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Über dieses Buch

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, MobiQuitous 2013, held in Tokyo, Japan, in December 2013. The 67 revised full papers presented were carefully reviewed and selected from 141 submissions. The papers and 2 invited talks cover a wide range of topics such as mobile applications, social networks, networking, data management and services.

Inhaltsverzeichnis

Frontmatter

Main Conference Session

Frontmatter

OPSitu: A Semantic-Web Based Situation Inference Tool Under Opportunistic Sensing Paradigm

Opportunistic sensing becomes a competitive sensing paradigm nowadays. Instead of pre-deploying application-specific sensors, it makes use of sensors that just happen to be available to accomplish its sensing goal. In the opportunistic sensing paradigm, the sensors that can be utilized by a given application in a given time are unpredictable. This brings the Semantic-Web based situation inference approach, which is widely adopted in situation-aware applications, a major challenge, i.e., how to handle uncertainty of the availability and confidence of the sensing data. Although extending standard semantic-web languages may enable the situation inference to be compatible with the uncertainty, it also brings extra complexity to the languages and makes them hard to be learned. Unlike the existing works, this paper developed a situation inference tool, named

OPSitu

, which enables the situation inference rules to be written in the well accepted standard languages such as OWL and SWRL even under opportunistic sensing paradigm. An experiment is also described to demonstrate the validity of

OPSitu

.

Jiangtao Wang, Yasha Wang, Yuanduo He

Model-Driven Public Sensing in Sparse Networks

Public Sensing (PS) is a recent trend for building large-scale sensor data acquisition systems using commodity smartphones. Limiting the energy drain on participating devices is a major challenge for PS, as otherwise people will stop sharing their resources with the PS system. Existing solutions for limiting the energy drain through model-driven optimizations are limited to dense networks where there is a high probability for every point of interest to be covered by a smartphone. In this work, we present an adaptive model-driven PS system that deals with

both

dense and sparse networks. Our evaluations show that this approach improves data quality by up to 41 percentage points while enabling the system to run with a greatly reduced number of participating smartphones. Furthermore, we can save up to 81 % of energy for communication and sensing while providing data matching an error bound of

$$1\,^\circ $$

C up to 96 % of the time.

Damian Philipp, Jarosław Stachowiak, Frank Dürr, Kurt Rothermel

An Integrated WSN and Mobile Robot System for Agriculture and Environment Applications

Agriculture and environment issues are becoming increasingly important and are facing some new challenges. It is believed that wireless Sensor Networks (WSNs) and machine automation are among the key enabling technologies to address these challenging issues. Although extensive research has been conducted on individual technologies, their seamless integration to solve complex environmental problems has not been done before. This paper provides a design concept and some preliminary results for an integrated autonomous monitoring system. The integrated system will provide a powerful and cost-efficient tool for optimal, profitable, and sustainable management of environment and agriculture and thus bring significant social and economic benefits.

Hong Zhou, Haixia Qi, Thomas M. Banhazi, Tobias Low

Sensor Deployment in Bayesian Compressive Sensing Based Environmental Monitoring

Sensor networks play crucial roles in the environmental monitoring. So far, the large amount of resource consumption in traditional sensor networks has been a huge challenge for environmental monitoring. Compressive sensing (CS) provides us a method to significantly decrease the number of sensors needed and Bayesian compressive sensing (BCS) makes it possible to deploy sensors selectively rather than randomly. By deploying sensors to the most informative places, we expect to reduce the reconstruction errors further compared with random sensor deployment. In this paper we employ multiple sensor deployment algorithms and BCS based signal recovery algorithm to build novel environmental monitoring systems, in which the environmental signals can be recovered accurately with undersampled measurements. Besides, we apply these environmental monitoring models to ozone data experiments to evaluate them and compare their performance. The results show a significant improvement in the recovery accuracy from random sensor deployment to selective sensor deployment. With 100 measurements for 16641 data points, the reconstruction error of one of the sensor deployment approaches was 40 % less than that of random sensor deployment, with 3.52 % and 6.08 % respectively.

Chao Wu, Di Wu, Shulin Yan, Yike Guo

A Mobile Agents Control Scheme for Multiple Sinks in Dense Mobile Wireless Sensor Networks

In Mobile Wireless Sensor Networks (MWSNs) where mobile sensor nodes densely exist, it is desirable to gather sensor data from the minimum number of sensor nodes which are necessary to guarantee the sensing coverage in order to reduce communication traffic. In the past, we have proposed a data gathering method using mobile agents in dense MWSNs. However, since this method assumes that only one sink is present in a network, it cannot effectively reduce traffic in environments where multiple sinks exist. In this paper, we propose a mobile agents control scheme which guarantees multiple sinks’ coverages and efficiently gathers sensor data. In the proposed method, mobile agents are communalized if their sensing points overlap, and sensor data are aggregated to transmit them to same direction.

Keisuke Goto, Yuya Sasaki, Takahiro Hara, Shojiro Nishio

Highly Distributable Associative Memory Based Computational Framework for Parallel Data Processing in Cloud

One of the main challenges for large-scale computer clouds dealing with massive real-time data is in coping with the rate at which unprocessed data is being accumulated. In this regard, associative memory concepts open a new pathway for accessing data in a highly distributed environment that will facilitate a parallel-distributed computational model to automatically adapt to the dynamic data environment for optimized performance. With this in mind, this paper targets a new type of data processing approach that will efficiently partition and distribute data for clouds, providing a parallel data access scheme that enables data storage and retrieval by association where data records are treated as patterns; hence, finding overarching relationships among distributed data sets becomes easier for a variety of pattern recognition and data-mining applications. The ability to partition data optimally and automatically will allow elastic scaling of system resources and remove one of the main obstacles in provisioning data centric software-as-a-service in clouds.

Amir Hossein Basirat, Asad I. Khan, Balasubramaniam Srinivasan

MobiPLACE*: A Distributed Framework for Spatio-Temporal Data Streams Processing Utilizing Mobile Clients’ Processing Power

The problem of continuous spatio-temporal queries’ processing was addressed by many papers. Some papers introduced solutions using single server architecture while others using distributed server one. In this paper, we introduce MobiPLACE*, an extension to PLACE* [

13

] system, a distributed framework for spatio-temporal data streams processing exploiting mobile clients’ processing power. We will extend the Query-Track-Participate (QTP) query processing model, introduced as a system architecture in PLACE*, by moving the Query server role to mobile clients. This will reduce memory and processing load on our regional servers in exchange for a little additional communication and memory load on mobile devices. This makes the system more scalable and enhances average query response time. Improvements in mobile devices’ and communication links’ capabilities encouraged us to introduce this extension. In this paper, we will focus on range and k-NN continuous queries and their evaluation on MobiPLACE*. Experimental study is made to compare between MobiPLACE* and PLACE* in terms of server response time and memory.

Victor Zakhary, Hicham G. Elmongui, Magdy H. Nagi

Modelling Energy-Aware Task Allocation in Mobile Workflows

Mobile devices are becoming the platform of choice for both business and personal computing needs. For a group of users to efficiently collaborate over the execution of a set workflow using their mobile devices, the question then arises as to which device should run which task of the workflow and when? In order to answer this question, we study two common energy requirements: in the

minimum group energy cost problem (MGECP)

we build the model as a quadratic 0–1 program and solve the optimisation problem with the objective to minimise the total energy cost of the devices as a group. In the

minimum max-utilisation problem (MMUP)

we aim to improve the fairness of the energy cost within the group of devices and present two adjustment algorithms to achieve this goal. We demonstrate the use of a Mixed Integer Quadratic Programming (MIQP) solver in both problem’s solutions. Simulation result shows that both problems are solved to good standards. Data generated by different workload pattern also give us a good indication of the type of workflow that benefit the most from MMUP. The model used in this work can also be adapted for other energy critical scenarios.

