GloMoSim: a library for parallel simulation of large-scale wireless networks
PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
Wireless integrated network sensors
Communications of the ACM
System architecture directions for networked sensors
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
AIDA: Adaptive application-independent data aggregation in wireless sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
On Real-Time Capacity Limits of Multihop Wireless Sensor Networks
RTSS '04 Proceedings of the 25th IEEE International Real-Time Systems Symposium
The capacity of wireless networks
IEEE Transactions on Information Theory
IEEE Communications Magazine
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Wireless Sensor Network (WSN) should be capable of fulfilling its mission, in a timely manner and without loss of important information. In this paper, we propose a new analytical model for calculating RRT (Reliable Real-Time) degree in multihop WSNs, where RRT degree describes the percentage of real-time data that the network can reliably deliver on time from any source to its destination. Also, packet loss probability is modeled as a function of the probability of link failure when the buffer is full and the probability of node failure when node's energy is depleted. Most of network properties are considered as random variables and a queuing-theory based model is derived. In this model, the effect of network load on the packets delay, RRT degree, and nodes energy depletion rate is considered. Simulation results are used to validate the proposed model. The simulation results agree very well with the model.