Effects of MAC Approaches on Non-Monotonic Saturation with COPE - A Simple Case Study

We construct a simple network model to provide insight into network design strategies. We show that the model can be used to address various approaches to network coding, MAC, and multi-packet reception so that their effects on network throughput can be evaluated. We consider several topology components which exhibit the same non-monotonic saturation behavior found within the Katti et. al. COPE experiments. We further show that fairness allocation by the MAC can seriously impact performance and cause this non-monotonic saturation. Using our model, we develop a MAC that provides monotonic saturation, higher saturation throughput gains and fairness among flows rather than nodes. The proposed model provides an estimate of the achievable gains for the cross-layer design of network coding, multi-packet reception, and MAC showing that super-additive throughput gains on the order of six times that of routing are possible.United States. Dept. of Defense (Air Force Contract FA8721-05-C-0002)Irwin Mark Jacobs and Joan Klein Jacobs Presidential FellowshipInformation Systems of ASD(R&E

MAC Centered Cooperation - Synergistic Design of Network Coding, Multi-Packet Reception, and Improved Fairness to Increase Network Throughput

We design a cross-layer approach to aid in develop- ing a cooperative solution using multi-packet reception (MPR), network coding (NC), and medium access (MAC). We construct a model for the behavior of the IEEE 802.11 MAC protocol and apply it to key small canonical topology components and their larger counterparts. The results obtained from this model match the available experimental results with fidelity. Using this model, we show that fairness allocation by the IEEE 802.11 MAC can significantly impede performance; hence, we devise a new MAC that not only substantially improves throughput, but provides fairness to flows of information rather than to nodes. We show that cooperation between NC, MPR, and our new MAC achieves super-additive gains of up to 6.3 times that of routing with the standard IEEE 802.11 MAC. Furthermore, we extend the model to analyze our MAC's asymptotic and throughput behaviors as the number of nodes increases or the MPR capability is limited to only a single node. Finally, we show that although network performance is reduced under substantial asymmetry or limited implementation of MPR to a central node, there are some important practical cases, even under these conditions, where MPR, NC, and their combination provide significant gains

Proceedings Work-In-Progress Session of the 13th Real-Time and Embedded Technology and Applications Symposium

The Work-In-Progress session of the 13th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS\u2707) presents papers describing contributions both to state of the art and state of the practice in the broad field of real-time and embedded systems. The 17 accepted papers were selected from 19 submissions. This proceedings is also available as Washington University in St. Louis Technical Report WUCSE-2007-17, at http://www.cse.seas.wustl.edu/Research/FileDownload.asp?733. Special thanks go to the General Chairs – Steve Goddard and Steve Liu and Program Chairs - Scott Brandt and Frank Mueller for their support and guidance

Learning to Cooperate and Communicate Over Imperfect Channels

Information exchange in multi-agent systems improves the cooperation among agents, especially in partially observable settings. In the real world, communication is often carried out over imperfect channels. This requires agents to handle uncertainty due to potential information loss. In this paper, we consider a cooperative multi-agent system where the agents act and exchange information in a decentralized manner using a limited and unreliable channel. To cope with such channel constraints, we propose a novel communication approach based on independent Q-learning. Our method allows agents to dynamically adapt how much information to share by sending messages of different sizes, depending on their local observations and the channel's properties. In addition to this message size selection, agents learn to encode and decode messages to improve their jointly trained policies. We show that our approach outperforms approaches without adaptive capabilities in a novel cooperative digit-prediction environment and discuss its limitations in the traffic junction environment

Inductive pattern-based land use/cover change models: A comparison of four software packages

International audienceLand use/cover change (LUCC), as an important factor in global change, is a topic that has recently received considerable attention in the prospective modeling domain. There are many approaches and software packages for modeling LUCC, many of them are empirical approaches based on past LUCC such as CLUES , DINAMICA EGO, CA_MARKOV and Land Change Modeler (both available in IDRISI). This study reviews the possibilities and the limits of these four modeling software packages. First, a revision of the methods and tools available for each model was performed, taking into account how the models carry out the different procedures involved in the modeling process: quantity of change estimate, change potential evaluation, spatial allocation of change, reproduction of temporal and spatial patterns, model evaluation and advanced modeling options. Additional considerations, such as flexibility and user friendliness were also taken into account. Then, the four models were applied to a virtual case study to illustrate the previous descriptions with a typical LUCC scenario that consists of four processes of change (conversion of forest to two different types of crops, crop abandonment and urban sprawl) that follow different spatial patterns and are conditioned by different drivers. The outputs were compared to assess the quantity of change estimates, the change potential and the simulated prospective maps. Finally, we discussed some basic criteria to define a " good " model