Abstract
Current mobile applications treat the end-user device as a 'thin client,' with all of the heavy computations being offloaded to an infrastructure cloud. However, the computational capabilities of mobile devices are constantly improving, and it is worthwhile considering whether an edge-cloud that consists purely of mobile devices (operating effectively as 'thick clients') can perform as well as, or even better than, an infrastructure cloud. In this paper, we study the trade-offs between offloading computation to an infrastructure cloud versus retaining the computation within a mobile edge-cloud. To this end, we develop and run two classes of applications on both types of clouds, and we analyze the performance of the two clouds in terms of the time taken to run the application, along with the total amount of battery power consumed in both cases. Our results indicate that there are indeed classes of applications where an edge-cloud can outperform an infrastructure cloud in terms of both latency and battery power. © 2013 IEEE.

Abstract
The Android permissions system provides all-or-nothing access to users' photos stored on smartphones, and the permissions which control access to stored photos can be confusing to the average user. Our analysis found that 73% of the top 250 free apps on the Google Play store have permissions that may not reflect their ability to access stored photos. We propose CHIPS, a unique content-based fine-grained run-time access control system for stored photos for Android which requires minimal user assistance, runs entirely locally, and provides low-level enforcement. CHIPS can recognize faces with minimal user training to deny apps access to photos with known faces. CHIPS's privacy identification has low overheads as privacy checks are cached, and is accurate, with false-positive and false-negative rates of less than 8%. © 2014 ACM.

Abstract
In the era of high competition with E-commerce and online shops, brick-and-mortar retail industry seeks new opportunities to enhance shopping experience through engaging technologies. Even though retailers are applying their omnichannel strategies to attract more shoppers through technology-driven solutions including websites, mobile apps, and so forth, we find that these technologies are somewhat basic and do not represent the "disruptive" innovations. Along with these current technologies, retailers should leverage their store physical real estate, and transform it into immersive store environments (ISEs) that allow shoppers to navigate in 3D store aisles through rich media interface ported onto networked devices. Therefore, we propose our own study of what ISE use-cases are most desirable by customers and retailers in such contexts; we describe the implementation of our cloud-based interactive shopping interface for ISE, before discussing the promising results of its deployment in a "real-world" store. © 2013 Springer-Verlag Berlin Heidelberg.

Abstract
The rapid growth in mobile devices will give rise to the trend of the leasing out of compute and data resources on mobile devices to third-parties for applications to be run on multiple mobile devices. However, these third-party applications running on leased mobile devices are typically written by unknown entities, and cannot be trusted by mobile device owners. Current mobile device platforms (e.g. Android) have permissions and access control systems designed for mobile apps that are written by reputable developers and vetted by authoritative app stores, and they are not suitable for untrusted apps. We propose STOVEPipe, an observable access control system for user data on mobile devices for untrusted third-party applications. STOVEPipe ensures that untrusted code is isolated and cannot directly access system data, and performs all data accesses on behalf of untrusted apps. This enables STOVEPipe to observe all data accessed by untrusted apps, implement content-based access control, perform accounting and auditing on accessed data easily, and perform privacy-preserving data transformations. © 2014 IEEE.

Abstract
The overall performance improvement in Byzantine fault-tolerant state machine replication algorithms has made them a viable option for critical high-performance systems. However, the construction of the proofs necessary to support these algorithms are complex and often make assumptions that may or may not be true in a particular implementation. Furthermore, the transition from theory to practice is difficult and can lead to the introduction of subtle bugs that may break the assumptions that support these algorithms. To address these issues we have developed Hermes, a fault-injector framework that provides an infrastructure for injecting faults in a Byzantine fault-tolerant state machine. Our main goal with Hermes is to help practitioners in the complex process of debugging their implementations of these algorithms, and at the same time increase the confidence of possible adopters, e.g., systems researchers, industry, by allowing them to test the implementations. In this paper, we discuss our experiences with Hermes to inject faults in BFT-SMaRt, a high-performance Byzantine fault-tolerant state machine replication library. © IFIP International Federation for Information Processing 2013.

Abstract
The ever-increasing popularity of online stores is reshaping traditional commerce models. In particular, brick-and-mortar stores are presently facing the challenge of reinventing themselves and their business models to offer attractive yet low-cost alternatives to e-commerce. Other industries have already introduced new concepts to fight inefficiency (i.e., 'Just-in-Time' inventory management in Automotive), retail stores face a more challenging environment which these models cannot accommodate. Stores remain heavily vested in battling the overhead costs of personnel management when, instead, a robotic automation scheme with retail-oriented behaviors could reduce the detection latency of out-of-stock and compliance error phenomena throughout the store. These behaviors must be automated, multi-purpose, and schedulable; they must also ensure that the robot coordinates store nuances to adapt its functionality appropriately. In this paper, we present our architecture that defines retail robot behaviors as a collection of reusable activities, which, when permuted various ways, allows for various high-level and application-specific tasks to be accomplished effectively. We evaluate this system on our robotic platform by scrutinizing the integrity of navigation and machine vision tasks, which we perform concurrently in an experimental store setup. Results show the feasibility and efficiency of our proposed architecture. © 2013 IEEE.