A lightweight framework for secure life-logging in smart environments

As the world becomes an interconnected network where objects and humans interact with each other, new challenges and threats appear in the ecosystem. In this interconnected world, smart objects have an important role in giving users the chance for life-logging in smart environments. However, smart devices have several limitations with regards to memory, resources and computation power, hindering the opportunity to apply well-established security algorithms and techniques for secure life-logging on the Internet of Things (IoT) domain. The need for secure and trustworthy life-logging in smart environments is vital, thus, a lightweight approach has to be considered to overcome the constraints of Smart Objects. The purpose of this paper is to present in details the current topics of life-logging in smart environments, while describing interconnection issues, security threats and suggesting a lightweight framework for ensuring security, privacy and trustworthy life-logging. In order to investigate the efficiency of the lightweight framework and the impact of the security attacks on energy consumption, an experimental test-bed was developed including two interconnected users and one smart attacker, who attempts to intercept transmitted messages or interfere with the communication link. Several mitigation factors, such as power control, channel assignment and AES-128 encryption were applied for secure life-logging. Finally, research into the degradation of the consumed energy regarding the described intrusions is presented.     

A smart intuitionistic fuzzy-based framework for round-robin short-term scheduler

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Data stream visualization framework for smart cities

Monitoring smart cities is a key challenge due the variety of data streams generated from different process (traffic, human dynamics, pollution, energy supply, water supply, etc.). All these streams show us what is happening and as to where and when in the city. The purpose of this paper was to apply different types of glyphs for showing real-time stream evolution of data gathered in the city. The use of glyphs is intended to make the most out of the human capacity for detecting visual patterns.     

A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities

Neural Computing and Applications - Blockchains-based smart contracts are disrupting the smart real estate sector of the smart cities. The current study explores the literature focused on...     

A unified framework for data integrity protection in people-centric smart cities

Multimedia Tools and Applications - With the rapid increase in urbanisation, the concept of smart cities has attracted considerable attention. By leveraging emerging technologies such as the...     

A framework for incremental construction of real global smart space applications

This article describes a standardised way to build context-aware global smart space applications using information that is distributed across independent (legacy, sensor-enabled, and embedded) systems by exploiting the overlapping spatial and temporal attributes of the information maintained by these systems. The framework supports a spatial programming model based on a topographical approach to modelling space that enables systems to independently define and use potentially overlapping spatial context in a consistent manner and in contrast to topological approaches, in which geographical relationships between objects are described explicitly. This approach is supported by an extensible data model that implicitly captures the relationships between information provided by separate underlying systems and facilitates the incremental construction of global smart spaces since the underlying systems to be incorporated are largely decoupled. The framework has been evaluated using a prototype that integrates legacy systems and context-aware services for multi-modal urban journey planning and for visualising traffic congestion.     

A linguistic self-organizing process controller

A heuristic controller for dynamic processes is presented in this paper whose control policy is able to develop and improve automatically. The controller's heuristics take the form of a set of linguistic decision rules which are expressed quantitatively and manipulated by using the theory of fuzzy sets. A series of experiments are described which show that the controller can be applied to a wide range of different processes which can be multivariable and also nonlinear and demonstrate the controller's robustness. This form of control can therefore find an application in those complex systems which have been too difficult to control or which in the past have had to rely on the experience of a human operator.     

SmartData 4.0: a formal description framework for big data

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Context-based person identification framework for smart video surveillance

Smart video surveillance (SVS) applications enhance situational awareness by allowing domain analysts to focus on the events of higher priority. SVS approaches operate by trying to extract and interpret higher “semantic” level events that occur in video. One of the key challenges of SVS is that of person identification where the task is for each subject that occurs in a video shot to identify the person it corresponds to. The problem of person identification is especially challenging in resource-constrained environments where transmission delay, bandwidth restriction, and packet loss may prevent the capture of high-quality data. Conventional person identification approaches which primarily are based on analyzing facial features are often not sufficient to deal with poor-quality data. To address this challenge, we propose a framework that leverages heterogeneous contextual information together with facial features to handle the problem of person identification for low-quality data. We first investigate the appropriate methods to utilize heterogeneous context features including clothing, activity, human attributes, gait, people co-occurrence, and so on. We then propose a unified approach for person identification that builds on top of our generic entity resolution framework called RelDC, which can integrate all these context features to improve the quality of person identification. This work thus links one well-known problem of person identification from the computer vision research area (that deals with video/images) with another well-recognized challenge known as entity resolution from the database and AI/ML areas (that deals with textual data). We apply the proposed solution to a real-world dataset consisting of several weeks of surveillance videos. The results demonstrate the effectiveness and efficiency of our approach even on low-quality video data.     