Bo Gao, Ligang He

Recognition of Periodic Behavioral Patterns from Streaming Mobility Data

Ubiquitous location-aware sensing devices have facilitated collection of large volumes of mobility data streams from moving entities such as people and animals, among others. Extraction of various types of periodic behavioral patterns hidden in such large volume of mobility data helps in understanding the dynamics of activities, interactions, and life style of these moving entities. The ever-increasing growth in the volume and dimensionality of such Big Data on the one hand, and the resource constraints of the sensing devices on the other hand, have made not only high pattern recognition accuracy but also low complexity, low resource consumption, and real-timeness important requirements for recognition of patterns from mobility data. In this paper, we propose a method for extracting periodic behavioral patterns from streaming mobility data which fulfills all these requirements. Our experimental results on both synthetic and real data sets confirm superiority of our method compared with existing techniques.

Mitra Baratchi, Nirvana Meratnia, Paul J. M. Havinga

Detection of Real-Time Intentions from Micro-blogs

Micro-blog forums, such as Twitter, constitute a powerful medium today that people use to express their thoughts and intentions on a daily, and in many cases, hourly, basis. Extracting ‘Real-Time Intention’ (RTI) of a user from such short text updates is a huge opportunity towards web personalization and social networking around dynamic user context. In this paper, we propose novel ensemble approaches for learning and classifying RTI expressions from micro-blogs, based on a wide spectrum of linguistic and statistical features of RTI expressions (

viz.

high dimensionality, sparseness of data, limited context, grammatical in-correctness, etc.). We demonstrate our approach achieves significant improvement in accuracy, compared to word-level features used in many social media classification tasks. Further, we conduct experiments to study the run-time performance of such classifiers for integration with a variety of applications. Finally, a prototype implementation using an Android-based user device demonstrates how user context (intention) derived from social media sites can be consumed by novel social networking applications.

Nilanjan Banerjee, Dipanjan Chakraborty, Anupam Joshi, Sumit Mittal, Angshu Rai, B. Ravindran

Fast and Accurate Wi-Fi Localization in Large-Scale Indoor Venues

An interest and development of indoor localization has grown along with the scope of applications. In a large and crowded indoor venue, the population density of access points (APs) is typically much higher than that in small places. This may cause a client device such as a smartphone to capture an

imperfect

Wifi fingerprints (FPs), which is essential piece of data for indoor localization. This is due to the limited access time allocated per channel and collisions of responses from APs. It results in an extended delay for localization and a massive unnecessary traffic in addition to a high estimation error. This paper proposes a fast and accurate indoor localization method for large-scale indoor venues using a small subset of APs, called

representative APs

(rAPs). According to our experimental study in a large venue with 1,734 APs, the proposed method achieves the estimation error of 1.8

$$\sim $$

2.1 m, which can be considered a very competitive performance even in small-scale places with a few hundreds of APs.

Seokseong Jeon, Young-Joo Suh, Chansu Yu, Dongsoo Han

Reality Mining: Digging the Impact of Friendship and Location on Crowd Behavior

Crowd behavior of human deserves to be studied since it is common that people are influenced and change their behavior when being in a group. In pervasive computing research, an amount of work has been directed towards discovering human movement patterns based on wireless networks, mainly focusing on movements of individuals. It is surprising that social interaction among individuals in a crowd is largely neglected. Mobile phones offer on-body tracking and they are already deployed on a large scale, allowing the characterization of user behavior through large amounts of wireless information collected by mobile phones. In this paper, we observe and analyze the impact of friendship and location attributes on crowd behavior, using location-based wireless mobility information. This is a cornerstone for predicting crowd behavior, which can be used in a large number of applications such as crowdsourcing-based technology, traffic management, crowd safety, and infrastructure deployment.

Yuanfang Chen, Antonio M. Ortiz, Noel Crespi, Lei Shu, Lin Lv

Robust Overlay Routing in Structured, Location Aware Mobile Peer-to-Peer Systems

Mobile Peer-to-Peer architectures provide object and service lookup functionality in absence of a preexisting communication infrastructure. Therefore, those architectures can be harnessed in several application scenarios like disaster relief scenarios where no infrastructure can be assumed and mobility is required. Yet, Mobile Peer-to-Peer architectures inherit the vulnerability to routing attacks from the underlying communication technologies. Further, even though many security mechanisms were developed for traditional Peer-to-Peer architectures, those mechanisms cannot be applied without adaptations to Mobile Peer-to-Peer architectures due to the wireless, mobile underlay network. In this paper, we analyze the vulnerability of the overlays routing algorithm of structured, location aware Mobile Peer-to-Peer architectures against a prominent routing attack. Therefore, we discuss and analyze existing security mechanisms that were developed to ensure a reliable routing process of these architectures. Moreover, we validate and adapt analytic models for the routing algorithm and those previously mentioned security mechanisms.

Christian Gottron, Sonja Bergsträßer, Ralf Steinmetz

Crossroads: A Framework for Developing Proximity-based Social Interactions

Proximity-based Social Interaction (PSI) apps are emerging on mobile platforms. While both industries and academic communities have developed frameworks to simplify the PSI app development, our framework, Crossroads, brings a set of features to balance the development overhead and developer expressiveness. We argue that APIs with application hints give developers the expressiveness, and core services (such as virtual links over the star topology) simplify network maintenance. Finally, PSI-specific primitives (such as presence beaconing with interval decaying and group dissemination) improve the energy efficiency. Evaluation results on real smartphones show the energy efficiency gain, topology robustness, and lower group dissemination load.

Chieh-Jan Mike Liang, Haozhun Jin, Yang Yang, Li Zhang, Feng Zhao

Merging Inhomogeneous Proximity Sensor Systems for Social Network Analysis

Proximity information is a valuable source for social network analysis. Smartphone based sensors, like GPS, Bluetooth and ANT

+

, can be used to obtain proximity information between individuals within a group. However, in real-life scenarios, different people use different devices, featuring different sensor modalities. To draw the most complete picture of the spatial proximities between individuals, it is advantageous to merge data from an inhomogeneous system into one common representation. In this work we describe strategies how to merge data from Bluetooth sensors with data from ANT

+

sensors. Interconnection between both systems is achieved using pre-knowledge about social rules and additional infrastructure. Proposed methods are applied to a data collection from 41 participants during an 8 day pilgrimage. Data from peer-to-peer sensors as well as GPS sensors is collected. The merging steps are evaluated by calculating state-of-the art features from social network analysis. Results indicate that the merging steps improve the completeness of the obtained network information while not altering the morphology of the network.

Amir Muaremi, Franz Gravenhorst, Julia Seiter, Agon Bexheti, Bert Arnrich, Gerhard Tröster

Device Analyzer: Understanding Smartphone Usage

We describe Device Analyzer, a robust data collection tool which is able to reliably collect information on Android smartphone usage from an open community of contributors. We collected the largest, most detailed dataset of Android phone use publicly available to date. In this paper we systematically evaluate smartphones as a platform for mobile ubiquitous computing by quantifying access to critical resources in the wild. Our analysis of the dataset demonstrates considerable diversity in behaviour between users but also over time. We further demonstrate the value of handset-centric data collection by presenting case-study analyses of human mobility, interaction patterns, and energy management and identify notable differences between our results and those found by other studies.

