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IRI Computer Communications Corporation

IRI Computer Communications Corporation provides the following services:

Network simulations, design and analyses
Simulation evaluations and performance design and analyses for computer communications and telecommunications networks, wireless networks, mobile ad hoc wireless networks, integrated network management and for UAV aided C4ISR networked systems.

Consulting
Computer communications and telecommunications system modeling and analysis; network and C4ISR system planning; communications network oriented business planning; network management; traffic engineering; network simulation; design of network testing schemes; network protocols and architectures; modeling, planning, analysis of wireline and wireless network systems; analysis and synthesis of queueing / processing systems and networks; UAV aided mobile ad hoc wireless networks; sensor networks.

Legal consulting
Technical support to law companies in areas of communications network systems and related subsystems.

Courses
Customized in-house training and update short courses in the area of computer communications and telecommunication networks, IP networking, and UAV aided mobile ad hoc wireless networks.

Sample Tools developed by IRI Computer Communications Corporation:

Planyst 2.0, the first hybrid analytical/simulation user-friendly Windows-based tool for advanced modeling and analysis of communication networks.

Powerful and Easy to Use

Planyst is a fast and user-friendly Windows-based hybrid analytical/simulation network planning and analysis tool, employing a powerful graphical user interface that requires no user programming. Icons and models are provided for common network system building blocks that the user employs to rapidly construct a configuration of the network.

Your Networking Spreadsheet

Planyst provides comprehensive tools for modeling application-oriented sessions, connections and traffic processes. Peer-to-peer and client-server message flows are defined and automatically routed across the inter-network. This allows the network planner to rapidly identify the traffic loads imposed on each network component and instantaneously identify bottlenecks.

More than just a Simulator

Planyst provides the network planner, engineer, designer, manager and tester with a computer model upon which the user can record results, manage the network configuration, perform what-if analyses, plan the network system and allocate resources.

Rather than carry out actual network oriented experimentations, Planyst offers significant time, manpower, and cost savings, requiring no actual network operations, test flow loadings and extensive network measurements. Planyst serves as a generic high-granularity (less detailed), PC-based, user friendly tool for the modeling and analysis of communications network systems, without requiring the experience of the user: no computer programming is involved.

Planyst employs a unique hybrid simulation engine that introduces the use of burst level Monte-Carlo based simulation executions combined with the extensive use of mathematical formulas that carry out transient state and steady state performance analyses of the involved queueing system and networking protocols, schemes and algorithms.

PLANYST contains an analytical facility for performing queueing system analyses through rapid definition of basic single and multi server queuing system models, using FIFO or priority based service policies and prescribing limited or unlimited buffer capacity levels. Performance analyses are carried out instantaneously through the use of mathematical formulas developed at IRI Corporation. Sensitivity evaluations and performance curves are displayed as traffic loading rates are swept from low to high intensity values.

MBNP_Simulator: Mobile Backbone Network Protocol Simulator

The MBNP_Simulator uses cutting edge techniques for the synthesis of a robust mobile ad-hoc wireless network employing our innovative mathematical modeling techniques. Both flat and hierarchical networking architectures can be employed. In modeling mobile ad hoc wireless networks that use hierarchical networking structures, distributed scalable algorithms and protocols are employed to dynamically construct backbone tiers. The UV-MBN schemes developed by Professor Izhak Rubin and his research group are effectively employed for the construction of backbone networks (Bnets) and access networks (Anets) and regular (flat) ad hoc sub-networks that constitute a Mobile Backbone Network (MBN). This ad-hoc communication network is aided by Unmanned Air Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) that are dynamically placed at critical locations to enhance network performance and system survivability.

MBN based QoS routing (MBNR ) schemes can be employed. Cross layer nodal congestion (and robustness) control schemes combined with flow admission control mechanisms are included to guarantee admitted flow (on a priority basis) their desired quality of service transport across the network.

Extensive performance indicators display the performance behavior of the mobile ad hoc wireless network, in terms of the QoS measures provided to each traffic class of offered and carried network flows.

MBNP_Sense_Simulator

MBNP_Sense_Simulator is a tool that combines the techniques and models used by our powerful MBNP simulator (as described above) with network centric C4ISR system operations. It provides users with an effective method of modeling and studying sensor networks and the interactions between sensor networks and the wireless communications networks that are used to transport the sensor data. In addition to including models for the formation of ad hoc wireless networks (such as MBN based networks), the tool includes models and analyses for sensor networks, mobility of intruders (the phenomena being sensed), engagement interactions, use of pursuit vehicles to track and engage detected intruders, including the employment of ground based and UAV based multi tasked pursuit vehicles.

Extensive performance results are provided to exhibit the performance behavior of the involved communications networks, entity attrition, sensing and engagement outcomes, and performance upgrades attained through the guided placement of UAV platforms.