GOING BEYOND TRADITIONAL NETWORKS: SMART GRID,
INTELLIGENT INFRASTRUCTURE, AND SOCIAL NETWORKS
SPCOM is evolving to incorporate new networking concepts. The smart grid adds communication networking  c apability to an infrastructure network like the power grid or water grid. For example, the power grid can deliver power much more effi-ciently when real-time information about the grid state is available through net-worked meters and power infrastructure. Challenges in the smart power grid include developing machine-to-machine network protocols for reporting measure-ments, better techniques for large-scale network state estimation, and robustness to cyber attacks. The smart grid is just one example of a more general trend of networks-of-networks where different net-working concepts are used to make infra-structure more intelligent. More examples include intelligent transportation sys-tems, which use vehicle-to-vehicle net-works to improve transportation network safety and efficiency. Mathematical tools from SPCOM are also being used to
understand noncommunication networks like social networks.
COMPRESSIVE SENSING IN SPCOM Compressive sensing (CS) refers to effi-cient compression and reconstruction of analog signals that are sparse in some domain, e.g., space, time, or fre-quency. CS is a component of many dif-ferent technical committees. In SPCOM, CS has been applied to the detection of impulse radio ultrawide-band (exploits time-domain sparsity), radar (exploits sparsity in angle, Doppler, and/or range domain), and spectrum sensing (exploits sparsity in the spectrum). There are many applica-tions of CS remaining in SPCOM, including localization and tracking through radar, or better navigation through global network satellite sys-tems. Challenges remain, especially in evaluating the viability of CS versus non-CS techniques.
LOCALIZATION
Determining location is receiving renewed interest in SPCOM coupled
with applications such as sensor net-works for telemetry and satellite naviga-tion. For low-energy sensor applications, range-based localization is receiving attention where distance measurements between sensors and beacons and/or among the sensors themselves are exploited to compute the location of the sensors. Assisted satellite naviga-tion is also likely to become more important, where signals of opportuni-ty are exploited. New mathematical tools that are being exploited in local-ization include CS and multidimen-sional scaling.
AUTHORS
Shuguang (Robert) Cui  (cui@ece.tamu.edu) is an associate professor at Texas A&M University.
Robert W. Heath Jr. (rheath@ece.utexas.edu) is an associate professor at
The University of Texas
at Austin.
Geert Leus  (G.J.T.Leus @tudelft.nl) is an associ-ate  p rofessor at the Delft University of Technology.
A.M. Zoubir, V. Krishnamurthy, and A.H. Sayed
T
he scope of the IEEE Signal Processing Theory and Methods (SPTM) Technical Committee has a broad span, ranging from digital filtering
and adaptive signal processing to statisti-cal signal analysis, estimation, and detec-tion. There have also been significant advances in the estimation of sparse sys-tems. These areas continue to play a key role in classical and timely applications.
Under the unifying theme “how sim-ple local behavior generates rational
global behavior,” an SPTM expert ses-sion was organized by the authors dur-ing ICASSP 2011 in Prague. This article summarizes the session and raises chal-lenging questions for future research. It is by no means representative of all emerging topics in the areas of SPTM, but it includes trends and challenges that, in our opinion, will become important activities in SPTM in the coming years. The bibliography is not exhaustive due to space limitations; it
only gives some representative referenc-es the readers may want to consult. IN-NETWORK PROCESSING, LEARNING, AND ADAPTATION
Cognitive or adaptive networks are com-posed of spatially distributed agents that share information over a graph. The topol-ogy of the graph may evolve dynamically over time due to movement of the agents or because agents wish to collaborate with other agents and form coalitions  (see [1] and the references therein). Each agent possesses adaptation and learning abilities
Signal Processing Theory and Methods
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ate of publication: 22 August 2011 D
igital Object Identifier 10.1109/MSP.2011.941987Slides
[2], [3]; for example, each agent can have the capability of running an adaptive or a Bayesian signal processing algorithm based on local data and also information from other nodes. There are at least two questions one can pose:
1) If each agent possesses limited capa-
bilities, can the global behavior of the network be sophisticated? This can be viewed as an analysis problem—how do simple algorithms interact resulting in sophisticated behavior.
2) A related question is: what algo-
rithm should each agent run for the global behavior of the network to achieve a particular objective? This can be viewed as a synthesis problem, since one is interested in designing distrib-
uted algorithms.
The combination of in-network processing and adaptive cooperation leads to the emergence of learning and self-organiza-tion features across the network. Nature provides an abundance of examples of self-organization over biological networks consisting of mobile agents. While individ-ual agents tend to exhibit limited cogni-tive abilities, it is the coordinated behavior among the agents that leads to the mani-festation of decentralized intelligence and enables the agents to perform sophisticat-ed maneuvers to evade predators. In many biological networks, no single agent is in command and yet complex patterns of for-mation are evident. Examples include fish joining together in schools [1], birds flying in formation [4], bees seeking a new hive, and bacteria foraging for food.
