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Alhussein A. Abouzeid - One of the best experts on this subject based on the ideXlab platform.
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Weak State Routing for Large-Scale Dynamic Networks
IEEE ACM Transactions on Networking, 2010Co-Authors: Utku Günay Acer, Shivkumar Kalyanaraman, Alhussein A. AbouzeidAbstract:Forwarding decisions in routing protocols rely on information about the destination nodes provided by routing table states. When paths to a destination change, corresponding states become invalid and need to be refreshed with control messages for resilient routing. In large and highly Dynamic networks, this overhead can crowd out the capacity for data traffic. For such networks, we propose the concept of weak state, which is interpreted as a probabilistic hint, not as absolute truth. Weak state can remain valid without explicit messages by systematically reducing the confidence in its accuracy. Weak State Routing (WSR) is a novel routing protocol that uses weak state along with random directional walks for forwarding packets. When a packet reaches a node that contains a weak state about the destination with higher confidence than that held by the packet, the walk direction is biased. The packet reaches the destination via a sequence of directional walks, punctuated by biasing decisions. WSR also uses random directional walks for disseminating routing state and provides mechanisms for aggregating weak state. Our simulation results show that WSR offers a very high packet delivery ratio ( ≥ 98%). Control traffic overhead Scales as O(N), and the state complexity is Θ(N3/2), where N is the number of nodes. Packets follow longer paths compared to prior protocols (OLSR , GLS-GPSR , ), but the average path length is asymptotically efficient and Scales as O(√N). Despite longer paths, WSR's end-to-end packet delivery delay is much smaller due to the dramatic reduction in protocol overhead.
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weak state routing for large Scale Dynamic networks
ACM IEEE International Conference on Mobile Computing and Networking, 2007Co-Authors: Utku Günay Acer, Shivkumar Kalyanaraman, Alhussein A. AbouzeidAbstract:Routing in communication networks involves the indirection from a persistent name (or ID) to a locator and delivering packets based upon the locator. In a large-Scale, highly Dynamic network, the ID-to-locator mappings are both large in number, and change often. Traditional routing protocols require high overhead to keep these in directions up-to-date. In this paper, we propose Weak State Routing (WSR), a routing mechanism for large-Scale highly Dynamic networks. WSR's novelty is that it uses random directional walks biased occasionally by weak indirection state information in intermediate nodes. The indirection state information is weak, i.e. interpreted not as absolute truth, but as probabilistic hints. Nodes only have partial information about the region a destination node is likely to be. This method allows us to aggregate information about a number of remote locations in a geographic region. In other words, the state information maps a set-of-IDs to a it geographical region. The intermediate nodes receiving the random walk use a method similar to longest-prefix-match in order to prioritize their mappings to decide how to bias and forward the random walk. WSR can also be viewed as an unstructured distributed hashing technique. WSR displays good rare-object recall with scalability properties similar to structured DHTs, albeit with more tolerance to dynamism and without constraining the degree distribution of the underlying network. Through simulations, we show that WSR offers a high packet delivery ratio, more than 98%. The control packet overhead incurred in the network Scales as O(N) for N-node networks. The number of mappings stored in the network appears to Scale as Θ(N(3/2)). We compare WSR with Dynamic Source Routing (DSR) and geographic forwarding (GPSR) combined with Grid Location Service (GLS). Our results indicate that WSR delivers more packets with less overhead at the cost of increased path length.
Utku Günay Acer - One of the best experts on this subject based on the ideXlab platform.