Daniel T. Wagner, Andrew Rice, Alastair R. Beresford

Evaluation of Energy Profiles for Mobile Video Prefetching in Generalized Stochastic Access Channels

This paper evaluates the energy cost reduction of Over-The-Top mobile video content prefetching in various network conditions. Energy cost reduction is achieved by reducing the time needed to download content over the radio interface by prefetching data on higher data rates, compared to the standard on demand download. To simulate various network conditions and user behavior, a stochastic access channel model was built and validated using the actual user traces. By changing the model parameters, the energy cost reduction of prefetching in different channel settings was determined, identifying regions in which prefetching is likely to deliver the largest energy gains. The results demonstrate that the largest gains (up to 70 %) can be obtained for data rates with strong correlation and low noise variation. Additionally, based on statistical properties of data rates, such as peak-to-mean and average data rate, prefetching strategy can be devised enabling the highest energy cost reduction that can be obtained using the proposed prefetching scheme.

Alisa Devlic, Pietro Lungaro, Zary Segall, Konrad Tollmar

MITATE: Mobile Internet Testbed for Application Traffic Experimentation

This paper introduces a Mobile Internet Testbed for Application Traffic Experimentation (MITATE). MITATE is the first programmable testbed to support the prototyping of application communications between mobiles and cloud datacenters. We describe novel solutions to device security and resource sharing behind MITATE. Finally, we show how MITATE can answer network performance questions crucial to mobile application design.

Utkarsh Goel, Ajay Miyyapuram, Mike P. Wittie, Qing Yang

Declarative Programming for Mobile Crowdsourcing: Energy Considerations and Applications

This paper introduces

LogicCrowd

, a declarative programming platform for mobile crowdsourcing applications (using social media networks and peer-to-peer networks), developed as an extension of Prolog. We present a study of energy consumption characteristics for our

LogicCrowd

prototype. Based on the measurements, we develop an energy-crowdsourcing consumption model for

LogicCrowd

on the Android platform and also extend the

LogicCrowd

meta-interpreter for computing with an energy budget corresponding to a certain battery lifetime.

Jurairat Phuttharak, Seng W. Loke

Types in Their Prime: Sub-typing of Data in Resource Constrained Environments

Sub-typing of data improves reuse and allows for reasoning at different levels of abstraction; however, it is seldom applied in resource constrained environments. The key reason behind this is the increase in overhead that is caused by including hierarchical information in data types as compared to a flat list. Where hierarchical data typing is used, it is often represented using verbose textual identifiers or numerical encodings that are suboptimal with regards to space. In this paper, we present an encoding function for hierarchically typed information, based on the properties of prime numbers. It provides a compact representation of types, fast subsumption testing even on resource constrained platforms and support for the evolution of the data type hierarchy. We demonstrate the feasibility of our approach on two representative communication models in constrained environments; a publish/subscribe event bus and a RESTful application protocol. We evaluate the performance of our encoding function and show that it has limited overhead compared to a flat list of data types and that this overhead is outweighed by reduced memory and communication overhead once applied.

Klaas Thoelen, Davy Preuveneers, Sam Michiels, Wouter Joosen, Danny Hughes

Privacy-Aware Trust-Based Recruitment in Social Participatory Sensing

The main idea behind social participatory sensing is to leverage social networks as the underlying infrastructure for recruiting social friends to participate in a sensing campaign. Such recruitment requires the transmission of messages (i.e., tasks and contributions) between the requester and participants via routes consisting of social links. When selecting the routes, the recruitment scheme should consider two fundamental factors. The first factor is the level of trustworthiness of a route, which evaluates its reliability to ensure that the integrity of the message is preserved. The second factor is the privacy level of the route, which measures information leakage in the form of disclosure of private information contained in the message by intermediate nodes. The best route will be the route with maximum credibility, i.e., highest trust score and lowest likelihood of privacy breach. In this paper, we propose a privacy-preserving trust-based recruitment framework which is aimed at finding the best route from the requester to the selected participants. We propose to quantify the privacy score of a route by utilising the concept of entropy to measure the level of privacy breach in each intermediate node along the route. The trust score of the route is obtained by multiplying the mutual trust rates of all links along the route. Simulation results demonstrate the efficacy of our framework in terms of recruiting suitable participants through the most secure and trustable routes.

Haleh Amintoosi, Salil S. Kanhere

Privacy-Preserving Calibration for Participatory Sensing

By leveraging sensors embedded in mobile devices, participatory sensing tries to create cost-effective, large-scale sensing systems. As these sensors are heterogeneous and low-cost, regular calibration is needed in order to obtain meaningful data. Due to the large scale, on-the-fly calibration utilizing stationary reference stations is preferred. As calibration can only be performed in proximity of such stations, uncalibrated measurements might be uploaded at any point in time. From the data quality perspective, it is desirable to apply backward calibration for already uploaded values as soon as the device gets calibrated. To protect the user’s privacy, the server should not be able to link all user measurements. In this paper, we therefore present a privacy-preserving calibration mechanism that enables both forward and backward calibration. The latter is achieved by transferring calibration parameters to already uploaded measurements without revealing the connection between the individual measurements. We demonstrate the feasibility of our approach by means of simulation.

Kevin Wiesner, Florian Dorfmeister, Claudia Linnhoff-Popien

Complexity of Distance Fraud Attacks in Graph-Based Distance Bounding

Distance bounding

(DB) emerged as a countermeasure to the so-called

relay attack

, which affects several technologies such as RFID, NFC, Bluetooth, and Ad-hoc networks. A prominent family of DB protocols are those based on graphs, which were introduced in 2010 to resist both mafia and distance frauds. The security analysis in terms of distance fraud is performed by considering an adversary that, given a vertex labeled graph

$$G = (V, E)$$

and a vertex

$$v \in V$$

, is able to find the most frequent

$$n$$

-long sequence in

$$G$$

starting from

$$v$$

(MFS problem). However, to the best of our knowledge, it is still an open question whether the distance fraud security can be computed considering the aforementioned adversarial model. Our first contribution is a proof that the MFS problem is NP-Hard even when the graph is constrained to meet the requirements of a graph-based DB protocol. Although this result does not invalidate the model, it does suggest that a

too-strong

adversary is perhaps being considered (

i.e.

, in practice, graph-based DB protocols might resist distance fraud better than the security model suggests.) Our second contribution is an algorithm addressing the distance fraud security of the tree-based approach due to Avoine and Tchamkerten. The novel algorithm improves the computational complexity

$$O(2^{2^n+n})$$

of the naive approach to

$$O(2^{2n}n)$$

where

$$n$$

is the number of rounds.

Rolando Trujillo-Rasua

Protecting Movement Trajectories Through Fragmentation

Location-based applications (LBAs) like geo-social networks, points of interest finders, and real-time traffic monitoring applications have entered people’s daily life. Advanced LBAs rely on location services (LSs) managing movement trajectories of multiple users in a scalable fashion. However, exposing trajectory information raises user privacy concerns, in particular if LSs are non-trusted. For instance, an attacker compromising an LS can use the retrieved user trajectory for stalking, mugging, or to trace user movement. To limit the misuse of trajectory data, we present a new approach for the secure management of trajectories on non-trusted servers. Instead of providing the complete trajectory of a user to a single LS, we split up the trajectory into a set of fragments and distribute the fragments among LSs of

different

providers. By distributing fragments, we avoid a single point of failure in case of compromised LSs, while different LBAs can still reconstruct the trajectory based on user-defined access rights.

In our evaluation, we show the effectiveness of our approach by using real world trajectories and realistic attackers using map knowledge and statistical information to predict and reconstruct the user’s movement.

