VECTORS: Video communication through opportunistic relays and scalable video coding

Crowd-sourced video distribution is frequently of interest in the local vicinity. In this paper, we propose a novel design to transfer such content over opportunistic networks with adaptive quality encoding to achieve reasonable delay bounds. The video segments are transmitted between source and destination in a delay tolerant manner using the Nearby Connections Android library. This implementation can be applied to multiple domains, including farm monitoring, wildlife, and environmental tracking, disaster response scenarios, etc. In this work, we present the design of an opportunistic contact based system, and we discuss basic results for the trial runs within our institute.Comment: 13 pages, 6 figures, and under 3000 words for submission to the SoftwareX journa

MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications

Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing, 2014. arXiv admin note: substantial text overlap with arXiv:1310.405

A novel on-board Unit to accelerate the penetration of ITS services

In-vehicle connectivity has experienced a big expansion in recent years. Car manufacturers have mainly proposed OBU-based solutions, but these solutions do not take full advantage of the opportunities of inter-vehicle peer-to-peer communications. In this paper we introduce GRCBox, a novel architecture that allows OEM user-devices to directly communicate when located in neighboring vehicles. In this paper we also describe EYES, an application we developed to illustrate the type of novel applications that can be implemented on top of the GRCBox. EYES is an ITS overtaking assistance system that provides the driver with real-time video fed from the vehicle located in front. Finally, we evaluated the GRCbox and the EYES application and showed that, for device-to-device communication, the performance of the GRCBox architecture is comparable to an infrastructure network, introducing a negligible impact

Evaluation of network coding techniques for a sniper detection application

This paper experimentally studies the reliability and delay of flooding based multicast protocols for a sniper detection application. In particular using an emulator it studies under which conditions protocols based on network coding deliver performance improvements compared to classic flooding. It then presents an implementation of such protocols on mobile phones

Delay Tolerant Networking over the Metropolitan Public Transportation

We discuss MDTN: a delay tolerant application platform built on top of the Public Transportation System (PTS) and able to provide service access while exploiting opportunistic connectivity. Our solution adopts a carrier-based approach where buses act as data collectors for user requests requiring Internet access. Simulations based on real maps and PTS routes with state-of-the-art routing protocols demonstrate that MDTN represents a viable solution for elastic nonreal-time service delivery. Nevertheless, performance indexes of the considered routing policies show that there is no golden rule for optimal performance and a tailored routing strategy is required for each specific case

PASSIVE TIME SYNCHRONIZATION IN SENSOR NETWORKS USING OPPORTUNISTIC FM RADIO SIGNALS

ABSTRACT Time synchronization is a critical piece of infrastructure for any wireless sensor network. It is necessary for applications such as audio localization, beam-forming, velocity calculation, and duplicate event detection. All of which require the coordination of multiple nodes. Recent advances in low-cost, low-power wireless sensors have led to an increased interest in large-scale networks of small, wireless, low-power sensor nodes. Because of the more stringent power and cost requirements that this technology is driving, current time synchronization techniques must be updated to capitalize on these advances. One time synchronization method developed specifically for wireless sensor networks is Reference Broadcast Synchronization. In RBS, a reference broadcast is transmitted to sensor nodes that require synchronization. Be recording the time of arrival, nodes can then use those time stamps to synchronize with each other. This project aimed to make the RBS system even more robust, energy efficient, and cost effective by replacing the reference broadcast with an ambient RF signal (FM, TV, AM, or satellite signals) already prevalent in the environment. The purpose of this project was to demonstrate the viability of using Opportunistic RF synchronization by 1.) quantifying error, 2.) applying this synchronization method in a real world application, and 3.), implementing a wireless sensor network using Android smart phones as sensor nodes. Many of the objectives for the project were successfully completed. For convenience and economic reasons, an FM signal was chosen as the reference broadcast. FM Radio Synchronization error was then quantified using local FM Radio stations. The results of this experiment were very favorable. Using 5 second segments for correlation, total error was found to be 0.208±4.499μs. Using 3 second segments, average error was 2.33 ± 6.784μs. Using 400ms segments, synchronization error was calculated to be 4.76 ± 8.835μs. These results were comparable to sync errors of methods currently in widespread use. It was also shown that Opportunistic RF Synchronization could be used in real world applications as well. Again FM was the RF signal of choice. FM Radio Synchronization was tested in an Audio Localization experiment with favorable results. Implementation of an Android Wireless Sensor Network according to our specifications, however, could not be achieved. HTC EVO 4G’s were programmed to communicate through TCP / IP network connections, record audio with a microphone, and to record FM Radio streams from the EVO’s internal FM radio. Although recording these two sources separately as different data tracks was successful, simultaneous recording of these streams could not be accomplished (simultaneous recording is essential for Opportunistic RF Synchronization). Although the Android smart phone implementation was not a total success, this project still provided data that supported the practical use of Opportunistic RF Synchronization.AFRLNo embarg

