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Über dieses Buch

This book constitutes the proceedings of the 7th International Conference on Mobile Computing, Applications, and Services (MobiCASE 2015) held in Berlin, Germany, in November 2015. The 16 full and 4 poster papers were carefully reviewed and selected from 43 submissions, and are presented together with 4 papers from the First Workshop on Situation Recognition by Mining Temporal Information (SIREMETI 2015). The conference papers cover the following topics: intelligent caching, activity recognition and crowdsourcing, mobile frameworks, middleware, interactive applications and mobility.



Intelligent Caching


Network Data Buffering for Availability Improvement of Mobile Web Applications

In recent years, we have seen an explosion in the use of smart devices. With several different competing OS platforms on these smart devices, developers have turned to web applications as an effective way to provide cross-platform services. However, because many web applications are designed to handle UI interactions locally on a user’s device and to process data remotely in the cloud, it is difficult for them to continue running while offline. In this paper, we propose a data synchronization technology that buffers and minimize network communications to address problems associated with dropped network connections as well as low bandwidth and/or high latency environments. To test the technology’s effectiveness, we applied it to some typical web applications and compared their performance in environments with dropped connections.

Tomoharu Imai, Kouichi Yamasaki, Masahiro Matsuda, Kazuki Matsui

Upgrading Wireless Home Routers for Enabling Large-Scale Deployment of Cloudlets

Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of range-restricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router’s native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.

Christian Meurisch, Alexander Seeliger, Benedikt Schmidt, Immanuel Schweizer, Fabian Kaup, Max Mühlhäuser

Activity Recognition and Crowdsourcing


Adaptive Activity and Context Recognition Using Multimodal Sensors in Smart Devices

The continuous development of new technologies has led to the creation of a wide range of personal devices embedded with an ever increasing number of miniature sensors. With accelerometers and technologies such as Bluetooth and Wi-Fi, today’s smartphones have the potential to monitor and record a complete history of their owners’ movements as well as the context in which they occur. In this article, we focus on four complementary aspects related to the understanding of human behaviour. First, the use of smartwatches in combination with smartphones in order to detect different activities and associated physiological patterns. Next, the use of a scalable and energy-efficient data structure that can represent the detected signal shapes. Then, the use of a supervised classifier (i.e. Support Vector Machine) in parallel with a quantitative survey involving a dozen participants to achieve a deeper understanding of the influence of each collected metric and its use in detecting user activities and contexts. Finally, the use of novel representations to visualize the activities and social interactions of all the users, allowing the creation of quick and easy-to-understand comparisons. The tools used in this article are freely available online under a MIT licence.

Sébastien Faye, Raphael Frank, Thomas Engel

Characterization of User’s Behavior Variations for Design of Replayable Mobile Workloads

Mobile systems leverage heterogeneous cores to deliver a desired user experience. However, how these cores cooperate in executing interactive mobile applications in the hands of a real user is unclear, preventing more realistic studies on mobile platforms. In this paper, we study how 33 users run applications on modern smartphones over a period of a month. We analyze the usage of CPUs, GPUs and associated memory operations in real user interactions, and develop microbenchmarks on an automated methodology which describes realistic and replayable test runs that statistically mimic user variations. Based on the generated test runs, we further empirically characterize memory bandwidth and power consumption of CPUs and GPUs to show the impact of user variations in the system, and identify user variation-aware optimization opportunities in actual mobile application uses.

Shruti Patil, Yeseong Kim, Kunal Korgaonkar, Ibrahim Awwal, Tajana S. Rosing

Worker Selection for Reliably Crowdsourcing Location-Dependent Tasks

Obtaining accurate information about specific locations is of great importance to today’s many crowdsourced smartphone applications. To verify information about a location, smartphone users are selected to go to the location and answer a yes/no question about the location. Our research focuses on the location-aware worker selection problem, which is the problem of selecting a group of workers who, together, can give the most accurate answer to the location-based question. We define the location-aware worker selection problem, mathematically formulate it, and then show that an optimal solution is exponential in time complexity. We present our heuristic solutions that take into account both the reliability of the users and the level of convenience for each user to complete the task. We evaluate and compare our approaches to three other heuristic algorithms via simulation.

