Free Fixed Qualnet 5 0 Simulator
EVE-NG is available in free and paid editions with vastly different features. Although the free version comes with all the basics of this tool, it lacks some things such as Docker container support, NAT clouds, or Wireshark integrations.
Free Qualnet 5 0 Simulator
The OPNET network simulator is an open-source piece of software with pre-built models of protocols and devices, allowing you to create a wide range of network topologies. Aside from that, it incorporates a large number of project scenarios.
Parts of this chapter may serve as a primer on using the ns-2 simulator, though a primer focused on the goal of illuminating some of the basic operation and theory of TCP through experimentation. However, some of the outcomes described may be of interest even to those not planning on designing their own simulations.
The tool used for much research-level network simulations is ns, for network simulator and originally developed at the Information Sciences Institute. The ns simulator grew out of the REAL simulator developed by Srinivasan Keshav [SK88]; later development work was done by the Network Research Group at the Lawrence Berkeley National Laboratory.
We will describe in this chapter the ns-2 simulator, hosted at www.isi.edu/nsnam/ns. There is now also an ns-3 simulator, available at www.nsnam.org. Because ns-3 is not backwards-compatible with ns-2 and the programming interface has changed considerably, we take the position that ns-3 is an entirely different package, though one likely someday to supercede ns-2 entirely. While there is a short introduction to ns-3 in this book (32 The ns-3 Network Simulator), its use is arguably quite a bit more complicated for beginners, and the particular simulation examples presented below are well-suited to ns-2. While ns-3 supports more complex and realistic modeling, and is the tool of choice for serious research, this added complexity comes at a price in terms of configuration and programming. The standard ns-2 tracefile format is also quite easy to work with using informal scripting.
In the ns-2 simulator, counting individual lost packets and TCP loss responses is straightforward enough. For TCP Reno, there are only two kinds of loss responses: Fast Recovery, in which cwnd is halved, and coarse timeout, in which cwnd is set to 1.
The Channel/WirelessChannel class represents the physical terrestrial wireless medium; there is also a Channel/Sat class for satellite radio. The Propagation/TwoRayGround is a particular radio-propagation model. The TwoRayGround model takes into account ground reflection; for larger inter-node distances d, the received power level is proportional to 1/d4. Other models are the free-space model (in which received power at distance d is proportional to 1/d2) and the shadowing model, which takes into account other types of interference. Further details can be found in the Radio Propagation Models chapter of the ns-2 manual.
Access points communicate with wireless devices based on the superframe structure in beacon-enabled networks . Access points in beacon-enabled networks manage slots defined by partitioned time logically. The beacon interval in this network is defined by the summation of the active and inactive period, as shown in Figure 1. In the inactive period, all devices belonging to the same network are powered down to save power. The active period is composed of the beacon, the contention-access period (CAP) and the contention-free period (CFP). Beacons transmit by access points to report network information and synchronize devices. In the CAP, devices attempt to connect to access points by random access. In the CFP, devices connect to access points with guaranteed resources by the time-division access in the clear channel. The devices try to associate with access points for addressing. After finalizing the association procedure, the devices are eligible to communicate with access points. Sleep mode is easy to configure for CFP, since the devices only need to continue to receive the mode during the allocated period from the access points .
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In the paradigm of Internet of Things (IoT), sensors, actuators and smart devices are connected to the Internet. Application providers utilize the connectivity of these devices with novel approaches involving cloud computing. Some applications require in depth analysis of the interaction between IoT devices and clouds. Research in this area is facing questions like how we should govern such large cohort of devices, which may easily go up often to tens of thousands. In this chapter we investigate IoT Cloud use cases, and derive a general IoT use case. Distributed systems simulators could help in such analysis, but they are problematic to apply in this newly emerging domain, since most of them are either too detailed, or not extensible enough to support the to be modelled devices. Therefore we also show how generic IoT sensors could be modelled in a state of the art simulator using our generalized case to exemplify how the fundamental properties of IoT entities can be represented in the simulator. Finally, we validate the applicability of the introduced IoT extension with a fitness and a meteorological use case.
In this research work we develop extensions for the DISSECT-CF  simulator, which already has the ability to model cloud systems, and has the potential to provide accurate representation of IoT systems. Therefore the goal of this research is to: (i) investigate IoT Cloud use cases, and (ii) derive a general IoT use case. We also show (iii) how generic IoT sensors could be modelled in a state of the art simulator using our generalized case to exemplify how the fundamental properties of IoT entities can be represented in the simulator. Finally, we (iv) validate the applicability of the introduced IoT extension with a fitness and a meteorological use case.
There are many simulators available to examine distributed and specifically cloud systems. These existing simulators are mostly general network simulators, e.g. Qualnet  and OMNeT++ . With these tools IoT-related processes can be examined such as device placement planning and network interference. The OMNeT++ discrete event simulation environment  is one of these examples, and it can be used in numerous domains from queuing network simulations to wireless and ad-hoc network simulations, from business process simulation to peer-to-peer network, optical switch and storage area network simulations.
There are more specific IoT simulators, which are closer to our approach. As an example, Han et al.  have designed DPWSim, which is a simulation toolkit to support the development of service-oriented and event-driven IoT applications with secure web service capabilities. Its aim is to support the OASIS standard Devices Profile for Web Services (DPWS) that enables the use of web services on smart and resource-constrained devices. SimIoT  is derived from the SimIC simulation framework , which provides a deeper insight into the behavior of IoT systems, and introduces several techniques that simulates the communication between an IoT sensor and the cloud, but it is limited by its compute oriented activity modeling.
Khan et al.  introduce a novel infrastructure coordination technique that supports the use of larger scale IoT systems. They build on CloudSim , which can be used to model a community cloud based on residential infrastructures. On top of CloudSim they provide customizations that are tailored for their specific home automation scenarios and therefore limit the applicability of their extensions for evaluating new IoT coordination approaches. These papers are also limited on sensors/smart objects thus not allowing to evaluate a wide range of IoT applications that are expected to rise to widespread use in the near future. Zeng et al.  proposed IOTSim that supports and enables simulation of big data processing in IoT systems using the MapReduce model. They also presented a real case study that validates the effectiveness of their simulator.
The following section provides a small selection of use cases that display a wide range of behaviours, communication models, and data flows. A wide scope of use cases can provide a much better understanding of the drawbacks with current simulation solutions and will allow us to gain an insight into how we can find a common ground between them. This list is only a partial selection of possible use cases as they were selected based on the potential differences they may have, together building a fairly large pool of behavioural patterns after which introducing more use cases would have had little impact on the overall experiment. The use case figures primarily display data flows (With minor context actions when necessary) as they provide an accurate enough description of the system to understand its behaviour and because simulators generally work via modelling the data transactions between entities.
The proposed extension provides a runnable Application interface that can take an XML file defining the Machine Data (Such as Physical Machines, Repositories, and their Connection data) and an XML file defining the Simulation Data (Such as the Devices and their behaviours). The Simulation Data can contain a scalable number of Devices and each device has its own independent behaviour model defined. The behaviour of the Device can be modelled in a combination of 3 ways; a direct link to a Trace File (Which should contain the target device, timestamp, and data size), a Trace Producer Model which contains the Distribution set to produce an approximation of the device trace, or finally the simulator can accept device extensions which allow custom devices to be included in the source to programmatically model more specific behaviours. 350c69d7ab