in high‐density urban environments such as Kolkata, India. Traditional cloud‐based systems often face challenges in latency, energy consumption, and execution cost. Methods This study proposes an Early Response, Zone‐Based Accident Detection and Resource Allocation framework utilizing a multi‐tier fog–cloud architecture. Historical accident data from 2017 to 2023 are used to identify accident‐prone zones. Advanced machine learning models, including Decision Tree, Random Forest, and XGBoost, are employed to predict accident counts. Optimal emergency routes to nearby ambulance centers, police stations, and hospitals are calculated using a shortest‐path algorithm. The framework is implemented and evaluated using the iFogSim2 simulator. Spatial analysis and visualization are performed using Quantum Geographic Information System (QGIS). Results The proposed architecture shows significant performance improvements, including a 16.77% reduction in energy consumption and an 89.66% reduction in execution cost. Latency and execution times are improved by 11%–20% and 12.08%, respectively. The predictive models demonstrate high accuracy in forecasting accident counts, supporting efficient emergency resource deployment. Conclusion The zone‐prioritized fog computing framework enhances emergency response efficiency by minimizing latency and energy consumption while offering scalable, real‐time accident detection capabilities. The integration of spatial visualization and predictive modeling provides policymakers and urban planners with critical, actionable insights to improve road safety across metropolitan regions. This solution offers a promising direction for safety‐critical applications in smart city environments.

In this is work we propose a sustainable livelihood framework (SLF) with backend spatial data infrastructure (SDI) using geospatial analysis. The proposed SLF uses both econometric and spatial indices to estimate the livelihood. A detailed case study has been presented to show the efficacy of the proposed framework. The proposed framework is scalable and can be easily replicated for other region.

Road accidents claim the lives of over four hundred individuals daily in India. While several researchers have focused on identifying accident-prone locations using different parameters and machine learning models, current solutions often lack a comprehensive framework for identifying blackspots based on recent fatality information. In this paper, we present a geospatial framework for identifying accident blackspots, considering multiple complex parameters such as accident frequency, total fatalities, fatal accidents, fatal and major accidents, and Equivalent Property Damage Only (EPDO). We employ an upper-tail critical test on each parameter to calculate the blackspots. Additionally, we group the blackspots using k-means clustering based on the severity of the accident locations within the spatial data infrastructure. We consider four clusters representing different zones, and each cluster is differentiated by its risk factor, determined by accident density. The blackspot identification is conducted on all roads in Kolkata, West Bengal, India. The zones are ranked in increasing order of risk factors as west, east, north, and south. The proposed model is replicable, scalable, and applicable to any road network.

Spatiotemporal (ST) climatic signals are used exclusively in the analysis and prediction of weather and climate. These signals are prone to noise due to sensor defects, environmental interference and so on. A novel ST signal denoising method is presented that computes ST signal variability measure at multiple data scales obtained from multivariate variational mode decomposition algorithm. This aids in joint multi-zonal climatic signal denoising directly in multidimensional space$\mathbb {R}^{Z\times N}$where input signal resides, with the usage of interval thresholding applied on multiple data scales in$\mathbb {R}^{Z\times N}$. The performance of the proposed method is assessed and compared against closely related state-of-the-art methods using qualitative and quantitative analysis.

Online public shaming events on Twitter often have devastating consequences for ordinary victims. Yet, little has been explored about such events from the perspective of these victims. The research gap is starker in comparison to the related domain of corporate crisis communication, which by virtue of a prolonged interest of both the academia and the industry spanning over decades has established theories and practices for managing a crisis event. This work attempts to bridge the gap by addressing two specific questions about managing an ongoing public shaming event. First, once an event has started, can the victim estimate the progress of the event? Second, how should a victim responds—whether by tendering an apology or posting a denial, based on the current progress to restrain the shamers efficiently? We try to address these by providing a way to measure and predict the progress of an ongoing shaming event with considerable accuracy and devising a response strategy depending on the present progress. The progress is measured from the event peak making it uniform for events of different lengths and intensities. The victim’s response can be one of denial or apology. Learning from past shaming events, we recommend the best response type depending on event progress. Moreover, the entire pipeline is language-agnostic benefiting even non-English tweet shamed victims.

The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. This paper focuses on how individuals’ GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, namedMobilyticsdesigned to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) and a deep learning architecture that automatically annotates the GPS log. Additionally, we propose a novel “transfer learning” approach to explore movement dynamics in a geographically distant area by leveraging knowledge obtained from a comparable region, such as an academic campus. In terms of major contributions and novelty, this is the first work to present end-to-end daily mobility trip purpose extraction and mobility knowledge transfer for trip annotation and POI-tagging where the labeled data are insufficient. Experimental results on real-life datasets of five different regions demonstrate the efficacy of our proposed Mobilytics framework which outperforms the baselines for trip-purpose extraction and POI annotations by a significant margin ($\approx$18% to$\approx$30%). Moreover, the analysis on huge volume of simulated traces (10,000 users) illustrates the scalability and robustness of the framework.

Resource-constrained Internet of Things (IoT) devices depend on remote Cloud/Fog Nodes (FNs) to execute deadline-sensitive services. Offloading computations of real-time services to a remote cloud server results in intolerable latency due to intermittent channels, higher transmission delays, and scarce spectrum resources. Therefore, offloading to nearby FNs is preferable; however, it introduces several significant issues: (i) allocation of limited FN resources, (ii) deadline constraint of heterogeneous services, and (iii) requirement of computationally inexpensive and scalable strategies. This article proposes a M-DAFTO model to tackle the abovementioned issues and generate a fair offloading plan in polynomial time. The offloading problem is modeled as a many-to-one matching game with maximum and minimum quotas at each FN. Because the deferred acceptance (DA) algorithm fails to operate with minimum quotas, we adopt a variant of the DA algorithm, a multistage deferred acceptance (MSDA) algorithm, to solve the offloading problem. The overall goal of M-DAFTO is to reduce the aggregate offloading delay with increased assignment of tasks to FNs. Extensive simulation and analysis confirm a 30.26% and a 93.53% reduction in offloading delay and outages (unassigned tasks) compared to the baselines.

Exploring human mobility dynamics is a challenging semantic data analysis task as conventional information retrieval techniques fail to detect "why people travel". We propose a mobility analytics framework to discover such trip-purposes from individual’s GPS traces using a mobility knowledge graph (MKG) and a deep-learning architecture that automatically annotates the GPS log. Further, a novel transfer learning technique is proposed to analyze the movement dynamics in another geographically dispersed region with the help of the knowledge gained from a region of similar type (say, academic campus).

Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model calledCoMCLOUDto select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines.

