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Coordination and Cooperation in Ad-Hoc Networks in Congested and Contested Environments


TECHNOLOGY AREA(S): Electronics 

OBJECTIVE: The objective of this topic is to develop network resource-aware methodologies for coordination and cooperation that will optimize the information flow for Army ad-hoc tactical networks operating in congested and contested environments. 

DESCRIPTION: The Army has a definite need to understand the degree to which ad-hoc wireless communications can still occur in congested and contested environments where power sources are short-lived, the spectrum is limited and where jamming (both friendly and adversarial) can occur spontaneously and without warning. The achieving of wireless communications may entail that the agents/nodes on the network cooperate and coordinate to an extent that will enable them to pass needed information to each other despite being embedded in a congested and contested environments. The degree of cooperation and coordination between these agents can be obtained by viewing the interactions between agents as an optimization problem, the solution of which will indicate what kinds of activities and just how much data exchange between the agents are necessary for passing needed information. The methodology to be developed will address the problem as one that will utilize coordination and cooperation in obtaining the solution. This is to be contrasted with one where the agents alone and in isolation decide the optimal course of action for operating effectively in a congested and contested environment. The methodology should make use of any of the techniques coming from the fields of machine learning and artificial intelligence but should not make use of techniques from game theory. This analysis should be done assuming a base frequency ranging from 400 MHz to 2.4 GHz. Free space communications is assumed. It will be assumed that there are N (friendly) network nodes enclosed in bounded region R of space in the environment and distributed uniformly in this region. Two other (adversarial) nodes will be assumed to be outside of R but transmitting a periodic signal of period T and power P at the same frequency as the friendly nodes. These two nodes will represent adversarial jamming nodes. Two of the friendly nodes are assumed to transmit at ten (10) different frequencies (to be chosen by the investigator) between 400 MHz and 2.4 GHz but at twice the power of the other friendly nodes. The duration and starting times of these signals will be chosen according to a deterministic pattern. These two nodes represent friendly jammers. It will be assumed also that all nodes have finite power sources that could be replenished either with batteries or with RF energy harvesting. The goal will be to develop a practical computable methodology that will: (i) Show explicitly how cooperation and coordination among agents can optimize network resources (spectrum, bandwidth, energy), and the processing power of the agents using algorithms that will scale with the number of agents. The exact notion of cooperation and coordination should be innovative and must not utilize those from the game theory literature. (ii) Using computational geometry or similar techniques, segment the environment to indicate which geographical regions can optimally support wireless communications. Each of these regions is to be ranked as to its effectiveness in supporting wireless communications using a quantitative risk measure. For each agent this risk measure must reflect the many reasons why the agent won’t be able to obtain useful information from other agents, such as congestion, unavailability of slots or routes, or signal interference. (iii) Indicate explicitly which network factors/covariates and their measurements are needed for cooperation and coordination and to what degree these factors contribute to optimal wireless communications in contested and congested environments. (iv) Estimate the amount of needed information that can be exchanged in a tactical network using coordination and cooperation. 

PHASE I: Explore and define a mathematical framework to capture the interactions between agents in a tactical network embedded in a contested and congested environment. Use this framework to show how cooperation and coordination between agents is to occur and show explicitly what network data is needed to accomplish this. Identify the geographical segmentation algorithm to be used and the risk measure(s) to be assigned to each geographical segment. 

PHASE II: Create simulation and/or analytical models to illustrate the optimality of the cooperation and coordination framework. Give examples of the models over real Army tactical networks. Develop software that can be implemented in a tactical network that will realize what was shown in these models. This software is to be written in a language that is implementable on an Army tactical communications platform. 

PHASE III: Demonstrate a radio system that is field-ready utilizing the software developed in Phase II, and demonstrate interoperability with other transceivers in a tactical network environment that would be used in all Army echelons. 


1: Additive consistency of risk measures and its application to risk-averse routing in networks, R. Cominetti and A. Turrico, arXiv: 1312.4193v1 [math.OC] 15 Dec 2013.

2:  Cooperative learning in multi-agent systems from intermittent measurements, N. Leonard, A. Olshevsky, arXiv: 1209.2194v2 Sept 2013.

3:  Learning of coordination: exploiting sparse interactions in multiagent systems, F. S. Melo and M. Veloso, Procs of 8th Int. Conf on Autonomous Agents and Multiagent Systems, 2009.

4:  Learning of coordination: exploiting sparse interactions in multiagent systems, F. S. Melo and M. Veloso, Procs of 8th Int. Conf on Autonomous Agents and Multiagent Systems, 2009.

5:  Collective decision-making in ideal networks: the speed-accuracy tradeoff, V. Srivastave, N. E Leonard, arXiv 1402.3634v1 Feb 2014.

6:  The topology of wireless communication, E. Kantor, Z. Lotker, M. Parter, D. Peleg, arXiv 1103.4566v2 Mar 2011.

7:  A review of properties and variations of Voronoi diagrams, A. Dorbin.

8:  Risk Measures for the 21st Century, Giorgio Szego (Editor), Wiley

9:  1 edition 2004.

KEYWORDS: Cooperation, Coordination, Wireless, Ad-hoc, Optimization, Risk, Tactical, Network 


Bart Panettieri 

(443) 395-7371 

Gilbert Green 

(443) 395-7629 

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