Marius Wernke, Frank Dürr, Kurt Rothermel

Trust-Based, Privacy-Preserving Context Aggregation and Sharing in Mobile Ubiquitous Computing

In ubiquitous computing environments, we are surrounded by significant amounts of context information about our individual situations and the situations we share with others around us. Along with the widespread emergence of ubiquitous computing and the availability of context information comes threats to personal privacy that result from sharing information about ourselves with others in the vicinity. We define an individual’s context to be a potentially private piece of information. Given the individual context of multiple participants, one can compute an

aggregate

context that represents a shared state while at the same time preserves individual participants’ privacy. In this paper, we describe three approaches to computing an aggregate measure of a group’s context while maintaining a balance between the desire to share information and the desire to retain control over private information. Our approaches allow dynamic tuning of information release according to

trust levels

of the participants within communication range. By evaluating our approaches through simulation, we show that sharing aggregate context can significantly increase the rate at which a group of co-located users learns an aggregate measure of their shared context. Further, our approaches can accomplish high quality context sharing even in situations with low levels of trust, assuming the availability of a small number of highly trustworthy partners.

Michael Xing, Christine Julien

A Novel Approach for Addressing Wandering Off Elderly Using Low Cost Passive RFID Tags

Wandering (e.g. elopement) by elderly persons at acute hospitals and nursing homes poses a significant problem to providing patient care as such incidents can lead to injury and even accidental morbidity. These problems are particularly serious given aging populations around the world. While various technologies exit (such as door alarms), they are expensive and the reliability of such systems have not been evaluated in the past. In this article we propose two novel methods for a very low cost solution to address the problem of wandering off patients using body worn low cost passive Radio Frequency Identification (RFID) tags using phase based measurements. Our approach requires no modification to the air interface protocols, firmware or hardware. Results from extensive experiments show that: (i) the proposed algorithms can accurately identify whether a person is moving into or out of, for example, a room; and (ii) it can be implemented in real-time to develop a low cost wandering off alarm.

Mingyue Zhou, Damith C. Ranasinghe

Focus and Shoot: Efficient Identification Over RFID Tags in the Specified Area

In RFID systems, the reader usually identifies all the RFID tags in the interrogation region with the maximum power. However, some applications may only need to identify the tags in a specified area, which is usually smaller than the reader’s default interrogation region. In this paper, we respectively present two solutions to identify the tags in the specified area. The principle of the solutions can be compared to the picture-taking process of a camera. It first focuses on the specified area and then shoots the tags. The design of the two solutions is based on the extensive empirical study on RFID tags. Realistic experiment results show that our solutions can reduce the execution time by

$$46\,\%$$

compared to the baseline solution.

Yafeng Yin, Lei Xie, Jie Wu, Athanasios V. Vasilakos, Sanglu Lu

Middleware – Software Support in Items Identification by Using the UHF RFID Technology

This article deals with RFID technology, which is a part of automatic identification and data capture. Nowadays, the identification of items in logistic sector is carried through barcodes. In this article we would like to specify, how items can be located in metal container, identified in the transmission process of logistics chain by UHF RFID technology. All results are verified by measurement in our AIDC laboratory, which is located at the University of Žilina. Our research contains of 12 different types of orientation tags and antennas and more than 1000 tests. Our identification performance was close to 100 %. All tested items have been located in metal container. The results of our research bring the new point of view and indicate the ways of using UHF RFID technology in logistic applications. The utilization of the RFID technology in logistics chain is characterized at the end of this article.

Peter Kolarovszki, Juraj Vaculík

A Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns

Much work have been done in activity recognition using wearable sensors organized in a body sensor network. The quality and communication reliability of the sensor data much affects the system performance. Recent studies show the potential of using RFID radio information instead of sensor data for activity recognition. This approach has the advantages of low cost and high reliability. Radio-based recognition method is also amiable to packet loss and has the advantages including MAC layer simplicity and low transmission power level. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system using passive tags which are smaller and more cost-effective to recognize human activities in real-time. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address two issues - the false negative issue of tag readings and tag/antenna calibration, and design a fast online recognition system. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6 % with a latency of 5 s.

Liang Wang, Tao Gu, Hongwei Xie, Xianping Tao, Jian Lu, Yu Huang

Evaluation of Wearable Sensor Tag Data Segmentation Approaches for Real Time Activity Classification in Elderly

The development of human activity monitoring has allowed the creation of multiple applications, among them is the recognition of high falls risk activities of older people for the mitigation of falls occurrences. In this study, we apply a graphical model based classification technique (conditional random field) to evaluate various sliding window based techniques for the real time prediction of activities in older subjects wearing a passive (batteryless) sensor enabled RFID tag. The system achieved maximum overall real time activity prediction accuracy of

$$95\,\%$$

using a time weighted windowing technique to aggregate contextual information to input sensor data.

Roberto Luis Shinmoto Torres, Damith C. Ranasinghe, Qinfeng Shi

MobiSLIC: Content-Aware Energy Saving for Educational Videos on Mobile Devices

We present a context-aware system that simultaneously increases energy-efficiency and readability for educational videos on smartphones with OLED displays. Our system analyzes the content of each frame of the video and intelligently modifies the colors and presentations of specific regions of the frame to drastically reduce display energy consumption while retaining relevant content of the lecture video. We achieve this by leveraging the mapping between frames and electronic versions of slides used in the lecture. This enables separate manipulation of the slide area and the background. Further, since the slides can themselves be analyzed for content (e.g. text and images within) this approach provides substantive control over energy use and user experience. We evaluate the system using extensive energy measurements performed on phones using two different display technologies. Our method was able to reduce energy usage up to 59.2 % of the energy used by the display which amounts to 27 % of the total energy used by the device.

Qiyam Tung, Maximiliano Korp, Chris Gniady, Alon Efrat, Kobus Barnard

An Un-tethered Mobile Shopping Experience

Smart phones with access to apps from online stores are ideal candidates to replace expensive hardware like PoS terminals for retail. A standard set of shopper and retailer apps can replace the conventional retailer IT setup in settings ranging from rural areas with low connectivity to dense urban areas. We describe how we built such a set of apps for mobile shoppers and retailers equipped only with smart phones and tablets, and who require little to no training to use them. These apps are flexible enough to be used by small shops with small inventories as well as large grocery chains. Our apps enable retailers to manage their inventories and finances, and shoppers to discover retailers, match shopping lists, and make purchases. We describe a user study of retailers in North India to understand the ecosystem in emerging markets, and ascertain their needs, helping us build a useful platform for mobile shopping.

Venkatraman Ramakrishna, Saurabh Srivastava, Jerome White, Nitendra Rajput, Kundan Shrivastava, Sourav Bhattacharya, Yetesh Chaudhary

Gestyboard BackTouch 1.0: Two-Handed Backside Blind-Typing on Mobile Touch-Sensitive Surfaces

This paper presents a new and innovative gesture-based text-input concept designed for high-performance blind-typing on mobile devices with a touch-sensitive surface on their back-side. This concept is based on the Gestyboard concept which has been developed by the Technische Universität München for stationary use on larger multi-touch devices like tabletop surfaces. Our new mobile concept enables the user to type text on a tablet device while holding it in both hands, such as the thumbs are in the front of the tablet and the other eight fingers are in the back. The user can hence type text using these fingers on the back of the device. Although, the gesture-based finger movements are quite unfamiliar and the participants need to mentally rotate the QWERTY layout by

$$-90$$

and 90 degrees respectively, our multi-session evaluation shows that despite the fact that their fingers are occluded by the tablet, our concept enables the users to blind-type and that they improve their performance in each session. Consequently, the user can use all ten fingers simultaneously to type text on a mobile touchscreen device while holding it comfortably in both hands. This implies that our concept has a high potential to yield to an high-performance text-input concept for mobile devices in the near future.

