On the feasibility of attribute-based encryption on Internet of Things devices

Attribute-based encryption (ABE) could be an effective cryptographic tool for the secure management of Internet of Things (IoT) devices, but its feasibility in the IoT has been under-investigated thus far. This article explores such feasibility for well-known IoT platforms, namely, Intel Galileo Gen 2, Intel Edison, Raspberry pi 1 model B, and Raspberry pi zero, and concludes that adopting ABE in the IoT is indeed feasible

Implementation of image enhancement techniques based on Intel Edison platform

Image enhancement (IE) is to produce images with suitable visual quality. These enhanced images can then be used in many image processing applications, for e.g., remote sensing, medical imaging, and aerial imaging. However, most of the previous applications have been implemented on traditional computers, which are expensive and large. This work addresses the implementation of IE techniques like color balance, brightness, contrast, and sharpness, on a low cost platform (Intel Edison). The proposed enhancements are programmed by Python, with the assistance of Python imaging library (PIL). Two systems are being run, traditional computer and Intel Edison. The performance evaluation has been done by comparing the visual appearance and histogram of the enhanced images. The result shows that both systems produce similar outputs. Hence, with the low cost (75$) and tiny size (6×2.9×0.8 cm) of Intel Edison compared to the traditional computer (500$), performing IE on the Intel Edison is a viable low-cost alternative

A Cyber-Physical System

The team was tasked with the creation of an autonomous cyber-physical system that could be continually developed as a post-capstone class by future STEM students and as a means to teach future engineering students. The strict definition of a cyber-physical system is a computation machine that networks with an embedded computer that performs a physical function. The autonomous aspect was achieved through two sonic sensors to monitor object distances in order to avoid walls and obstacles. The integrated system was based on the Intel Edison computation module. A primary goal for future addition is automation capabilities and machine learning applications

FogGIS: Fog Computing for Geospatial Big Data Analytics

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

An Experimental Evaluation of Datacenter Workloads On Low-Power Embedded Micro Servers

This paper presents a comprehensive evaluation of an ultra-low power cluster, built upon the Intel Edison based micro servers. The improved performance and high energy efficiency of micro servers have driven both academia and industry to explore the possibility of replacing conventional brawny servers with a larger swarm of embedded micro servers. Existing attempts mostly focus on mobile-class micro servers, whose capacities are similar to mobile phones. We, on the other hand, target on sensor-class micro servers, which are originally intended for uses in wearable technologies, sensor networks, and Internet-of-Things. Although sensor-class micro servers have much less capacity, they are touted for minimal power consumption (< 1 Watt), which opens new possibilities of achieving higher energy efficiency in datacenter workloads. Our systematic evaluation of the Edison cluster and comparisons to conventional brawny clusters involve careful workload choosing and laborious parameter tuning, which ensures maximum server utilization and thus fair comparisons. Results show that the Edison cluster achieves up to 3.5× improvement on work-done-per-joule for web service applications and data-intensive MapReduce jobs. In terms of scalability, the Edison cluster scales linearly on the throughput of web service workloads, and also shows satisfactory scalability for MapReduce workloads despite coordination overhead.This research was supported in part by NSF grant 13-20209.Ope

Multi-bot Easy Control Hierarchy

The goal of our project is to create a software architecture that makes it possible to easily control a multi-robot system, as well as seamlessly change control modes during operation. The different control schemes first include the ability to implement on-board and off-board controllers. Second, the commands can specify either actuator level, vehicle level, or fleet level behavior. Finally, motion can be specified by giving a waypoint and time constraint, a velocity and heading, or a throttle and angle. Our code is abstracted so that any type of robot - ranging from ones that use a differential drive set up, to three-wheeled holonomic platforms, to quadcopters - can be added to the system by simply writing drivers that interface with the hardware used and by implementing math packages that do the required calculations. Our team has successfully demonstrated piloting a single robots while switching between waypoint navigation and a joystick controller. In addition, we have demonstrated the synchronized control of two robots using joystick control. Future work includes implementing a more robust cluster control, including off-board functionality, and incorporating our architecture into different types of robots

TrusNet: Peer-to-Peer Cryptographic Authentication

Originally, the Internet was meant as a general purpose communication protocol, transferring primarily text documents between interested parties. Over time, documents expanded to include pictures, videos and even web pages. Increasingly, the Internet is being used to transfer a new kind of data which it was never designed for. In most ways, this new data type fits in naturally to the Internet, taking advantage of the near limit-less expanse of the protocol. Hardware protocols, unlike previous data types, provide a unique set security problem. Much like financial data, hardware protocols extended across the Internet must be protected with authentication. Currently, systems which do authenticate do so through a central server, utilizing a similar authentication model to the HTTPS protocol. This hierarchical model is often at odds with the needs of hardware protocols, particularly in ad-hoc networks where peer-to-peer communication is prioritized over a hierarchical model. Our project attempts to implement a peer-to-peer cryptographic authentication protocol to be used to protect hardware protocols extending over the Internet. The TrusNet project uses public-key cryptography to authenticate nodes on a distributed network, with each node locally managing a record of the public keys of nodes which it has encountered. These keys are used to secure data transmission between nodes and to authenticate the identities of nodes. TrusNet is designed to be used on multiple different types of network interfaces, but currently only has explicit hooks for Internet Protocol connections. As of June 2016, TrusNet has successfully achieved a basic authentication and communication protocol on Windows 7, OSX, Linux 14 and the Intel Edison. TrusNet uses RC-4 as its stream cipher and RSA as its public-key algorithm, although both of these are easily configurable. Along with the library, TrusNet also enables the building of a unit testing suite, a simple UI application designed to visualize the basics of the system and a build with hooks into the I/O pins of the Intel Edison allowing for a basic demonstration of the system

Distributed-Memory Breadth-First Search on Massive Graphs

This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchronous top-down algorithm as well as the recently discovered direction optimizing algorithm. We analyze the performance and scalability trade-offs in using different local data structures such as CSR and DCSC, enabling in-node multithreading, and graph decompositions such as 1D and 2D decomposition.Comment: arXiv admin note: text overlap with arXiv:1104.451

FIT A Fog Computing Device for Speech TeleTreatments

There is an increasing demand for smart fogcomputing gateways as the size of cloud data is growing. This paper presents a Fog computing interface (FIT) for processing clinical speech data. FIT builds upon our previous work on EchoWear, a wearable technology that validated the use of smartwatches for collecting clinical speech data from patients with Parkinson's disease (PD). The fog interface is a low-power embedded system that acts as a smart interface between the smartwatch and the cloud. It collects, stores, and processes the speech data before sending speech features to secure cloud storage. We developed and validated a working prototype of FIT that enabled remote processing of clinical speech data to get speech clinical features such as loudness, short-time energy, zero-crossing rate, and spectral centroid. We used speech data from six patients with PD in their homes for validating FIT. Our results showed the efficacy of FIT as a Fog interface to translate the clinical speech processing chain (CLIP) from a cloud-based backend to a fog-based smart gateway.Comment: 3 pages, 5 figures, 1 table, 2nd IEEE International Conference on Smart Computing SMARTCOMP 2016, Missouri, USA, 201