Spatial big data analysis is very important in disaster scenarios to understand distribution patterns of situations, e.g., people's movements, people's requirements, resource shortage situations, and so on. In a general case, spatial big data is generated from distributed sensing devices and analyzed in a centralized way, e.g., a cloud center with high-performance computing resources. However, data transmission from sensing devices to cloud centers always takes a long time, especially in disaster scenarios with an unstable network. Fog computing is a promising technique to solve the above problem by offloading data processing tasks from the cloud to nearby computation devices. But data resolution also decreases after local processing in the fog nodes. It is necessary to investigate the optimal task distribution solutions to efficiently use computation resources in the fog layer. In this paper, we take the above research problem, and study fog-computing supported spatial big data processing. We analyze the process for spatial clustering, which is a typical category for spatial data analysis, and propose an architecture to integrate data processing into fog computing. We formalize a problem to maximize the data-resolution efficiency by considering data resolution and delay. We further propose core algorithms to enable spatial clustering in a fog-computing environment and implement the above algorithms in a real system. We have performed both simulations and experiments on a real Twitter dataset collected when Kumamoto-city suffered an earthquake. Through the simulations and the experiments, we have determined that the proposed solution significantly outperforms the other solutions.

Big data analytics has started to use data collected from the sensors in smartphones. This data may be used by disaster response teams for locating problems. But the regular communication infrastructure can be destroyed after disasters. Movable base stations (MBS), as studied by the company NTT, offer an easily deployable solution to construct an emergency communication network~ (ECN), but are not suitable for transmitting big data from sensing devices to the cloud for data processing in the cloud. To address this issue, MBSs have been equipped with processing capabilities of their own, which creates an MBS-based Fog-computing Network. We proposed a novel algorithm to minimize the overall cost of the system while maintaining 0 data overflow. This will allow the resources to be used at the most efficient level. Our genetic algorithm solution had a reduced system cost over various network sizes when compared to some conventional solutions. During the simulation, it was clear that the best conventional method for preventing data overflow was the fog-based solution, but its cost was quite high. The cloud-based solution had the lowest cost but would lead to a large amount of data overflow, which would need to be cached. The GA-based solution maintained the ideal solution throughout the variation of all bandwidth parameters: the processing rate, the data compression ratio, and the cost coefficient ratio. Because none of the conventional solutions were able to match the capabilities of the GA for the current constraints, we believe that this should be investigated further with a faster algorithm.

Spatial big data analytics has become possible with the data collected from the sensors in smart phones, which can support decision-making in disaster scenarios. However, sometimes the regular communication infrastructure can be destroyed after disasters. Movable base stations (MBS), as studied by the company NTT, offer an easily deployable solution to construct an emergency communication network, but are not suitable for transmitting big data from sensing devices to the cloud for data processing in the cloud. To solve this issue, we studied a novel algorithm to process spatial big data efficiently in a wirelessly networked disaster area that uses multiple MBSs. More specifically, we proposed a novel algorithm to minimize overall delay for spatial data processing in wirelessly-networked disaster areas (SDP-WNDA), to enable quick responses to data analysis. Our proposed model and genetic algorithm solution showed to have a reduced maximum end- to-end (E2E) delay over various network sizes, when compared to some conventional solutions. For the realistic constraints, the cloud solution was the best conventional method, followed by the system which used the fog nodes to process as much data as possible, but the genetic algorithm (GA) had a slight advantage over all other methods. However, as the computation rate, μk, was increased, the maximum processing algorithm got much stronger. Also, as the communication capacity, R, was increased, the cloud computing solution was more successful. The fact that none of the conventional cases matched the capabilities of the GA for increased computation or increased transmission rates suggests the need for this to be investigated even further.

Three-Dimensional Networks-on-Chips (3D-NoCs) have been proposed as an auspicious solution, merging the high parallelism of the Network-on-Chip (NoC) paradigm with the high-performance and low-power cost of 3D-ICs. However, as technology scales down, the reliability issues are becoming more crucial, especially for complex 3D-NoC which provides the communication requirements of multi and many-core systems-on-chip. Reliability assessment is prominent for early stages of the manufacturing process to prevent costly redesigns of a target system. In this paper, we present an accurate reliability assessment and quantitative evaluation of a soft-error resilient 3D-NoC based on a soft-error resilient mechanism. The system can recover from transient errors occurring in different pipeline stages of the router. Based on this analysis, the effects of failures in the network's principal components are determined.

In conventional hybrid PNoC systems, the end-to end optical data transfer is accompanied with electrical control functions including path-setup, acknowledgment, and tear-down. These functions directly a ect the performance and power characteristics of these circuit-switching based systems. In this paper, we propose a novel hybrid PNoC system, named PHENIC-II. 1 Thanks to the adopted non-blocking photonic switch and light-weight electronic router, PHENIC-II is capable of alleviating the congestion in the electronic control layer which is considered as the main source of latency and power overhead in hybrid PNoC systems. From the performance evaluation, we demonstrate that the proposed system has a better performance and low energy dissipation when compared to the previously proposed systems.

Optical Network-on-Chip is a solution to for power and throughput bottlenecks of current technology. The higher bandwidth is achieved by the light speed transmissions, and the power required to transmit data in the optical domain is much lower. This is a disruptive technology solution to problems arising from silicon-based computing. In this paper, we present a fault-tolerant optical router (FTTDOR) with its electrical control module towards the design of a highly-reliable low-power three dimensional Networks-on-Chip (PHENIC). FTTDOR uses redundancy only in critical locations, to assure accuracy of the packet transmission even after a faulty ring resonator appears. The proposed optical router is decomposed non-blocking, with minimal ring resonators, and requires no resonators for straight travel (East to West, North to South, and Up to Down, as well as their inverses). Simulation results show that the network can maintain a 98% throughput after 3% faults, and 89% after 20% faults. These results come with a reduction of micro-ring resonators to 65% of the amount present in a conventional crossbar router. Simulation of the electrical control module and router show that it has a total area of around 20,000 μm2and consumes 3.8mW at 600MHz.