Tayfur Coskun, Christoph Bruns, Amal Benzina, Manuel Huber, Patrick Maier, Marcus Tönnis, Gudrun Klinker

Passive, Device-Free Recognition on Your Mobile Phone: Tools, Features and a Case Study

We investigate the detection of activities and presence in the proximity of a mobile phone via the WiFi-RSSI at the phone. This is the first study to utilise RSSI in received packets at a mobile phone for the classification of activities. We discuss challenges that hinder the utilisation of WiFi PHY-layer information, recapitulate lessons learned and describe the hardware and software employed. Also, we discuss features for activity recognition (AR) based on RSSI and present two case studies. We make available our implemented tools for AR based on RSSI.

Stephan Sigg, Mario Hock, Markus Scholz, Gerhard Tröster, Lars Wolf, Yusheng Ji, Michael Beigl

AcTrak - Unobtrusive Activity Detection and Step Counting Using Smartphones

In this paper we introduce “AcTrak”, a system that provides training-free and orientation-and-placement-independent step-counting and activity recognition on commercial mobile phones, using only 3D accelerometer. The proposed solution uses “step-frequency” as a feature to classify various activities. In order to filter out noise generated due to normal handling of the phone, while the user is otherwise physically stationary, AcTrak is armed with a novel algorithm for step validation termed as Individual Peak Analysis (IPA). IPA uses peak-height and inter-peak interval as features. AcTrak provides realtime step count. It also classifies current activity, and tags each activity with the associated steps, resulting in a detailed analysis of activity recognition. Using our model, a step-count accuracy of 98.9 % is achieved. Further, an accuracy of 95 % is achieved when classifying stationary, walking and running/jogging. When brisk-walking is added to the activity set, still a reasonable level of accuracy is achieved. Since AcTrak is largely orientation and position agnostic, and requires no prior training, this makes our approach truly ubiquitous. Classification of step-based activity is done as walking, brisk-walking and running (includes jogging). So, after a session of workout, the subject can easily self-assess his/her accomplishment.

Vivek Chandel, Anirban Dutta Choudhury, Avik Ghose, Chirabrata Bhaumik

Practical Image-Enhanced LBS for AR Applications

We have designed a multisensor indoor LBS suitable for augmented reality applications which, mainly based on computer vision techniques, provides precise estimations of both the 3D position and rotation of the device. Our proposal makes use of state-of-the-art IMU data processing techniques during the training phase in order to reliably generate a 3D model of the targeted environment, thus solving typical scalability issues related to visually repetitive structures in large indoor scenarios. A very efficient camera resection technique will then be used in the on-line phase, able to provide accurate 6 degrees of freedom estimations of the device position, with mean errors in the order of 5 cm and response times below 250 ms.

Antonio J. Ruiz-Ruiz, Pedro E. Lopez-de-Teruel, Oscar Canovas

Appstrument - A Unified App Instrumentation and Automated Playback Framework for Testing Mobile Applications

Mobile Test Automation is gaining significant importance for an app-tester because it helps to alleviate the voluminous effort and time associated in thoroughly testing an application. Challenges like diversity in mobile hardware, multiple operating systems, ever-increasing application complexity and high volume of test cases etc. reiterate the importance of exploiting automation techniques for mobile application testing. In order to exhaustively capture user actions during the record-phase, faithfully reproduce those actions during playback-phase and also to capture the relevant metrics while playing back, instrumentation of the

Application-under-test

(AUT) becomes an imperative process. However, the type and level of instrumentation is different and is very specific to the category of testing which has to be automated. This paper presents

Appstrument

, a unified framework for instrumenting mobile applications to make them ready for functional, performance and accessibility testing. This framework allows instrumenting the application to get it ready for either a single category of testing or a combination of two or more of these categories, with multiple optional features for each category. In addition to this, given a test script, the framework also supports automated playback of instrumented applications.

Appstrument

has been deployed and tested against some popular applications from

Google Play

(Android apps) and some IBM in-house iOS applications. Results indicate that this framework is able to successfully instrument a sizeable number of applications and effectively playback user-defined test cases automatically to collect relevant metrics/results corresponding to each category of testing.

Vikrant Nandakumar, Vijay Ekambaram, Vivek Sharma

A Layered Secret Sharing Scheme for Automated Profile Sharing in OSN Groups

We propose a novel Layered secret sharing scheme and its application to Online Social Networks (OSNs). In current, commercially offered OSNs, access to users’ profile information is managed by the service provider e.g. Facebook or Google+, based on the user defined privacy settings. A limited set of rules such as those governing the creation of groups of friends as defined by the user (e.g. circles, friend groups or lists) allow the users to define different levels of privacy, however they are arguably complex and rely on a trusted third party (the service provider) to ensure compliance. The proposed scheme enables automated profile sharing in OSN groups with fine grained privacy control, via a multi-secret sharing scheme comprising layered shares, created from user’s profile attributes (multiple secrets), that are distributed to group members; with no reliance on a trusted third party. The scheme can be implemented via e.g. a browser plugin, enabling automation of all operations for OSN users. We study the security of the scheme against attacks aiming to acquire knowledge about user’s profile. We also provide a theoretical analysis of the resulting level of protection for specific (privacy sensitive) attributes of the profile.

Guillaume Smith, Roksana Boreli, Mohamed Ali Kaafar

Distributed Key Certification Using Accumulators for Wireless Sensor Networks

In this work, we propose a key certification protocol for wireless sensor networks that allows nodes to autonomously exchange their public keys and verify their authenticity using one-way accumulators. We examine and compare different accumulator implementations for our protocol on the Sun SPOT platform. We observe that our protocol performs best with accumulators based on Elliptic Curve Cryptography (ECC): ECC-based accumulators have roughly the same speed as Secure Bloom filters, but they have a smaller memory footprint.

Jun-Young Bae, Claude Castelluccia, Cédric Lauradoux, Franck Rousseau

On Malware Leveraging the Android Accessibility Framework

The number of Android malware has been increasing dramatically in recent years. Android malware can violate users’ security, privacy and damage their economic situation. Study of new malware will allow us to better understand the threat and design effective anti-malware strategies. In this paper, we introduce a new type of malware exploiting Android’s accessibility framework and describe a condition which allows malicious payloads to usurp control of the screen, steal user credentials and compromise user privacy and security. We implement a proof of concept malware to demonstrate such vulnerabilities and present experimental findings on the success rates of this attack. We show that 100 % of application launches can be detected using this malware, and 100 % of the time a malicious Activity can gain control of the screen. Our major contribution is two-fold. First, we are the first to discover the category of new Android malware manipulating Android’s accessibility framework. Second, our study finds new types of attacks and complements the categorization of Android malware by Zhou and Jiang [

21

]. This prompts the community to re-think categorization of malware for categorizing existing attacks as well as predicting new attacks.

Joshua Kraunelis, Yinjie Chen, Zhen Ling, Xinwen Fu, Wei Zhao

Safe Reparametrization of Component-Based WSNs

Modern Wireless Sensor Networks are moving from singe-purpose custom built solutions towards multi-purpose application hosting platforms. These platforms support multiple concurrent applications managed by multiple actors. Reconfigurable component-models are a viable solution for supporting these scenarios by reducing management and development overhead while promoting software reuse. However, implicit parameter dependencies spanning component compositions make reconfiguration complex and error-prone. This paper proposes composition-safe reparametrization of components. This is accomplished by offering language annotations that allow component developers to make dependencies explicit and network protocols to resolve and enforce parameter constraints. Our approach greatly simplifies reparametrization while imposing minimal runtime overhead.

