Improving the Performance Metric of Wireless Sensor Networks with Clustering Markov Chain Model and Multilevel Fusion

The paper proposes a performance metric evaluation for a distributed detection wireless sensor network with respect to IEEE 802.15.4 standard. A distributed detection scheme is considered with presence of the fusion node and organized sensors into the clustering and non-clustering networks. Sensors are distributed in clusters uniformly and nonuniformly and network has multilevel fusion centers. Fusion centers act as heads of clusters for decision making based on majority-like received signal strength (RSS) with comparison the optimized value of the common threshold. IEEE 802.15.4 Markov chain model derived the performance metric of proposed network architecture with MAC, PHY cross-layer parameters, and Channel State Information (CSI) specifications while it is including Path-loss, Modulation, Channel coding and Rayleigh fading. Simulation results represent significant enhancement on performance of network in terms of reliability, packet failure, average delay, power consumption, and throughput.

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A Markov chain model for IEEE 802.15.4 in time critical wireless sensor networks under periodic traffic with reneging packets

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-02984-6 ◽

2021 ◽

Author(s):

Hossein Hadadian Nejad Yousefi ◽

Yousef Kavian ◽

Alimorad Mahmoudi

Keyword(s):

Wireless Sensor Networks ◽

Markov Chain ◽

Sensor Networks ◽

Ieee 802.15.4 ◽

Markov Chain Model ◽

Wireless Sensor ◽

Chain Model ◽

Periodic Traffic ◽

Time Critical

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Four-dimensional Markov chain model of single-hop data aggregation with IEEE 802.15.4 in wireless sensor networks

Wireless Networks ◽

10.1007/s11276-011-0412-1 ◽

2012 ◽

Vol 18 (5) ◽

pp. 469-479 ◽

Cited By ~ 8

Author(s):

Xiaoyun Li ◽

David K. Hunter

Keyword(s):

Wireless Sensor Networks ◽

Markov Chain ◽

Sensor Networks ◽

Data Aggregation ◽

Ieee 802.15.4 ◽

Markov Chain Model ◽

Wireless Sensor ◽

Chain Model

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Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Journal of Sensor and Actuator Networks ◽

10.3390/jsan7030039 ◽

2018 ◽

Vol 7 (3) ◽

pp. 39 ◽

Cited By ~ 2

Author(s):

George Koufoudakis ◽

Konstantinos Oikonomou ◽

Georgios Tsoumanis

Keyword(s):

Markov Chain ◽

Energy Harvesting ◽

Information Dissemination ◽

Markov Chain Model ◽

Wireless Sensor ◽

Chain Model ◽

Network Coverage ◽

Termination Time ◽

Probabilistic Flooding ◽

Network Environments

Technological advantages in energy harvesting have been successfully applied in wireless sensor network environments, prolonging network’s lifetime, and, therefore, classical networking approaches like information dissemination need to be readdressed. More specifically, Probabilistic Flooding information dissemination is revisited in this work and it is observed that certain limitations arise due to the idiosyncrasies of nodes’ operation in energy harvesting network environments, resulting in reduced network coverage. In order to address this challenge, a modified version of Probabilistic Flooding is proposed, called Robust Probabilistic Flooding, which is capable of dealing with nodes of about to be exhausted batteries that resume their operation after ambient energy collection. In order to capture the behavior of the nodes’ operational states, a Markov chain model is also introduced and—based on certain observations and assumptions presented here—is subsequently simplified. Simulation results based on the proposed Markov chain model and a solar radiation dataset demonstrate the inefficiencies of Probabilistic Flooding and show that its enhanced version (i.e., Robust Probabilistic Flooding) is capable of fully covering the network on the expense of increased termination time in energy harvesting environments. Another advantage is that no extra overhead is introduced regarding the number of disseminated messages, thus not introducing any extra transmissions and therefore the consumed energy does not increase.

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