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Sujata T. (Intel) added a comment on project Shared Sensor Network for Vehicles and Smart Roads

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Shared Sensor Network for Vehicles and Smart Roads


Create a large network with mobile sensor nodes installed inside vehicles connected to each other in order to detect, analyze and send data automatically and anonymously in data collection centers through check-points placed on smart roads. The aim is to have as much data as possible on air quality, fine dusts, km driven by vehicles, and on which roads mainly; Data collection centers will be checkpoints on smart roads. Smart roads connected with 5G technology allow vehicles to connect and send data, this idea will also allow to report data about vehicles that generally do not hit external roads (highways for example) and, therefore, remaining more in the city accumulate interesting amounts of routes citizen data.


Continuous technological advances in miniaturization and in the construction of low energy consumption circuits and the continuous progress in the field of information coupled with the high level of efficiency achieved by radio communication devices, increasingly affirm a new technological perspective: Sensor networks (WSN - Wireless sensor network). This technology arise from the fact that electronic devices are becoming smaller and more complex, and the tendency is to distribute intelligence into objects with relatively lower computing power but strongly related each other, instead of bring it together to a single expensive unit, cumbersome and hardly manageable; The birth of WSN is due to a basic idea which involves the use of a large number of nodes, allowing to perform reliefs and elaborations with greater precision and frequency than the single sensor case of use, capable of providing better performances at detriment of higher costs. All those premise is the logic from which the project starts, distributing analysis and reliefs among multiple mobile nodes (related vehicles) that converge acquired data into defined checkpoints (points on the smart roads) connected to data collections center. Mobile nodes consist essentially of:

  • Processor (CPU): usually represented by a microcontroller that performs all management functions, including translation of transducers electrical signals, actuator management and communication control.

  • Memory: Represents a volatile memory block that acts as an aid to execution.

  • Transducers: can be more than one, transform some physical dimension (temperature, gps perpendicular paths, air quality, fine dust pad etc etc) into an electrical signal.

  • Actuators: they can be more than one and of a different kind (Leds, acoustic sonar etc..).

  • Communication Unit: Allows inter-node communication.

Network description

One of the primary features of this network type is the management of logical addressing and routing of packets. In a WSN, addressing can follow a particular technique, so the network is partitioned into node clusters and for each cluster it is assigned a unique address. In every cluster there is a coordinator that collects data from nodes and directs them to the collection center or possibly to the cluster group coordinator to which it belongs. In this way, the address space is safeguarded while retaining the possibility of referring to certain portions of the network. A typical application example is showed in the picture (down-below). Here, in fact, there is a practical application where clusters are grouped together in a large network. Red-circled knots act as coordinators for top-level clusters, while blue circled nodes act as coordinators for second-level clusters (the largest external ones) until they reach the data collection center. As for the routing algorithms, it was chosen a hybrid typology, that is an algorithm that allows to interface, create nodes whenever encounters a mobile node and to communicate and understand if that node will reach first the data collection center.

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Sujata T. (Intel) added a comment on project Containerizing Deep Learning Workloads on Xeon Phi Cluster for AI Web Applications

Medium bae1ed0d 3a3a 40d6 8fba b5327e569eb3

Containerizing Deep Learning Workloads on Xeon Phi Cluster for AI Web Applications

Today's Web applications are data intensive and demand environments like tensorflow to execute the workloads. Hence, Containers are best suited to provide the framework and compute resources like CPU and memory for each workload. It decouples the app environment from the running machine/host and encapsulates all dependencies in a single portable unit. Nomad is a state of the art tool for scheduling Docker Containers. Test Model Workload is generated from Model Zoo for each framework.

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Sujata T. (Intel) added a comment on project Create REAL-TIME SDR/SDN/NFV Mobile Broadband Networks with Intel FPGA & Intel General Purpose Processors

Medium a4435a6d 2a2b 41c9 9f21 0c74cbb0c182

Create REAL-TIME SDR/SDN/NFV Mobile Broadband Networks with Intel FPGA & Intel General Purpose Processors

We, WindyCitySDR, are utilizing a Intel FPGA based SDR working + Intel General Purpose Processors to work with what created: "5G-EmPOWER: a open Mobile Network Operating System for Wi-Fi and LTE networks". Its flexible architecture and high-level programming APIs allow for fast prototyping of apps and services.

5G-EmPOWER blurs the line between radio and core network introducing the concept of Programmable Data Plane which abstracts the radio and packet processing resources available in a network.

5G-EmPOWER natively support multi-tenancy which allows supporting verticals with orthogonal requirements over the same network while ensuring performance isolation and efficient spectrum utilization.


In 5G-EmPOWER everything, including radio access, is treated as Virtual Network Function. 5G-EmPOWER currently supports Wi-Fi, LTE, and Click Virtual Network Functions.

For more information you can begin here:

For the source code and installation instructions:

We've gotten the "5G-Empower Controller" working on a LiveUSB pictured here:

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