Recent advancements in artificial intelligence (AI) and IoT technology have revolutionized the healthcare industry by providing effective remote healthcare. Furthermore, with the aging of the world’s population, remote health monitoring and recommendations are becoming imperative to provide cost-effective healthcare solutions for improving the quality of life of our senior citizens. The explosive growth of wearable sensors (IoT sensors) and health bands has facilitated the interconnection among patients and caregivers to enable assisted living by leveraging AI techniques. This work proposes an end-to-end connected smart home healthcare system (FEEL) for elderly people. Our proposed framework addresses the main challenges of the Internet of Medical Things (IoMT) system namely, the scarcity of labeled data and user’s diverse needs. The major contributions of the work are: 1) few-shot learning-enabled novel federated learning (FL) framework for health data and context information analysis and recommendation; 2) user and context-based knowledge graph (UKG) to represent and model health parameters and environmental impacts on recommendations; 3) deep learning architecture for activity monitoring and location estimation of the users; and 4) edge-fog-IoMT collaborative framework to collect, store, and share medical recommendations while protecting the privacy of the users. FEEL is specifically beneficial for elderly homes where several aged people stay together and require constant care. We aim to develop a novel AI module where along with the health parameters, the social context of the home can be augmented to provide an accurate and improved healthcare service. FEEL has been evaluated for three tasks, namely: 1) activity monitoring and location estimation; 2) fall detection; and 3) medical recommendations for unusual health conditions. A customized wearable device has been used to collect, store, and send health-related parameters. The experimental evaluation demonstrates promising accuracy (F1 score 0.86–0.94 range) for the tasks and outperforms the baselines by a significant margin ($\approx 10$%–16%).

Traffic Sign Recognition (TSR) is an essential component of Intelligent Transportation Systems (ITS) and intelligent vehicles. TSR systems based on deep learning have grown in popularity in recent years. However, since these models belong to the closed-world-oriented learning paradigm, they are only capable of accurately identifying traffic signs that are easy to collect and cannot adapt to the real world. Furthermore, the sample utilization of these methods is insufficient, the resource consumption of model training may become unbearable as the data scale grows. To address this problem, we propose a novel “knowledge + data” co- driven solution (i.e., Joint Semantic Representation algorithm, JSR) for TSR. JSR creates a hybrid feature representation by extracting general and principal visual features from traffic sign images. It also realizes the model’s reasoning ability to zero-shot TSR based on prior knowledge of traffic sign design standards. The effectiveness of JSR is demonstrated by experiments on four benchmark datasets and two self-built TSR datasets.

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications’ power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is$\mathcal {NP}$-Hard, we propose anAnt Colony Optimization(ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%–30% and electricity cost by approximately 25% compared to the baseline.

Skill-based volunteering is an expanding branch of crowdsourcing where one may acquire sustainable services, solutions, and ideas from the crowd by connecting with them online. The optimal mapping between volunteers and tasks with collaboration becomes challenging for complex tasks demanding greater skills and cognitive ability. Unlike traditional crowdsourcing, volunteers like to work on their own schedule and locations. To address this problem, we propose a novel two-phase frame-work consisting of Initial Volunteer-Task Mapping (i-VTM) and Adaptive Common Slot Finding (a-CSF) algorithms. The i-VTM algorithm assigns volunteers to the tasks based on their skills and spatial proximity, whereas the a-CSF algorithm recommends appropriate common working time slots for successful volunteer collaboration. Both the algorithms aim to maximise the overall utility of the crowdsourcing platform. Experimenting with the UpWork dataset demonstrates the efficacy of our framework over existing state-of-the-art methods.

Summary In the Internet of Things framework, crowdsourcing (CS) has played a significant role. A sufficient number of participants carrying sensors or IoT devices are necessary to obtain maximum coverage within a given budget for CS a task. Cloud computing is used for centralized processing, storage, and large‐scale data analysis. The delay associated with transferring data to cloud servers creates a time‐consuming decision‐making process. Fog computing is responsible for this capability. As a result, FogiRecruiter, a novel framework, is offered to efficiently choose participants for data collection from the vital environment while staying within a budget. We also utilize fuzzy logic to pick the best fog nodes for relaying data to them and then to faraway cloud servers. Realizing emergency communication, despite the fact that a direct connection to the cloud is inconvenient. Simulations and prototype testing are used to show the efficacy of the proposed approach.

Abstract Approaches to evaluate comments based on whether they increase code comprehensibility for software maintenance tasks are important, but largely missing. We proposeCommentfor automated classification and quality evaluation of code comments of C codebases based on how they can help to understand existing code. We conduct surveys and document developers' perceptions on the type of comments that prove useful to maintaining software in the form of comment categories. A total of 20,206 comments have been collected from open‐sourceGithubprojects and annotated with assistance from industry experts. We develop features to semantically analyze comments to locate concepts related to categories of usefulness. Additionally, features based on code and comment correlation are designed to infer whether the comment is also consistent and not superfluous. Using neural networks, comments are classified asuseful,partially useful, andnot usefulwith precision and recall scores of 86.27% and 86.42%, respectively. The proposed framework for comment quality evaluation incorporates industry practices and adds significant value to companies wanting to formulate better code commenting strategies. Furthermore, large codebases can be de‐cluttered by removing comments not helpful in maintaining code.

Abstract Internet of Things (IoT) has a pivotal role in developing intelligent and computational solutions to facilitate varied real‐life applications. To execute high‐end computations and data analytics, IoT and cloud‐based solutions play the most significant role. However, frequent communication with long distant cloud servers is not a delay‐aware and energy‐efficient solution while providing time‐critical applications such as healthcare. This article explores the possibilities and opportunities of integrating cloud technology with fog and edge‐based computing to provide healthcare services to users in exigency. Here, we propose an end‐to‐end framework namedRESCUE(enabling green healthcare services using integrated iot‐edge‐fog‐cloud computing environments), consisting efficient spatio‐temporal data analytics module for efficient information sharing, spatio‐temporal data analysis to predict the path for users to reach the destination (healthcare center or relief camps) with minimum delay in the time of exigency (say, natural disaster). This module analyzes the collected information through crowd‐sourcing and assists the user by extracting optimal path postdisaster when many regions are nonreachable. Our work is different from the existing literature in varied aspects: it analyses the context and semantics by augmenting real‐time volunteered geographical information (VGI) and refines it. Furthermore, the novel path prediction module incorporates such VGI instances and predicts routes in emergencies avoiding all possible risks. Also, the design of development of a latency‐aware, power‐aware data‐driven analytics system helps to resolve any spatio‐temporal query more efficiently compared to the existing works for any time‐critical application. The experimental and simulation results outperform the baselines in terms of accuracy, delay, and power consumption.

Abstract Several global health incidents and evidences show the increasing likelihood of pandemics (large‐scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data‐sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio‐temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio‐temporal data and computing infrastructure using fog/edge based architecture; and (ii) spatio‐temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic‐knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home‐health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real‐life datasets related to COVID‐19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio‐temporal datasets and reduces the delay in predicting health status compared to the existing studies.

