Wireless sensor networks are nowadays used in various applications to facilitate monitoring and actuation tasks, e.g., for smart grids and industrial automation. Some of these applications require guarantees or at least assurances on reliability. Such applications expect predictable throughput and delay, which are hard to maintain in environments with changing radio conditions. QoS-aware MAC protocols capable of handling such environments are well explored. They require however protocol changes and are therefore difficult to deploy. This paper presents an application layer forwarding service that offers proportional differentiation while limiting network load to preserve high utilization and predictability. Demands for capacity are expressed as fractions of the overall channel throughput. We show that this service can be implemented with a cognitive load controller (CLC) based on fuzzy logic and quality assessed with utility functions for application layer packet loss and throughput. We evaluate the CLC for 802.15.4 with CSMA/CA through NS-3 simulations showing that it offers the intended service while adjusting load for high overall throughput and low delay.
Wireless sensor networks (WSN) commonly use ZigBee to communicate, especially when low power consumption is demanded. Zig-Bee may however provide unpredictable throughput although transmission distances are short. This is especially evident in difficult environments with complicated reflections and various materials through which radio signals need to pass through. Distributed scheduling based on cognitive networking principles may improve both network predictability and overall throughput. This paper presents measurements of key parameters for such cognitive scheduling, and discusses their potential for predicting suitable per-node transmission rates. Results include variability of throughput, RSSI and LQI observed for different transmission powers, transmission ranges, and number of transmitting nodes.
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