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Acoustic source localization has many important applications particularly for military tracking foreign objects. Even though Wireless Sensor Networks (WSNs) have been developed, this localization problem remains a big challenge. A system for solving source localization must have the ability to deal with the problems of recorded convolved mixture signals while minimizing the high communication and computation cost. This paper introduces a distributed design for positioning multiple independent moving sources based on acoustic signals in which we focus on utilizing the relative information of magnitudes recorded at different sensors. The sensors perform preprocessing on the sensed data to capture the most important information before compressing and sending extracted data to the base. At the base, the data is uncompressed and the source locations are inferred via two clustering stages and an optimization method. Analysis and simulation results lead to the conclusion that our system provides good accuracy and needs neither much communication nor complex computation in a distributed manner. It works well when there exists high noise with Rayleigh multipath fading under Doppler effect and even when the number of independent sources is greater than the number of microphone sensors. [ABSTRACT FROM AUTHOR]

Availability is one of the most important requirements in production system. Keeping a persistent level of high availability in the Infrastructure-as-a-Service (IaaS) cloud computing is a challenge due to the complexity of service providing. By definition, the availability can be maintained by coupling with the fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them adequately consider the fault detection aspect, which is critical to issue the appropriate recovery actions just in time. In this paper, based on a rigorous analysis on the nature of failures, we would like to introduce a method to early identify the faults occurring in the IaaS system. By engaging fuzzy logic algorithm and prediction technique, the proposed approach can provide better performance in terms of accuracy and reaction rate, which subsequently enhances the system reliability. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of data governance in a cloud-based storage system. To achieve fine-grained access control over the outsourced data, we propose Self-Healing Attribute-based Privacy Aware Data Sharing in Cloud (SAPDS). The proposed system delegates the key distribution and management process to a cloud server without seeping out any confidential information. It facilitates data owner to restrain access of the user with whom data has been shared. User revocation is achieved by merely changing one attribute associated with the decryption policy, instead of modifying the entire access control policy. It enables authorized users to update their decryption keys followed by each user revocation, making it self-healing, without ever interacting with the data owner. Computation analysis of the proposed system shows that data owner can revoke n′ users with the complexity of O( n′). Besides this, legitimate users can update their decryption keys with the complexity of O(1). [ABSTRACT FROM AUTHOR]

Synchronization/desynchronization and clustering are important techniques in multi-node computing systems, especially for sensor networks (SN) which is broadly considered to be a type of multi-node computing environment. However, most of the existing algorithms' clustering criteria are limited to the node location information and ignore the nature and characteristics of the nodes as well as the requirements of the applications. In this paper, an autonomic concurrent General Criteria-based Clustering (GCC) method is proposed for multi-node computing systems. The GCC method is based on the neuron oscillator pulse-coupling model and its clustering criteria can come from any node-related data or properties. The cluster member nodes share similar physical or logical properties and represent those relationships in the form of Logical Clusters (LCs). Due to the neuron dynamic system basis of the method, there is concurrency that exists both on the whole network and on each individual node. The simulation shows that the GCC method can generate diverse logical clusters and synchronization/desynchronization coexistence results with acceptable time and energy usage. [ABSTRACT FROM AUTHOR]

In the past few years the role of e-health applications has taken a remarkable lead in terms of services and features inviting millions of people with higher motivation and confidence to achieve a healthier lifestyle. Induction of smart gadgetries, people lifestyle equipped with wearables, and development of IoT has revitalized the feature scale of these applications. The landscape of health applications encountering big data need to be replotted on cloud instead of solely relying on limited storage and computational resources of handheld devices. With this transformation, the outcome from certain health applications is significant where precise, user-centric, and personalized recommendations mimic like a personal care-giver round the clock. To maximize the services spectrum from these applications over cloud, certain challenges like data privacy and communication cost need serious attention. Following the existing trend together with an ambition to promote and assist users with healthy lifestyle we propose a framework of Health Fog where Fog computing is used as an intermediary layer between the cloud and end users. The design feature of Health Fog successfully reduces the extra communication cost that is usually found high in similar systems. For enhanced and flexible control of data privacy and security, we also introduce the cloud access security broker (CASB) as an integral component of Health Fog where certain polices can be implemented accordingly. The modular framework design of Health Fog is capable of engaging data from multiple resources together with adequate level of security and privacy using existing cryptographic primitives. [ABSTRACT FROM AUTHOR]

One of the main issues with data sharing in cloud environment is to manage user access and its auto revocation in a controlled and flexible way. The issue becomes more complex when privacy on user access has to be ensured as well to hide additional leakage of information. For automatic revocation over cloud data, access can be bounded within certain anticipated time limit so that the access expires beyond effective time period. This time-oriented approach is more rigid and not a one-size-fits-all solution. In certain circumstances, exact time anticipation is not an easy choice. Instead, the alternate solution could be task oriented to restrict user beyond certain number of permissible attempts to access the data. We have proposed oblivious user management (OUM) in which a user can have access on cloud data for certain number of attempts without imposing any time restriction. For user authorization and her subsequent revocation, owner will perform one time setup activity and that is same for all users. The model also alleviates the burden of managing different access parameters at user end with each request as she will always use the same parameter for all valid attempts. Our approach also conceals the privacy of user attempts throughout the communication. Hiding this information helps to avoid distinguishing importance of particular user that has more authorization over others. Evaluation results have proved that OUM hides $$(N-1)$$ number of permissible attempts until $$N\mathrm{th}$$ request arrives at Cloud Storage. The Performance analysis conducted on Google App Engine revealed that the cost of operations performed in OUM is within the range of 0.097-0.278 $ per 1,000 requests. [ABSTRACT FROM AUTHOR]