We are pleased to present a special issue that focuses on the software systems for managing the impact of the coronavirus disease 19 (COVID-19) pandemic. The COVID-19 pandemic has affected around 192 million people worldwide and has led to ˜4.13 million deaths as of July 22, 2021. Globally, most of the countries have implemented lockdowns to protect their citizens. However, lockdown over an extended period is unsustainable. Hence, it is widely believed that virus testing and tracking is the best approach to ease lockdown measures. There is a need for innovative software systems to manage the impact of the COVID-19 pandemic effectively in many areas such as healthcare system, transport systems, supply-chain system, educational system, government-service delivery, pharmaceutical companies, manufacturing, software industries, and multinational companies. For example, in healthcare, smart-software systems would be able to remotely measure a person's body temperature, heart and respiratory rates, identifying their movements (including sneezing, coughing, shivering, etc.) to identify whether a person is displaying symptoms of COVID-19 or not. An essential aspect associated with these technologies is data privacy, scalability, and quality of service (QoS) in terms of reliability, availability, security, latency, and energy which need to be considered throughout the development of the software systems. In countries like India, UK, Russia, Brazil, and USA, the system would also help to ensure that isolated communities have access to testing, delivered in a fast, accurate, and efficient manner. These software systems would help and support the assessment of public-health strategies and policies such as social distancing and assess further interventions to control the spread of the virus. Innovative software systems can increase stakeholder participation, as cost-effective assistance in the COVID-19 pandemic monitoring is of great interest to many countries. To manage the impact of this pandemic, there is a need to design and develop scalable, reliable, and energy-efficient sustainable software solutions for different COVID-19 scenarios. In consideration of the existing systems and their features, an Internet of Things (IoT)-based system suitable for COVID-19 or pandemic situations associated with other influenza viruses can be developed. Furthermore, these systems can be integrated with artificial-intelligence (AI) processes for effective data-collection, analysis, statistical visualization, sharing, and decision making. Moreover, these systems can be implemented using both simulations and real-time testbeds for COVID-19 operations (sanitization, medication, monitoring, thermal imaging, etc.) to test their performance in terms of scalability, reliability, availability, and energy efficiency. There is a need to use AI methods, such as reinforcement learning, deep learning, and genetic algorithms while developing IoT-based software systems to achieve self-learning, self-adaptation, and autonomous decision-making capabilities in order to improve efficiency of the systems. Meanwhile, a huge voluminous amount of complex data is generated from various sources including World Health Organization (WHO), social networking, edge devices, private and public hospitals, patients and academic institutes, which needs an effective big data analytics mechanism to manage this data proficiently. Furthermore, there is a need to study the impact of system configuration on workload processing at different cloud nodes while maintaining the QoS dynamically. The data are collected in databases, it is subsequently examined and monitored, and it is important to manage data consistency and integrity. In this context, we argue that it is essential to employ decentralized data-gathering approaches, maintaining the privacy of the population as a high priority. This special issue has received articles by researchers and practitioners from both academia and industry to develop innovative software systems for managing the impact of the COVID-19 pandemic. This special issue, therefore, aims to focus the attention of its readers to four research articles carefully selected after multiple rounds of peer-review. The brief contributions of these papers are discussed in the following section: The first paper entitled "An approach to forecast impact of COVID-19 using supervised machine learning model" by Mohan et al.1 proposes a hybrid model to predict the effect of COVID-19 using moving regressive, autoregressive, and ensemble learning model. This work uses two datasets from Worldometer and Ministry of Health & Family Welfare of India to conduct the countrywise predictions across the world and statewise predictions of India, respectively. The second paper entitled "NovidChain: Blockchain-based privacy-preserving platform for COVID-19 test/vaccine certificates" by Abid et al.2 includes various promising ideas such as maintains the immutability and data integrity using Blockchain technology, enhances the privacy by incorporating encryption for personal information and verifies the COVID-19 proof using W3C verifiable credentials standard immediately. The third paper entitled "Software System to Predict the Infection in COVID-19 Patients using Deep Learning and Web of Things" by Singh et al.3 generates synthetic data using various data augmentation techniques. Proposed system uses U-Net and WoT to segment the COVID Medseg and Radiopedia datasets in an autonomic manner. Experimental results show that the system gives better performance in terms of network latency, response time, and server latency. The fourth paper entitled "Advanced Data Integration in Banking, Financial, and Insurance Software in the Age of COVID-19" by Maiti et al.4 contributes to recognize the effect of the COVID-19 pandemic on the global Banking Financial Services and Insurance landscape. Further, a hype cycle has been developed to find out the important software technologies to handle real-world challenges related to corporate. We believe the work that has been approved in this special issue will assist readers of the journal and a broader research community to learn about the topics of software systems and impacts of COVID-19 pandemic, and inspire them to study more in this area. We would like to express our gratitude to the Editor-in-Chief (Prof. Rajkumar Buyya) and editorial board members for allowing us to bring out this special issue and guiding us throughout the process. We also want to express our gratitude to and further acknowledge the administrative staff, reviewers, and especially the authors for their contributions to the success of this issue.

This article presents a new fast, highly scalable distributed matrix multiplication algorithm on Apache Spark, calledStark, based on Strassen’s matrix multiplication algorithm. Stark preserves Strassen’s seven multiplications scheme in a distributed environment and thus achieves asymptotically faster execution time. It creates a distributed recursion tree of computation where each level of the tree corresponds to division and combination of distributed matrix blocks stored in the form of Resilient Distributed Datasets (RDDs). It processes each divide and combine step in parallel and memorises the sub-matrices by intelligently tagging matrix blocks in it. To the best of our knowledge, Stark is the first implementation of a distribute Strassen’s algorithm on Spark platform. We also report a detailed complexity analysis for the proposed algorithm, taking into account computation and communication costs. Experimental results suggest that Stark outperforms existing distributed matrix multiplication implementations on Spark –MarlinandMLLib, for high matrix sizes ($\geq 16384\times 16384$). Our experiments reveal optimal block sizes for each matrix size, which is also shown from theoretical analysis. We also show that the experimental and theoretical running times for Stark match closely. It has also been shown experimentally that Stark exhibits strong scalability with increasing number of executors.

Catastrophic forgettingof previously acquired knowledge is a major setback suffered by the neural networks (NNs) when these are trained on tasks in sequential fashion. The NN variants, including the deep network models, as commonly used in remote sensing data prediction are also not free from this limitation. The issue becomes more prominent when the prediction is performed over data collected from spatial zones with a large degree of subregional variations or heterogeneity. In order to tackle this problem, in this article, we propose a multilayered adaptive recurrent incremental network (MARINE) model, offering a spatial heterogeneity-aware self-adaptation scheme that resolves the catastrophic forgetting issue in an autonomous manner. Typically, the proposed MARINE is equipped with an intrinsic mechanism of clustering the spatial subregions as per their heterogeneity levels and auto-constructing the recurrent network layers in an ensemble fashion so that the knowledge acquired about one group of heterogeneity level does not overwrite that acquired about other groups. With respect to spatio-temporal prediction of normalized difference vegetation index (NDVI) time series, as derived from MODIS Terra satellite remote sensing imagery, we demonstrate that our proposed MARINE achieves competitive results when compared with the state-of-the-arts. More significantly, in the presence of higher degree of spatial heterogeneity, MARINE outperforms others by betteravoiding the catastrophic forgetting issue.

With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Processing the vast spatio-temporal data requires scalable computing infrastructure. In this regard, an efficient query resolution system can be deployed if we predict the infrastructure requirement of the user query apriori along with the identification of the geospatial service chain. In this work, we propose a framework, namelyLYRIC(deadLine and budget aware spatio-temporal querYpRocessingInCloud), where the spatio-temporal queries are resolved efficiently considering user-defined deadline and budget constraint. Our framework shows high deadline completion accuracy in the range of 1.0 - 0.937, which is more accurate than SparkGIS, GeoSpark, GeoMesa and JUST. This also reduces the resource prediction error by 11 percent, considering the geospatial service chain than without it. The cost of the spatio-temporal query is reduced by$\approx$23% in LYRIC, further, the simulation study (using CloudSim) illustrates the efficacy and scalability of LYRIC in terms of optimal budget usage and execution time compared to four baseline approaches.