Code offloading in opportunistic computing

With the advent of cloud computing, applications are no longer tied to a single device, but they can be migrated to a high-performance machine located in a distant data center. The key advantage is the enhancement of performance and consequently, the users experience. This activity is commonly referred computational offloading and it has been strenuously investigated in the past years. The natural candidate for computational offloading is the cloud, but recent results point out the hidden costs of cloud reliance in terms of latency and energy; Cuervo et. al. illustrates the limitations on cloud-based computational offloading based on WANs latency times. The dissertation confirms the results of Cuervo et. al. and illustrates more use cases where the cloud may not be the right choice. This dissertation addresses the following question: is it possible to build a novel approach for offloading the computation that overcomes the limitations of the state-of-the-art? In other words, is it possible to create a computational offloading solution that is able to use local resources when the Cloud is not usable, and remove the strong bond with the local infrastructure? To this extent, I propose a novel paradigm for computation offloading named anyrun computing, whose goal is to use any piece of higher-end hardware (locally or remotely accessible) to offloading a portion of the application. With anyrun computing I removed the boundaries that tie the solution to an infrastructure by adding locally available devices to augment the chances to succeed in offloading. To achieve the goals of the dissertation it is fundamental to have a clear view of all the steps that take part in the offloading process. To this extent, I firstly provided a categorization of such activities combined with their interactions and assessed the impact on the system. The outcome of the analysis is the mapping to the problem to a combinatorial optimization problem that is notoriously known to be NP-Hard. There are a set of well-known approaches to solving such kind of problems, but in this scenario, they cannot be used because they require a global view that can be only maintained by a centralized infrastructure. Thus, local solutions are needed. Moving further, to empirically tackle the anyrun computing paradigm, I propose the anyrun computing framework (ARC), a novel software framework whose objective is to decide whether to offload or not to any resource-rich device willing to lend assistance is advantageous compared to local execution with respect to a rich array of performance dimensions. The core of ARC is the nference nodel which receives a rich set of information about the available remote devices from the SCAMPI opportunistic computing framework developed within the European project SCAMPI, and employs the information to profile a given device, in other words, it decides whether offloading is advantageous compared to local execution, i.e. whether it can reduce the local footprint compared to local execution in the dimensions of interest (CPU and RAM usage, execution time, and energy consumption). To empirically evaluate ARC I presented a set of experimental results on the cloud, cloudlet, and opportunistic domain. In the cloud domain, I used the state of the art in cloud solutions over a set of significant benchmark problems and with three WANs access technologies (i.e. 3G, 4G, and high-speed WAN). The main outcome is that the cloud is an appealing solution for a wide variety of problems, but there is a set of circumstances where the cloud performs poorly. Moreover, I have empirically shown the limitations of cloud-based approaches, specifically, In some circumstances, problems with high transmission costs tend to perform poorly, unless they have high computational needs. The second part of the evaluation is done in opportunistic/cloudlet scenarios where I used my custom-made testbed to compare ARC and MAUI, the state of the art in computation offloading. To this extent, I have performed two distinct experiments: the first with a cloudlet environment and the second with an opportunistic environment. The key outcome is that ARC virtually matches the performances of MAUI (in terms of energy savings) in cloudlet environment, but it improves them by a 50% to 60% in the opportunistic domain