Kevin Emery, Taylor Sallee, Qi Han

Mobile Frameworks


AppSachet: Distributed App Delivery from the Edge Cloud

With total app installs touching 100 Billion in 2015, the increasing number of active devices that support apps are posed to result in 200 billion downloads by 2017. Data center based App stores offering users convenient app access, however, cause congestion in the last mile of the Internet, despite use of content delivery networks (CDNs) or ISP-based caching. This paper explores the new paradigm of eBoxes, situated in the ‘edge cloud’ tier beyond the last mile, which can be used to alleviate this congestion. With redesigned app caches – termed AppSachet – such edge cloud based distributed caching can achieve a hit ratio of up to 83 %, demonstrated on real-world Internet traffic. The redesign leverages proposed new caching policies, termed p-LRU and c-LRU, specifically targeted at eBoxes’ limited storage and for the traffic caused by app installs and updates. A cost benefit analysis shows that the additional cost required to deploy AppSachet on eBoxes can be recovered within the first three months of operation.

Ketan Bhardwaj, Pragya Agrawal, Ada Gavrilovska, Karsten Schwan

Typed JS: A Lightweight Typed JavaScript Engine for Mobile Devices

Web applications have been gaining huge popularity due to being platform independent and also enabling fast development. Unfortunately, due to insufficient performance of web applications, they are generally limited to non-performance-critical use. The performance of web applications is largely affected by the performance of JavaScript. To address this problem, modern JavaScript engines such as Google’s V8 incorporate many state-of-the-art optimization and engineering techniques. In industry, recent approaches are to extend JavaScript to decorate objects with types to better utilize just-in-time (JIT) compilers.In this paper, we present Typed JS, a subset of JavaScript that utilizes type-decorated syntax. Unlike previous approaches, Typed JS supports most of the JS core operations while utilizing the ahead-of-time (AOT) compilation technique, which was not possible in the existing solution. Typed JS is specifically designed for running Web applications on mobile devices with goals of having smaller memory footprint while achieving high-performance, which is accomplished by utilizing the type information and AOT technique. Experiments show that Typed JS requires significantly much less memory usage while performing better than industry-leading JavaScript engines on a mobile platform.

Ryan H. Choi, Youngil Choi

Pervasive Context Sharing in Magpie: Adaptive Trust-Based Privacy Protection

Today’s mobile and pervasive computing devices are embedded with increasingly powerful sensing capabilities that enable them to provide exceptional spatio-temporal context acquisition that is not possible with traditional static sensor networks alone. As a result, enabling these devices to share context information with one another has a great potential for enabling mobile users to exploit the nearby cyber and physical environments in participatory or human-centric computing. However, because these mobile devices are owned by and sense information about individuals, sharing the acquired context raises significant privacy concerns. In this paper, we define Magpie, which implements an alternative to existing all-or-nothing sharing solutions. Magpie integrates a decentralized context-dependent and adaptive trust scheme with a privacy preserving sharing mechanism to evaluate the risk of disclosing potentially private data. The proposed method uses this assessment to dynamically determine the sharing strategy and the quality of the context shared. Conceptually, Magpie allows devices to actively obfuscate context information so that sharing is still useful but does not breach user privacy. To our knowledge this is the first work to take both trust relationships and users’ individual privacy sensitivities into account to balance sharing and privacy preservation. We describe Magpie and then evaluate it in a series of application-oriented experiments running on the Opportunistic Network Environment (ONE) simulator.

Chenguang Liu, Christine Julien



Panorama: A Framework to Support Collaborative Context Monitoring on Co-located Mobile Devices

A key challenge in wide adoption of sophisticated context-aware applications is the requirement of continuous sensing and context computing. This paper presents Panorama, a middleware that identifies collaboration opportunities to offload context computing tasks to nearby mobile devices as well as cloudlets/cloud. At the heart of Panorama is a multi-objective optimizer that takes into account different constraints such as access cost, computation capability, access latency, energy consumption and data privacy, and efficiently computes a collaboration plan optimized simultaneously for different objectives such as minimizing cost, energy and/or execution time. Panorama provides support for discovering nearby devices and cloudlets/cloud, computing an optimal collaboration plan, distributing computation to participating devices, and getting the results back. The paper provides an extensive evaluation of Panorama via two representative context monitoring applications over a set of Android devices and a cloudlet/cloud under different constraints.

