369 research outputs found
Tree-Chain: A Fast Lightweight Consensus Algorithm for IoT Applications
Blockchain has received tremendous attention in non-monetary applications
including the Internet of Things (IoT) due to its salient features including
decentralization, security, auditability, and anonymity. Most conventional
blockchains rely on computationally expensive consensus algorithms, have
limited throughput, and high transaction delays. In this paper, we propose
tree-chain a scalable fast blockchain instantiation that introduces two levels
of randomization among the validators: i) transaction level where the validator
of each transaction is selected randomly based on the most significant
characters of the hash function output (known as consensus code), and ii)
blockchain level where validator is randomly allocated to a particular
consensus code based on the hash of their public key. Tree-chain introduces
parallel chain branches where each validator commits the corresponding
transactions in a unique ledger. Implementation results show that tree-chain is
runnable on low resource devices and incurs low processing overhead, achieving
near real-time transaction settlement
Causal Inference in Disease Spread across a Heterogeneous Social System
Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635
In-Network Distributed Solar Current Prediction
Long-term sensor network deployments demand careful power management. While
managing power requires understanding the amount of energy harvestable from the
local environment, current solar prediction methods rely only on recent local
history, which makes them susceptible to high variability. In this paper, we
present a model and algorithms for distributed solar current prediction, based
on multiple linear regression to predict future solar current based on local,
in-situ climatic and solar measurements. These algorithms leverage spatial
information from neighbors and adapt to the changing local conditions not
captured by global climatic information. We implement these algorithms on our
Fleck platform and run a 7-week-long experiment validating our work. In
analyzing our results from this experiment, we determined that computing our
model requires an increased energy expenditure of 4.5mJ over simpler models (on
the order of 10^{-7}% of the harvested energy) to gain a prediction improvement
of 39.7%.Comment: 28 pages, accepted at TOSN and awaiting publicatio
Guiding Ebola Patients to Suitable Health Facilities: An SMS-based Approach
We propose to utilize mobile phone technology as a vehicle for people to
report their symptoms and to receive immediate feedback about the health
services readily available, and for predicting spatial disease outbreak risk.
Once symptoms are extracted from the patients text message, they undergo
complex classification, pattern matching and prediction to recommend the
nearest suitable health service. The added benefit of this approach is that it
enables health care facilities to anticipate arrival of new potential Ebola
cases
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
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
- …