Faezeh Hajiaghajani

Economy driven content dissemination in Delay Tolerant Networks

Abstract Majority of the existing Delay Tolerant Network (DTN) routing protocols attempt to minimize the message delay, forwarding count, or required storage. However, for many DTN applications such as distributing commercial content, targeting the best performance for only one index and neglecting the others is insufficient. A more practical solution would be to strike a balance between multiple of those performance indices. This paper introduces a Gain-aware Dissemination Protocol (GDP) which attempts to reach the maximum economic gain of content delivery by maintaining a balance between the value achieved via message delivery and the involved forwarding costs given out as user rebates. Economic gain from disseminating content is defined as the generated value upon content delivery minus its forwarding costs. The key concept behind the proposed protocol is to adaptively balance between dissemination latency and forwarding costs in order to maximize the economic gain. Using the DTN simulation software ONE, we characterize the GDP protocol with varying mobility models, content sources, and content generation times.

Device-to-device commercial content dissemination in social wireless networks

Majority of existing Delay Tolerant Network (DTN) multicast routing approaches attempt to individually optimize performance indices such as message delay, forwarding cost, and required storage. Very few define composite cost factors by combining multiple such indices into a single index. This paper introduces a composite index, namely economic gain, which combines revenue from delivery and forwarding cost from disseminating commercial content such as coupons. In this context, an Economy Driven Content Dissemination (EDCD) protocol is proposed that controls D2D content dissemination with an end goal of maximizing the economic gain for a coupon-generator. Using the DTN simulation software ONE, we run experiments for functional validation and performance evaluation of the proposed protocol with respect to few other competitive protocols under different mobility scenarios.

Contact-less indoor activity analysis using first-reflection echolocation

This paper presents an ultrasound echolocation-based approach for human activity recognition in indoor settings. The key novelty of the proposed approach is to perform activity analysis using distance estimated through “first-reflection echolocation”. The distance to the nearest obstructing object is computed using the first reflected ultrasound signal. All subsequent reflected signal components from other distant objects are ignored. This leads to an extremely simple signal (i.e., time-series distance data) analysis approach with very low computational complexity. Especially so, when compared with the existing approaches in literature in which full reflected signal analysis, often with Doppler Shift computation, is performed for activity classification. It is demonstrated that for the goal of isolating workplace sedentary behavior, the proposed approach can differentiate between sitting, standing, and walking (i.e., in-office pacing) with more than 80% accuracy. This was validated with different classifiers applied on data collected from multiple subjects in multiple sessions. Recorded video was used as the ground-truth for training the classifiers.

Device-to-device coupon distribution using economic routing utilities

This paper presents a multicast Delay Tolerant Network (DTN) routing protocol that uses an economic gain based routing utility for device-to-device (D2D) commercial coupon dissemination. Majority of the existing DTN routing approaches are not practically applicable to commercial content dissemination since they attempt to individually optimize performance indices such as message delay, forwarding cost, and required storage, but not their combined economic benefits. This paper uses a composite routing performance index, namely economic gain, which combines revenue from delivery and forwarding cost from disseminating commercial content such as coupons. It then develops a new gain-aware DTN multicast routing utility that is designed around the notion of consumption interest and coupon redemption probability. We propose two routing protocols, namely, Predictive Gain Utility Routing Individual (PGUR-I), and a more scalable version, Predictive Gain Utility Routing Aggregated (PGUR-A), to control D2D coupon dissemination with an end goal of maximizing the economic gain for a coupon-generator. Using the DTN simulation software ONE, we run experiments for functional validation and performance evaluation of the proposed protocols with respect to an existing competitive protocol under different protocol constraints and mobility scenarios.

MAC Protocol Design Using Evolvable State-Machines

This paper explores a framework of Medium Access Control (MAC) protocol synthesis using evolving finite state machines. A network protocol is coded as a genotype and its resulting state machine behavior manifests in the form of the corresponding phenotype, leading to specific protocol performance. The genotype or state machine is allowed to evolve under the pressure of desired performance fitness. In this paper, the proposed framework is applied specifically to the well-known pure-ALOHA protocol for which protocol performance is known under a certain network and loading conditions. Using a C based protocol fitness evaluator and a Java based evolution fabric, we show that a generalized state machine is able to evolve towards known pure-ALOHA solutions under a wide range of network and loading conditions.