Allocating tasks to the best-fit candidates is a classical problem in crowdsourcing (CS). Most of the existing approaches assume that the task and candidate knowledge is known in advance and ignore the effect of enrolled candidates' willingness on the CS system's selection decision. For instance, an unwilling candidate assigned to a task may quit without completing it, thus depreciating the utility of the CS platform. In practice, a task or candidate may arrive or leave the CS system dynamically. Moreover, a complex task may be broken into smaller sub-tasks, each requiring a variety of computations and expertise. To overcome these challenges, based on a greedy algorithm, we propose a novel approach for skill-oriented dynamic task allocation with willingness factor for complex assignments (SWill-TAC). This approach iteratively attempts to delegate candidates (workers) to tasks depending on the skills required for executing the tasks and the candidates' skill set. SWill-TAC also considers the willingness of eligible candidates and keeps track of the budget constraints of tasks. Finally, the feasibility and efficiency of our approach are demonstrated using the UpWork dataset. Experimental results show that SWill-TAC outperforms Online Greedy, TM-Uniform, Random selection-based, and Minimum payment-based task allocations in terms of the completed tasks count, the utility gained, and success ratio.

Purpose The purpose of this study is to make an attempt to identify the factors responsible for the adoption of an artificial intelligence (AI)-integrated customer relationship management (CRM) system in Indian organizations with a special focus on security and privacy issues. Design/methodology/approach This study was conducted for the identification of factors responsible for the adoption of the AI-integrated CRM system in Indian organizations focusing attention on security and privacy perspective. For this, the adoption theories and models have been studied. The literature available in this context has also been studied with a focus on security and privacy issues. After the initial study, few hypotheses have been formulated and a conceptual model has been developed. These hypotheses were validated with the help of statistical tools by conducting sample survey with 324 usable responses against 36 questionnaires. Findings The results of this study highlight that of the eight hypotheses conceptually formulated, one hypothesis was not supported as is evident from the application of statistical analysis. This is the influence of perceived ease of use on attitude of the stakeholders intending to use the AI-integrated CRM system in Indian organizations. The results also transpire that the model so provided has achieved 87% explanative power. Research limitations/implications The model so provided has taken the help of the technological acceptance model. It has also used the issues circumscribing menace of security and privacy vulnerabilities. Consideration of the technological acceptance model and aspects of issues of security and privacy has enriched the model rendering its explanative power to 87%. Practical implications The model is simple. Practitioners can execute this model without having any complexity. The policymakers could also get inputs from the model, as it has focused specially on security and privacy issues that could help to enhance the trust of the potential users. Originality/value Not many studies are found covering the adoption of the AI-integrated CRM system by Indian organizations with a special focus on security and privacy aspects. In this light, this study is a novel attempt.

Cholesky Decomposition is the primary approach which is used to solve Symmetric and Positive Definite (SPD) systems but is inherently iterative making it very difficult to parallelize as calculations at each partition require elements from other partitions. In this paper, we present two distributed block-recursive approaches to solve large SPD systems — the symmetric version of the state-of-the-art Strassen’s algorithm and Cholesky based inversion algorithm. We show experimentally that both the approaches have good scalability and Cholesky based approach is more efficient as it uses fewer matrix multiplications in each recursion level than Strassen based algorithm.

With the major development of sensor technologies and advancements of communication network infrastructures, there is a growing interest to add more intelligence in the e-health monitoring for facilitating an effective healthcare system. While IoT devices are capable of continuous health-parameter sensing and providing notifications to the user, an effective business process management (BPM) facilitates effective system integration and data processing workflow. This paper proposes an efficient framework for managing emergency situations (specifically, health-related) through the analysis of heterogeneous data sources. The proposed framework, named CLAWER (CLoud-Fog bAsed Workflow for Emergency seRvice) aims to bridge the gap between process management and data analytics by providing an automated workflow for personalized health-monitoring and efficient recommendation system. Here, the IoT devices are used for collecting the movement and health data. The smart phone can act as an edge device to acquire data with user movement information. The accumulated data is initially processed inside the fog device, and finally the analysis and recommendations are generated by the cloud. In this paper the indoor health-status of the users are analysed in small cell cloud enhanced eNode B, which is used as fog device. The generated recommendations are stored in the fog device to provide the recommendations to the users with low latency and in timely manner. The experimental analysis of CLAWER yields better precision and recall values than the existing methods.

Both analyzing mobility traces and understanding a user's movement semantics from mobile sensor data are challenging issues in ubiquitous computing systems. With the pervasiveness of sensor technologies, wireless networks and GPS-equipped devices, a huge volume of location information is being accumulated. Several techniques have been proposed to analyze the mobility traces and extract informative knowledge for varied location-aware applications. However, all of these applications necessitate an effective mobility-analysis framework to capture the movement behavior of individuals in minimum delay. This paper aims to develop a cloud-based mobility analytics framework to model peoples' mobility behaviour in varied granular scale and extract usable knowledge to provision location-aware services. The preliminary experimental results on real-life dataset depict the effectiveness of our proposed framework.

In the presence of latency sensitive geo-distributed applications, users require fast service for their queries. Cloud computing provides physical servers from its data center in order to process user requests. The cloud data center consumes a huge amount of energy due to lack of management of the data center servers as the container-based service consolidation is a nontrivial task. Since the containers require less resource footprint, consolidating it in servers might make resource availability sparse. In order to reduce the energy consumption of the cloud data center, we have proposed a green container-based consolidation of the services so that the maximum number of servers can be put into idle mode without affecting the application quality of experience. The service consolidation problem has been formulated as an optimization problem considering minimization of total energy consumption of the data center as the objective, and an algorithm named Energy Aware Service consolidation using baYesian optimization (EASY) has been proposed to solve the optimization. We have evaluated the EASY algorithm in simulation using python. The experimental results have shown that EASY improves the total energy consumption of the data centers. This improvement comes at the cost of a small increase of service response time as there exists a trade-off between energy consumption and service response time.

Vehicular cyber-physical systems (VCPS), among several other applications, may help address an ever-increasing challenge of traffic congestion in large cities. Nevertheless, VCPS can be hindered by information falsification problem, resulting due to the wrong perception of a traffic event or deliberate faking by the participating vehicles. Such information fabrication causes the re-routing of vehicles and artificial congestion, leading to economic, safety, environmental, and health hazards. Thus, it is imperative to infer truthful traffic information in real-time to restore the operational reliability of the VCPS. In this work, we propose a novel reputation scoring and decision support framework, called Spoofed and False Report Eradicator (SAFE) , which offers a cost-effective and efficient solution to handle information falsification problem in the VCPS domain. The framework includes humans in the sensing loop by exploiting the paradigm of participatory sensing , a concept of a mobile security agent (MSA) to nullify the effects of deliberate false contribution, and a variant of the distance bounding mechanism to thwart location-spoofing attacks. A regression-based model integrates these effects to generate the expected truthfulness of a participant’s contribution. To determine if any contribution is true or false, a generalized linear model is used to transform the expected truthfulness into a Quality of Contribution (QoC) score. The QoC of different reports is aggregated to compute user reputation. Such reputation enables classification of different participation behaviors. Finally, an Expected Utility Theory (EUT) -based decision model is proposed that utilizes the reputation score to determine if event-specific information should be published or dropped. To evaluate the SAFE framework through experimental study, we used both simulated and real data to compare its reputation-based user segregation performance with state-of-the-art frameworks. Experimental results exhibit that SAFE captures the fine differences in participants’ behavior through the quality and quantity of participation, and the accuracy of their informed location. It also significantly improves operational reliability through publishing the information of only legitimate events.