A close synergy is emerging between studies on self-organization in the biologi-cal [5] and social sciences and studies on cognitive networks in signal processing and communications. There are ample opportunities for cross-disciplinary research that seeks to understand and reverse-engineer the decentralized intelli-gence encountered in socio-economic-bio-logical networks, by exploring connections with adaptation over networks and by using enhanced signal processing tech-niques. Adaptive diffusion methods [1] and game theoretic methods [6], [7] are ideal tools for the synthesis and analysis of cog-nitive networks with varied capabilities. By spreading intelligence throughout the
sys-tem, such methods eliminate the need to
transport information to and from a cen-
tral point, while still allowing local infor-
mation exchange to any desired degree.
ROLE OF ADAPTATION THEORY
An important feature of cognitive net-
works is that the individual nodes are not
expected to rely mainly on information
fed from their neighbors. Such coopera-
tion among the nodes is only one factor
in the learning process. The individual
nodes should also possess local adaptive
processing abilities that enable them to
assess and react to the quality of the
information received from their neigh-
bors against their own personal beliefs
[1], [4]. F or this reason, cognitive net-
works do not expect all nodes to reach
global agreement over the state of the
environment, as is common in some use-
ful consensus seeking strategies [8], [9];
adaptive
nodes in a cognitive network do not need
to converge to the same global value [2].
Actually, such variations in the individual
levels of performance across a network
are commonly observed in nature.
Animals in a group do not act in absolute
synchrony. There are variations in their
patterns of motion and in their individual
reactions to obstacles in the environment
[1]. The same phenomenon is observed
even in agent-based models of macro-
economies: the nodes (such as sellers and
buyers) do not need to converge to the
same equilibrium state. Instead, their
state (and behavior) can fluctuate
depending on their individual beliefs and
preferences.
Solving estimation and tracking prob-
lems over cognitive networks generally
require optimizing certain global cost
functions in a distributed manner.
Consensus-based techniques are useful in
enabling networks to evaluate average val-
ues across the network. Adaptive diffusion
techniques, on the other hand, allow net-
works to more generally optimize global
cost functions and to perform real-time
adaptation and learning [2]. This level of
generality is useful in modeling mobile
adaptive networks, which serve as good
models for various patterns of motion
observed in biological networks. The anal-
ysis of such learning algorithms poses sev-
eral challenges. What assumptions on
graph connectivity, information patterns,
rate of information sharing, adaptation
rules, and learning dynamics are needed
to ensure convergence to within accept-
able levels of performance?
ROLE OF GAME THEORY
Game-theoretic methods [6], [7] can also
derive rich dynamics through the interac-
tion of simple components and can be
used either as descriptive tools, to predict
the outcome of complex interactions, or as
prescriptive tools, to design systems
around given interaction rules. Game the-
ory is a complexity-based theory, along
with percolation theory, cellular automa-
ta, and ecology modeling. Game-theoretic
learning algorithms [6], [10] can allow
individual agents to perform simple algo-
rithms and, under suitable assumptions,
ensure that the global performance con-
verges to desired equilibrium sets.
The game theoretic approach has also
several appealing features for system
design and analysis. Simple devices with
limited awareness can be equipped with
preconfigured or configurable utility func-
tions and routines for maximizing their
utility in an interactive, even unknown
environment. Such devices can then be
deployed to organize themselves effective-
ly in a dynamic and unknown environ-
ment. As long as the utility functions are
properly specified, these networked devic-
es can be made to exhibit many desired
behaviors [7]. The game theoretic design
approach echoes natural systems in a form
of biomimicry; biological agents such as
insects have long since evolved the proper
procedures and utility preferences to
accomplish collective tasks. The same can
be said of humans, who orient their utili-
ties according to economics; the proper
specification of utilities in this case are SOLVING ESTIMATION AND
TRACKING PROBLEMS OVER
COGNITIVE NETWORKS
GENERALL Y REQUIRE
OPTIMIZING CERTAIN GLOBAL
COST FUNCTIONS IN A
DISTRIBUTED MANNER.
dictated by the structure of the economic system and realized through such mecha-nisms as pricing. The self-organizing fea-ture of game-theoretic (decentralized) systems results in a specific set of benefits and challenges.
APPLICATIONS
Cognitive networks can be designed to perform a variety of tasks. Examples of applications include environmental moni-toring, event detection, resource monitor-ing, target tracking, communications over cognitive radio channels, processing and control over smart power grids, analysis of swarm and animal flocking behavior [1], [4], design of multiagent systems, and analysis of collective decision ma
king. While it is generally possible to find cen-tralized or hierarchical processing mecha-nisms that are faster or more accurate in performing a given task, cognitive
n etworks are generally more scalable, adaptable, and resilient.