Wilfried Daniels, Pedro Javier del Cid Garcia, Wouter Joosen, Danny Hughes

Toward Agent Based Inter-VM Traffic Authentication in a Cloud Environment

Ubiquitous simply means being everywhere. The concept of Cloud Computing (CC) further strengthens the idea of Ubiquitous computing. On the other hand, one of the key enablers of CC is Virtualization. However, with the many advantages of virtualization comes certain limitations, especially related to security. Virtualization vulnerabilities and more specifically isolation, creates new targets for intrusion due to the complexity of access and difficulty in monitoring all interconnection points between systems, applications, and data sets. Hence, without strict control put in place within the Cloud, guests could violate and bypass security policies, intercept unauthorized client data, and initiate or become the target of security attacks. This article discusses the security and the visibility issues of inter-VM traffic, by proposing a solution for it within the Cloud context. The proposed approach provides Virtual Machines (VMs) authentication, communication integrity, and enforces trusted transactions, through security mechanisms, structures, policies, and various intrusion detection techniques.

Benzidane Karim, Saad Khoudali, Abderrahim Sekkaki

Adaptive Wireless Networks as an Example of Declarative Fractionated Systems

Adaptive wireless networks can morph their topology and support information gathering and delivery activities to follow high-level goals that capture user interests. Using a case study of an adaptive network consisting of smart phones, robots, and UAVs, this paper extends a declarative approach to networked cyber-physical systems to incorporate quantitative aspects. This is done by distinguishing two levels of control. The temporal evolution of the macroscopic system state is controlled using a logical framework developed in earlier work while the microscopic state is controlled by an optimization algorithm or heuristic. This two-level declarative approach is built on top of a partially-ordered knowledge sharing model for loosely coupled distributed computing and is an example of a so-called fractionated system that can operate with any number of wireless nodes and quickly adapt to changes. Feasibility of the approach is demonstrated simulation and in a hybrid cyber-physical testbed consisting of robots, quadcopters, and Android devices.

Jong-Seok Choi, Tim McCarthy, Minyoung Kim, Mark-Oliver Stehr

Elastic Ring Search for Ad Hoc Networks

In highly dynamic mobile ad hoc networks, new paths between nodes can become available in a short amount of time. We show how to leverage this property in order to efficiently search for paths between nodes using a technique we call elastic ring search, modeled after the popular expanding ring search. In both techniques, a node searches up to a certain number of hops, waits long enough to know if a path was found, and searches again if no path was found. In elastic ring search, the delays between search attempts are long enough for shorter paths to become available, and therefore the optimal sequence of search extents may increase and even decrease. In this paper, we provide a framework to model this network behavior, define two heuristics for optimizing elastic ring search sequences, and show that elastic ring search can incur significantly lower search costs than expanding ring search.

Simon Shamoun, David Sarne, Steven Goldfeder

Suitability of a Common ZigBee Radio Module for Interaction and ADL Detection

In this contribution we analyze whether it is possible to use a ZigBee module to detect interactions. The detection is done using modules with adjustable communication range. From the data we want to draw conclusions about the activities of daily living (ADL). This is important to detect because small changes in behavior which might indicate the beginning of dementia or a mild cognitive impairment. We have already done promising experiments with a radio module. In this paper we analyze whether it is also possible to use a common ZigBee module. Therefore we compare it with the module we used already and then modify the antenna to shorten the range. Our findings show that it is possible to use the ZigBee module for interaction and ADL detection by adjustable range after modifications in the antenna circuit.

Jakob Neuhaeuser, Tim C. Lueth, Lorenzo T. D’Angelo

The Need for QoE-driven Interference Management in Femtocell-Overlaid Cellular Networks

Under the current requirements for mobile, ubiquitous and highly reliable communications, internet and mobile communication technologies have converged to an all-Internet Protocol (IP) packet network. This technological evolution is followed by a major change in the cellular networks’ architecture, where the traditional wide-range cells (macrocells) coexist with indoor small-sized cells (femtocells). A key challenge for the evolved heterogeneous cellular networks is the mitigation of the generated interferences. In the literature, this problem has been thoroughly studied from the Quality of Service (QoS) point of view, while a study from the user’s satisfaction perspective, described under the term “Quality of Experience (QoE)”, has not received enough attention yet. In this paper, we study the QoE performance of VoIP calls in a femto-overlaid Long Term Evolution – Advanced (LTE-A) network and we examine how QoE can drive a power controlled interference management scheme.

Dimitris Tsolkas, Eirini Liotou, Nikos Passas, Lazaros Merakos

Modeling Guaranteed Delay of Virtualized Wireless Networks Using Network Calculus

Wireless network virtualization is an emerging technology that logically divides a wireless network element, such as a base station (BS), into multiple slices with each slice serving as a standalone virtual BS. In such a way, one physical mobile wireless network can be partitioned into multiple virtual networks each operating as an independent wireless network. Wireless virtual networks, as composed of these virtual BSs, need to provide quality of service (QoS) to mobile end user services. One such key QoS parameter is network delay, in particular upper bound delay. This paper presents a delay model for such a wireless virtual network. This delay model considers resources (in particular queues) of both physical nodes and virtual nodes and provides a realistic modelling of the delay behaviours of wireless virtual networks. Network calculus, which usually provides finer insight into a system, is utilized to fulfil the modelling task. The numerical results have shown the effectiveness of the proposed model. The model is useful for both off-line network planning and online network admission control.

Jia Liu, Lianming Zhang, Kun Yang

A Data Distribution Model for Large-Scale Context Aware Systems

Very large scale context aware systems are becoming a reality with the emerging paradigms such as machine-to-machine communications, crowdsensing, etc. Scalable data distribution is a critical requirement in such large scale systems for optimal usage of computing and communication resources. In this paper, we present a novel theoretical model for middleware design for such large-scale context aware systems that distributes only relevant data based on its

effective utility

. We also present extensive experimental results to validate the efficacy of our proposed model.

Soumi Chattopadhyay, Ansuman Banerjee, Nilanjan Banerjee

EduBay: A Mobile-Based, Location-Aware Content Sharing Platform

Most natural sciences streams (zoology, botany, anthropology, agriculture etc.) require collecting a lot of artifacts from natural environments to understand their properties such as shape, size and appearance. Currently this is done through what are called “field trips” where students and researchers go to different places and collect/study specimens from that unique habitat. These field trips are mostly very notional since logistically and monetarily they are not very scalable. The interesting locations for field trips may also be quite far and remote from the current user location. We present in this work a location based content brokering platform EduBay where we bring together the

creators

of content (researchers, students or local people who get photographs, videos of the specimens; audio such as bird sounds etc.) and the

consumers

of the content (fellow students and researchers who need these artifacts for their studies). EduBay is built around the mobile platform since collecting audio, video, or photographs from natural surroundings can leverage the camera, microphone, and other sensors on the mobile device. The platform strongly leverages the location sensor on the mobile device to connect a content to its creation location since most of these interesting artifacts can sometimes be found only in some unique locations. The platform presents interesting location centric views to the creator as well as to the consumer for easy discoverability and searching of the content. The platform also provides a mechanism to validate new content or ascertain the value of a content using peer group crowdsourcing. To keep the creation and consumption of the content under balance, the platform keeps check on the creation and consumption behavior of each user and incentivizes them to create good content as much as they consume them.

Amit M. Mohan, Prasenjit Dey, Nitendra Rajput

Enhancing Context-Aware Applications Accuracy with Position Discovery

Detecting user context with high accuracy using smartphone sensors is a difficult task. A key challenge is dealing with the impact of different smartphone positions on sensor values. Users carry their smartphones in different positions such as holding in their hand or keeping inside their pants or jacket pocket, and each of these smartphone positions affects various sensor values in different ways. This paper addresses the issue of poor accuracy in detecting user context due to varying smartphone positions. It describes the design and prototype development of a smartphone position discovery service that accurately detects a smartphone position, and then demonstrates that the accuracy of an existing context aware application is significantly enhanced when run in conjunction with this proposed smartphone position discovery service.