This paper proposes a virtual machine (VM) migration model to reduce the power consumption while migrating a set of VMs over geo-distributed clouds. We develop an approach to find out the migration path across different Internet Service Providers (ISPs) leading to the most feasible destination. For this, we make use of the variation in the electricity price at the ISPs for deciding the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. Hence, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between the power consumption and the migration time to make the implementation realistic. Thorough simulation analysis of the proposed approach shows that it can achieve low power consumption cost with acceptable migration time.

Understanding human interests and intents from movement data are fundamental challenges for any location-based service. With the pervasiveness of sensor embedded smartphones and wireless networks and communication, the availability of spatio-temporal mobility trace (timestamped location information) is increasingly growing. Analysing these huge amount of mobility data is another major concern. This paper proposes a cloud-based framework named MovCloud to efficiently manage and analyse mobility data. Specifically, the framework presents a hierarchical indexing schema to store trajectory data in different spatio-temporal resolution, clusters the trajectories based on semantic movement behaviour instead of only raw latitude, longitude point and resolves mobility queries using MapReduce paradigm. MovCloud is implemented over Google Cloud Platform (GCP) and an extensive set of experiments on real-life data yield the effectiveness of the proposed framework. MovCloud has achieved ~ 28% better clustering accuracy and also executed three times faster than the baseline methods.

In the presence of the Internet of Things (IoT) devices, the end-users require a response within a short amount of time which the cloud computing alone cannot provide. Fog computing plays an important role in the presence of IoT devices in order to meet such delay requirements. Though beneficial in these latency-sensitive scenarios, the fog has several implementation challenges. In order to solve the problem of micro-service placement in the fog devices, we propose a framework with the objective of achieving low response time. This problem has been formulated as an optimization problem to improve the response time by considering the time-varying resource availability of the fog devices as constraints. We propose an orchestration framework named Pick-Test-Choose (PTC) to solve the problem. PTC uses Bayesian Optimization based iterative reinforcement learning algorithm to find out a micro-service allocation based on the current workload of the fog devices. PTC employs containers for service isolation and migration of the micro-services. The proposed architecture is implemented over an in-house testbed as well as in iFogSim simulator. The experimental results show that the proposed framework performs better in terms of response time compared to various other baselines.

Regardless of attempts to extract knowledge from code bases to aid in program comprehension, there is an absence of a framework to extract and integrate knowledge to provide a near-complete multifaceted understanding of a program. To bridge this gap, we propose SMARTKT (Smart Knowledge Transfer) to extract and transfer knowledge related to software development and application-specific characteristics and their interrelationships in form of a knowledge graph. For an application, the knowledge graph provides an overall understanding of the design and implementation and can be used by an intelligent natural language query system to convert the process of knowledge transfer into a developer-friendly Google-like search. For validation, we develop an analyzer to discover concurrency-related design aspects from runtime traces in a machine learning framework and obtain a precision and recall of around 97% and 95% respectively. We extract application-specific knowledge from code comments and obtain 72% match against human-annotated ground truth.

Public shaming in online social networks and related online public forums like Twitter has been increasing in recent years. These events are known to have a devastating impact on the victim's social, political, and financial life. Notwithstanding its known ill effects, little has been done in popular online social media to remedy this, often by the excuse of large volume and diversity of such comments and, therefore, unfeasible number of human moderators required to achieve the task. In this paper, we automate the task of public shaming detection in Twitter from the perspective of victims and explore primarily two aspects, namely, events and shamers. Shaming tweets are categorized into six types: abusive, comparison, passing judgment, religious/ethnic, sarcasm/joke, and whataboutery, and each tweet is classified into one of these types or as nonshaming. It is observed that out of all the participating users who post comments in a particular shaming event, majority of them are likely to shame the victim. Interestingly, it is also the shamers whose follower counts increase faster than that of the nonshamers in Twitter. Finally, based on categorization and classification of shaming tweets, a web application called BlockShame has been designed and deployed for on-the-fly muting/blocking of shamers attacking a victim on the Twitter.

A proper assessment of reservoir water dynamics is of utmost importance for the development of any region. However, the reservoir water dynamics is not merely a periodic event. Rather, it is a result of complex interplay among various water balancing components, especially the meteorological factors. So, the key objectives of this research work are to model the influence of spatial variability of meteorological variables on the hydrological processes in a reservoir, and to utilize this knowledge of spatial variability to aid in prediction of reservoir dynamics. In this regard, we propose FORWARD, a forecasting model based on spatial Bayesian network (SpaBN) which has inherent capability of efficiently modeling the spatial impact of meteorological and topographical factors distributed over the watershed. The forecasting efficiency of FORWARD has been compared with a set of linear and non-linear prediction techniques with respect to a case study on forecasting daily live capacity of the Mayurakshi reservoir in India. The experimental results show the superiority of FORWARD over the others.

Computing optimal routes in a road network is both space and time-consuming. This paper proposes an innovative way of finding a route, given the traffic conditions of every edge present in the map. This article aims to bring about a convergence between methods used in routing data over a network and path planning used in AI technique. It makes use of certain concepts of network routing tables in order to develop path planners suitable for urban conditions. It also has a linear space complexity.

Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset.

Human mobility prediction is a challenging and crucial task for fostering wide spectrum of location based applications, namely, traffic planning, travel-plan recommendation etc. However, modeling human movement behaviour and predicting next location are non-trivial tasks. We consider the human mobility pattern modeling and predicting next location problem for semantic trajectory data, wherein sparse GPS records are associated with other contextual information. Intuitively, human moves with an intent and the confluence of movement history (GPS traces) and contextual information may help in capturing people's intents at a fine granularity and has potential to improve the efficacy of location prediction problem. In this paper, a hierarchical and layered hidden markov model (HMM) framework for mobility prediction from sparse trajectories has been proposed. Experiment on real-life mobility dataset of Nokia Mobile Data Challenge (MDC) demonstrates the efficacy of the proposed framework even when with sparse GPS traces.

This paper proposes a novel data-driven model (BESTED), based on spatial Bayesian network with incorporated exponential smoothing mechanism, for predicting precipitation time series on daily basis. In BESTED, the spatial Bayesian network helps to efficiently model the influence of spatially distributed variables. Moreover, the incorporated exponential smoothing mechanism aids in tuning the network inferred values to compensate for the unknown factors, influencing the precipitation rate. Empirical study has been carried out to predict the daily precipitation in West Bengal, India, for the year 2015. The experimental result demonstrates the superiority of the proposed BESTED model, compared to the other benchmarks and state-of-the-art techniques.

Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real‐time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim , to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

Natural systems, like the hydrological, climatological, atmospheric, or any other environmental processes, are extremely complex as well as dynamic in nature. It is therefore difficult to forecast, analyze, and quantify these processes by using simple empirical equations. Modeling and forecasting of reservoir water dynamics are not exceptions in this respect, as these involve various challenges due to the effect of meteorological factors, natural processes of stream flow, climatic change, and so on. The intent of our present work is to propose a novel forecasting model, FORWARD, that handles some of these issues in complex reservoir dynamics. FORWARD is based on a variant of spatial Bayesian network (SpaBN), having inherent capability of modeling impact of spatial variability of meteorological factors over the river catchment. The forecasting efficiency of FORWARD has been compared with four other linear and non-linear techniques based on six different statistical performance measures. The experimental results show the superiority of FORWARD over the other techniques. Though FORWARD has been demonstrated with respect to a case study on forecasting reservoir live capacity, the model possesses a generic structure that can also be applied in other domains by introducing minimal augmentation.

