Uninformed Search using Lévy Walk in Graphs
Lévy Walk, a type of random walk, is a model for the foraging behavior of marine predators such as albatrosses, bees and killer whales, and is known to provide a statistical approximation to human behavioral patterns. Very little research has been done on Lévy Walk.
In this study, we perform a blind search based on Lévy Walk on unit disk graphs, which are models of road networks, to determine the relationship between the search performance of Lévy Walk and the characteristics of the graph (e.g., diameter) and the distance to the search target. We believe that Lévy Walk-based mobile sensor sampling can efficiently detect critical incidents, such as those in sensor networks that do not assume a network infrastructure (e.g., on a DTN).
Cuckoo Search based Replication Protocol for Mobile Ad-Hoc Networks
Cuckoos do not incubate their own eggs but beg for eggs in the nests of other birds. In this case, which nest to go to is very important to increase the success rate of hatching. Cuckoos use an original mendicant algorithm to select their nest for mendacity very skillfully, so that their offspring can flourish.
Cuckoo Search is a meta-heuristic algorithm which is based on the brood parasitic behavior of Cuckoos.
In this research, we aim to develop a data replication mechanism using an algorithm that mimics the cuckoo’s mendicance to prevent data loss on ad hoc networks while minimizing the consumption of resources on the network for low-demand, but very important information (e.g., personal safety information in a disaster).
Secure IoT Environment using Trusted Execution Environment
While the IoT environment is becoming the infrastructure for providing various services, the data collected by IoT devices, including personal information, requires a high level of security and availability to continue the services. However, IoT devices such as surveillance cameras are more vulnerable in terms of security than ordinary computer systems, and there is a possibility that an external attack could steal the collected data and leak personal information. The same thing can happen in a normal computer system if there is a vulnerability in the operating system. In recent years, the Trusted Execution Environment (TEE), which is a security mechanism that cooperates with hardware, has been attracting attention for these problems.
In this research, we realize advanced security and system availability using TEE in the IoT environment consisting of various devices such as IoT devices and computer systems.
Accrual Failure Detectors
The detection of failures is a fundamental issue for fault-tolerance in distributed systems. Recently, many people have come to realize that failure detection ought to be provided as some form of generic service, similar to IP address lookup or time synchronization. However, this has not been successful so far; one of the reasons being the fact that classical failure detectors were not designed to satisfy several application requirements simultaneously. We present a novel abstraction, called accrual failure detectors, that emphasizes flexibility and expressiveness and can serve as a basic building block to implementing failure detectors in distributed systems. Instead of providing information of a binary nature (trust vs. suspect), accrual failure detectors output a suspicion level on a continuous scale. The principal merit of this approach is that it favors a nearly complete decoupling between application requirements and the monitoring of the environment. In this paper, we describe an implementation of such an accrual failure detector, that we call the phi failure detector. The particularity of the phi failure detector is that it dynamically adjusts to current network conditions the scale on which the suspicion level is expressed. We analyzed the behavior of our phi failure detector over an intercontinental communication link over a week. Our experimental results show that if performs equally well as other known adaptive failure detection mechanisms, with an improved flexibility.
Many applications, services, and platforms, such as
Apache Cassandra,
Akka,
Fluentd, use Accrual Failure Detectors and their implementations (e.g., phi-failure detector).