Khaled Alanezi, Xinyang Zhou, Lijun Chen, Shivakant Mishra

Jouler: A Policy Framework Enabling Effective and Flexible Smartphone Energy Management

Smartphone energy management is a complex challenge. Considerable energy-related variation exists between devices, apps, and users; and while over-allocating energy can strand the user with an empty battery, over-conserving energy can unnecessarily degrade performance. But despite this complexity, current smartphone platforms include “one-size-fits-all” energy management policies that cannot satisfy the diverse needs of all users. To address this problem we present Jouler, a framework enabling effective and flexible smartphone energy management by cleanly separating energy control mechanisms from management policies. Jouler provides both imperative mechanisms that can control all apps, and cooperative mechanisms that allow modified apps to adapt to the user’s energy management goals. We have implemented Jouler for Android and used it to provide three new energy management policies to 203 smartphone users. Results from our deployment indicate that users appreciate more flexible smartphone energy management and that Jouler policies can help users achieve their energy management goals.

Anudipa Maiti, Yihong Chen, Geoffrey Challen

CSSWare: A Middleware for Scalable Mobile Crowd-Sourced Services

The growing ubiquity of a variety of personal connected computational devices – each with a number of sensors – has created the opportunity for a wide range of crowd-sourced services. A busy professional could find a restaurant to go to for a quick lunch based on information available from smartphones of other people already there. Sensors on smartphones could detect whether their owners are having lunch, waiting to be seated, or even heading there.Although the programming required for offering a new service of this sort can be significant if done from scratch, we identify core communication mechanisms underlying such services, which can be implemented as part of a middleware. Service designers can then launch novel services over this middleware by plugging in small pieces of service-specific code.This paper describes the multi-origin communication mechanism which we believe to underlie many crowd-sourced services. It presents our design and prototype Actor-based implementation of middleware for crowd-sourced services, CSSWare. We present the code for a realistic crowd-sourced service to illustrate the ease with which new services can be specified and launched. Finally, we present our experimental results demonstrating scalability, performance and data-contributor side energy efficiency of the approach.

Ahmed Abdel Moamen, Nadeem Jamali

Interactive Applications


Quality Assurance in Additive Manufacturing Through Mobile Computing

The increase in use of consumer 3D printers for in-home or small business manufacturing may signal the start of an additive manufacturing revolution, but unfortunately these printers are often error prone. In order to remedy the time and materials lost when a failed print continues on a low-end 3D printer, a cost-effective method is needed to monitor the quality of a print and stop it when an error occurs. This paper presents an approach to using a commodity smartphone and computer vision to perform quality assurance on selected layers of a 3D print. Our results indicate that a commodity mobile device using our technique is capable of accurately detecting printing errors and then effectively determining whether or not a print should continue.

Sam Hurd, Carmen Camp, Jules White

Interactively Set up a Multi-display of Mobile Devices

We provide a method to interactively set up a multi-display using a combination of multi and single device gestures. An initial setup provides a coarse grained model. Test pictures and user judgement based on the human visual system then guide a fine grained interactive process. This allows the user to move and rotate single screens until differences between physical and model position are no longer perceived. To this end, a central computer holds the model and connects among all participating smartphones and tablets with different physical dimensions and display resolutions. In addition, it evaluates gestures and prepares as well as distributes images on the multi-display.

Peter Barth, Manuel Pras

SURFLogo - Mobile Tagging with App Icons

Mobile tagging became more and more popular in commercials, magazines, newspapers, and other applications during the last years. In context of commercials, a bar code containing the advertisers internet address is often used to refer a customer to related online content. Due to their robustness as well as their comparably high fault-tolerance in case of low quality pictures, QR-Code systems are commonly used for that task. Connected to that topic we present a special procedure for mobile tagging, which uses a distinct logo or image in order to refer to certain information instead of a QR-Code. Our procedure was optimized to work with a conventional smartphone – the only prerequisite for usage is the possession of a smartphone capable of capturing and analyzing the different logos with our smartphone application. To match the logos with related information and to determine their uniqueness we introduce a new similarity measure on basis of SURF feature points and a contour comparison.

Chadly Marouane, Andre Ebert



Towards Indoor Transportation Mode Detection Using Mobile Sensing

Transportation mode detection (TMD) is a growing field of research, in which a variety of methods have been developed, foremost for outdoor travels. It has been employed in application areas such as public transportation and environmental footprint profiling. For indoor travels the problem of TMD has received comparatively little attention, even though diverse transportation modes, such as biking and electric vehicles, are used indoors. The potential applications are diverse, and include scheduling and progress tracking for mobile workers, and management of vehicular resources. However, for indoor TMD, the physical environment as well as the availability and reliability of sensing resources differ drastically from outdoor scenarios. Therefore, many of the methods developed for outdoor TMD cannot be easily and reliably applied indoors.In this paper, we explore indoor transportation scenarios to arrive at a conceptual model of indoor transportation modes, and then compare challenges for outdoor and indoor TMD. In addition, we explore methods for TMD we deem suitable in indoor settings, and we perform an extensive real-world evaluation of such methods at a large hospital complex. The evaluation utilizes Wi-Fi and accelerometer data collected through smartphones carried by hospital workers throughout four days of work routines. The results show that the methods can distinguish between six common modes of transportation used by the hospital workers with an F-score of $$84.2\,\%$$.

