PLOMaR: An Ontology Framework for Context Modeling and Reasoning on Crowd-Sensing Platforms

Crowd-sensing is a popular way to sense and collect data using smartphones that reveals user behaviors and their correlations with device performance. PhoneLab is one of the largest crowd-sensing platform based on the Android system. Through experimental instrumentations and system modifications, researchers can tap into a sea of insightful information that can be further processed to reveal valuable context information about the device, user and the environment. However, the PhoneLab data is in JSON format. The process of inferring reasons from data in this format is not straightforward. In this paper, we introduce PLOMaR — an ontology framework that uses SPARQL rules to help researchers access information and derive new information without complex data processing. The goals are to (i) make the measurement data more accessible, (ii) increase interoperability and reusability of data gathered from different sources, (iii) develop extensible data representation to support future development of the PhoneLab platform. We describe the models, the JSON to RDF mapping processes, and the SPARQL rules used for deriving new information. We evaluate our framework with three application examples based on the sample dataset provided

Service Compositions in Challenged Mobile Environments Under Spatiotemporal Constraints

Opportunistic network created among mobile devices in challenged environments can be effectively exploited to provide application services. However, data and services may be subject to space and time constraints in challenged environments where it is critical to complete application services within given spatiotemporal limits. This paper discusses an analytical framework that takes into account human mobility traces and provides quantitative measures of the spatiotemporal requirements for service sharing and composition in challenged opportunistic environments. The analytical results provide estimates on feasibility of service sharing and service compositions for various mobility models. To validate the framework, we conduct simulation experiments using multiple human mobility and synthesized datasets. In these experiments, we analyze service composition feasibility, service completion rate and time for resource utilization

COSC: Paths with Combined Optimal Stability and Capacity in Opportunistic Networks

Opportunistic networks are characterized by the dynamic connectivity created when mobile devices encounter each other, as they are within close proximity. During these transient opportunities, devices are typically within one-hop wireless range of their neighbors. Opportunistic networks are an effective way, in terms of bandwidth and battery consumption to distribute large volume content among peers. Many existing proposals consider opportunistic networks as a best-effort content delivery approach, which limits their applications. We exploit characteristics of human mobility to derive an effective data forwarding scheme that achieves Combined Optimal Stability and Capacity (COSC) for opportunistic networks. COSC includes a path selection algorithm to maximize the utility of link capacity and stability. We validate theoretical findings with rigorous simulation studies using synthetic and real-world mobility traces. When compared with other approaches, COSC shows significant improvement due to the consideration of link capacity and stability

Context-aware and resource efficient sensing infrastructure for context-aware applications

Middleware for wireless sensor networks and middleware for context-aware applications both provide information abstraction and programming support for gathering, pre-processing, and managing sensor data. However the former mostly concentrates on optimising the operations of the resource constrained hardware and simplifying access to the raw sensor data while the latter focuses on gathering sensor data, pre-processing it to the abstract context information required by the applications and providing reasoning on this data. In this paper, we explore the idea of enhancing middleware for context-aware applications with solutions from sensor networks middle ware to allow resource efficient and contextaware management of sensing infrastructure. The decisions on which sensor data needs to be delivered to the middleware for evaluation are based on current contextual situations. The approach allows to trade the level of confidence in context information for resource efficiency in context provisioning without a detrimental effect on the functionality of contextaware applications. © 2010 IEEE

Evaluation of Homomorphic Primitives for Computations on Encrypted Data for CPS systems

In the increasingly connected world, cyber-physical systems (CPS) have been quickly adapted in many application domains, such as smart grids or healthcare. There will be more and more highly sensitive data important to the users being collected and processed in the cloud computing environments. Homomorphic Encryption (HE) offers a potential solution to safeguard privacy through cryptographic means while allowing the service providers to perform computations on the encrypted data. Throughout the process, only authorized users have access to the unencrypted data. In this paper, we provide an overview of three recent HE schemes, analyze the new optimization techniques, conduct performance evaluation, and share lessons learnt from the process of implementing these schemes. Our experiments indicate that the YASHE scheme outperforms the other two schemes we studied. The findings of this study can help others to identify a suitable HE scheme for developing solutions to safeguard private data generated or consumed by CPS

Homomorphic Proximity Computation in Geosocial Networks

With the growing popularity of mobile devices that have sophisticated localization capability, it becomes more convenient and tempting to give away location data in exchange for recognition and status in the social networks. Geosocial networks, as an example, offer the ability to notify a user or trigger a service when a friend is within geographical proximity. In this paper, we present two methods to support secure distance computation on encrypted location data; that is, computing distance functions without knowing the actual coordinates of users. The underlying security is ensured by the homomorphic encryption scheme which supports computation on encrypted data. We demonstrate feasibility of the proposed approaches by conducting various performance evaluations on platforms with different specifications. We argue that the novelty of this work enables a new breed of pervasive and mobile computing concepts, which was previously not possible due to the lack of feasible mechanisms that support computation on encrypted location data

Advancing Android Activity Recognition Service with Markov Smoother: Practical Solutions

Common use of smartphones is a compelling reason for performing activity recognition with on-board sensors as it is more practical than other approaches, such as wearable sensors and augmented environments. Many solutions have been proposed by academia, but practical use is limited to experimental settings. Ad hoc solutions exist with different degrees in recognition accuracy and efficiency. To ease the development of activity recognition for the mobile application eco-system, Google released an activity recognition service on their Android platform. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we identified scenarios in which the recognition accuracy was barely acceptable. We analyze the cause of the inaccuracy and propose four practical and light-weight solutions to significantly improve the recognition accuracy and efficiency. Our evaluation confirmed the improvement. As a contribution, we released the proposed solutions as open-source projects for developers who want to incorporate activity recognition into their applications

X-Cipher: Achieving Data Resiliency in Homomorphic Ciphertexts

Homomorphic encryption (HE) allows for computations on encrypted data without requiring decryption. HE is commonly applied to outsource computation on private data. Due to the additional risks caused by data outsourcing, the ability to recover from losses is essential, but doing so on data encrypted under an HE scheme introduces additional challenges for recovery and usability. This work introduces X-Cipher, which aims to make HE data resilient by ensuring it is private and fault-tolerant simultaneously at all stages during data-outsourcing. X-Cipher allows for data recovery without decryption, and maintains its ability to recover and keep data private when a cluster server has been compromised. X-Cipher allows for reduced ciphertext storage overhead by introducing novel encoding and leveraging previously introduced ciphertext packing. X-Cipher\u27s capabilities were evaluated on synthetic dataset, and compared to prior work to demonstrate X-Cipher enables additional security capabilities while enabling privacy-preserving outsourced computations