Office activity classification using first-reflection ultrasonic echolocation

Excessive sedentary time poses considerable health risks for individuals predominately engaged in desk-bound work. To empower interventions aimed at addressing this problem, reliable technologies for continuous activity monitoring within an office environment are required. As an alternative to existing solutions, we propose the Echolocation-based Activity Detector, a contactless sensor array of four first-reflection ultrasonic distance sensors. The research described herein demonstrates the capacity of the sensor to distinguish between common activities performed at a workstation within an office environment, including sedentary sitting, typing, writing, and standing. Cubic support vector machine classifiers are developed using dispersion-related features computed from the time-series array outputs. Average classification accuracy for sedentary activities exceeds 85%, while classification accuracy for the entire activity set exceeds 80% for a controlled experiment conducted with six participants.

Pulse position coded PDUs: A new approach to networking energy economy

This paper develops a brand new approach towards data transfer using position-modulated pulses. The core idea is to encode a data value in terms of the time gap between two transmitted pulses whose positions are modulated accordingly. It is shown that this concept allows sending a data value using significantly less number of pulses or logical bits as used in conventional packet-based transports. Significant savings may come from the fact that it does not require the large number of preamble bits needed for synchronization in packets. Fewer pulses can lead to reduced-energy overhead compared to packets. The framework is mainly targeted towards sensing applications in which energy is a premium. Building on this fundamentally new concept, methods are developed in this paper for: 1) pulse-based transport on transmit-only links, and 2) code compression to reduce the overall energy and transmission delay. Analytical models and extensive simulations have been performed for evaluating the performance of the system relative to comparable packet-based solutions.

Distributed caching in mobile networks with heterogeneous content demand

The proposed architecture implements a content sharing infrastructure using device-to-device wireless links towards the goal of reducing the usage cost of 3G/4G cellular links. For downloading digital content, a device can first search within the local mobile network at the edge for the requested content before downloading it from the CPs server. The expected usage of 3G/4G bandwidth in this approach can be lower. Building on a content pricing and sharing incentive structure, the paper first develops a new heterogeneous model for content request from mobile devices. Using this model, it then develops an optimal cooperative caching strategy with the goal of minimizing the content provisioning cost in wireless mobile networks. Using the mobile network simulator ONE, it is shown that the proposed mechanism is able to reduce bandwidth usage and the resulting content provisioning cost compared to traditional caching mechanisms in both monolithic and community-based mobility scenario.

Heterogeneous content caching in wireless networks

This work develops a cooperative content caching framework for Social Wireless Networks (SWNETs) in which content demands are hierarchically heterogeneous. The heterogeneous request model incorporates user preference for different categories/genres, and contents under each category, both following power law distributions at local as well as global levels. Based upon such request generation model, an optimal Heterogeneous Split Caching (HSC) algorithm is proposed which can minimize electronic content provisioning cost using cooperative caching policies. Detailed simulation experiments for functional validation and performance evaluation with respect to traditional caching strategies are then performed under various protocol and network parameters. The baseline HSC mechanism is also tested against a special benchmark situation that provides a steady state performance bound, which can occur after an infinite request horizon.

Learning based gain-aware content dissemination in delay tolerant networks

This paper presents a multicast Delay Tolerant Network (DTN) routing protocol with the goal of maximizing an economic gain for Device-to-Device (D2D) commercial content dissemination. The defined economic gain is a composite routing performance index, which combines revenue from delivery, and forwarding cost from disseminating commercial content such as coupons. Majority of existing approaches, due to lack of gain-awareness, cannot control the economic gain of dissemination, as defined in this context. In the proposed approach, which is inspired by Q-learning, nodes are able to learn a set of forwarding actions which lead to a higher expected reward, i.e., gain. Due to the online learning framework, this Q-learning based Gain-aware Routing (QGR) protocol is expected to be robust in diverse and dynamic mobility environments. Using the DTN simulation software ONE, we run experiments for functional validation and performance evaluation of the proposed protocol with respect to two existing competitive protocols under different protocol constraints and mobility scenarios.