editorial embedded systems for mobile...

EditorialEmbedded Systems for Mobile Sensors

Marco Anisetti,1 Valerio Bellandi,1 Abdellah Chehri,2 Yurong Qian,3 and Gwanggil Jeon4

1Dipartimento di Informatica, Universita degli Studi di Milano, Via Bramante 65, 26013 Crema, Italy2School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Avenue,Ottawa, ON, Canada K1N 6N53Software College, Xinjiang University, 14 Sheng Li Road, Urumqi, Xinjiang 830008, China4Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu,Incheon 406-772, Republic of Korea

Correspondence should be addressed to Gwanggil Jeon; [email protected]

Received 21 July 2016; Accepted 22 July 2016

Copyright © 2016 Marco Anisetti et al.This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In the era of IoT, embedded systems based on mobile sensorideas turn into increasingly compound and need under-standing in various training tools such as signal process-ing, artificial intelligence, and multimedia communication.After a long period of computational capacity centralization,we are facing a period where the computation capacity ismigrating back to the periphery of the systems and thento smart objects and sensors. Currently IoT is constitutedof computationally heterogeneous devices for which theability of executing preelaborations or complete processingis fundamental as well as the ability of doing them, keepinglow power consumptions and maintaining a certain level ofsecurity. The combination of signal processing techniquesfor embedded systems is a principal and timely research inmobile sensors and communications. The articles containedin the present issue include both reviews and basic scien-tific studies focused on mobile multimedia communicationusing fundamental or applied signal processing methodsfor embedded systems and applications of IoT. This issuecomprises the description of streams processing (e.g., images,video, and audio) for embedded systems involved in IoTas mobile sensors/aggregators including preprocessing forsignal quality enhancement (e.g., images, noise reduction),security concerns (e.g., privacy), and evolution of communi-cation protocols and mobile sensor network architectures.

Many challenges exist in constructing smart build-ing/home system including interconnectivity issues between

different technologies and the need of continuous connectiv-ity between heterogeneous sensors. A smart home gatewaytaking responsibility for the connection between the networklayer and the ubiquitous sensor network layer plays an impor-tant role in automated home Internet of Things system. Thecontribution of F. Ding et al. “A Smart Gateway Architecturefor Improving Efficiency of Home Network Applications”proposes an integrated access gateway providing significantflexibility for user to configure and deploy supporting mul-tiubiquitous sensor network. One of the basic issues forinformation collection and data processing in wireless sensornetworks is the network coverage. The contribution of F.Ding and A. Song “Development and Coverage Evaluationof ZigBee-Based Wireless Network Applications” presentedcoverage evaluation and efficient development of a ZigBee-based home network application based on adaptive weightedfusion (AWF) of all data of sensing nodes and a gateway-based architecture to reexecute packet processing and thenreported to the monitoring center.

Quality of Service (QoS) parameters such as bit rate andpacket losses are good indicators for optimizing network ser-vices but not for characterizing user perception calledQualityof Experience (QoE). The contribution of G. Sarwar et al.“QoS and QoE Aware N-Screen Multicast Service” proposedQoS and QoE-aware adaptive mapping of N-Screen devicesto multicast groups at the application layer to ensure thevisual quality requirements in varying network conditions.

Hindawi Publishing CorporationJournal of SensorsVolume 2016, Article ID 8312032, 3 pageshttp://dx.doi.org/10.1155/2016/8312032

2 Journal of Sensors

Power consumption is another crucial aspect in IoTand mobile sensor network systems. The contribution of A.Ahmad et al. “Context-Aware Mobile Sensors for SensingDiscrete Events in Smart Environment” proposed a smarthome architecture introducing the notion of context-awarelow power mobile sensors sharing the same communicationmodel based on IoT paradigm. The author tested the per-formances on Hadoop and the energy consumption of thesensors showing the advantages of their solution.

Themobile phone is becoming a powerful environmentalsensing unit that represents a strong support for severalapplications domains, ranging from traffic management toadvertisement and social studies. However, the limited bat-tery capacity of mobile devices represents a big obstacle. Thecontribution of M. Boukhechba et al. “Energy Optimizationfor Outdoor Activity Recognition” presented an approachfor the recognition of users’ outdoor activities able to keepmobile resources consumption under control behaving vari-ably in function of users’ behaviors and the remaining batterylevel.

Dedicated sensors can harvest the energy from theenvironment, but they cannot charge and discharge at thesame time. The contribution of X.-F. Zhang and C.-C. Yin“Energy Harvesting and Information Transmission Protocolin Sensors Networks” studied a wireless system under energyharvesting conditions proposing a method to optimize thesystem outage performance via finding the optimal save ratio.

