FamiWare: a family of event-based middleware for ambient intelligence

Most of the middlewares currently available focus on one type of device (e.g., TinyOS sensors) and/or are designed with one requirement in mind (e.g., data management). This is an important limitation since most of the AmI applications work with several devices (such as sensors, smartphones or PDAs) and use a high diversity of low-level services. Ideally, the middleware should provide a single interface for accessing all those services able to work in heterogeneous devices. To address this issue, we propose a family of configurable middleware (FamiWare) with a really flexible architecture, instead of building a single version of a middleware with a rigid structure. In this work, we present the architecture of our middleware that can be configured, following a Software Product Line approach, in order to be instantiated in a particular device fulfilling specific application requirements. Furthermore, we evaluate that the decisions taken at architecture and implementation are the adequate ones for this kind of constrained devices.     

Deep learning analysis of mobile physiological,environmental and location sensor data for emotion detection

The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%)     

Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method

In this paper, a new approach for detecting previously unencountered malware targeting mobile device is proposed. In the proposed approach, time-stamped security data is continuously monitored within the target mobile device (i.e., smartphones, PDAs) and then processed by the knowledge-based temporal abstraction (KBTA) methodology. Using KBTA, continuously measured data (e.g., the number of sent SMSs) and events (e.g., software installation) are integrated with a mobile device security domain knowledge-base (i.e., an ontology for abstracting meaningful patterns from raw, time-oriented security data), to create higher level, time-oriented concepts and patterns, also known as temporal abstractions. Automatically-generated temporal abstractions are then monitored to detect suspicious temporal patterns and to issue an alert. These patterns are compatible with a set of predefined classes of malware as defined by a security expert (or the owner) employing a set of time and value constraints. The goal is to identify malicious behavior that other defensive technologies (e.g., antivirus or firewall) failed to detect. Since the abstraction derivation process is complex, the KBTA method was adapted for mobile devices that are limited in resources (i.e., CPU, memory, battery). To evaluate the proposed modified KBTA method a lightweight host-based intrusion detection system (HIDS), combined with central management capabilities for Android-based mobile phones, was developed. Evaluation results demonstrated the effectiveness of the new approach in detecting malicious applications on mobile devices (detection rate above 94% in most scenarios) and the feasibility of running such a system on mobile devices (CPU consumption was 3% on average).     

Reliable before-fabrication forecasting of MEMS piezoresistive pressure sensor: mathematical modelling and numerical simulation

Microsystem Technologies - Micro-mechanical systems (MEMS) based piezoresistive pressure sensors have significant importance in several pressure sensor devices in real world, i.e., aviation, IoT...     

Design,modeling, and control of an aerial manipulator for placement and retrieval of sensors in the environment

On‐site inspection of large‐scale infrastructure often involves high risks for the operators and high insurance costs. Despite several safety measures already in place to avoid accidents, an increasing concern has brought the need to remotely monitor hard‐to‐reach locations, for which the use of aerial robots able to interact with the environment has arisen. In this paper a novel approach to aerial manipulation is presented, where a compact manipulator with a single degree‐of‐freedom is tailored for the placement and retrieval of sensors in the environment. The proposed design integrates on‐board sensing, a high‐performance force controller on the manipulator, and a thrust‐to‐force mapping on the flight controller. Experimental results demonstrate the high reliability achieved during both placement and retrieval tasks on flat surfaces (e.g., a bridge wall) and cylindrical surfaces (e.g., tree trunks). A total number of 89 flight experiments were carried out to demonstrate the robustness and potential of the compact, bespoke aerial design.     

EasiSMP: A Resource-Oriented Programming Framework Supporting Runtime Propagation of RESTful Resources

In order to simplify programming for building sensor networks, macro-programming methods have been pro- posed in prior work. Most of them are designed for the dedicated networks and specific scenarios where devices are mostly homogeneous. Nevertheless the methods rarely consider those shared networks which are composed of heterogeneous de- vices, e.g., sensors, actuators, mobile devices, and share resources among themselves. In this paper, we present EasiSMP, a resource-oriented programming framework for these shared networks and generic application scenarios. In this framework, the devices and their functionalities are abstracted into RESTful virtual resources （VRs） each of which is labelled by a uni- form resource identifier （URI）. The post-deployment VR can be globally accessed and reused to propagate new resource（s） at runtime. To support the resource propagation, programming primitives are proposed and a virtual resource engine （VRE） is studied. To perform evaluation, EasiSMP is deployed into a relic monitoring network. Experimental results show that programming using Ea-siSMP is concise, and the average deployment overhead is decreased by up to 27% compared with the node-level programming.     