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Weak State Routing for Large-Scale Dynamic Networks
IEEE ACM Transactions on Networking, 2010Co-Authors: Utku Günay Acer, Shivkumar Kalyanaraman, Alhussein A. AbouzeidAbstract:Forwarding decisions in routing protocols rely on information about the destination nodes provided by routing table states. When paths to a destination change, corresponding states become invalid and need to be refreshed with control messages for resilient routing. In large and highly Dynamic networks, this overhead can crowd out the capacity for data traffic. For such networks, we propose the concept of weak state, which is interpreted as a probabilistic hint, not as absolute truth. Weak state can remain valid without explicit messages by systematically reducing the confidence in its accuracy. Weak State Routing (WSR) is a novel routing protocol that uses weak state along with random directional walks for forwarding packets. When a packet reaches a node that contains a weak state about the destination with higher confidence than that held by the packet, the walk direction is biased. The packet reaches the destination via a sequence of directional walks, punctuated by biasing decisions. WSR also uses random directional walks for disseminating routing state and provides mechanisms for aggregating weak state. Our simulation results show that WSR offers a very high packet delivery ratio ( ≥ 98%). Control traffic overhead Scales as O(N), and the state complexity is Θ(N3/2), where N is the number of nodes. Packets follow longer paths compared to prior protocols (OLSR , GLS-GPSR , ), but the average path length is asymptotically efficient and Scales as O(√N). Despite longer paths, WSR's end-to-end packet delivery delay is much smaller due to the dramatic reduction in protocol overhead.
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weak state routing for large Scale Dynamic networks
ACM IEEE International Conference on Mobile Computing and Networking, 2007Co-Authors: Utku Günay Acer, Shivkumar Kalyanaraman, Alhussein A. AbouzeidAbstract:Routing in communication networks involves the indirection from a persistent name (or ID) to a locator and delivering packets based upon the locator. In a large-Scale, highly Dynamic network, the ID-to-locator mappings are both large in number, and change often. Traditional routing protocols require high overhead to keep these in directions up-to-date. In this paper, we propose Weak State Routing (WSR), a routing mechanism for large-Scale highly Dynamic networks. WSR's novelty is that it uses random directional walks biased occasionally by weak indirection state information in intermediate nodes. The indirection state information is weak, i.e. interpreted not as absolute truth, but as probabilistic hints. Nodes only have partial information about the region a destination node is likely to be. This method allows us to aggregate information about a number of remote locations in a geographic region. In other words, the state information maps a set-of-IDs to a it geographical region. The intermediate nodes receiving the random walk use a method similar to longest-prefix-match in order to prioritize their mappings to decide how to bias and forward the random walk. WSR can also be viewed as an unstructured distributed hashing technique. WSR displays good rare-object recall with scalability properties similar to structured DHTs, albeit with more tolerance to dynamism and without constraining the degree distribution of the underlying network. Through simulations, we show that WSR offers a high packet delivery ratio, more than 98%. The control packet overhead incurred in the network Scales as O(N) for N-node networks. The number of mappings stored in the network appears to Scale as Θ(N(3/2)). We compare WSR with Dynamic Source Routing (DSR) and geographic forwarding (GPSR) combined with Grid Location Service (GLS). Our results indicate that WSR delivers more packets with less overhead at the cost of increased path length.
Lei Zhang - One of the best experts on this subject based on the ideXlab platform.
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optimal energy management of wind battery hybrid power system with two Scale Dynamic programming
IEEE Transactions on Sustainable Energy, 2013Co-Authors: Lei Zhang, Yaoyu LiAbstract:This study is concerned with the optimal energy management for a wind-battery hybrid power system (WBHPS) with local load and grid connection, by including the current and future information on generation, demand, and real-time utility price. When applying typical Dynamic optimization schemes to such a problem with a single time Scale, the following dilemma usually presents: it is more beneficial to plan the (battery) storage setpoint trajectory for the longer horizon, while prediction of renewable generation, utility price, and load demand is more accurate for the shorter term. To relieve such conflict, a two-Scale Dynamic programming (DP) scheme is applied based on multiScale predictions of wind power generation, utility price, and load. A macro-Scale Dynamic programming (MASDP) is first performed for the whole operational period, based on long-term ahead prediction of electricity price and wind energy. The resultant battery state-of-charge (SOC) is thus obtained as the macro-Scale reference trajectory. As the operation proceeds, the micro-Scale Dynamic programming (MISDP) is applied to the short-term interval based on short-term three-hour ahead predictions. The MASDP battery SOC trajectory is used as the terminal condition for the MISDP. Simulation results show that the proposed method can significantly decrease the energy cost compared with the single Scale DP method.
