In this paper we describe T-MAC, a contention-based Medium Access Control protocol for wireless sensor networks. Applications for these networks have some characteristics (low message rate, insensitivity to latency) that can be exploited to reduce energy consumption by introducing an activesleep duty cycle. To handle load variations in time and location T-MAC introduces an adaptive duty cycle in a novel way: by dynamically ending the active part of it. This reduces the amount of energy wasted on idle listening, in which nodes wait for potentially incoming messages, while still maintaining a reasonable throughput.We discuss the design of T-MAC, and provide a head-to-head comparison with classic CSMA (no duty cycle) and S-MAC (fixed duty cycle) through extensive simulations. Under homogeneous load, T-MAC and S-MAC achieve similar reductions in energy consumption (up to 98%) compared to CSMA. In a sample scenario with variable load, however, T-MAC outperforms S-MAC by a factor of 5. Preliminary energy-consumption measurements provide insight into the internal workings of the T-MAC protocol.

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An adaptive energy-efficient MAC protocol for wireless sensor networks

SUMMARY Clusters, Grids, and peer-to-peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving largescale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers’ or owners’ requirements, and continuously adapt to changes in resource availability. The management of resources and scheduling of applications in such large-scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java-based discrete-event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time- and space-shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod-G

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GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing

The OMNeT++ discrete event simulation environment has been publicly available since 1997. It has been created with the simulation of communication networks, multiprocessors and other distributed systems in mind as application area, but instead of building a specialized simulator, OMNeT++ was designed to be as general as possible. Since then, the idea has proven to work, and OMNeT++ has been 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. This paper presents an overview of the OMNeT++ framework, recent challenges brought about by the growing amount and complexity of third party simulation models, and the solutions we introduce in the next major revision of the simulation framework.

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An overview of the OMNeT++ simulation environment

Recently, many efforts have been made to develop more efficient Inter-Vehicle Communication (IVC) protocols for on-demand route planning according to observed traffic congestion or incidents, as well as for safety applications. Because practical experiments are often not feasible, simulation of network protocol behavior in Vehicular Ad Hoc Network (VANET) scenarios is strongly demanded for evaluating the applicability of developed network protocols. In this work, we discuss the need for bidirectional coupling of network simulation and road traffic microsimulation for evaluating IVC protocols. As the selection of a mobility model influences the outcome of simulations to a great extent, the use of a representative model is necessary for producing meaningful evaluation results. Based on these observations, we developed the hybrid simulation framework Veins (Vehicles in Network Simulation), composed of the network simulator OMNeT++ and the road traffic simulator SUMO. In a proof-of-concept study, we demonstrate its advantages and the need for bidirectionally coupled simulation based on the evaluation of two protocols for incident warning over VANETs. With our developed methodology, we can advance the state-of-the-art in performance evaluation of IVC and provide means to evaluate developed protocols more accurately.

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Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis

This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

The paper introduces OMNeT++, a C++-based discrete event simulation package primarily targeted at simulating computer networks and other distributed systems. OMNeT++ is fully programmable and modular, and it was designed from the ground up to support modeling very large networks built from reusable model components. Large emphasis was placed also on easy traceability and debuggability of simulation models: one can execute the simulation under a powerful graphical user interface, which makes the internals of a simulation model fully visible to the person running the simulation: it displays the network graphics, animates the message flow and lets the user peek into objects and variables within the model. These features make OMNeT++ a good candidate for both research and educational purposes. The OMNeT++ simulation engine can be easily embedded into larger applications. OMNeT++ is opensource, free for non-profit use, and it has a fairly large user

The omnet++ discrete event simulation system

AN OVERVIEW OF THE OMNeT++ SIMULATION ENVIRONMENT

The intent of this paper is to contribute to the teaching of computer networks, parallel and distributed systems and discrete event simulation by presenting a simulation system that is ideally suited for educational use. OMNeT++ is a C++-based discrete event simulator which uses the process-interaction approach. An OMNeT++ model consists of modules communicating by message passing. Modules can be arbitrarily nested. Model topology is specified by a topology description language which supports separation of interface and functionality and facilitates model reuse. One of the strengths of OMNeT++ is that one can execute the simulation under a powerful graphical user interface. The GUI makes the internals of a simulation model fully visible to the person running the simulation: it displays the network graphics, animates the message flow and lets the user peek into objects and variables within the model. The use of the tracing/debugging capabilities does not require extra code to be written by the simulation programmer. The combination of these features make OMNeT++ a good choice for use in the education. OMNeT++ is open-source and free for non-profit use. The CD-ROM contains the full source distribution, the manual in HTML format, and a Win95/NT executable with several sample simulation models and their sources.

Using the OMNeT++ discrete event simulation system in education

This chapter introduces OMNeT++, a comprehensive software package that provides infrastructure and tools for writing simulations for communication networks and other distributed systems. One of the fundamental ingredients of this infrastructure is a generic component architecture for simulation models. Models are assembled from reusable components termed modules. Well-written modules are truly reusable, and can be combined in various ways to form larger units. The power of OMNeT++ comes from 3rd-party model frameworks that cover various problem domains; countless open-source model frameworks have been published in the time since OMNeT++ became popular. This chapter introduces OMNeT++ using a practical approach and aims at giving the reader an overview and just enough details to help them getting started.

Andras Varga

Papers

The omnet++ discrete event simulation system

An overview of the OMNeT++ simulation environment

AN OVERVIEW OF THE OMNeT++ SIMULATION ENVIRONMENT

Using the OMNeT++ discrete event simulation system in education

A Practical Introduction to the OMNeT++ Simulation Framework