Thor Siiger Prentow, Henrik Blunck, Mikkel Baun Kjærgaard, Allan Stisen

Indoor Navigation with a Smartphone Fusing Inertial and WiFi Data via Factor Graph Optimization

Mobile devices are getting more capable every year, allowing a variety of new applications, such like supporting pedestrian navigation in GPS-denied environments. In this paper we deal with the problem of combining in real-time dead reckoning data from the inertial sensors of a smartphone, and the WiFi signal fingerprints, which enable to detect the already visited places and therefore to correct the user’s trajectory. While both these techniques have been used before for indoor navigation with smartphones, the key contribution is the new method for including the localization constraints stemming from the highly uncertain WiFi fingerprints into a graphical problem representation (factor graph), which is then optimized in real-time on the smartphone. This method results in an Android-based personal navigation system that works robustly with only few locations of the WiFi access points known in advance, avoiding the need to survey WiFi signal in the whole area. The presented approach has been evaluated in public buildings, achieving localization accuracy which is sufficient for both pedestrian navigation and location-aware applications on a smartphone.

Michał Nowicki, Piotr Skrzypczyński

Workshop Papers


Using Interaction Signals for Job Recommendations

Job recommender systems depend on accurate feedback to improve their suggestions. Implicit feedback arises in terms of clicks, bookmarks and replies. We present results from a member inquiry conducted on a large-scale job portal. We analyse correlations between ratings and implicit signals to detect situations where members liked their suggestions. Results show that replies and bookmarks reflect preferences much better than clicks.

Benjamin Kille, Fabian Abel, Balázs Hidasi, Sahin Albayrak

A Spatiotemporal Approach for Social Situation Recognition

The development of virtual personal assistants requires situation awareness. For this purpose, lightweight approaches for the processing of sensor data to derive situation information from available sensor data (e.g., mobile phone data) are required.In this paper, we propose a spatiotemporal approach to derive situational information about social interactions only based on location and time, using data collected with off-the-shelf smartphones. We examine the approach, using location traces of 163 users collected over four weeks. The proposed spatiotemporal approach shows an average social situation recognition result of $$45.8\pm 23.2\,\%$$$$F_1$$-measure across the data set using Random Forest classifiers.

Christian Meurisch, Tahir Hussain, Artur Gogel, Benedikt Schmidt, Immanuel Schweizer, Max Mühlhäuser

Managing Wireless Mesh Networks – A Survey of Recent Fault Recovery Approaches

Wireless Mesh Network (WMN) is a technology which has evolved in recent years and fits well in today’s technological needs. However, due to the wireless nature of WMNs and their deployment in heterogeneous and large scale areas, wireless links often face significant quality fluctuations and performance degradation or weak connectivity. Therefore, failure detection and recovery plays crucial role in performance of WMN. This paper presents a study report on comparison of recent research and techniques developed for the issue of fault tolerance in WMNs. In this survey we present the existing techniques for fault tolerance in WMNs in categories; node failure approach, communication failure approach, routing schemes, fault tolerance techniques, and autonomous reconfiguration systems. The paper also provides an outline of areas which need further research and studies.

Akmal Yaqini

Threat Model Based Security for Wireless Mesh Networks

Wireless Mesh Network (WMN) is a technology, which has gained popularity due to its cost effective design, robustness, and reliable service coverage. Despite the advantages, WMNs are considered vulnerable to security breaches. Thereby, it is important to consider security in the early design phase in WMNs. Identifying security threats helps the system designer in developing rational security requirements. In this paper we propose threat modeling as a systematic approach to pinpoint the security threats for WMNs as basis for developing security requirements. We identify assets, value them and categorize possible attacks that target the assets in a layer-wise manner. We further elucidate our threat model by use of Attack Trees to clearly define vulnerabilities in the system during early design phase. We take the example of Schools’ WMN in a district of Kabul City in Afghanistan as our scenario. We briefly discuss how to assess the risks that are associated with the specified WMN based on the information that is derived from the threat model.

Freshta Popalyar


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