Multi-hop scheduling and local data link aggregation dependant Qos in modeling and simulation of power-aware wireless sensor networks
Conference
Iyer, V, Iyengar, SS, Murthy, R et al. (2009). Multi-hop scheduling and local data link aggregation dependant Qos in modeling and simulation of power-aware wireless sensor networks
. 844-848. 10.1145/1582379.1582562
Iyer, V, Iyengar, SS, Murthy, R et al. (2009). Multi-hop scheduling and local data link aggregation dependant Qos in modeling and simulation of power-aware wireless sensor networks
. 844-848. 10.1145/1582379.1582562
In this study of wireless sensor networks (WSN) protocols, the application Qos, system, and protocol performance metrics are measured for a large scalable wireless deployment using a typical wireless radio and an energy model. As there are many different types of WSN algorithms, we have categorized it into pro-active, re-active, and query driven information processing. A typical Qos is based on the useful lifetime of sensor nodes, after which reliability of the sensor data cannot be guaranteed and typically, a threshold such as a percentage of the sensor drains out of energy or a minimum through-put of real-time data from the sensor network is expected, which is used to compare the Qos of the routing algorithm. The results from lifetime based Qos, measured in simulation seconds, for the implemented protocols show that with varying sampled data sources for a BE Qos multi-hop deployment and varying percentage of cluster heads in a time synchronized deployment, the lifetime is based on network size and protocol invariant. However, low sensing ranges result in dense networks, and therefore, it becomes necessary to achieve an efficient medium-access protocol subjected to power constraints. Scalability of sensor network applications are based on energy energy-harvesting techniques in which the various layers of the network interoperate and extend the system network lifetime, the battery residual power per node, and the application reliability in terms of cross-layer energy savings. In this study, we have extended the lifetime metrics from a constant metrics into a break down of how much percentage of time is spent for Tx, Rx, and Idle tasks, respectively. This helps one to highlight the cross-layer energy dissipation per node and how the performance of an algorithm differs in terms of duty-cycling. Copyright 200X ACM.