Preprocessing of sensors data is a fundamental step forimproving the quality of the signal to be effectively used inthe following steps of the target application (e.g., image recog-nition, speech recognition, and human computer interactionsystems). More specifically preprocessing for noise reductionhas a paramount importance. Noise in video streams impactnegatively any type of processing activities aimed at detectingobjects. Often object detection uses edge detection as oneof the basic features for the recognition. Considering thehigh computational complexity of applying edge detectionand filtering to 1080p video, hardware implementation onreconfigurable hardware fabric is necessary.The contributionof I. Yoon et al. “Zynq-Based Reconfigurable System for Real-Time EdgeDetection ofNoisy Video Sequences” proposed anembedded system utilizes dynamic reconfiguration featuresof Zynq SoC to perform partial reconfiguration of differentfilter bitstreams during run-time according to the detectednoise density level in the incoming video frames.

Noise estimation algorithms are essential components ofmany modern mobile communications. The contribution ofS. Lee and G. Lee “Noise Estimation and Suppression UsingNonlinear FunctionwithAPriori SpeechAbsence Probabilityin Speech Enhancement” proposed a noise-based compensa-tion of minimum statistics method for speech enhancementin highly nonstationary noisy environments using a nonlin-ear function and a priori speech absence probability. Activenoise cancellation is a technique based on adaptive feed-forward control mostly to superpose an artificial sound to anunwanted disturbance noise. The contribution of Y.-S. Lee etal. “Length Variation Effect of the Impulse Response Modelof a Secondary Path in Embedded Control” investigated onthe length variation effect of the impulse response function

for the secondary path model in active noise control using anembedded control board.

The contribution of S. Ryu and Y.-S. Lee “Characteristicsof Relocated Quiet Zones Using Virtual Microphone Algo-rithm in an Active Headrest System” proposed a theoreticaland experimental investigation on the characteristics of therelocated zone of quiet as effective antinoise solution usingvirtual microphone based FxLMS algorithm suitable to beembedded in a real-time digital controller for an activeheadrest system.

The principal and most challenging requirement forimage enlargement methods in embedded systems is keepinga good balance between performance, low computationalcost, and lowmemory usage.The contribution ofH.Hua et al.“An efficient Image Enlargement Method for Image Sensorsof Mobile in Embedded Systems” proposes an efficient imageenlargement method based on different kinds of features fordifferent morphologies with different approaches and the rel-ative learned dictionaries for efficient image representation.The authors adopt clustering approach and projection matrixon dictionaries for improving speed and memory usage.

Seaming finding is an important step for creatingpanorama images for smoothing the differences observed atboundaries between stitched images. The contribution of J.Jeong and K. Jun “A Novel Seam Finding Method UsingDownscaling and Cost for Image Stitching” proposed animproved seam finding method based on a cost function todetermine how each pixel affects stitching and a downscaledversion of overlapped area to approximate a seam and theninterpolate the seam to the original region.

Privacy-preserving elaboration and transmission of sens-ing data have drawn much attention recently. Encryptionof a digital image is fundamental in applications of bodyarea networks where the captured image may include anumber of privacy issues. Some of the disadvantages of thepast encryption methods are the small key space and lowability of resistance to attacks. The contribution of W. Wanget al. “A Novel Encryption Algorithm Based on DWT andMultichaos Mapping” proposes a two-step discrete wavelettransform-based encryption algorithm enhanced with multi-chaosmatrix.The authors proved that the proposed approachshows large key space, high key sensitivity, and ability ofresistance to attacks.

In addition to encryption of data while in transit, privacy-preserving data queries are crucial in wireless sensor net-work. The contribution of H. Dai et al. “Random SecureComparator Selection Based Privacy-Preserving MAX/MINQuery Processing in Two-Tiered Sensor Networks” proposeda privacy-preserving MAX/MIN query processing approachbased on random secure comparator selection in two-tieredsensor network denoted as RSCS-PMQ.

Context awareness is a fundamental property of a sys-tem expressing the capacity of being aware of its physicalenvironment or situation and responding in a proactiveand intelligent manner. It is fundamental in the context ofwireless sensor network for opening to advanced applicationsinvolving autonomous reasoning and decision-making. Thecontribution of M. Hussain et al. “CRAM: A ConditionedReflex Action inspired Adaptive Model for Context Addition

Journal of Sensors 3

in Wireless Sensor Networks” proposed a context addedsystem in which the actuations once performed by the systemhelp the system itself to internally evolve enriching contextrepository through retrospective contexts and improvingintrospection.

We think that this special issue would shed light onmajor achievements in the area of embedded systems formobile sensors and IoT and attract attention by the scientificcommunity to pursue further investigations leading to therapid development of these crucial technologies.

Acknowledgments

We would like to express our appreciation to all the authorsfor their informative contributions and the reviewers for theirsupport and constructive critiques in making this specialissue possible.

Marco AnisettiValerio BellandiAbdellah Chehri

Yurong QianGwanggil Jeon

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