Khaled A. Harras

Delay tolerant mobile networks (DTMNs): controlled flooding in sparse mobile networks

The incredible growth in the capabilities and functionality of mobile devices has enabled new applications to emerge. Due to the potential for node mobility, along with significant node heterogeneity, characteristics such as very large delays, intermittent links and high link error rates pose a new set of challenges. Along with these challenges, end-to-end paths are assumed not to exist and message relay approaches are often adopted. While message flooding happens to be a simple and robust solution for such cases, its cost in terms of network resource consumption is unaffordable. In this paper, we focus on the evaluation of different controlled message flooding schemes over large-scale, sparse mobile networks. We study the effect of these schemes on message delay and network resource consumption. Our simulations show that our schemes can save substantial network resources while incurring a negligible increase in the message delivery delay.

Transport layer issues in delay tolerant mobile networks

The tremendous increase in wireless devices and user mobility have ultimately resulted in a new set of networking challenges that previously did not exist. Some of these challenges include large delays, intermittent connectivity and most importantly, the absence of an end-to-end path from sources to destinations. Networks characterized by one or more of these challenges are called Delay Tolerant Networks (DTNs). Researchers have studied DTNs with a major focus on routing issues in such extreme environments. As a result, in this paper, we shift this focus towards addressing and studying transport layer issues in extreme networking environments. We particularly concentrate on investigating and comparing several reliability approaches in a specific category of DTNs known as Delay Tolerant Mobile Networks (DTMNs). We present four different reliability approaches in DTMNs. We also evaluate these approaches under various network conditions via simulation. Our goals from this study are to examine the impact of these reliability approaches, understand the tradeoffs between them, and open the way for further work in transport layer issues in delay tolerant networks.

Inter-regional messenger scheduling in delay tolerant mobile networks

The evolution of wireless devices along with the increase in user mobility have created new challenges such as network partitioning and intermittent connectivity. These new challenges have become apparent in many situations where the transmission of critical data is of high priority. Disaster rescue groups, for example, are equipped with numerous devices which constantly gather and transmit various forms of data. The challenge of establishing communication between groups of this type has led to an evolutionary form of networks which we consider in this paper, namely, delay tolerant mobile networks (DTMNs). Nodes in DTMNs usually form clusters that we define as regions. Nodes within each region have end-to-end paths between them. Both regions, as well as nodes within a region, can be either stationary or mobile. For such environments, we propose using a dedicated set of messengers that relay message bundles between these regions. Our goal is to understand how messenger scheduling can be used to improve network performance and connectedness. We develop several classes of messenger scheduling algorithms which can be used to achieve inter-regional communication in such environments. We use simulation to better understand the performance and tradeoffs between these algorithms

Towards resource sharing in mobile device clouds: power balancing across mobile devices

Despite the increased capabilities of mobile devices, mobile application resource requirements can often transcend what can be accomplished on a single device. This has been addressed through several proposals for efficient computation offloading from mobile devices to remote cloud resources or closely located computing resources known as cloudlets. In this paper we consider an environment in which computational offloading is performed among a set of mobile devices. We call this environment a Mobile Device Cloud (MDC). We are interested in MDCs where nodes are {\em highly collaborative}. We develop computational offloading schemes that {\em maximize the lifetime} of the ensemble of mobile devices where we consider the network to be alive as long as no device has depleted its battery. As a secondary contribution in this work, we develop and use an experimentation platform that allows us to evaluate a range of computational models and profiles derived from a realistic testbed. We use this platform as a first step in an evaluation exercise that demonstrates the effectiveness of our computation offloading algorithms in extending the lifetime of an MDC.

UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator

Monitoring breathing rates and patterns helps in the diagnosis and potential avoidance of various health problems. Current solutions for respiratory monitoring, however, are usually invasive and/or limited to medical facilities. In this paper, we propose a novel respiratory monitoring system, UbiBreathe, based on ubiquitous off-the-shelf WiFi-enabled devices. Our experiments show that the received signal strength (RSS) at a WiFi-enabled device held on a persons chest is affected by the breathing process. This effect extends to scenarios when the person is situated on the line-of-sight (LOS) between the access point and the device, even without holding it. UbiBreathe leverages these changes in the WiFi RSS patterns to enable ubiquitous non-invasive respiratory rate estimation, as well as apnea detection. We propose the full architecture and design for UbiBreathe, incorporating various modules that help reliably extract the hidden breathing signal from a noisy WiFi RSS. The system handles various challenges such as noise elimination, interfering humans, sudden user movements, as well as detecting abnormal breathing situations. Our implementation of UbiBreathe using off-the-shelf devices in a wide range of environmental conditions shows that it can estimate different breathing rates with less than 1 breaths per minute (bpm) error. In addition, UbiBreathe can detect apnea with more than 96% accuracy in both the device-on-chest and hands-free scenarios. This highlights its suitability for a new class of anywhere respiratory monitoring.

