
Distributed Detection and Estimation
The literature on distributed detection and estimation is quite extensive,
including the topic of multisensor data fusion. Initially, let us consider
distributed detection; a good, relatively recent tutorial on the subject is
given by Viswanathan and Varshney [1]. The basic idea is to have a number of
independent sensors each make a local decision (typically a binary one) and
then to combine these decisions at a fusion center to generate a global
decision. The figure below illustrates the parallel fusion topology,
which implements this processing.
Either the Bayesian or the NeymanPearson criterion can be used.
Assuming the NeymanPearson formulation where one assumes a bound on the
global probability of false alarm, the goal is to determine the optimum local
and global decision rules that minimize the global probability of miss (or
equivalently maximize the global probability of detection). When the sensors
are deployed so that their observations are conditionally independent, one can
show that these decision rules are threshold rules based on likelihood ratios
[2]. The problem now becomes one of determining the optimal threshold at each
sensor, as well as at the fusion center. While this task is quite nontrivial,
it can still be done for a reasonably small number of sensors using iterative
techniques [3]. More importantly, by using soft, multibit decisions at each
of the sensors, it is possible to increase the performance so that it is
asymptotically close to the optimal centralized scheme [4].
Depending on the sensor network topology, it may be more useful to implement
the distributed detection or estimation using a tree structure. Tsistsiklis
[3] shows that the optimal decision rules are still in the form of threshold
tests. Tang et al. [5] consider the case where the local decisions
made at a number of sensors are communicated to multiple root nodes for data
fusion. With each sensor node characterized by a receiver operating curve
(ROC) and assuming a Bayes' risk criterion, they reformulate the problem as
a nonlinear optimal control problem that can be solved numerically.
Furthermore, they briefly examine communication and robustness issues for
two types of tree structures: a functional hierarchy and a decentralized
market. One conclusion is that if the communication costs are a primary
concern, then the functional hierarchy is preferred because it leads to
less network traffic. However if robustness is the primary issue, then
the decentralized market structure may be a better choice.
In the cases discussed above, the information flows in one direction from the
sensors to either the single fusion center or to a number of root nodes. Even
in the decentralized market topology, where numerous sensors report to
multiple intermediate nodes, the graph of the network is still acyclic. If the
communication network is able to handle the increased load, performance can
be improved through the use of decision feedback [6, 7].
Pados et al. [6] examine two distributed structures:
1.) a network where the fusion center provides decision feedback connections
to each of the sensor nodes, and 2.) a set of sensors that are fully
interconnected via decision feedback. The performance of the fully connected
network is quantifiably better, but their initial system was nonrobust to
variations in the statistical descriptions of the two hypotheses. Robust
testing functions are able to overcome this problem, and they show that
robust networks tend to reject the feedback when operating with contaminated
data. Alhakem and Varshney [7] study a distributed detection system with
feedback and memory. That is, each sensor not only uses its present input
and the previous fedback decision from the fusion center, but it also uses
its own previous inputs. They derive the optimal fusion rule and local
decision rules, and they show that the probability of error in a Bayesian
formulation goes to zero asymptotically. Additionally, they address
the communication requirements by developing two data transmission protocols
that reduce the number of messages sent among the nodes.
Swaszek and Willet propose a more extensive feedback approach that they denote
parleying [8]. The basic idea is that each sensor makes an initial binary
decision that is then distributed to all the other sensors. The goal is to
achieve a consensus on the given hypothesis through multiple iterations.
They develop two versions of the algorithm; the first is a greedy approach
that achieves fast convergence at the expense of performance. The nthroot
approach constrains the consensus to be optimum in that it would match that
of a centralized processor having access to all the data. The main issue is
the number of parleys (iterations) required to reach this consensus.
Wireless Networks
The second component of a smart sensor network is the wireless communications
network used to relay the sensor information. In essentially all of the work
discussed above, the initialization, routing, and reconfiguration details of
this network are not considered. The effect of the distributed algorithm on
the use of networking resources is often not examined. When it has been
examined, the effects of lost or corrupted messages on the performance of the
detection or estimation algorithm have been typically neglected. An
exception is Tang et al. [5], who studied robustness to the loss of
communication links. Also, Thomopoulos and Zhang [9] make some assumptions
about networking delays and channel errors. Recent work in distributed
estimation [10] assumes errorfree communication channels with capacity
constraints.
Since a real wireless network imposes channel errors, delays, packet losses,
and power and topology constraints, it is essential that the overall sensor
network design consider these factors. Typically after deployment, the first
action for a sensor network is to determine its topology. This step is done
because many of the traditional routing protocols require topological
information for initialization. This is especially true for link state
routing, which forms the basis for the open shortest path first algorithm
used within autonomous systems in the Internet [11]. In order to both
conserve battery power and reduce the probability of detection by hostile
forces, it is better to use a reactive routing protocol. This is a protocol
that determines a route only when it is required.
Another design choice is whether the network has a flat or hierarchical
architecture. Both have advantages and disadvantages. The former is more
survivable since it does not have a single point of failure; it also allows
multiple routes between nodes. The latter provides simpler network
management, and can help further reduce transmissions.
In a mobile adhoc network, the changing network topology leads to a
requirement that the network periodically reconfigure itself. Not only must
the routing protocols be able to handle this situation, but also the media
access mechanism. Link parameters, such as modulation type, amount of
channel coding, transmitter power, etc. , must adapt to the new
configuration. While we are initially assuming that the sensors are
stationary, the possibility of (deliberate) sensor destruction requires that
the communications network be reconfigurable. Moreover, the distributed
detection and estimation algorithms must also have this capability.
Summary of Research Issues
As discussed above, there has been a relatively long history of research in
distributed detection and estimation in one research community, and wireless
networking in another. However, there has been much less overlap between
these two communities.
The DARPAsponsored SensIt program
is also beginning to address this topic, with the focus primarily on
collaborative signal processing, network routing protocols, and query
procedures. The main contribution of our work is to combine the two
disciplines of distributed detection/estimation and wireless networking
so that realistic smart sensor network configurations are developed,
evaluated, and optimized. Based on these designs, research is being
conducted to answer the following interrelated questions:

