scholarly journals A Hybrid Secure Scheme for Wireless Sensor Networks against Timing Attacks Using Continuous-Time Markov Chain and Queueing Model

Sensors ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1606 ◽  
Author(s):  
Tianhui Meng ◽  
Xiaofan Li ◽  
Sha Zhang ◽  
Yubin Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Markov Chain ◽  
Sensor Networks ◽  
Continuous Time ◽  
Wireless Sensor ◽  
Queueing Model ◽  
Continuous Time Markov Chain ◽  
Timing Attacks

scholarly journals Adaptive Duty Cycling in Sensor Networks With Energy Harvesting Using Continuous-Time Markov Chain and Fluid Models

2015 ◽  
Vol 33 (12) ◽  
pp. 2687-2700 ◽  
Author(s):  
Wai Hong Ronald Chan ◽  
Pengfei Zhang ◽  
Ido Nevat ◽  
Sai Ganesh Nagarajan ◽  
Alvin C. Valera ◽  
...  
Keyword(s):  
Markov Chain ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Continuous Time ◽  
Fluid Models ◽  
Continuous Time Markov Chain ◽  
Duty Cycling

scholarly journals Enabling Green Wireless Sensor Networks: Energy Efficient T-MAC Using Markov Chain Based Optimization

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 534 ◽  
Author(s):  
Mahendra Ram ◽  
Sushil Kumar ◽  
Vinod Kumar ◽  
Ajay Sikandar ◽  
Rupak Kharel
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Markov Chain ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Access Control ◽  
Analytical Model ◽  
Wireless Sensor ◽  
Medium Access ◽  
Service Delay

Due to the rapidly growing sensor-enabled connected world around us, with the continuously decreasing size of sensors from smaller to tiny, energy efficiency in wireless sensor networks has drawn ample consideration in both academia as well as in industries’ R&D. The literature of energy efficiency in wireless sensor networks (WSNs) is focused on the three layers of wireless communication, namely the physical, Medium Access Control (MAC) and network layers. Physical layer-centric energy efficiency techniques have limited capabilities due to hardware designs and size considerations. Network layer-centric energy efficiency approaches have been constrained, in view of network dynamics and available network infrastructures. However, energy efficiency at the MAC layer requires a traffic cooperative transmission control. In this context, this paper presents a one-dimensional discrete-time Markov chain analytical model of the Timeout Medium Access Control (T-MAC) protocol. Specifically, an analytical model is derived for T-MAC focusing on an analysis of service delay, throughput, energy consumption and power efficiency under unsaturated traffic conditions. The service delay model calculates the average service delay using the adaptive sleep wakeup schedules. The component models include a queuing theory-based throughput analysis model, a cycle probability-based analytical model for computing the probabilities of a successful transmission, collision, and the idle state of a sensor, as well as an energy consumption model for the sensor’s life cycle. A fair performance assessment of the proposed T-MAC analytical model attests to the energy efficiency of the model when compared to that of state-of-the-art techniques, in terms of better power saving, a higher throughput and a lower energy consumption under various traffic loads.

scholarly journals Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5379
Author(s):  
Gustavo A. Nunez Segura ◽  
Cintia Borges Margi
Keyword(s):  
Wireless Sensor Networks ◽  
Markov Chain ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Performance ◽  
Resource Constraints ◽  
Software Defined Networking ◽  
Wireless Sensor ◽  
Prediction Algorithm ◽  
Energy Prediction

Resource Constraints in Wireless Sensor Networks are a key factor in protocols and application design. Furthermore, energy consumption plays an important role in protocols decisions, such as routing metrics. In Software-Defined Networking (SDN)-based networks, the controller is in charge of all control and routing decisions. Using energy as a metric requires such information from the nodes, which would increase packets traffic, impacting the network performance. Previous works have used energy prediction techniques to reduce the number of packets exchanged in traditional distributed routing protocols. We applied this technique in Software-Defined Wireless Sensor Networks (SDWSN). For this, we implemented an energy prediction algorithm for SDWSN using Markov chain. We evaluated its performance executing the prediction on every node and on the SDN controller. Then, we compared their results with the case without prediction. Our results showed that by running the Markov chain on the controller we obtain better prediction and network performance than when running the predictions on every node. Furthermore, we reduced the energy consumption for topologies up to 49 nodes for the case without prediction.

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