Consensus in anonymous asynchronous systems with crash-recovery and omission failures

In anonymous distributed systems, processes are indistinguishable because they have no identity and execute the same algorithm. Currently, anonymous systems are receiving a lot of attention mainly because they preserve privacy, which is an important property when we want to avoid impersonation attacks. On the other hand, Consensus is a fundamental problem in distributed computing. It is well-known that Consensus cannot be deterministically solved in pure asynchronous anonymous systems if processes can crash (the so-called crash-stop failure model). This impossibility holds even if message losses never occur in transmission. Failure detectors are an elegant and powerful abstraction for achieving deterministic Consensus in asynchronous distributed systems. A failure detector is a distributed object that gives the processes information about crashed processes. Failure detectors have attracted so much attention in the crash-stop failure model because they provide a totally independent abstraction. $$\varOmega $$ Ω is the weakest failure detector to solve Consensus in classic asynchronous systems when a majority of processes never crash, and $$A\varOmega '$$ A Ω ′ is its implementable version for anonymous systems. As far as we know, there is a lack of works in the literature which tackle Consensus in anonymous asynchronous systems where crashed process can recover (the so-called crash-recovery failure model) and also assuming errors in transmission operations (the so-called omission failure model). Extending failure models in the system allows us to design more realistic systems and solve more practical security problems (i.e., fair exchange and the secure multiparty computation). We present, in this paper, an algorithm to solve Consensus using $$A\varOmega '$$ A Ω ′ in anonymous asynchronous systems under the crash-recovery and omission failure models. Another important contribution of this paper is a communication-efficient and latency-efficient implementation of $$A\varOmega '$$ A Ω ′ for these new failure models.

A Crash Recovery Scheme for a Hybrid Mapping FTL in NAND Flash Storage Devices

An FTL (flash translation layer), which most flash storage devices are equipped with, needs to guarantee the consistency of modified metadata from a sudden power failure. This crash recovery scheme significantly affects the writing performance of a flash storage device during its normal operation, as well as its reliability and recovery performance; therefore, it is desirable to make the crash recovery scheme efficient. Despite the practical importance of a crash recovery scheme in an FTL, few works exist that deal with the crash recovery issue in FTL in a comprehensive manner. This study proposed a novel crash recovery scheme called FastCheck for a hybrid mapping FTL called Fully Associative Sector Translation (FAST). FastCheck can efficiently secure the newly generated address-mapping information using periodic checkpoints, and at the same time, leverages the characteristics of an FAST FTL, where the log blocks in a log area are used in a round-robin way. Thus, it provides two major advantages over the existing FTL recovery schemes: one is having a low logging overhead during normal operations in the FTL and the other to have a fast recovery time in an environment where the log provisioning rate is relatively high, e.g., over 20%, and the flash memory capacity is very large, e.g., 32 GB or 64 GB.

The saccharibacterium TM7x elicits differential responses across its host range

Host range is a fundamental component of symbiotic interactions, yet it remains poorly characterized for the prevalent yet enigmatic subcategory of bacteria/bacteria symbioses. The recently characterized obligate bacterial epibiont Candidatus Nanosynbacter lyticus TM7x with its bacterial host Actinomyces odontolyticus XH001 offers an ideal system to study such a novel relationship. In this study, the host range of TM7x was investigated by coculturing TM7x with various related Actinomyces strains and characterizing their growth dynamics from initial infection through subsequent co-passages. Of the twenty-seven tested Actinomyces, thirteen strains, including XH001, could host TM7x, and further classified into “permissive” and “nonpermissive” based on their varying initial responses to TM7x. Ten permissive strains exhibited growth/crash/recovery phases following TM7x infection, with crash timing and extent dependent on initial TM7x dosage. Meanwhile, three nonpermissive strains hosted TM7x without a growth-crash phase despite high TM7x dosage. The physical association of TM7x with all hosts, including nonpermissive strains, was confirmed by microscopy. Comparative genomic analyses revealed distinguishing genomic features between permissive and nonpermissive hosts. Our results expand the concept of host range beyond a binary to a wider spectrum, and the varying susceptibility of Actinomyces strains to TM7x underscores how small genetic differences between hosts can underly divergent selective trajectories.

Accident Prediction and Crash Recovery by using Car Black Box

Millions of peoples are losing their lives because of accidents that occur every year. The main cause of death was recorded due to the unavailability of medical services at the time of accident, and also the reason for the cause of accident is also not known. So to overcome these problems, a car black box system came into existence. Data received from the sensors are stored on the SD card mounted on raspberry pi for investigation purpose after the accident. This paper presents a technique for designing and development of GSM-GPS based intelligent vehicle tracking system using Raspberry pi controller. The proposed system uses light sensor, MQ135 Alcohol sensor, temperature sensor, Accelerometer, video recorder, Limit switch sensor, GPS and GSM modems to prevent vehicles from collisions and alert the collision of vehicles to owners. The data recorded is used for investigation purpose. The additional feature of this project is that it warns the driver whenever the sensor values exceed than the normal specification value. The data is stored in the SD card that is externally connected to the raspberry