A survey of urban vehicular sensing platforms

Vehicular sensing where vehicles on the road continuously gather, process, and share location-relevant sensor data (e.g., road condition, traffic flow) is emerging as a new network paradigm for sensor information sharing in urban environments. Recently, smartphones have also received a lot of attention for their potential as portable vehicular urban sensing platforms, as they are equipped with a variety of environment and motion sensors (e.g., audio/video, accelerometer, and GPS) and multiple wireless interfaces (e.g., WiFi, Bluetooth and 2/3G). The ability to take a smartphone on board a vehicle and to complement the sensors of the latter with advanced smartphone capabilities is of immense interest to the industry. In this paper we survey recent vehicular sensor network developments and identify new trends. In particular we review the way sensor information is collected, stored and harvested using inter-vehicular communications (e.g., mobility-assist mobility-assisted dissemination and geographic storage), as well using the infrastructure (e.g., centralized and distributed storage in the wired Internet). The comparative performance of the various sensing schemes is important to us. Thus, we review key results by carefully examining and explaining the evaluation methodology, in the process gaining insight into vehicular sensor network design. Our comparative study confirms that system performance is impacted by a variety of factors such as wireless access methods, mobility, user location, and popularity of the information.     

High-performance IoT streaming data prediction system using Spark: a case study of air pollution

Neural Computing and Applications - Internet-of-Things (IoT) devices are becoming prevalent, and some of them, such as sensors, generate continuous time-series data, i.e., streaming data. These IoT...     

Towards fusing uncertain location data from heterogeneous sources

Properly incorporating location-uncertainties – which is, fully considering their impact when processing queries of interest – is a paramount in any application dealing with spatio-temporal data. Typically, the location-uncertainty is a consequence of the fact that objects cannot be tracked continuously and the inherent imprecision of localization devices. Although there is a large body of works tackling various aspects of efficient management of uncertainty in spatio-temporal data – the settings consider homogeneous localization devices, e.g., either a Global Positioning System (GPS), or different sensors (roadside, indoor, etc.).In this work, we take a first step towards combining the uncertain location data – i.e., fusing the uncertainty of moving objects location – obtained from both GPS devices and roadside sensors. We develop a formal model for capturing the whereabouts in time in this setting and propose the Fused Bead (FB) model, extending the bead model based solely on GPS locations. We also present algorithms for answering traditional spatio-temporal range queries, as well as a special variant pertaining to objects locations with respect to lanes on road segments – augmenting the conventional graph based road network with the width attribute. In addition, pruning techniques are proposed in order to expedite the query processing. We evaluated the benefits of the proposed approach on both real (Beijing taxi) and synthetic (generated from a customized trajectory generator) data. Our experiments demonstrate that the proposed method of fusing the uncertainties may eliminate up to 26 % of the false positives in the Beijing taxi data, and up to 40 % of the false positives in the larger synthetic dataset, when compared to using the traditional bead uncertainty models.     

Wafer bonding with nano-imprint resists as sacrificial adhesive for fabrication of silicon-on-integrated-circuit (SOIC) wafers in 3D integration of MEMS and ICs

In this paper, we present the use of thermosetting nano-imprint resists in adhesive wafer bonding. The presented wafer bonding process is suitable for heterogeneous three-dimensional (3D) integration of microelectromechanical systems (MEMS) and integrated circuits (ICs). Detailed adhesive bonding process parameters are presented to achieve void-free, well-defined and uniform wafer bonding interfaces. Experiments have been performed to optimize the thickness control and uniformity of the nano-imprint resist layer in between the bonded wafers. In contrast to established polymer adhesives such as, e.g., BCB, nano-imprint resists as adhesives for wafer-to-wafer bonding are specifically suitable if the adhesive is intended as sacrificial material. This is often the case, e.g., in fabrication of silicon-on-integrated-circuit (SOIC) wafers for 3D integration of MEMS membrane structures on top of IC wafers. Such IC integrated MEMS includes, e.g., micro-mirror arrays, infrared bolometer arrays, resonators, capacitive inertial sensors, pressure sensors and microphones.     

Sensing Assessment in Unknown Environments: A Survey

This paper surveys sensing assessment solutions from the literature with a particular focus on techniques which can be used in unknown environments, including the following: sensor fault detection and identification (FDI), sensor or source evaluation, and isolating poorly sensed regions. Each approach is evaluated in terms of its ability to perform sensing assessment tasks in unknown environments and its coverage of the range of potential sensing problems. These tasks include sensing problem detection and characterization, as well as performance evaluation (e.g., estimating accuracy or reliability), for a sensor or group of sensors. This survey shows that over 40 existing approaches are focused on either detection and identification of traditional sensor faults (e.g., drift or physical damage) in known environments or evaluation of the reliability of a source (e.g., sensor or agent). Only eight approaches surveyed have tackled environment-dependent problems (e.g., exteroceptive sensor FDI, miscalibration, or use of an inappropriate sensor) in a useful manner for unknown environments. Even less work (two studies) appears to have been done on isolating poorly sensed regions. The survey concludes with a list of opportunities for future research, including developing methods for detecting and characterizing environment-dependent problems and creating comprehensive sensing assessment systems.     