How do the communication and
networking effects, specifically routing, bandwidth, and power constraints
determine the quality of the distributed detection and/or estimation?

What is the load on the
communication network caused by the distributed processing? How many
messages/second and how many system resources are required to achieve a
desired quality of service?

How robust is the resulting sensor
network to lost nodes? What is the mechanism for reconfiguration that
allows the network to adapt to such loss events?
References

R. Viswanathan and P. K. Varshney, "Distributed detection with multiple
sensors: Part I  fundamentals," Proceedings of the IEEE, vol. 85, no. 1,
pp. 5463, Jan. 1997.

S. C. A. Thomopoulos, R. Viswanathan, and D. K. Bougoulias, "Optimal
distributed decision fusion," IEEE Trans. Aerospace Elect. Syst., vol. 25,
pp. 761765, Sept 1989.
 J. N. Tsistsiklis, "Decentralized detection," in Advances in Statistical
Signal Processing, Signal Detection, vol. 2, H. V. Poor and J. B. Thomas,
Eds. Greenwich, CT: JAI, 1993.
 C. C. Lee and J. J. Chao, "Optimum local decision space partioning for
distributed detection," IEEE Trans. Aerospace Elect. Syst., vol. 25, no. 4.
pp. 536544, July 1989.
 Z. B. Tang, K. R. Pattipati, and D. L. Kleinman, "Optimization of
distributed detection networks: Part II generalized tree structures,"
IEEE Trans. Syst., Man Cybern., vol. 23, pp 211221, Jan./Feb. 1993.
 D. A. Pados, K. W. Halford, D. Kazakos, and P. PapantoniKazakos,
"Distributed binary hypothesis testing with feedback," IEEE Trans. Syst.,
Man and Cybern., vol. 25, pp. 2142, Jan. 1995.
 S. Alhakeem and P. K. Varshney, "Decentralized bayesian hypothesis
testing with feedback," IEEE Trans. Syst., Man Cybern., vol. 26,
pp. 503513, July 1996.
 P. F. Swaszek and P. Willett, "Parley as an approach to distributed
detection," IEEE Trans. Aerospace Elect. Syst., vol. 31, pp. 447457,
Jan. 1995.
 S. C. A. Thomopoulos and L. Zhang, "Distributed decision fusion with
networking delays and channel errors," Inform. Sci, vol. 66, no. 1,
pp. 91118, Dec, 1992.
 V. Megalooikonomou and Y. Yesha, "Quantizer design for distributed
estimation with communication constraints and unknown observation
statistics," IEEE Trans. on Comm., vol. 48, no. 2,
pp. 181184, Feb. 2000.
 J. Moy, "OSPF Version 2," IETF Network Working Group, RFC 2328,
Apr. 1998.