Khaled Alanezi, Shivakant Mishra

How’s My Driving? A Spatio-Semantic Analysis of Driving Behavior with Smartphone Sensors

Road accident is one of the major reasons for loss of human lives, especially in developing nations with poor road infrastructure and a driver needs to constantly negotiate with several adverse conditions to ensure safety. In this paper, we study several such adverse conditions that are relevant to safe driving and propose a novel method for identifying them as well as characterizing driving behavior for such conditions. Experimental results reveal that our proposed methodology is promising and more flexible than prior work in this area. In particular, our prediction results reveal that our methodology is an aggressive one where most of the bad driving behaviors are determined at the cost of a few instances of good behavior being falsely characterized as bad ones.

Dipyaman Banerjee, Nilanjan Banerjee, Dipanjan Chakraborty, Aakash Iyer, Sumit Mittal

Impact of Contextual Factors on Smartphone Applications Use

The development of methodologies and techniques to evaluate smartphones usability is an emerging topic in the scientific community and triggers discussions about which methodology is most appropriate. The lack of consensus is due to the inherent difficulty on capturing context data in the scenarios where the experiments take place and on relating them to the results found. This work aims to correlate potential usability problems in mobile applications with contextual factors that may occur during users’ interactions on different devices, such as luminosity, device screen resolution, and the user’s activity while interacting with the application. The methodology applied to carry out a field experiment take the following steps: identification of contextual factors that may influence users’ interaction; use of the UXEProject infrastructure to support the automatic capture of applications’ context data; implementation of long term experiments with real users using three different mobile applications over almost one year period. In this paper, we present and discuss the results obtained during this study.

Artur H. Kronbauer, Celso A. S. Santos

Short-Paper Session

Frontmatter

A Highly Accurate Method for Managing Missing Reads in RFID Enabled Asset Tracking

RFID based tracking systems have to overcome some significant challenges such as uncertainty to improve accuracy. We describe a highly accurate and scalable location tracking algorithm achieved by integrating an object compression technique with particle filtering.

Rengamathi Sankarkumar, Damith Ranasinghe, Thuraiappah Sathyan

A New Method for Automated GUI Modeling of Mobile Applications

It is often necessary to construct GUI models for automated testing of event-driven GUI applications, so test cases can be generated by traversing the GUI models systematically. It is, however, difficult to apply traditional modeling techniques directly for mobile platforms as common static models cannot reflect application behaviors under different contexts. To address these challenges, we propose a novel approach for automated GUI modeling of mobile applications and introduce our unique definition of GUI state equivalence, which can reduce state space and facilitate model merging. The proposed modeling method can already discover subtle implementation issues. Real-world case studies show that the proposed approach is effective for adaptive GUI modeling on the Android platform.

Jing Xu, Xiang Ding, Guanling Chen, Jill Drury, Linzhang Wang, Xuandong Li

Towards Augmenting Legacy Websites with Context-Awareness

Emerging context frameworks enable Websites to interact with the Internet of Things directly from the browser; however, Websites must be specifically designed to utilize such context framework support. As such, the majority of “legacy” Websites remains context-unaware. This paper presents an open approach for dynamically injecting context-awareness capabilities into legacy Websites on-demand, without requiring browser extensions, proxies or Website reengineering. Towards this end, we developed an extensible Bookmarklet framework that serves as a conduit between the user’s browser and a server-side repository of enhancement plug-ins, which can used to dynamically augment any 3

rd

party Website with new content, adapted behavior and context framework support.

Darren Carlson, Lukas Ruge

Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks

We present and evaluate a procedure that utilizes mobility and throughput prediction to prefetch video streaming data in integrated cellular and WiFi networks. The effective integration of such heterogeneous wireless technologies will be significant for supporting high performance and energy efficient video streaming in ubiquitous networking environments. Our evaluation is based on trace-driven simulation considering empirical measurements and shows how various system parameters influence the performance, in terms of the number of paused video frames and the energy consumption. The proposed approach has been implemented in an Android-based prototype.

Vasilios A. Siris, Maria Anagnostopoulou, Dimitris Dimopoulos

Integration and Evolution of Data Mining Models in Ubiquitous Health Telemonitoring Systems

Ubiquitous Health Telemonitoring Systems collect low level data with the aim to ameliorate the health condition of patients. Models from data mining are created to compute indicators regarding their status and activity (habits, abnormalities). Models can also help generate feedbacks and recommendations for patients as well as for remote formal and informal care givers. Essential features are that the models can be easily updated whenever new information is available and that data generated from the models can be readily accessible as well as sensed data. This paper addresses the challenge of conveniently incorporating in a Ubiquitous Health Telemonitoring System the creation, the use, and the updating of data mining models. We conducted first runs and generated results showing the feasibility as well as the effectiveness of the system.

Vladimer Kobayashi, Pierre Maret, Fabrice Muhlenbach, Pierre-René Lhérisson

ITS-Light: Adaptive Lightweight Scheme to Resource Optimize Intelligent Transportation Tracking System (ITS) – Customizing CoAP for Opportunistic Optimization

In this paper we aim to reduce overall resource usage and improve throughput of an intelligent transportation tracking application. Primary improvements in terms of bandwidth and latency are achieved using CoAP (Constrained Application Protocol). We propose a novel approach to adapt CoAP’s reliability mode for data transfer by inferring vehicle’s motion-state from tracking information. Further, we make salient modifications in protocol to achieve even better optimization and improved throughput and scalability while using non-reliable data transfer mode of CoAP. Proposed scheme is analyzed based on results obtained both in real and emulated environments.

Abhijan Bhattacharyya, Soma Bandyopadhyay, Arpan Pal

MELON: A Persistent Message-Based Communication Paradigm for MANETs

In this paper we introduce MELON, a new communication paradigm tailored to mobile ad hoc networks, based on novel interactions with a distributed shared message store. MELON provides remove-only, read-only, and private messages, as well as bulk message operations. The dynamic nature of MANETs is addressed with persistent messages, completely distributed message storage, and flexible communication patterns. We quantitatively compare a prototype implementation of MELON to existing paradigms to show its feasibility as the basis for new MANET applications. Experiments demonstrate 40 % better throughput on average than traditional paradigms, as well as 70 % faster local insertion and removal operations compared to an existing tuple space library.

Justin Collins, Rajive Bagrodia

MVPTrack: Energy-Efficient Places and Motion States Tracking

Contextual information such as a person’s meaningful places (Different from a person’s location (raw coordinates), place is an indoor or outdoor area where a person usually conducts some activity, in other words where it is meaningful to the person, such as home, office rooms, restaurants etc.) could provide intelligence to many smartphone apps. However, acquiring this context attribute is not straightforward and could easily drain the battery. In this paper, we propose M(Move)V(Vehicle)P(Place)Track, a continuous place and motion state tracking framework with a focus on improving the energy efficiency of place entrance detection through two techniques: (1) utilizing the mobility change not only for finding the sleeping opportunities for the high energy sensors, but also for providing hint for place entrance detection, (2) leveraging the place history for fast place entrance detection. We evaluated MVPTrack based on traces collected by five persons over two weeks. The evaluation results showed that MVPTrack used 58 % less energy than previous work and provided a much faster place entrance detection approach.

Chunhui Zhang, Ke Huang, Guanling Chen, Linzhang Wang

Neighbourhood-Pair Attack in Social Network Data Publishing

Vertex re-identification is one of the significant and challenging problems in social network. In this paper, we show a new type of vertex re-identification attack called neighbourhood-pair attack. This attack utilizes the neighbourhood topologies of two connected vertices. We show both theoretically and empirically that this attack is possible on anonymized social network and has higher re-identification rate than the existing structural attacks.