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Optimal Energy Management of Hybrid Power System With Two-Scale Dynamic Programming
ASME 2011 Dynamic Systems and Control Conference and Bath ASME Symposium on Fluid Power and Motion Control Volume 1, 2011Co-Authors: Lei ZhangAbstract:Energy management is one of the main issues in operating the HPS, which needs to be optimized with respect to the current and future change in generation, demand, and market price, particularly for HPS with strong renewable penetration. Optimal energy management strategies such as Dynamic programming (DP) may become significantly suboptimal under strong uncertainty in prediction of renewable generation and utility price. In order to reduce the impact of such uncertainties, a two-Scale Dynamic programming scheme is proposed in this study to optimize the operational benefit based on multi-Scale prediction. First, a macro-Scale Dynamic programming (MASDP) is performed for the long term period, based on long term ahead prediction of hourly electricity price and wind energy (speed). The battery state-of-charge (SOC) is thus obtained as the macro-Scale reference trajectory. The micro-Scale Dynamic programming (MISDP) is then applied with a short term interval, based on short term-hour ahead auto-regressive moving average (ARMA) prediction of hourly electricity price and wind energy. The nodal SOC values from the MASDP result are used as the terminal condition for the MISDP. The simulation results show that the proposed method can significantly decrease the operation cost, as compared with the single Scale DP method.Copyright © 2011 by ASME
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Optimal energy management of hybrid power system with two-Scale Dynamic programming
Proceedings of the 2011 American Control Conference, 2011Co-Authors: Lei ZhangAbstract:Hybrid power system (HPS) is the power system consists of renewable energy sources and traditional energy sources used together to increase system efficiency and reduce operation cost. Energy management is one of the main issues in operating the HPS, which needs to be optimized with respect to the current and future change in generation, demand, and market price, particularly for HPS with strong renewable penetration. Optimal energy management strategies such as Dynamic programming (DP) may become significantly suboptimal under strong uncertainty in prediction of renewable generation and utility price. In order to reduce the impact of such uncertainties, a two-Scale Dynamic programming scheme is proposed in this study to optimize the operational benefit based on multi-Scale prediction. The proposed idea is illustrated with a simple HPS which consists of wind turbine and battery storage with grid connection. The system is expected to satisfy certain load demand while minimizing the cost via peak-load shaving. First, a macro-Scale Dynamic programming (MASDP) is performed for the long term period, based on long term ahead prediction of hourly electricity price and wind energy (speed). The battery state-of-charge (SOC) is thus obtained as the macro-Scale reference trajectory. The micro-Scale Dynamic programming (MISDP) is then applied with a short term interval, based on short term-hour ahead auto-regressive moving average (ARMA) prediction of hourly electricity price and wind energy. The nodal SOC values from the MASDP result are used as the terminal condition for the MISDP. The simulation results show that the proposed method can significantly decrease the operation cost, as compared with the single Scale DP method.
Shivkumar Kalyanaraman - One of the best experts on this subject based on the ideXlab platform.
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Weak State Routing for Large-Scale Dynamic Networks
IEEE ACM Transactions on Networking, 2010Co-Authors: Utku Günay Acer, Shivkumar Kalyanaraman, Alhussein A. AbouzeidAbstract:Forwarding decisions in routing protocols rely on information about the destination nodes provided by routing table states. When paths to a destination change, corresponding states become invalid and need to be refreshed with control messages for resilient routing. In large and highly Dynamic networks, this overhead can crowd out the capacity for data traffic. For such networks, we propose the concept of weak state, which is interpreted as a probabilistic hint, not as absolute truth. Weak state can remain valid without explicit messages by systematically reducing the confidence in its accuracy. Weak State Routing (WSR) is a novel routing protocol that uses weak state along with random directional walks for forwarding packets. When a packet reaches a node that contains a weak state about the destination with higher confidence than that held by the packet, the walk direction is biased. The packet reaches the destination via a sequence of directional walks, punctuated by biasing decisions. WSR also uses random directional walks for disseminating routing state and provides mechanisms for aggregating weak state. Our simulation results show that WSR offers a very high packet delivery ratio ( ≥ 98%). Control traffic overhead Scales as O(N), and the state complexity is Θ(N3/2), where N is the number of nodes. Packets follow longer paths compared to prior protocols (OLSR , GLS-GPSR , ), but the average path length is asymptotically efficient and Scales as O(√N). Despite longer paths, WSR's end-to-end packet delivery delay is much smaller due to the dramatic reduction in protocol overhead.