Cognitive networks can also be used to model herding behavior in macroeco-nomic systems. In agent-based econom-ic models, the individual agents such as buyers, sellers, traders, brokers, and dealers, are capable of behavioral adapta-tion. The nodes are embodied with goals and beliefs related to patterns they see in pricing and profitability, and they react according to certain behavioral rules. Agent-based models are also prevalent in social networks, where they are used to model social interactions and the spread of disease or information. Extensive studies in computer science and graph theory have been devoted to understand-ing the structure of social networks in terms of properties such as their central-ity (a measure of the influence of a node), closeness (how close individuals are on a social network), and clustering, and network degree (how many connec-tions a node has). BIOMIMICRY
There has already been extensive work in the literature on exploit-ing  n aturally occur-ring phenomena in the development of biolog-
ically inspired techniques for applica-tion in various domains such as robotics and optimization. F or
e
xample, the ant colony optimization (ACO) procedure is based on how ants find the shortest path to food, and the particle swarm optimization (PSO) pro-cedure is based on how birds flock to find food. Other research efforts have focused instead on rules that emulate the emergence of organized behavior in animal colonies. F or example, in con-sensus-seeking models, the individual members in a colony adjust their veloc-ities according to the average velocity of their neighbors. While consensus methods can be effective in emulating the coordinated motion of (animal) agents, they are nevertheless limited in their ability to model the remarkable adaptation, learning, and tracking capabilities that moving (animal) net-works exhibit, especially when travers-ing an environment with unpredictable obstacles and predators. Adaptive diffu-sion methods provide effective model-ing tools in these situations  [1], [4]. Research efforts are needed to address broader questions such as understand-ing how and why complex patterns of behavior arise in biological networks under highly dynamic conditions. How do mobility and the changing topolo-gies influence learning and cognitive abilities? How does information flow through a cognitive network? Are there similarities across different domains? Interestingly, there is evidence to sug-gest that certain patterns of behavior may b
e independent of the population. F or example, when faced with two identical food sources, ants have been observed to focus on one of these sources for some time before switching to the other source. The same behavior
has been observed in humans choosing between two restaurants—this is mod-eled by social learning where agents learn from the actions of other agents. A related question is: how can a deci-sion maker make global decisions based on local decisions made by self-ish individual agents? It can be shown that even for elementary sequential detection problems, the optimal deci-sion policy no longer has a threshold behavior [11].
AUTHORS
A.M. Zoubir (zoubir@spg.tu-darmstadt.de) is a professor with Technische Universität Darmstadt, Germany.
V. Krishn amurthy  (vikramk@ece.ubc.ca) is a professor with The University of British Columbia, Canada.
A.H. Sayed  (sayed@ee.ucla.edu) is a professor with the University of California, Los Angeles, United States.REFERENCES
[1] S.-Y. Tu and A. H. Sayed, “Mobile adaptive net-works,” IEEE J. Select. Topics Signal Processing , vol. 5, 2011.
[2] F. Cattivelli and A. H. Sayed, “Diffusion LMS strat-egies for distributed estimation,” IEEE Trans. Signal Processing , vol. 58, no. 3, pp. 1035–1048, Mar. 2010.[3] C. G. Lopes and A. H. Sayed, “Diffusion least-mean squares over adaptive networks: Formulation and per-formance analysis,” IEEE Trans. Signal Processing , vol. 56, no. 7, pp. 3122–3136, July 2008.
[4] F. Cattivelli and A. H. Sayed, “Modeling bird flight formations using diffusion adaptation,” IEEE Trans. Signal Processing , vol. 59, no. 5, pp. 2038–2051, 2011.[5] S. Camazine, J. L. Deneubourg, N. R. Franks, J. Sneyd, G. Theraulaz, and E. Bonabeau, Self-Organization in Biological Systems . Princeton, NJ: Princeton Univ. Press, 2003.
[6] S. Hart, “Adaptive heuristics,” Econometrica , vol. 73, no. 5, pp. 1401–1430, 2005.
[7] M. Maskery, V. Krishnamurthy, and Q. Zhang, “Decentralized dynamic spectrum access for cogni-tive radios: Cooperative design of a non-cooperative game,” IEEE Trans. Commun., vol. 57, pp. 459–469, Feb. 2008.
[8] S. Kar and J. Moura, “Sensor networks with ran-dom links: Topology design for distributed consensus,” IEEE Trans. Signal Processing , vol. 56, no. 7, pp. 3315–3326, July 2008.
[9] V. Krishnamurthy K Topley, and G. Yin, “Consen-sus formation in a two-time-scale Markovian system,” SIAM J. Multiscale Model. Simul., vol. 7, no. 4, pp. 1898–1927, 2009.
[10] M. Benaim, J. Hofbauer, and S. Sorin, “Stochastic approximations and differential inclusions,” SIAM J. Control Optim., vol. 44, pp. 328–348, Jan. 2005.[11] V. Krishnamurthy. (2011). Bayesian sequential detec-tion with phase-distributed
change time and nonlinear
penalty—A lattice program-ming approach.
IEEE Trans. Inform. Theory
[Online]. Available: /abs/1011.5298
Video
Slides
INTERESTINGL Y, THERE IS EVIDENCE TO SUGGEST THAT CERTAIN PATTERNS OF BEHAVIOR MAY BE INDEPENDENT OF THE
POPULATION.

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