Trust-enhanced Security in Location-based Adaptive Authentication

We propose trust to enhance security in adaptive and non-intrusive user authentication in controlled and pervasive environments. In addition to who a user is (e.g., via biometrics) and what a user knows (e.g., a password, a PIN), recent authentication solutions evaluate what a user has. The user's identity is then derived from what detectable accredited items (e.g., badges, RFIDs) and personal devices (e.g., smart-phones, PDAs) the user shows when authenticating. The level of security of the access is set consequently. Position information is also considered in authentication; only those users carrying authorised items in proximity of certain places can benefit from available resources at those places. Unfortunately, items such as badges, mobile phones, smart phones, RFID-ed cards can be stolen, forgotten, or lost with a consequent risk of identity theft and intrusion. In controlled environment like buildings, where sensors can detect a wide range of different types of items, the security of authentication can be improved by evaluating the amount of trust that can be reposed on the user standing in the area from where he tries to access a resource. This piece of information can be calculated from the positions of all the items linkable to the requester as sensed along time by the different sensors available. Sensors are seen as recommenders that give opinions on a user being in a requested position depending on what they have perceived in the environment. We apply Subjective Logics to model recommendations that originate from different types of location detectors and to combine them into a trust value. Our solution has been tested to improve authentication in an intelligent coffee corner of our research institute. A user at the coffee corner can see, displayed on a wall screen, the position of his colleagues depending on the level of authentication he obtains. The user authentication level depends on the number and on the quality of tokens he provides when authenticating. We comment how the use of a location-based trust (on the requester standing at the coffee corner) improves the adaptability, the non-intrusiveness, and the security of the authentication process. We validate our proposal with a simulation that shows how location-based trust changes when a user device moves away from the coffee corner.     

Energy consumption of data mining algorithms on mobile phones: Evaluation and prediction

The pervasive availability of increasingly powerful mobile computing devices like PDAs, smartphones and wearable sensors, is widening their use in complex applications such as collaborative analysis, information sharing, and data mining in a mobile context. Energy characterization plays a critical role in determining the requirements of data-intensive applications that can be efficiently executed over mobile devices. This paper presents an experimental study of the energy consumption behavior of representative data mining algorithms running on mobile devices. Our study reveals that, although data mining algorithms are compute- and memory-intensive, by appropriate tuning of a few parameters associated to data (e.g., data set size, number of attributes, size of produced results) those algorithms can be efficiently executed on mobile devices by saving energy and, thus, prolonging devices lifetime. Based on the outcome of this study we also proposed a machine learning approach to predict energy consumption of mobile data-intensive algorithms. Results show that a considerable accuracy is achieved when the predictor is trained with specific-algorithm features.     

Four-layer framework for combinatorial optimization problems domain

Four-layer framework for combinatorial optimization problems/models domain is suggested for applied problems structuring and solving: (1) basic combinatorial models and multicriteria decision making problems (e.g., clustering, knapsack problem, multiple choice problem, multicriteria ranking, assignment/allocation); (2) composite models/procedures (e.g., multicriteria combinatorial problems, morphological clique problem); (3) basic (standard) solving frameworks, e.g.: (i) Hierarchical Morphological Multicriteria Design (HMMD) (ranking, combinatorial synthesis based on morphological clique problem), (ii) multi-stage design (two-level HMMD), (iii) special multi-stage composite framework (clustering, assignment/location, multiple choice problem); and (4) domain-oriented solving frameworks, e.g.: (a) design of modular software, (b) design of test inputs for multi-function system testing, (c) combinatorial planning of medical treatment, (d) design and improvement of communication network topology, (e) multi-stage framework for information retrieval, (f) combinatorial evolution and forecasting of software, devices. The multi-layer approach covers ‘decision cycle’, i.e., problem statement, models, algorithms/procedures, solving schemes, decisions, decision analysis and improvement.     