Meteorological parameters are often considered as crucial factors for climatological pattern analysis. Predictions of these parameters have been studied extensively in the field of remote sensing and GIS. It is one of the most critical steps involved in most of the meteorological data mining process. Spatial interpolation is an efficient technique to yield minimal error in prediction. From existing literatures, it is evident that the land-use/land-cover (LULC) distribution of the terrain influences these parameters in a varying manner and it is important to model their behaviour for climatological analyses. However, this semantic LULC knowledge of the terrain is generally ignored in the prediction process of the meteorological parameters. Recently, we have proposed a new spatial interpolation technique, namely semantic kriging (SemK) [3,5,7], which considers the semantic LULC knowledge for land-atmospheric interaction modeling and incorporates it into the existing interpolation process for better accuracy. However, the a-priori correlation analysis of SemK ignores the effect of other nearby LULC classes on each other. This article presents a new variant of SemK, namely a-posterior probabilistic Bayesian SemK (BSemK), which extends the a-priori correlation analysis of SemK with a-posterior probabilistic analysis. The proposed approach provides more accurate estimation of the parameters. Experimentation with LST data advocates the efficacy of the proposed approach compared to the a-priori SemK and other existing interpolation techniques.

Spatial autocorrelation (SA), describing correlation of a particular feature/phenomenon with itself across space, is one of the major properties of any spatial data. Among the various measures of SA proposed till date, the Moran's index (I) is the most common as well as significant one. However, measuring Moran's I, which needs to deal with spatial weight between each pair of spatial data objects, becomes almost unfeasible in case of large-scale raster data, like geostationary satellite data, containing several millions of pixels. This paper proposes a method based on the Hadoop MapReduce framework for computing Moran's I in large-scale raster data. The main contribution of the work lies in the implementation of the Mapper and Reducer processes for a cost effective estimation of Moran's I, considering both rook case and queen case of spatial contiguity. The key feature of these algorithms is an efficient manipulation of the spatial weight matrix, and thereby reducing the overall memory and time requirement. The experimentation shows a promising result in this regard.

This work proposes a new deep convolution architechture for predicting urban growth using satellite images. The case study shows that even without any feature extraction algorithm, performance of DCAP is comparable to MLP and RBFN. Further, other kinds of deep architechtures, such as deep belief net or recurrent neural net, can be applied and their performance can be compared.

Participatory sensing (PS) is an emerging socio-technological paradigm in which citizens voluntarily participate and contribute to a distributed information system using applications installed in their hand-held devices. It can be found in a number of real-life applications, viz. traffic monitoring, air/sound pollution, garbage monitoring, social networking, commodity pricing, and so on. In these systems, information sensed by the user helps the peers in decision making. Present work considers vehicular participatory sensing systems, where registered user senses (perceives) the traffic incident and submits its report(s) to a PS application server. PS application server in turn, broadcasts those reports as alerts to its subscribers. To promote the participation, the PS systems used to have incentive schemes for the participants. However, a common problem in participatory sensing is the generation of false reports either due to wrong perception of an event or to maliciously increase the degree of participation to gain undue incentives. Such false reports make the usage of the PS system unreliable and vulnerable to the illusion attack. This work proposes a novel approach to make PS applications more reliable by identifying and filtering out the falsely reported event through automated confidence assignment based on a probabilistic model. Waze traffic alerts have been used as the dataset to validate the proposed filtering mechanism. Finally, simulation-based experiments and performance evaluation have been done to demonstrate that the proposed approach is relatively accurate.

In the state‐of‐the‐art single channel speech enhancement techniques, the short‐time spectral amplitude is modified while the effect of the phase corruption due to the contamination of additive noise is neglected. This study introduces an improved speech enhancement algorithm based on a phase‐aware multi‐band spectral subtraction technique which estimates the spectral amplitude of the clean speech signal by considering the phase of the speech and noise signal components, and uses the estimated phase of the clean speech signal for signal reconstruction in the time domain. Experimental results show that the proposed algorithm yields better performance in terms of various objective and composite quality measures and other intelligibility assessment metrics while compared with other existing spectral subtraction methods. Using the composite objective measure quality evaluation technique, it is observed that the overall signal quality of the enhanced speech signal is improved on an average by 70% at 0 dB global input signal‐to‐noise ratio by using the proposed approach.

With the advent of advanced remote sensing technologies in past few decades, acquiring higher resolution satellite images has become easier and cheaper in recent days. However, on the other hand, it has offered a big challenge to the remote sensing community in smart image interpretation from such huge volume of data. Deep learning, which offers efficient algorithms for extracting multiple levels of feature abstractions, may be suitable to serve the purpose. This letter presents a deep learning approach (Deep-STEP) for spatiotemporal prediction of satellite remote sensing data. The proposed learning architecture is derived from a deep stacking network, consisting of a stack of multilayer perceptron, each of which models the spatial feature of the associated region at a particular time instant. The proposed method has been demonstrated on normalized difference vegetation index (NDVI) data sets, derived from satellite remote sensing imagery, containing several thousands to millions of pixels/records. The experimental results (related to NDVI prediction) reveal that the proposed architecture exhibits fairly satisfactory performance with promising learning capabilities.

Advancement in sensing and networking technology enhances the capability of mechatronic systems by many folds and improves the way it interacts with its physical environment. However, the dependency on network technology introduces a new set of challenges. Vehicular cyber-physical system (VCPS) is one such example that came into existence with the objective of enhancing road safety and convenience. In VCPS, the safety applications rely on the correctness of the kinematics information received from other vehicles in the vicinity. Incorrect kinematics information may result in increase in road hazards and creates more confusion than convenience. Irrespective of the reasons, detection of such errant nodes in VCPS is imperative. Moreover, the trust estimation of the nodes can play an important role to classify them as genuine and errant. In this work, an efficient scheme is proposed for trust estimation and detection of those errant nodes that disseminate incorrect kinematics information in VCPS. Simulations using ns-2 and VanetMobiSim under realistic vehicular network environment are conducted to evaluate the performance of the proposed scheme. The results are encouraging and reveal that the proposed scheme is suitable for implementation in real vehicular environment for detection of such errant nodes with high accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

Online collaboration service has become a popular offering of present day Software-as-a-Service (SaaS) clouds. It facilitates sharing of information among multiple participating domains and accessing them from remote locations. Owing to loosely-coupled nature of such collaborations, access request from a remote user is made in the form of a set of permissions. The cloud vendor maps the requested permissions into appropriate local roles in order to allow resource access. However, coexistence of such multiple simultaneous role activation requests may introduce conflicts which violate the principle of security. In this paper, we propose a distributed secure collaboration framework which enables collaborating domains to detect and remove these conflicts. Two features of our framework are: (i) it requires only local information, and (ii) it detects and removes conflicts on-the-fly. Formal proofs have been provided to establish the correctness of our approach. Experimental results and qualitative comparison with related work demonstrate the efficacy of our approach in terms of response time, thus addressing the scalability requirement of cloud services.

Chronic wound (CW) diagnosis is more demanding to monitor the healing process of the wound. However, the availability of specialist medical help in remote/rural areas in developing countries, like India, is a challenge. Further, visiting specially hospitals in city is both expensive and time consuming. This paper discusses the comprehensive CW diagnostic approach using three important modules, namely, wounds data acquisition (WDA), tele-wound technology network (TWTN), and wound screening and diagnostic (WSD) respectively. We have proposed a CW characterization and diagnosis under telemedicine framework to classify the tissue depending on percentage of wound and based on color variation at regular time intervals. The Bayesian classifier based wound characterization (BWC) method is proposed to identify the percentage of tissue with high accuracy. It has been observed that the BWC method provides overall accuracy of 87.11%.

