An Efficient Pairwise Key Establishment Scheme for Ad-hoc Mobile Clouds

An Ad-hoc Mobile Cloud (AMC) is a new computing model that allows sharing computing power of multiple mobile devices. For a diverse group of individuals that employ such computing model, in an ad-hoc manner, secure peer-to-peer communication becomes very important. Using private or pairwise keys to secure such communication is preferable to public-keys because of computation and energy requirements. With the advent of sensor enabled mobile devices, a protocol (SekGens) that uses sensor data to generate pairwise keys on demand has been proposed. To work successfully SekGens requires devices to be closely located and becomes infeasible for devices situated multiple hops away. SekGens is also expensive in computation and slow in key generation. In this thesis, we investigate how to enable devices in an AMC to establish pairwise keys. We propose an efficient solution which tries to reduce the number of executions of SekGens in the AMC, and establishes pairwise keys between mobile phones multiple hops away by distributing parts of the key on multiple routing paths. Our results show a reduction of up to 75% in the number of SekGens required to establish keys in an AMC, when compared to a naive approach. Also the execution time to come up with the optimal pairs is within 10s of seconds for reasonably large networks

Towards Energy-Efficient, Fault-Tolerant, and Load-Balanced Mobile Cloud

Recent advances in mobile technologies have enabled a new computing paradigm in which large amounts of data are generated and accessed from mobile devices. However, running resource-intensive applications (e.g., video/image storage and processing or map-reduce type) on a single mobile device still remains off bounds since it requires large computation and storage capabilities. Computer scientists overcome this issue by exploiting the abundant computation and storage resources from traditional cloud to enhance the capabilities of end-user mobile devices. Nevertheless, the designs that rely on remote cloud services sometimes underlook the available resources (e.g., storage, communication, and processing) on mobile devices. In particular, when the remote cloud services are unavailable (due to service provider or network issues) these smart devices become unusable. For mobile devices deployed in an infrastructureless network where nodes can move, join, or leave the network dynamically, the challenges on energy-efficiency, reliability, and load-balance are still largely unexplored. This research investigates challenges and proposes solutions for deploying mobile application in such environments. In particular, we focus on a distributed data storage and data processing framework for mobile cloud. The proposed mobile cloud computing (MCC) framework provides data storage and data processing services to MCC applications such as video storage and processing or map-reduce type. These services ensure the mobile cloud is energy-efficient, fault-tolerant, and load-balanced by intelligently allocating and managing the stored data and processing tasks accounting for the limited resources on mobile devices. When considering the load-balance, the framework also incorporates the heterogeneous characteristics of mobile cloud in which nodes may have various energy, communication, and processing capabilities. All the designs are built on the k-out-of-n computing theoretical foundation. The novel formulations produce a reliability-compliant, energy-efficient data storage solution and a deadline-compliant, energy-efficient job scheduler. From the promising outcomes of this research, a future where mobile cloud offers real-time computation capabilities in complex environments such as disaster relief or warzone is certainly not far

Energy-efficient fault-tolerant data storage & processing in dynamic networks

With the advance of mobile devices, cloud computing has enabled people to access data and computing resources without spatiotemporal constraints. A common assumption is that mobile devices are well connected to remote data centers and the data centers securely store and process data. However, for systems like mobile cloud deployed in infrastructureless dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility), reliability and energy efficiency remain largely unaddressed challenges. To address these issues, we develop the first κ-out-of-n computing framework that ensures nodes retrieve or process data stored in mobile cloud with minimum energy consumption as long as κ out of n storage/processing nodes are accessible. We demonstrate the feasibility and performance of our framework through both hardware implementation and extensive simulations. Copyright 2013 ACM