140 research outputs found
A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing
The idea of social participatory sensing provides a substrate to benefit from
friendship relations in recruiting a critical mass of participants willing to
attend in a sensing campaign. However, the selection of suitable participants
who are trustable and provide high quality contributions is challenging. In
this paper, we propose a recruitment framework for social participatory
sensing. Our framework leverages multi-hop friendship relations to identify and
select suitable and trustworthy participants among friends or friends of
friends, and finds the most trustable paths to them. The framework also
includes a suggestion component which provides a cluster of suggested friends
along with the path to them, which can be further used for recruitment or
friendship establishment. Simulation results demonstrate the efficacy of our
proposed recruitment framework in terms of selecting a large number of
well-suited participants and providing contributions with high overall trust,
in comparison with one-hop recruitment architecture.Comment: accepted in DCOSS 201
Recharging of Flying Base Stations using Airborne RF Energy Sources
This paper presents a new method for recharging flying base stations, carried
by Unmanned Aerial Vehicles (UAVs), using wireless power transfer from
dedicated, airborne, Radio Frequency (RF) energy sources. In particular, we
study a system in which UAVs receive wireless power without being disrupted
from their regular trajectory. The optimal placement of the energy sources are
studied so as to maximize received power from the energy sources by the
receiver UAVs flying with a linear trajectory over a square area. We find that
for our studied scenario of two UAVs, if an even number of energy sources are
used, placing them in the optimal locations maximizes the total received power,
while achieving fairness among the UAVs. However, in the case of using an odd
number of energy sources, we can either maximize the total received power, or
achieve fairness, but not both at the same time. Numerical results show that
placing the energy sources at the suggested optimal locations results in
significant power gain compared to nonoptimal placements.Comment: 6 pages, 5 figures, conference pape
MOF-BC: A Memory Optimized and Flexible BlockChain for Large Scale Networks
BlockChain (BC) immutability ensures BC resilience against modification or
removal of the stored data. In large scale networks like the Internet of Things
(IoT), however, this feature significantly increases BC storage size and raises
privacy challenges. In this paper, we propose a Memory Optimized and Flexible
BC (MOF-BC) that enables the IoT users and service providers to remove or
summarize their transactions and age their data and to exercise the "right to
be forgotten". To increase privacy, a user may employ multiple keys for
different transactions. To allow for the removal of stored transactions, all
keys would need to be stored which complicates key management and storage.
MOF-BC introduces the notion of a Generator Verifier (GV) which is a signed
hash of a Generator Verifier Secret (GVS). The GV changes for each transaction
to provide privacy yet is signed by a unique key, thus minimizing the
information that needs to be stored. A flexible transaction fee model and a
reward mechanism is proposed to incentivize users to participate in optimizing
memory consumption. Qualitative security and privacy analysis demonstrates that
MOF-BC is resilient against several security attacks. Evaluation results show
that MOF-BC decreases BC memory consumption by up to 25\% and the user cost by
more than two orders of magnitude compared to conventional BC instantiations
BlockChain: A distributed solution to automotive security and privacy
Interconnected smart vehicles offer a range of sophisticated services that
benefit the vehicle owners, transport authorities, car manufacturers and other
service providers. This potentially exposes smart vehicles to a range of
security and privacy threats such as location tracking or remote hijacking of
the vehicle. In this article, we argue that BlockChain (BC), a disruptive
technology that has found many applications from cryptocurrencies to smart
contracts, is a potential solution to these challenges. We propose a BC-based
architecture to protect the privacy of the users and to increase the security
of the vehicular ecosystem. Wireless remote software updates and other emerging
services such as dynamic vehicle insurance fees, are used to illustrate the
efficacy of the proposed security architecture. We also qualitatively argue the
resilience of the architecture against common security attacks
Design and analysis of fair, efficient and low-latency schedulers for high-speed packet-switched networks
A variety of emerging applications in education, medicine, business, and entertainment rely heavily on high-quality transmission of multimedia data over high speed networks. Packet scheduling algorithms in switches and routers play a critical role in the overall Quality of Service (QoS) strategy to ensure the performance required by such applications. Fair allocation of the link bandwidth among the traffic flows that share the link is an intuitively desirable property of packet schedulers. In addition, strict fairness can improve the isolation between users, help in countering certain kinds of denial-of-service attacks and offer a more predictable performance. Besides fairness, efficiency of implementation and low latency are among the most desirable properties of packet schedulers. The first part of this dissertation presents a novel scheduling discipline called Elastic Round Robin (ERR) which is simple, fair and efficient with a low latency bound. The perpacket work complexity of ERR is O(1). Our analysis also shows that, in comparison to all previously proposed scheduling disciplines of equivalent complexity, ERR has significantly better fairness properties as well as a lower latency bound. However, all frame-based schedulers including ERR suffer from high start-up latencies, burstiness in the output anddelayed correction of fairness. In the second part of this dissertation we propose a new scheduling discipline called Prioritized Elastic Round Robin (PERR) which overcomes the limitations associated with the round robin service order of ERR. The PERR scheduler achieves this by rearranging the sequence in which packets are transmitted in each round of the ERR scheduler. Our analysis reveals that PERR has a low work complexity which is independent of the number of flows. We also prove that PERR has better fairness and latency characteristics than other known schedulers of equivalent complexity. In addition to their obvious applications in Internet routers and switches, both the ERR and PERR schedulers also satisfy the unique requirements of wormhole switching, popular in interconnection networks of parallel systems. Finally, using real gateway traces and based on a new measure of instantaneous fairness borrowed from the field of economics, we present simulation results that demonstrate the improved fairness characteristics and latency bounds of the ERR and and PERR schedulers in comparison with other scheduling disciplines of equivalent efficiency.Ph.D., Electrical Engineering -- Drexel University, 200
Discretization-based ensemble model for robust learning in IoT
IoT device identification is the process of recognizing and verifying
connected IoT devices to the network. This is an essential process for ensuring
that only authorized devices can access the network, and it is necessary for
network management and maintenance. In recent years, machine learning models
have been used widely for automating the process of identifying devices in the
network. However, these models are vulnerable to adversarial attacks that can
compromise their accuracy and effectiveness. To better secure device
identification models, discretization techniques enable reduction in the
sensitivity of machine learning models to adversarial attacks contributing to
the stability and reliability of the model. On the other hand, Ensemble methods
combine multiple heterogeneous models to reduce the impact of remaining noise
or errors in the model. Therefore, in this paper, we integrate discretization
techniques and ensemble methods and examine it on model robustness against
adversarial attacks. In other words, we propose a discretization-based ensemble
stacking technique to improve the security of our ML models. We evaluate the
performance of different ML-based IoT device identification models against
white box and black box attacks using a real-world dataset comprised of network
traffic from 28 IoT devices. We demonstrate that the proposed method enables
robustness to the models for IoT device identification.Comment: 15 page
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