OPNET Simulations and Algorithms

OPNET® Simulations and Algorithms

Simulation Environment:

We enforced a Basic Service Set (BSS) to duplicate the mobile workplace experiments and so stretched it exploitation. Extended Service Set (ESS) to model a lot of complicated network configurations. The WLAN model is comparable to the one presented in and consists of three BSSs with the prime BSS enclosing wireless module underneath training. The 3 BSSs square measure connected to employ a WLAN LAN router Associate in LAN switch’s Behavior. Simulations were conducted for numerous network sizes number of nodes, data rates, and transmission power. Parameters for numerous network configurations square measure summarized in. As mentioned earlier WLAN card supports numerous transmission power levels and knowledge rates. For this study, we’ve got roughly drawn an attired and a revolting network since the one containing three nodes less load and twenty-five nodes (more load, typically outlined because the most number of nodes a current 802.11b access purpose ought to be organized to figure with) severally in every BSS. Further Description & Algorithm can be found in attached document.

We selected File Transfer Protocol (FTP) application for the node beneath take a look at since FTP is relatively irregular of exterior factors like handler profiles and info features and in comparison to emails and internet browsing committed secure sessions are supplementary related to subtle applications like FTP and cybernetic non-public grid VPNs.

So at first with the simple congestion control scenario, we ran the simulation & we got the following results in speed, Avg. speed & memory, Avg. memory respectively:



& when we changed the scenario & gave it some operational tasks we ran the simulation & we got the following results in speed, Avg. speed & memory, Avg. memory

Simulation Methodology:

Although OPNET® simulation setting permits integration of secret writing and different applicable process delays into the wireless local area network models we tend to use an additional elaborate analytical approach supported the observations created throughout our real-life measure experiments. E.g. the present consumed by cryptologic computations throughout active transmission is way over the total of current consumed by mobile workplace execution the cryptologic operations and current consumed by the idle system with idle wireless local area network card ( (4) – (2) + (2) = one hundred eighty mA). This is thanks to the actual fact that once transmittal the packet a wireless local area network card remains in active transmission mode until a timer timeouts. This timeout amount is mostly quite significant; for the image Spectrum24 wireless local area network card it’s two hundred mS in receive and five hundred mS in transmit mode. Thus, a nonstop stream of packet flow causes the current consumed by cryptologic computations to be close to the total of average current consumed by the mobile workplace with wireless local area network card in transmit mode (460 mA) and also the current consumed by the cryptologic operations ((4) – (2) = one hundred eighty – a hundred and ten = seventy mA).

Similarly, exploitation the typical current consumed by the system whereas transmittal Associate in treatment encrypted and attested packet is additionally dishonest since it depends upon the sustained output of the system, which, as mentioned earlier, successively depends upon a range of things. Therefore, we tend to derive energy models for every supply of energy consumption individually whereas taking into consideration the results of assorted inter-operation interactions on the energy consumption characteristics of every individual operation.

So the immediate above &  below simulation were ran keeping that in check & their time limit was changed so to see the results working in long term. As the congestion based also would help the scenario & task were kept the same as in the second simulation.

Suggested Congestion Algo. Based On our Study in final paper:


-when (takes an reply)

-update broadcast delay list;


-if (packet is lost)

          bool congest ← false;

/* network would be in congestion state if it’s true*/

-if (sum of broadcast delay list >β P )

Congest ← true;

-for (complete broadcast delay in the list)

-if (broadcast delay < former broadcast delay)

-congest ← false;



OPNET® Wi-Fi simulation results shown demonstrate that the energy consumed by knowledge transactions varies linearly with knowledge size and non-linearly with rate presumptuous different parameters like transmission power and control signals changed stay constant. This is often owing to the actual fact that on a median traffic flow of the simulation environment was maintained constant and sustained outturn of the network is ruled by many different factors besides the information transmission rate.


We found that below smart network conditions transmission knowledge at the best rate (11 Mbps) and low RF signal power level (5 mW) with no RTS-CTS exchange or fragmentation consumes least energy and offers most outturn, though the common delay per packet is worse than within the case of higher than configuration with RTS-CTS exchanges. This is often owing to the back-off rule overhead and packet loss from collisions. On the opposite hand, below dangerous network conditions transmission knowledge at the best rate (11 Mbps) and better RF signal power level (30 mW) with no RTS-CTS exchange or fragmentation consumes least energy and offers most outturn and least average delay per packet. This is often owing to thanks to attributable to} lesser collisions and packet losses as a result of less traffic and better signal strength. We have a tendency to utilize these configurations to check the impact of assorted optimization techniques on energy consumed by IPSec.


Existing square measure a network wireless fidelity Wi-Fi and local area system models of network simulators are still below development and support solely a set of characteristics active within the wireless atmosphere. For instance, OPNET® Wi-Fi models don’t support the consequences of attenuation, multi-path, and power-save modes of operation of network interface cards on the network characteristics. Also, they abstract the physical layer characteristics to the Wi-Fi mackintosh layer models (though a number of these characteristics will be accounted for indirectly) and don’t support dynamic variation of parameters like transmission power and rate.

Further, OPNET®’s square measure a network WLAN wireless fidelity Wi-Fi local area network models assume that everyone nodes with a similar BSS ID are within the same subnet, thereby prohibiting an instantaneous communication between the Wi-Fi nodes in several subnets. Similarly, all the present Wi-Fi models lack correct build-in security models. Integration of science primitives into the attributes of those Wi-Fi models can greatly ease the analysis of the impact of security mechanisms on the performance and energy consumption characteristics of the network also because the entire system.


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