Showing papers by "Sonia Fahmy published in 2014"
01 May 2014
TL;DR: This work designs energy-efficient provenance encoding and construction schemes, which are referred to as Probabilistic Provenance Flow (PPF) and integrates PPF with provenance-based trust frameworks and investigates the trade-off between trustworthy data items and transmission overhead.
Abstract: Assessing the trustworthiness of sensor data and transmitters of this data is critical for quality assurance. Trust evaluation frameworks utilize data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, resulting in high energy dissipation due to the extended period of radio communication. In this paper, we design energy-efficient provenance encoding and construction schemes, which we refer to as Probabilistic Provenance Flow (PPF). Our work demonstrates the feasibility of adapting the Probabilistic Packet Marking (PPM) technique in IP traceback to wireless sensor networks. We design two bit-efficient provenance encoding schemes along with a complementary vanilla scheme. Depending on the network size and bit budget, we select the best method based on mathematical approximations and numerical analysis. We integrate PPF with provenance-based trust frameworks and investigate the trade-off between trustworthiness of data items and transmission overhead. We conduct TOSSIM simulations with realistic wireless links, and perform testbed experiments on 15-20TelosB motes to demonstrate the effectiveness of PPF. Our results show that the encoding schemes of PPF have identical performance with a low bit budget (~32-bit), requiring 33% fewer packets and 30% less energy than PPM variants to construct provenance. With a twofold increase in bit budget, PPF with the selected encoding scheme reduces energy consumption by 46-60%.
35 citations
TL;DR: A platform-independent mechanism to partition a large network experiment into a set of small experiments that are sequentially executed, exposing the fundamental tradeoff between the simplicity of the partitioning and experimentation process, and the loss of experimental fidelity.
7 citations
01 Dec 2014
TL;DR: An optimization problem to identify a set of sensor nodes and their corresponding paths toward the base station that achieve a certain trustworthiness threshold, while keeping the energy consumption of the network minimal is formulated and ERUPT, a simulated annealing solution is proposed.
Abstract: Sensor nodes are inherently unreliable and prone to hardware or software faults. Thus, they may report untrustworthy or inconsistent data. Assessing the trustworthiness of sensor data items can allow reliable sensing or monitoring of physical phenomena. A provenance-based trust framework can evaluate the trustworthiness of data items and sensor nodes based on the intuition that two data items with similar data values but with different provenance (i.e., forwarding path) can be considered more trustworthy. Forwarding paths of data items generated from redundantly deployed sensors should consist of trustworthy nodes and remain dissimilar. Unfortunately, operating many sensors with dissimilar paths consumes significant energy. In this paper, we formulate an optimization problem to identify a set of sensor nodes and their corresponding paths toward the base station that achieve a certain trustworthiness threshold, while keeping the energy consumption of the network minimal. We prove the NP-hardness of this problem and propose ERUPT, a simulated annealing solution. Testbed and simulation results show that ERUPT achieves high trustworthiness, while reducing total energy consumption by 32–50% with respect to current approaches.
4 citations