Interference is one of the major factors influencing system performance of Wireless Sensor Networks (WSNs). This issue needs a serious attention, since WSNs are typically operating on the shared and unlicensed spectrum band. In other words, interference may not be controllable especially under the coexistence of devices operating with different standards within the same area. Collection Tree Protocol (CTP) is a routing protocol that is able to support large scale WSNs. With its ability, low level interference can be overcome by the routing process. This paper proposed an adaptation of CTP, so called multi-channel CTP, which provides higher system tolerance to the interference by allowing the multiple channel usage on top of the routing ability. The proposed system is evaluated via system level simulation and the results show the system performance enhancement under severe interference scenario in comparison with the traditional CTP.
With the increasing number of devices sharing the 2.4 GHz ISM band, coexistence problem becomes one of the major issues experienced by Wireless Sensor Networks (WSN). Cognitive Wireless Sensor Networks (CWSNs) has been proposed in order to achieve reliable and efficient communication via spectrum awareness and intelligent adaption. The learning and decision making technique is one of the core competences of such system. In this work, there machine-learning techniques under the umbrella of Reinforcement Learning (RL) including GPOMDP, Episodic-Reinforcement, and True Policy Gradient are implemented for our proposed learning and decision making engine of CWSN. Simulation model has been developed and used for the investigation and the results are obtained for performance comparison in terms of prediction accuracy and WSN system performance. From this study, True Policy Gradient offers better prediction accuracy in comparison with the other two techniques. As results, CWSN implementing True Policy Gradient offers lowest packet delay under interference environment. Keywords assignment, Gradient cognitive wireless sensor networks, channel GPOMDP, Episodic-Reinforcement, True Policy I.
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