In wireless sensor networks(WSNs),nodes are often scheduled to alternate between working mode and sleeping mode from energy efficiency point of view.When delay is tolerable,it is not necessary to preserve network conn...In wireless sensor networks(WSNs),nodes are often scheduled to alternate between working mode and sleeping mode from energy efficiency point of view.When delay is tolerable,it is not necessary to preserve network connectivity during activity(working or sleeping) scheduling,enabling more sensors to be switched to sleeping mode and thus more energy savings.In this paper,the nodal behavior in such delay-tolerant WSNs(DT-WSNs) is modeled and analyzed.The maximum hop count with a routing path is derived in order not to violate a given sensor-to-sink delay constraint,along with extensive simulation results.展开更多
This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe wea...This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.展开更多
基金This research is supported by the Provincial Natural Science Foundation of Jiangsu under Grant No.BK97047The Education Bureau Foundation of Jiangsu Province under Grant No. 00KJT11003.
基金Sponsored by the Shanghai Education Bureau(Grant No. 11YZ93,A-3101-10-035)the Shanghai Baiyulan Funding(Grant No. 2010B086)the National Natural Science Foundation of China(Grant No. 61003215)
文摘In wireless sensor networks(WSNs),nodes are often scheduled to alternate between working mode and sleeping mode from energy efficiency point of view.When delay is tolerable,it is not necessary to preserve network connectivity during activity(working or sleeping) scheduling,enabling more sensors to be switched to sleeping mode and thus more energy savings.In this paper,the nodal behavior in such delay-tolerant WSNs(DT-WSNs) is modeled and analyzed.The maximum hop count with a routing path is derived in order not to violate a given sensor-to-sink delay constraint,along with extensive simulation results.
文摘This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.