The abundance of semantically related information has resulted in semantic heterogeneity. Ontology matching is among the utilized techniques implemented for semantic heterogeneity resolution; however, ontology matching being a computationally intensive problem can be a time-consuming process. Medium to large-scale ontologies can take from hours up to days of computation time depending upon the utilization of computational resources and complexity of matching algorithms. This delay in producing results, makes ontology matching unsuitable for semantic web-based interactive and semireal-time systems. This paper presents SPHeRe, a performance-based initiative that improves ontology matching performance by exploiting parallelism over multicore cloud platform. Parallelism has been overlooked by ontology matching systems. SPHeRe avails this opportunity and provides a solution by: (i) creating and caching serialized subsets of candidate ontologies with single-step parallel loading; (ii) lightweight matcher-based and redundancy-free subsets result in smaller memory footprints and faster load time; and (iii) implementing data parallelism based distribution over subsets of candidate ontologies by exploiting the multicore distributed hardware of cloud platform for parallel ontology matching and execution. Performance evaluation of SPHeRe on a trinode (12-core) private cloud infrastructure has shown up to 3 times faster ontology load time with up to 8 times smaller memory footprint than Web Ontology Language (OWL) frameworks Jena and OWLAPI. Furthermore, by utilizing the computation resources most efficiently, SPHeRe provides the best scalability in contrast with other ontology matching systems, i.e., GOMMA, LogMap, AROMA, and AgrMaker. On a private cloud instance with 8 cores, SPHeRe outperforms the most performance efficient ontology matching system GOMMA by 40 % in scalability and 4 times in performance. [ABSTRACT FROM AUTHOR]

A lot of research efforts have been made to model the diffusion process in social networks that varies from adoption of products in marketing strategies to disease and virus spread. Previously, a diffusion process is usually considered as a single-objective optimization problem, in which different heuristics or approximate algorithms are applied to optimize an objective of spreading single piece of information that captures the notion of diffusion. However, in real social networks individuals simultaneously receive several pieces of information during their communication. Single-objective solutions are inadequate for collective spread of several information pieces. Therefore, in this paper, we propose a Multi-Objective Diffusion Model (MODM) that allows the modeling of complex and nonlinear phenomena of multiple types of information exchange, and calculate the information worth of each individual from different aspects of information spread such as score, influence and diversity. We design evolutionary algorithm to achieve the multi-objectives in single diffusion process. Through extensive experiments on a real world data set, we have observed that MODM leads to a richer and more realistic class of diffusion model compared to a single objective. This signifies the correlation between the importance of each individual and his information processing capability. Our results indicate that some individuals in the network are naturally and significantly better connected in terms of receiving information irrespective of the starting position of the diffusion process. [ABSTRACT FROM AUTHOR]

Over the last few years, activity recognition in the smart home has become an active research area due to the wide range of human centric-applications. With the development of machine learning algorithms for activity classification, dataset is significantly important for algorithms testing and validation. Collection of real data is a challenging process due to involved budget, human resources, and annotation cost that's why mostly researchers prefer to utilize existing datasets for evaluation purposes. However, openly available smart home datasets indicate variation in terms of performed activities, deployed sensors, and environment settings. Unfortunately, the analysis of existing datasets characteristic is a bottleneck for researchers while selecting datasets of their intent. In this paper, we develop a Framework for Smart Homes Dataset Analysis (FSHDA) to reflect their diverse dimensions in predefined format. It analyzes a list of data dimensions that covers the variations in time, activities, sensors, and inhabitants. For validation, we examine the effects of proposed data dimension on state-of-the-art activity recognition techniques. The results show that dataset dimensions highly affect the classifiers' individual activity label assignments and their overall performances. The outcome of our study is helpful for upcoming researchers to develop a better understanding about the smart home datasets characteristics with classifier's performance. [ABSTRACT FROM AUTHOR]

Encryption ensures confidentiality of the data outsourced to cloud storage services. Searching the encrypted data enables subscribers of a cloud storage service to access only relevant data, by defining trapdoors or evaluating search queries on locally stored indexes. However, these approaches do not consider access privileges while executing search queries. Furthermore, these approaches restrict the searching capability of a subscriber to a limited number of trapdoors defined during data encryption. To address the issue of privacy-aware data search, we propose Oblivious Term Matching (OTM). Unlike existing systems, OTM enables authorized subscribers to define their own search queries comprising of arbitrary number of selection criterion. OTM ensures that cloud service provider obliviously evaluates encrypted search queries without learning any information about the outsourced data. Our performance analysis has demonstrated that search queries comprising of 2 to 14 distinct search criteria cost only 0.03 to 1.09 $ per 1000 requests. [ABSTRACT FROM AUTHOR]