Sensor Fusion for Compliant Robot Motion Control

Force feedback is necessary for accurate force control in robotic manipulators, and thus far, wrist force/torque (F/T) sensors have been used. But an important problem arises when only these types of sensors are used. In a dynamic situation where the manipulator moves in either free or constrained space, the interaction forces and moments at the contact point and also the noncontact ones are measured by the mentioned sensor. In this paper, an estimator based on a sensor fusion strategy integrating the measurements of three different sensors (a wrist F/T sensor, an inertial sensor, and joint sensors) was developed to determine the contact force and torque exerted by the manipulator to its environment. The resulting observer helps to overcome some difficulties of uncertain world models and unknown environments since it reduces the high-frequency and low-frequency spectral contents, i.e., the low-frequency component due to inertia of a heavy tool mass and the high-frequency component due to impacts. The new improvement was experimentally validated in a force/position impedance control loop applied to a Staubli RX60 industrial robotic platform.     

A survey on context-aware mobile visual recognition

The phenomenal growth of the usage of mobile devices (e.g., mobile phones and tablet PCs) opens up a new service, namely mobile visual recognition, which has been widely used in many areas, such as mobile shopping and augmented reality. The rich contextual information (e.g., location, time and direction information), easily acquired by the mobile devices, provides useful clues to facilitate mobile visual recognition, including speeding up the recognition time and improving the recognition performance. This survey focuses on recent advances in Context-Aware Mobile Visual Recognition (CAMVR) and reviews related work regarding to different contextual information, recognition methods, recognition types, and various application scenarios. Finally, we discuss future research directions in this field.     

Calibration of a Computer Controlled Robotic Vision Sensor with a Zoom Lens

Active vision sensors are increasingly being employed in vision systems for their greater flexibility. For example, vision sensors in hand-eye configurations with computer controllable lenses (e.g., zoom lenses) can be set to values which satisfy the sensing situation at hand. For such applications, it is essential to determine the mapping between the parameters that can actually be controlled in a reconfigurable vision system (e.g., the robot arm pose, the zoom setting of the lens) and the higher-level viewpoint parameters that must be set to desired values (e.g., the viewpoint location, focal length). In this paper we present calibration techniques to determine this mapping. In addition, we discuss how to use these relationships in order to achieve the desired values of the viewpoint parameters by setting the controllable parameters to the appropriate values. The sensor setup that is considered consists of a camera in a hand-eye arrangement equipped with a lens that has zoom, focus, and aperture control. The calibration techniques are applied to the H6 × 12.5R Fujinon zoom lens and the experimental results are shown.     

A comprehensive data base for the design of manual materials handling

The main objective of the present paper is to provide a comprehensive data base of the material handling capabilities of the industrial work force. The data base encompasses the following: (1) five different types of manual materials handling activities, namely, lifting, lowering, carrying, pushing, and pulling, (2) worker variables (e.g., sex), and (3) task variables (e.g., frequency, and container size). The data base was developed for the IBM personal computer using the software package DBASE III PLUS.     

Quality factors in micron- and submicron-thick cantilevers

Micromechanical cantilevers are commonly used for detection of small forces in microelectromechanical sensors (e.g., accelerometers) and in scientific instruments (e.g., atomic force microscopes). A fundamental limit to the detection of small forces is imposed by thermomechanical noise, the mechanical analog of Johnson noise, which is governed by dissipation of mechanical energy. This paper reports on measurements of the mechanical quality factor Q for arrays of silicon-nitride, polysilicon, and single-crystal silicon cantilevers. By studying the dependence of Q on cantilever material, geometry, and surface treatments, significant insight into dissipation mechanisms has been obtained. For submicron-thick cantilevers, Q is found to decrease with decreasing cantilever thickness, indicating surface loss mechanisms. For single-crystal silicon cantilevers, significant increase in room temperature Q is obtained after 700°C heat treatment in either N₂ Or forming gas. At low temperatures, silicon cantilevers exhibit a minimum in Q at approximately 135 K, possibly due to a surface-related relaxation process. Thermoelastic dissipation is not a factor for submicron-thick cantilevers, but is shown to be significant for silicon-nitride cantilevers as thin as 2.3 μm     

Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures

This paper proposes a multi-objective genetic algorithm (MOGA) for optimal placements of control devices and sensors in seismically excited civil structures through the integration of an implicit redundant representation genetic algorithm with a strength Pareto evolutionary algorithm 2. Not only are the total number and locations of control devices and sensors optimized, but dynamic responses of structures are also minimized as objective functions in the multi-objective formulation, i.e., both cost and seismic response control performance are simultaneously considered in structural control system design. The linear quadratic Gaussian control algorithm, hydraulic actuators and accelerometers are used for synthesis of active structural control systems on large civil structures. Three and twenty-story benchmark building structures are considered to demonstrate the performance of the proposed MOGA. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement of actuators and sensors in seismically excited large buildings such that the performance on dynamic responses is also satisfied.