Towards Computational Offloading in Mobile Device Clouds

Many mobile applications overcome their device limitations in computational, energy, or data resources by offloading computations to the cloud. In this paper, we consider environments in which computational offloading occurs amongst a set of mobile devices. We call such an environment a mobile device cloud (MDC). In this work, we first highlight the gain in computation time and energy consumption that can be achieved by offloading tasks to nearby devices within an MDC compared to a cloud. We then propose and implement an MDC platform that enables the creation and assessment of various offloading algorithms in MDCs. This platform consists of an Android application deployable across MDC devices, and a test bed to measure power being consumed by a mobile device. We utilize this platform to carry out various offloading experiments on an MDC test bed from which we gain interesting insights into the potential for MDC offloading. Results from these experiments show up to 50% gain in time and 26% gain in energy. Finally, we address the off loadee selection problem in MDCs by proposing several social-based algorithms. The potential promise of this approach is shown by evaluating these algorithms using real data sets that include contact traces and social information of mobile devices in a conference setting.

Making the case for computational offloading in mobile device clouds

In this paper, we consider an environment in which computational offoading is adopted amongst mobile devices. We call such an environment a mobile device cloud (MDC). In this work, we highlight via emulation, experimenation and real measurements, the potential gain in computation time and energy consumption that can be achieved by offoading tasks within an MDC. We also propose and develop an experimental platform to enable researchers create and experiment with novel offoading algorithms in MDCs.

Bandwidth aggregation techniques in heterogeneous multi-homed devices

The widespread deployment of various networking technologies, coupled with the exponential increase in end-user data demand, have led to the proliferation of multi-homed, or multi-interface enabled devices. These trends drove researchers to investigate a wide spectrum of solutions, at different layers of the protocol stack, that utilize available interfaces in such devices by aggregating their bandwidth. In this survey paper, we provide an overview and examine the evolution of bandwidth aggregation solutions over time. We begin by describing the bandwidth aggregation problem. We then investigate the common features of proposed bandwidth aggregation systems and break them down into two major categories: layer-dependent and layer-independent features. Afterwards, we discuss the evolution trends in the literature and share some open challenges requiring further research. We end the survey with a brief presentation of related work in tangential research areas.

CAF: Community aware framework for large scale mobile opportunistic networks

The fundamental challenge in opportunistic networking is when and how to forward a message. Rank-based forwarding, one of the most promising methods for addressing this challenge, ranks nodes based on their social profiles or contact history in order to identify the most suitable forwarders. While these forwarding techniques have demonstrated great performance trends, we observe that they fail to efficiently forward messages in large scale networks. In this paper, we demonstrate using real mobility traces, the weakness of existing rank-based forwarding algorithms in large scale communities. We propose strategies for partitioning large communities into sub-communities based on geographic locality or social interests. We also propose exploiting particular nodes, named MultiHomed nodes, in order to disseminate messages across these sub-communities. We introduce CAF, a Community Aware Forwarding framework, which is designed to be integrated with state-of-the-art rank-based forwarding algorithms, in order to improve their performance in large scale networks. We use real mobility traces to evaluate our proposed techniques. Our results empirically show a delivery success rate increase of up to 40%, along with 5% to 30% improved success delivery rates compared to state-of-the-art rank-based forwarding algorithms; these results are obtained while incurring a marginal increase in cost which is less than 10%. We finally propose an extension of the original framework called Community Destination Aware Framework (CDAF). Assuming that the source node can determine the destinations community, CDAF further reduces the cost of CAF by a factor of 2 while maintaining similar success rates.

Towards Mobile Opportunistic Computing

With the advent of wearable computing and the resulting growth in mobile application market, we investigate mobile opportunistic cloud computing where mobile devices leverage nearby computational resources in order to save execution time and consumed energy. Our goal is to enable generic computation offloading to heterogeneous devices that include Cloud, mobile devices, and cloudlets. We propose a generic and flexible architecture that maximizes the computation gain with respect to various objective functions such as, minimizing the response time, reducing the overall energy consumption, and increasing the network lifetime. This novel architecture is designed to automate computation offloading to numerous compute resources over disrupted network connections.