Mohd Izuan Hafez Ninggal, Jemal H. Abawajy

On-demand Mobile Charger Scheduling for Effective Coverage in Wireless Rechargeable Sensor Networks

In this paper, we consider the problem of scheduling mobile chargers (MCs) in an on-demand way to maximize the covering utility (CU) in wireless rechargeable sensor networks (WRSNs), while nearly all previous related works assume the MCs move along predefined paths with perfect priori information. And the CU is defined to quantify the effectiveness of event monitoring. We propose three heuristics for this problem after proving its NP-Completeness. Finally we evaluate our solutions through extensive trace-driven simulations.

Lintong Jiang, Haipeng Dai, Xiaobing Wu, Guihai Chen

Tailoring Activity Recognition to Provide Cues that Trigger Autobiographical Memory of Elderly People

About a 19 % of elderly population is associated with poor performance in assessments of memory; the phenomenon is known as Age-related Memory Impairment (AMI). Lifelogging technologies can contribute to compensate for memories deficits. However, no matter how functional technology is, older people will not use it if they perceive it as intrusive or embarrassing. This paper shows our work to tailor current activity recognition techniques (based on Emerging Patterns) to provide value for AMI people from RFID reading and GPS positioning. Evaluation shows (1) increases in the recall of autobiographical memories, (2) recognition issues, which require the supervision of the e-Memory Diary, and (3) evidences that this approach didn’t suffer from the usual rejection showed to this technology by elderlies.

Lorena Arcega, Jaime Font, Carlos Cetina

Two-Way Communications Through Firewalls Using QLM Messaging

Nowadays, organizations make a point of protecting the confidentiality of their data and assets using firewalls, proxies and NATs, which goes against providing a high level of data usability and interoperability between distinct information systems, or “Things” in the so-called Internet of Things. Such security procedures often prevent two-way communications between nodes located on each side of the firewall. Quantum Lifecycle Management (QLM) messaging has been introduced as a messaging standard proposal that would fulfill the requirements for exchanging the kind of information required by an IoT. In this regard, the QLM piggy backing property proposed in that standard makes it possible to achieve two-way communication through a firewall. This property is introduced in this paper, along with the first proofs-of-concept.

Sylvain Kubler, Manik Madhikermi, Andrea Buda, Kary Främling

Towards a Privacy Risk Assessment Methodology for Location-Based Systems

Mobiquitous systems are gaining more and more weight in our daily lives. They are becoming a reality from our home and work to our leisure. The use of Location-Based Services (LBS) in these systems is increasingly demanded by users. Yet, while on one hand they enable people to be more “connected”, on the other hand, they may expose people to serious privacy issues. The design and deployment of Privacy-Enhancing Technologies (PETs) for LBS has been widely addressed in the last years. However, strikingly, there is still a lack of methodologies to assess the risk that using LBS may have on users’ privacy (even when PETs are considered). This paper presents the first steps towards a privacy risk assessment methodology to (i) identify (ii) analyse, and (iii) evaluate the potential privacy issues affecting mobiquitous systems.

Jesús Friginal, Jérémie Guiochet, Marc-Olivier Killijian

Workshop

Frontmatter

Mobility Models-Based Performance Evaluation of the History Based Prediction for Routing Protocol for Infrastructure-Less Opportunistic Networks

In Opportunistic Networks (OppNets), the sender and receiver of a packet are not assumed to be connected with each other through an end-to-end continuous path. They exploit the contact opportunity that arises between the nodes due to their mobility to pass the messages from one place to another in the network. They do not rely on any pre-existing topology; rather they belong to a dynamic network topology. In this paper, the performance of our recently proposed History-Based Prediction for Routing protocol for infrastructure-less OppNets (so-called HBPR) is evaluated on two different mobility models – the Random Waypoint (RWP) and the Custom Human Mobility Model (CHMM). The HBPR protocol is evaluated against four performance metrics, namely, the number of messages delivered, the overhead ratio, the average hop count, and the average latency. Simulation results show a significant decline in the performance of HBPR for the RWP model compared to the CHMM model.

Sanjay K. Dhurandher, Deepak Kumar Sharma, Isaac Woungang

LTE_FICC: A New Mechanism for Provision of QoS and Congestion Control in LTE/LTE-Advanced Networks

In Long Term Evolution (LTE)/LTE-Advanced architecture, the basic schedulers allocate resources without taking congestion at the Evolved NodeB (eNodeB’s) output buffer into account. This leads to buffer overflows and deterioration in overall Quality of Service (QoS). Congestion avoidance and fair bandwidth allocation is hardly considered in existing research for the LTE/LTE-Advanced uplink connections. This paper introduces a mechanism for LTE and LTE-Advanced,

LTE Fair Intelligent Congestion Control (LTE_FICC

), to control congestion at an eNodeB. LTE_FICC jointly exists with the scheduler at the eNodeB to guarantee efficient traffic scheduling, in order to make the output buffer operate around a target operating point. LTE_FICC also overcomes the problem of unfair bandwidth allocation among the flows that share the same eNodeB interface. LTE_FICC is simple, robust and scalable, as it uses per queue rather than per flow accounting. To evaluate the effectiveness of the proposed algorithm, simulations were performed in Opnet using LTE module. The results demonstrated that LTE_FICC controls the eNodeB buffer effectively; prevents overflows; and ensures the QoS of flows in terms of fair bandwidth allocation, improved throughput and reduced queuing delay.

Fatima Furqan, Doan B. Hoang

Virtual Wireless User: A Practical Design for Parallel MultiConnect Using WiFi Direct in Group Communication

Several MultiConnect technologies are actively discussed in research today. MultiPath TCP (MPTCP) is capable of splitting one flow into subflows and balance the load across multiple access technologies. Multihoming is an older technology that makes it possible for network providers to balance load across multiple up- and down-links dynamically. Finally, Software Defined Networking (SDN) achieves the ultimate flexibility of connection and routing decisions. However, none of these technologies enable true (network or otherwise) resource-pooling in communications within arbitrary size user groups such as occur in meetings, class discussions, and ad-hoc communities in the wild. This paper proposes the concept of a Virtual Wireless User (VWU) which represents the entire group and appears as single user to an over-the-network service. Each group member is capable of MultiConnect using Wi-Fi Direct in parallel with any other connection method. Modeling based on real measurements shows that VWUs can achieve throughput in the order of tens of Mbps even if throughput of individual users is very low. The paper also formulates a formal optimization problem in relation to VWU.

Marat Zhanikeev

Small Cell Enhancement for LTE-Advanced Release 12 and Application of Higher Order Modulation

The mobile data traffic is expected to grow beyond 1000 times by 2020 compared with it in 2010. In order to support 1000 times of capacity increase, improving spectrum efficiency is one of the important approaches. Meanwhile, in Long Term Evolution (LTE)-Advanced, small cell and hotspot are important scenarios for future network deployment to increase the capacity from the network density domain. Under such environment, the probability of high Signal to Interference plus Noise Ratio (SINR) region becomes larger which brings the possibility of introducing higher order modulation, i.e., 256 Quadrature Amplitude Modulation (QAM) to improve the spectrum efficiency. In this paper, we will firstly introduce the ongoing small cell enhancement discussion in the 3rd Generation Partnership Project (3GPP). And then focus on the application of higher order modulation in small cell environment. Important design issues and possible solutions will be analyzed particularly in the higher order modulation discussion.

Qin Mu, Liu Liu, Huiling Jiang, Hidetoshi Kayama

Backmatter

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