The causes of the many communicable diseases have some link with the geographical locations. In the developing countries, the patient information are not often collected systemically due to lack of technological support and scarcity of health professionals. As a result, the actual causes of different community diseases are not properly investigated and it delays to take preventive actions, especially in the rural areas. In this work, a service oriented framework has been proposed for collecting the health information of the patients along with their actual geo-locations to generate the health map of the region which may help in adopting mitigation measures. A mobile application has been developed for collecting these information by the health workers or the health volunteers. The application can be tuned to provide suggestive primary treatment of the disease. The collected volunteered disease information can be analyzed with respect to that geospatial information according to guidelines of the health experts and the disease map can produced on-the-fly. The disease map helps to predict the spreading trend of a disease in the context of geospatial attributes. This may facilitate, the public health decision makers to take preventive actions to mitigate the spread of the diseases.

The modern digital hearing aids suffer from the problem of degradation of perceived signal quality and intelligibility of audio signal when signal to noise ratio (SNR) of the input signal becomes very low. In this work, a speech enhancement algorithm is proposed where the magnitude spectrum of clean speech is estimated by using two separate Bayesian estimators derived from two different cost functions. For the voiced regions, the perceptually motivated adaptive β-order weighted minimum mean square error (MMSE) estimator is used and the phasestructure of the voiced region is also reconstructed using a harmonic model of speech. On the other hand, for the unvoiced segments, Bayesian estimator with modified Itakura-Saito (MIS) cost function is utilized for estimating the magnitude spectrum of clean unvoiced signal. The proposed algorithm is simulated under different non-stationary noisy environments at various signal to noise ratio values. The experimental results show that the proposed speech enhancement framework performs better than other standard benchmark methods in terms of several quality and intelligibility assessment metrics of perceived audio signal.

Spatio-temporal pattern analysis of meteorological parameters has been studied extensively in the field of remote sensing (RS) and geographic information system (GIS). It is an important data mining strategy for modeling the temporal dynamics of these parameters and forecasting them in future time instances. The meteorological parameters, measured near the earth surface, are eminently influenced by the surrounding land-cover distribution of the terrain. For the time-series prediction of these parameters, the knowledge of spatial land-covers can be contemplated within the space-time trade-off between the sample points. This work presents a new kriging based spatio-temporal interpolation method, namely times-series semantic kriging (SemKts) which deals with land-cover dynamics and incorporates this knowledge for better prediction accuracy. It is a time-series extension of our earlier work on semantic kriging (SemK) for spatial interpolation [1] [2]. Experimentation has been carried out by considering real land surface temperature data in the spatial region Kolkata, India. It has been observed that the semantically enhanced space-time trade-off analysis by SemKtsyields more accurate result than most of the popular methods for prediction.

Prediction of spatial attributes in geospatial data repositories is indispensable in the field of remote sensing and geographic information system. The semantic kriging (SemK) approach semantically captures the domain knowledge of the terrain in terms of local spatial features for spatial attribute prediction. It produces better results than ordinary kriging and other prediction methods. This letter focuses on the theoretical and empirical analyses of the SemK. A Euclidean vector analysis approach is adopted to theoretically prove the efficacy of SemK in capturing semantic knowledge.

Abstract It is increasingly important to implement a conflict‐free access control policies for co‐allied networks where different organizations are involve for a common goal. Mobile ad hoc networks are widely used for mission critical situations where teams from different organizational networks cooperate to form a single network to implement their respective operations. These teams (or quads) have different sets of local policies enforced for their own security resulting heterogeneity in access control. Each team wants to preserve its access control policies at a maximum level. Moreover, a set of allied policies govern the cooperation and interaction between the different teams, which may conflict with their local policies. The policy conflicts arise from the transitivity of policy rules, mobility of the nodes, cooperative behaviors, and so on. In addition, the policy rules may be temporal or static. To achieve the successful completion of the mission, it may be required to compromise with the stringency of the enforcement of the conflicting rules for the quads. In this paper, we propose an automated and formal framework to find the optimal conciliation of the policy rules to preserve the mission and thus ensure minimal compromise with the enforcement of policy for each quad. The efficacy of the work lies on optimizing the enforcement of access control policies to achieve the coalition instead of negating the policy. Copyright © 2014 John Wiley & Sons, Ltd.

With rapid technological advancements, cloud marketplace witnessed frequent emergence of new service providers with similar offerings. However, service level agreements (SLAs), which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services to store and process their clients' data. If loss of data occurs due to an outage, the customer's business gets affected. Therefore, the major challenge for a customer is to select an appropriate service provider to ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP, which combines trustworthiness and competence to estimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider's SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms.

This work presents GeomSMS as the first full-fledged SMS framework with the native support for geometric objects for sharing spatial information ubiquitously across mobile users. GeomSMS is an extension to Open GeoSMS Standard by Open GeoSpatial Consortium (OGC) that provides developers a Short Message Service (SMS) encoding for sharing only location information, namely latitude and longitude, between location based services (LBS) and applications. GeomSMS keeps the GeoSMS standard as it is, but adds support for sharing two other geometric features: line and polygon, apart from existing point feature. GeomSMS shares these features in the SMS payload without altering the GeoSMS standard. We describe the architecture of the system that utilizes the framework and demonstrates a real-life mobile application BeckonMe with one example from each of line and polygon feature.

Prediction of spatial attributes has attracted significant research interest in recent years. It is challenging especially when spatial data contain errors and missing values. Geostatistical estimators are used to predict the missing attribute values from the observed values of known surrounding data points, a general form of which is referred as kriging in the field of geographic information system and remote sensing. The proposed semantic kriging ( SemK) tries to blend the semantics of spatial features (of surrounding data points) with ordinary kriging (OK) method for prediction of the attribute. Experimentation has been carried out with land surface temperature data of four major metropolitan cities in India. It shows that SemK outperforms the OK and most of the existing spatial interpolation methods.

A fundamental challenge in the design of Wireless Sensor Network (WSN) is to enhance the network lifetime. The area around the Sink forms a bottleneck zone due to heavy traffic-flow, which limits the network lifetime in WSN. This work attempts to improve the energy efficiency of the bottleneck zone which leads to overall improvement of the network lifetime by considering a duty cycled WSN. An efficient communication paradigm has been adopted in the bottleneck zone by combining duty cycle and network coding. Studies carried out to estimate the upper bounds of the network lifetime by considering (i) duty cycle, (ii) network coding and (iii) combinations of duty cycle and network coding. The sensor nodes in the bottleneck zone are divided into two groups: simple relay sensors and network coder sensors. The relay nodes simply forward the received data, whereas, the network coder nodes transmit using the proposed network coding based algorithm. Energy efficiency of the bottleneck zone increases because more volume of data will be transmitted to the Sink with the same number of transmissions. This in-turn improves the overall lifetime of the network. Performance metrics, namely, packet delivery ratio and packet latency have also been investigated. A detailed theoretical analysis and simulation results have been provided to show the efficacy of the proposed approach.

