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