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weak state routing for large Scale Dynamic networks
ACM IEEE International Conference on Mobile Computing and Networking, 2007Co-Authors: Utku Günay Acer, Shivkumar Kalyanaraman, Alhussein A. AbouzeidAbstract:Routing in communication networks involves the indirection from a persistent name (or ID) to a locator and delivering packets based upon the locator. In a large-Scale, highly Dynamic network, the ID-to-locator mappings are both large in number, and change often. Traditional routing protocols require high overhead to keep these in directions up-to-date. In this paper, we propose Weak State Routing (WSR), a routing mechanism for large-Scale highly Dynamic networks. WSR's novelty is that it uses random directional walks biased occasionally by weak indirection state information in intermediate nodes. The indirection state information is weak, i.e. interpreted not as absolute truth, but as probabilistic hints. Nodes only have partial information about the region a destination node is likely to be. This method allows us to aggregate information about a number of remote locations in a geographic region. In other words, the state information maps a set-of-IDs to a it geographical region. The intermediate nodes receiving the random walk use a method similar to longest-prefix-match in order to prioritize their mappings to decide how to bias and forward the random walk. WSR can also be viewed as an unstructured distributed hashing technique. WSR displays good rare-object recall with scalability properties similar to structured DHTs, albeit with more tolerance to dynamism and without constraining the degree distribution of the underlying network. Through simulations, we show that WSR offers a high packet delivery ratio, more than 98%. The control packet overhead incurred in the network Scales as O(N) for N-node networks. The number of mappings stored in the network appears to Scale as Θ(N(3/2)). We compare WSR with Dynamic Source Routing (DSR) and geographic forwarding (GPSR) combined with Grid Location Service (GLS). Our results indicate that WSR delivers more packets with less overhead at the cost of increased path length.
Ping Zhang - One of the best experts on this subject based on the ideXlab platform.
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Large Scale Dynamic Testing of Rock Support System at Kiirunavaara Underground Mine
Rock Mechanics and Rock Engineering, 2016Co-Authors: Shahin Shirzadegan, Erling Nordlund, Ping ZhangAbstract:A series of five large Scale Dynamic tests were conducted at the LKAB Kiirunavaara mine using explosives to generate the Dynamic load on the support system. This was done with the aim of developing a testing methodology for in situ testing of ground support. Furthermore, the response of the installed rock support system to strong Dynamic loading was evaluated. The tests included ground motion measurements, fracture investigation, ground and support motion imaging, as well as deformation measurements. The results indicated that the relation between the burden and the used amount of explosive had a vital role in either reducing or involving the effect of the detonation gases in the test results. In addition, the type of explosive which was used in the tests had a great impact on minimising the gas expansion effects. Higher peak particle velocities were measured compared to those of similar large Scale tests carried out in other countries. However, the level of induced damage was limited to a fractured zone behind the support system and propagation of cracks in the shotcrete. Measured peak particle velocities were used to calculate the kinetic energy transmitted to the fractured zone of the test wall. The energy absorption by the Swellex, reinforced shotcrete and weld mesh was estimated by measuring the elongation/deflection of the support elements and relating these measurements to previously conducted laboratory tests. The comparison of maximum estimated energy absorbed by support system with the maximum estimated kinetic energy indicated that as the support system is still functional, the energy is partly reflected back to the surrounding rock. The results of the measurements in Tests 1, 2, 4 and 5 are presented in this paper and the methodology used to design the tests is discussed.
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Large-Scale Dynamic testing of rock support system at the Kiirunavaara underground mine: Tests 2 a 3
2013Co-Authors: Shahin Shirzadegan, Ulf Nyberg, Ping Zhang, Erling Nordlund, Lars Malmgren, Anders Nordqvist, Ulf Bertil AnderssonAbstract:Large-Scale Dynamic testing of rock support system at the Kiirunavaara underground mine: Tests 2 & 3