Increasing availability of geospatial data provides exceptional opportunities in knowledge creation and distribution. For the discovery of suitable data sources, keyword based search in the catalogue becomes inaccurate. The main reason behind this is the existing semantic heterogeneity in the database schema, deployed by different service providers. It necessitates the semantic management of the spatial catalogues. This paper presents an ontology based approach which is useful to create and manage catalogues semantically, hence resolving the semantic heterogeneity between geospatial repositories and incompatibility with spatial queries. Further, there is no standard available for semantic searching of the spatial catalogue till date. It will enhance the data extraction process by providing semantic meaning to the spatial catalogue.

Mobile ad hoc networks (MANETs) lacks enforcement of policy-based access control mechanism to restrict unauthorized accesses on the network resources. Policy-based security infrastructure in MANET is more complex than traditional network due to uncontrolled media access and absence of network perimeters. As access control needs to be applied in a distributed manner, considering the mobility of nodes, traditional security technologies like firewall, IDS etc. cannot fit for MANET. So, to ensure security, distribution and enforcement of the policy rules over different nodes in MANET are the major research challenges. This work proposes a distributed policy-based access control framework for MANET.

Abstract In today's organizations, the large scale deployment of wireless networks has opened up new directions in network security management. The organizational security policies aim at protecting the network resources from unauthorized accesses in the wireless local area networks (WLAN). In WLAN security policy management, the standard IP‐based access control mechanisms are not sufficient due to dynamic changes in network topology and access control states. The role‐based access control (RBAC) models may be appropriate to strengthen the security perimeter over the network resources. However, formalizing the dynamic binding of the access policies to the roles, depending on various control states, is a major challenge. In this paper, we propose a WLAN security policy management framework based on a formalspatio‐temporal RBAC(STRBAC) model. The present work primarily focuses on dynamic computation of security policies based on various control states, its formal representation using STRBAC model, and security property verification of the proposed STRBAC model. The proposed policy management framework logically partitions the WLAN topology into various security policy zones. The framework includes aCentral Authentication & Role Server(CARS) which authenticates the users (nodes) and access points (AP) and also assigns appropriate roles to the users; aGlobal Policy Server(GPS) that dynamically computes the global security policy and policy configurations for different policy zones based on local user‐role and control state information; a distributed policy zone control architecture. Each policy zone consists of aPolicy Zone Controller(WPZCon) which dynamically computes the low‐level access configurations. Finally, a SAT based verification procedure has been presented for verifying the security properties of the proposed STRBAC model. Copyright © 2010 John Wiley & Sons, Ltd.

In mobile ad hoc networks, the security enforcement and its implementation becoming increasingly difficult due to quasi-static nature of the mobile nodes (wireless communication devices), no fixed network topology and more importantly absence of centralized authority. In such networks, communication links between nodes may be bandwidth constrained, messages typically roamed in multi-hoped fashion, nodes may be powered by limited energy source and also have limited physical security. The major challenge in such networks is to give a robust security solution. The complexity of the problem is compounded by the fact that both active and passive attackers may present in the system, and nodes may not function properly in order to save its own energy by selective forwarding of the packets. This paper presents a distributed trust based security framework for ad hoc networks. We have proposed a clustering mechanism and security is enforced by local monitoring system by a new kind of nodes referred as guard nodes. This framework stems from cryptographic computation, which is not suitable in this scenario. The trust is computed depending upon some parameters which have a primary role in enforcing security and cooperation between the nodes. Also this solution conforms graceful leave and dynamic secure allocation of IP of the nodes.

The increasing use of wireless technologies in enterprise networks demands strong security management and policy enforcement mechanisms. The conventional security management frameworks used in wired LAN do not suit in wireless domain due to dynamic topology and mobility of hosts. The enforcement of organizational security policies in wireless LAN requires appropriate access control models as well as correct distribution of access control rules in the network access points. In this paper, we propose a WLAN security management framework supported by a spatio-temporal RBAC (STRBAC) model. The concept of mobile IP has been used to ensure a fixed layer 3 address of a mobile host. Each wireless policy zone consists of a Policy Zone Controller that coordinates with a dedicated Local Role Server to extract the low level access configurations corresponding to the zone access routers. The system can be mapped into a reconfigurable hardware to exploit the parallelism in computing. We also propose a formal STRBAC model to represent the global security policies formally and a SAT based decision procedure to verify the access configurations

In a typical enterprise network, there are several sub-networks or network zones corresponding to different departments or sections of the organization. These zones are interconnected through set of Layer-3 network devices (or routers). The service accesses within the zones and also with the external network (e.g., Internet) are usually governed by a enterprise-wide security policy. This policy is implemented through appropriate set of access control lists (ACL rules) distributed across various network interfaces of the enterprise network. Such networks faces two major security challenges, (i) conflict free representation of the security policy, and (ii) correct implementation of the policy through distributed ACL rules. This work presents a formal verification framework to analyze the security implementations in an enterprise network with respect to the organizational security policy. It generates conflict-free policy model from the enterprise-wide security policy and then formally verifies the distributed ACL implementations with respect to the conflict-free policy model. The complexity in the verification process arises from extensive use of temporal service access rules and presence of hidden service access paths in the networks. The proposed framework incorporates formal modeling of conflict-free policy specification and distributed ACL implementation in the network and finally deploys Boolean satisfiability (SAT) based verification procedure to check the conformation between the policy and implementation models.

Searching and accessing geospatial information in the open and distributed environments of geospatial information systems poses several challenges due to the heterogeneity in geospatial data. Geospatial data is highly heterogeneous — both at the syntactic and semantic level. The requirement for an integration architecture for seamless access of geospatial data has been raised over the past decades. The paper proposes a service-based model for geospatial integration where each geospatial data provider is interfaced on the web as services. The interface for these services has been described with Open Geospatial Consortium (OGC) specified service standards. Catalog service provides service descriptions for the services to be discovered. The semantic of each service description is captured in the form of ontology. The similarity assessment method of request service with candidate services proposed in this paper is aimed at resolving the heterogeneity in semantics of locational terms of service descriptions. In a way, we have proposed an architecture for enterprise geographic information system (E-GIS), which is an organization-wide approach to GIS integration, operation, and management. A query processing mechanism for accessing geospatial information in the service-based distributed environment has also been discussed with the help of a case study.

Geo-spatial data is increasingly becoming a key element for effective planning and decision-making in a variety of application domains. This has generated the need of sharing the spatial data repositories, collected/maintained by diverse organizations mostly for their own application domain. Enterprise geographic information system (E-GIS) is an organization-wide approach to GIS implementation, operation and management of these diverse spatial data repositories. The main focus of the paper is to propose an architecture for integrating diverse spatial data repositories for geographic applications using a service oriented architecture (SOA). The proposed architecture uses a central ontology of data repositories for seamless query processing across heterogeneous data sources. The ontology can be viewed as defining a database of objects, connected by roles, which will help in interpreting the semantics of users' request. Two level ontology namely, domain and application ontology is used for understanding and matchmaking the requests with services. The working principle of service-based discovery and retrieval mechanism has been incorporated in the integrated system.

In this paper, we present a graph-theoretic interpretation of convergence of fractal encoding based on partial iterated function system (PIFS). First we have considered a special circumstance, where no spatial contraction has been allowed in the encoding process. The concept leads to the development of a linear time fast decoding algorithm from the compressed image. This concept is extended for the general scheme of fractal compression allowing spatial contraction (on averaging) from larger domains to smaller ranges. A linear time fast decoding algorithm is also proposed in this situation, which produces a decoded image very close to the result obtained by an ordinary iterative decompression algorithm.