A multipath source self repair routing (MSSRR) algorithm for mobile ad hoc networks is proposed. By using multiple paths which can be repaired by themselves to transmit packets alternately, the network's load is b...A multipath source self repair routing (MSSRR) algorithm for mobile ad hoc networks is proposed. By using multiple paths which can be repaired by themselves to transmit packets alternately, the network's load is balanced, the link state in the network can be checked in time, the number of the times the route discovery mechanism starts is decreased. If only one route which will be broken can be used to transmit the packets, the route discovery mechanism is restarted.The algorithm is implemented on the basis of dynamic source routing (DSR). The effect of MSSRR on lifetime of the access from the source to the destination and the overhead is discussed. Compared with the performance of DSR,it can be seen that the algorithm can improve the performance of the network obviously and the overhead almost does not increase if the average hop count is larger.展开更多
A main shortcoming of mobile Ad-hoc network's reactive routing protocols is the large volume of far-reaching control traffic required to support the route discovery (RD) and route repair (RR) mechanism. Using a ra...A main shortcoming of mobile Ad-hoc network's reactive routing protocols is the large volume of far-reaching control traffic required to support the route discovery (RD) and route repair (RR) mechanism. Using a random mobility model, this paper derives the probability equation of the relative distance (RDIS) between any two mobile hosts in an ad-hoc network. Consequently, combining with average equivalent hop distance (AEHD), a host can estimate the routing hops between itself and any destination host each time the RD/RR procedure is triggered, and reduce the flooding area of RD/RR messages. Simulation results show that this optimized route repair (ORR) algorithm can significantly decrease the communication overhead of RR process by about 35%.展开更多
In this paper we introduce a novel energy-aware routing protocol REPU (reliable, efficient with path update), which provides reliability and energy efficiency in data delivery. REPU utilizes the residual energy availa...In this paper we introduce a novel energy-aware routing protocol REPU (reliable, efficient with path update), which provides reliability and energy efficiency in data delivery. REPU utilizes the residual energy available in the nodes and the re-ceived signal strength of the nodes to identify the best possible route to the destination. Reliability is achieved by selecting a number of intermediate nodes as waypoints and the route is divided into smaller segments by the waypoints. One distinct ad-vantage of this model is that when a node on the route moves out or fails, instead of discarding the whole original route, only the two waypoint nodes of the broken segment are used to find a new path. REPU outperforms traditional schemes by establishing an energy-efficient path and also takes care of efficient route maintenance. Simulation results show that this routing scheme achieves much higher performance than the classical routing protocols, even in the presence of high node density, and overcomes simul-taneous packet forwarding.展开更多
Restoration of infrastructure networks(INs)following large disruptions has received much attention lately due to examples of massive localized attacks.Within this challenge are two complex but critical problems:repair...Restoration of infrastructure networks(INs)following large disruptions has received much attention lately due to examples of massive localized attacks.Within this challenge are two complex but critical problems:repair route identification and optimizing the sequence of the repair actions for resilience improvement.Existing approaches have not,however,given due consideration to globally optimal enhancement in resilience,especially with multiple repair crews that have uneven capacities.To address this gap,this paper focuses on a resilience opti-mization(RO)strategy for coordinating multiple crews.The objective is to determine the optimal routes for each crew and the best sequence of repairs for damaged nodes and links.Given the two-layered decision-making required—coordinating between multiple crews and opti-mizing each crew's actions—this study develops a deep reinforcement learning(DRL)framework.The framework leverages an actor-critic neural network that processes IN damage data and guides Monte Carlo tree search(MCTS)to identify optimal repair routes and actions for each crew.A case study based on the 228-node power grid,simulated using Python,demonstrates that the proposed DRL approach effectively supports restoration decision-making.展开更多
文摘A multipath source self repair routing (MSSRR) algorithm for mobile ad hoc networks is proposed. By using multiple paths which can be repaired by themselves to transmit packets alternately, the network's load is balanced, the link state in the network can be checked in time, the number of the times the route discovery mechanism starts is decreased. If only one route which will be broken can be used to transmit the packets, the route discovery mechanism is restarted.The algorithm is implemented on the basis of dynamic source routing (DSR). The effect of MSSRR on lifetime of the access from the source to the destination and the overhead is discussed. Compared with the performance of DSR,it can be seen that the algorithm can improve the performance of the network obviously and the overhead almost does not increase if the average hop count is larger.
文摘A main shortcoming of mobile Ad-hoc network's reactive routing protocols is the large volume of far-reaching control traffic required to support the route discovery (RD) and route repair (RR) mechanism. Using a random mobility model, this paper derives the probability equation of the relative distance (RDIS) between any two mobile hosts in an ad-hoc network. Consequently, combining with average equivalent hop distance (AEHD), a host can estimate the routing hops between itself and any destination host each time the RD/RR procedure is triggered, and reduce the flooding area of RD/RR messages. Simulation results show that this optimized route repair (ORR) algorithm can significantly decrease the communication overhead of RR process by about 35%.
文摘In this paper we introduce a novel energy-aware routing protocol REPU (reliable, efficient with path update), which provides reliability and energy efficiency in data delivery. REPU utilizes the residual energy available in the nodes and the re-ceived signal strength of the nodes to identify the best possible route to the destination. Reliability is achieved by selecting a number of intermediate nodes as waypoints and the route is divided into smaller segments by the waypoints. One distinct ad-vantage of this model is that when a node on the route moves out or fails, instead of discarding the whole original route, only the two waypoint nodes of the broken segment are used to find a new path. REPU outperforms traditional schemes by establishing an energy-efficient path and also takes care of efficient route maintenance. Simulation results show that this routing scheme achieves much higher performance than the classical routing protocols, even in the presence of high node density, and overcomes simul-taneous packet forwarding.
文摘Restoration of infrastructure networks(INs)following large disruptions has received much attention lately due to examples of massive localized attacks.Within this challenge are two complex but critical problems:repair route identification and optimizing the sequence of the repair actions for resilience improvement.Existing approaches have not,however,given due consideration to globally optimal enhancement in resilience,especially with multiple repair crews that have uneven capacities.To address this gap,this paper focuses on a resilience opti-mization(RO)strategy for coordinating multiple crews.The objective is to determine the optimal routes for each crew and the best sequence of repairs for damaged nodes and links.Given the two-layered decision-making required—coordinating between multiple crews and opti-mizing each crew's actions—this study develops a deep reinforcement learning(DRL)framework.The framework leverages an actor-critic neural network that processes IN damage data and guides Monte Carlo tree search(MCTS)to identify optimal repair routes and actions for each crew.A case study based on the 228-node power grid,simulated using Python,demonstrates that the proposed DRL approach effectively supports restoration decision-making.