Multimedia based IoT-centric smart framework for eLearning paradigm

Multimedia Tools and Applications - Multimedia content boosts the learning trends. This paper is aimed to presents an electronic learning system based on Internet of Things (IoT) for the...     

Secure limitation analysis of public-key cryptography for smart card settings

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A new television audience measurement framework using smart devices

Television audience measurement is intended to collect information on the audiences watching a specific television program at a particular time. This information is crucial for television broadcasters and advertisers because they need to provide right television programs and commercials to right audiences to maximize their investments in broadcasting. For accurate measurements, a panel of representative audiences must be selected judiciously so that it accurately represents the entire target audience group. However, it is hard to secure a proper number of target audiences due to the expensive and cumbersome installations of measurement equipments. To resolve this issue in panel selection, we propose a novel television audience measurement framework using pervasive smart devices such as a smartphone. In the proposed framework, a short audio signal from a television set is recorded by a personal smart device and is sent to an audio matching server for the identification of the television program shown by the television set. For effective identification, we propose an accurate audio matching algorithm based on spectral coherence and efficient implementation techniques that exploit the inherent parallelism in the algorithm. To verify the plausibility of the framework and the effectiveness of the audio matching algorithm, we conduct experiments with diverse genres of television programs under various recording conditions.     

A neural-network-based model reference PID-like controller for process control

This paper presents a neural-network-based PID-like control strategy applicable to a class of nonlinear control problems commonly encountered in the process-control industry. An artificial neural network is used to provide compensation of the plant's nonlinear dynamics so that the overall closed-loop system can be described in terms of an equivalent error system. In the paper, the strategy is carefully described, and then evaluated and compared with an alternative control system design which uses conventional gain-scheduled PID controllers. The paper includes real-time experimental results in applying the proposed technique for level control of a coupled-tanks system.     

A multiregion fuzzy logic controller for nonlinear process control

Although a fuzzy logic controller is generally nonlinear, a PI-type fuzzy controller that uses only control error and change in control error is not able to detect the process nonlinearity and make a control move accordingly. In this paper, a multiregion fuzzy logic controller is proposed for nonlinear process control. Based on prior knowledge, the process to be controlled is divided into fuzzy regions such as high-gain, low-gain, large-time-constant, and small-time-constant. Then a fuzzy controller is designed based on the regional information. Using an auxiliary process variable to detect the process operating regions, the resulting multiregion fuzzy logic controller can give satisfactory performance in all regions. Rule combination and controller tuning are discussed. Application of the controller to pH control is demonstrated     

Model-free controller design for a single-link flexible smart materials robot

In this paper, controller design is investigated for a single-link flexible smart materials robot which combines the advantages of both flexible link robots and piezoelectric materials. To avoid the drawbacks resulting from model uncertainties and/or model truncations, model-free controllers (both decentralized and centralized) are proposed for tip regulation and residual vibration suppression. In contrast to traditional model-based methods, the controllers presented in the paper are derived from the basic energy-work relationship and can guarantee the asymptotic stability of the damped truncated system with arbitrarily any finite number of flexible modes. Furthermore, the controllers are easily implementable because all the signals can be chosen to be readily measurable. Simulations are carried out to show the effectiveness of the approach presented.     

A categorical framework for typing CCS-style process communication

Category theory has proved a useful tool in the study of type systems for sequential programming languages. Various approaches have been proposed to use categorical models to examine the type structures appropriate to concurrent systems. In this paper, we outline some of these approaches, such as interaction categories, and argue that they are not appropriate to model the handshake communication mechanism as used e.g. in CCS or the π-calculus. We propose an alternative general categorical framework for examining the type structure of such systems, and exhibit its categorical structure, which is similar to that of existing approaches. We then examine in detail an instance of this framework, based on a simple fragment of CCS. We prove that it is isomorphic to a syntactic category constructed from a process algebra similar to CCS, with a fusion operator, as in the fusion calculus. Thus, we make explicit some of the type structure implicitly present in such a process algebra.     

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated “smart” mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.