@conference {
	title = {Optimal Decision Fusion Under Order Effects},
	author = {Ehsan Nekouei, Wei, Jieqiang., John S. Baras, Mikael Skoglund, Johansson, Karl Henrik.},
	year = {2018},
	month = {12/2018},
	abstract = {This paper studies an optimal decision fusion problem with a group of human decision makers when an order effect is present. The order effect refers to situations wherein the process of decision making by a human is affected by the order of decisions. In our set-up, all human decision makers, called observers, receive the same data which is generated by a common but unknown hypothesis. Then, each observer independently generates a sequence of decisions which are modeled by employing noncommutative probabilistic models of the data and their relation to the unknown hypothesis. The use of non-commutative probability models is motivated by recent psychological studies which indicate that these non-commutative probability models are more suitable for capturing the order effect in human decision making, compared with the classical probability model. A central decision maker (CDM) receives (possibly a subset of) the observers{\textquoteright} decisions and decides on the true hypothesis. The considered problem becomes an optimal decision fusion problem with observations modeled using a non-commutative (Von Neumann) probability model. The structure of the optimal decision rule at the CDM is studied under two scenarios. In the first scenario, the CDM receives the entire history of the observers{\textquoteright} decisions whereas in the second scenario, the CDM receives only the last decision of each observer. The perfromance of the optimal fusion rule is numerically evaluated and compared with the optimal fusion rule derived when using a classical probability model.},
	address = {Miami, Florida},
	booktitle = {2nd IFAc Conference on Cyber-Physical \& Human-Systems (CPHS)2018}
}
@conference {
	title = {Towards Reduced-State Service Chaining with Source Routing},
	author = {Papagianni, Chrysa. , Panagiotis, Papadimitriou., John S. Baras},
	year = {2018},
	month = {11/2018},
	abstract = {The widespread adoption of Network Function Virtualizatio~ (NFV) poses significant dataplane scalability limitations in datacenters (DC), as switches will be required to maintain a large amount of forwarding state for service chaining. This problem is exacerbated by the small Forwarding Information Base (FIB) of commodity switches, which are typically deployed in NFV infrastructures.
To mitigate this problem, we present a routing fabric that combines source routing with pathlet switching in order to (i) achieve state reduction and (ii) provide support for longer paths, hence, meeting the increased hop-count requirement of service chains. Coupling the proposed source routing fabric with a service chain embedding method, we achieve significant gains in terms of FIB consumption, request acceptance, and revenue generation.},
	address = {Rome, Italy},
	booktitle = {14th International Conference on Network and Service Management (NSM)2018}
}
@conference {
	title = {Covariance-Based Transmission Power Control for Estimation over Wireless Sensor Networks},
	author = {Soleymani, Touraj., Zoppi, Samuel, Vilgelm, Mikhail, Hirche, Sandra., kellerer, Wolfgang, John S. Baras},
	year = {2018},
	month = {06/2018},
	abstract = {In this study, we would like to design a transmission power control mechanism for minimizing the transmit power of a wireless sensor node subject to a constraint on the quality of estimation at a remote estimator. In particular, we measure the estimation distortion by a mean square error function, and model the transmit power as a function of the packet success rate using the IEEE 802.15.4 standard which was formed for the specification of low-data-rate and low-power wireless communication. We formulate the problem with an infinite horizon discounted cost function. We show that there is a separation between the designs of the optimal estimator and optimal transmit power policy. Then, we use dynamic programming to characterize the optimal transmit power policy in terms of the estimation error covariance. Finally, we propose approximate value iteration as an approximation algorithm to calculate a near-optimal transmit power policy.},
	address = {Limassol, Cyprus},
	booktitle = {European Control Conference 2018},
	pages = {pp. 857-862}
}
