3,397 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Direct Acyclic Graph based Ledger for Internet of Things: Performance and Security Analysis
Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus
algorithm has been identified as a promising technology for Internet of Things
(IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have
been widely used in blockchain, the consensus mechanism designed on DAG
structure (simply called as DAG consensus) can overcome some shortcomings such
as high resource consumption, high transaction fee, low transaction throughput
and long confirmation delay. However, the theoretic analysis on the DAG
consensus is an untapped venue to be explored. To this end, based on one of the
most typical DAG consensuses, Tangle, we investigate the impact of network load
on the performance and security of the DAG-based ledger. Considering unsteady
network load, we first propose a Markov chain model to capture the behavior of
DAG consensus process under dynamic load conditions. The key performance
metrics, i.e., cumulative weight and confirmation delay are analysed based on
the proposed model. Then, we leverage a stochastic model to analyse the
probability of a successful double-spending attack in different network load
regimes. The results can provide an insightful understanding of DAG consensus
process, e.g., how the network load affects the confirmation delay and the
probability of a successful attack. Meanwhile, we also demonstrate the
trade-off between security level and confirmation delay, which can act as a
guidance for practical deployment of DAG-based ledgers.Comment: accepted by IEEE Transactions on Networkin
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