Unreliable wireless links can cause frequent link (and route) failures, creating a major challenge for routing protocols who need to constantly repair routes and find alternate paths. In this paper we propose DADR (Distributed Autonomous Depth-first Routing), a new distributed distance-vector routing protocol designed to adapt quickly to changing link conditions while minimizing network control overhead. In our algorithm, when a link fails, data packets are rerouted through an alternate next hop, and the information about the failed link is propagated with the data packet; therefore, routes are updated dynamically and with little overhead. We have implemented DADR on several link-layer technologies and deployed it in different applications, including AMI deployments in Japan [1]; all implementations resulted in reliable networks that were easy to set up, maintain, and resilient to changing conditions.
Advance Metering Infrastructure (AMI) networks are often deployed under challenging and unreliable conditions. One of the issues for the transmission of data packet in these unreliable networks is the routing of packets, because routing paths may behave differently from the time when the route is discovered to the time when a data packet is forwarded. In addition, control packets may get lost and give routers an inconsistent view of the network. While previous research has focused on designing the controlplane of routing protocols to deal with the AMI network conditions, there is comparatively a smaller amount of research on the advantages of new data forwarding mechanisms designed for unreliable networks. This paper introduces a set of data forwarding mechanisms inspired by distributed depth-first search algorithms, and designed for the challenging conditions of large-scale unreliable networks envisioned by smart-grid deployments. These forwarding mechanisms use data packets to detect loops, update routing tables, and perform rerouting of data packets through alternate paths, recovering thus, packets that would have been normally dropped due to failures at the link layer. We perform simulations based on a real field AMI deployment to evaluate the performance of the proposed mechanisms. We also provide the evaluation results for the data forwarding mechanisms that have been implemented in a real AMI network.
This paper describes a new framework of policy control sensor networks. Sensor networks ate shared by various applications, and have many nodes. Hence, sensor networks need to have ability to accept various applications, and to deploy application modules to nodes easily. Sensor nodes should have appropriate application modules. Afi.amework that is based on VPC on KODAM enables sensor nodes to have --qpropriate modules by assignment rules in a policy. When users only pur application policies to sensor neiworks, sensor nodes propagate the policies and perform appropriate roles in the applications. This paper also shows that sensor networks with policies change behavior corresponding to detected active W I D tags as an example.
Airport terminal decision makers in recent years need to deal with unexpected and sudden congestion situations. Although various types of mathematical research has analyzed the congestion situations and have succeed to manage a subsystem, they cannot sufficiently describe the variety of phenomena observed in a real airport terminal, because they have not considered the interactions between subsystems of the real airport terminal. A simulation approach enables us to describe the interactions between facilities and passenger behavior in detail as a whole airport system and to find various types of possible congestion situations. The simulation approach, however, cannot directly lead exact prediction that can be useful in practical management and operation for difficulties of modeling a complex airport terminal system and acquiring complete input data. In this paper, (1) we modeled Fukuoka airport international terminal in Japan as Complex Adaptive System and built a passenger flow simulation based on the Discrete Event Model. Validity of the simulation were confirmed by experiments. Moreover, (2) we confirmed that it is possible to get information, which is difficult to collect by observation, from discussing with stakeholders using the simulation. Therefore, we believe it is possible to reduce uncertainty of the simulation systematically by continuing modeling, predicting, and discussing with stakeholders, repeatedly.
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