@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}
}
@article {
	title = {Optimal LQG Control under Delay-dependent Costly Information},
	author = {Maity, Dipankar., Mamduhi, Mohammad H., Hirche, Sandra., Johansson, Karl Henrik., John S. Baras},
	year = {2018},
	month = {06/2018},
	abstract = {In the design of closed-loop networked control systems (NCSs), induced transmission delay between sensors and control station is an often-present issue which compromises control performance and may even cause instability. Taking into account the current advances of communication technology, a very relevant scenario within which induced delay needs to be investigated is cost-prone usage of communication resources. More precisely, if information exchange over the network is costly, then induced delay is the matter of decision maker{\textquoteright}s willingness to either pay the required cost to have immediate access to communication resource, or delay the access at a reduced price. In this article, we consider price-based decision making problem for a stochastic linear time-invariant system, and address the effect of induced delay on control performance based on LQG cost function. Assuming that network access with shorter delay is costlier, a decision maker installed at the control station determines real-time delay duration such that an optimal balance between the control performance and the communication cost is maintained. In this article, we show that, under mild assumptions on the available information for decision makers, the separation property holds between the optimal transmission and control policies. As the cost function is decomposable, the optimal policies are efficiently computed.},
	journal = {IEEE L-CSS (2018)}
}
@article {
	title = {Active View Point Control for Detection of Dynamic Targets in Human-Robot Interaction},
	author = {Luan, Wentao., John S. Baras, Fermuller, Cornelia., Mao, Ren.},
	year = {2017},
	month = {06/2017},
	abstract = {Object detection is a fundamental task for robots, and is particularly challenging in the dynamic setting of humanrobot interaction because of the user{\textquoteright}s movements. In this work, we put forward a practical viewpoint control mechanism for object detection in human-robot interaction. We not only consider constraints from vision but also incorporate the low level robot kinematics to guarantee reachability of the desired viewpoint. Using a linear time cost score function, our system selects very fast the viewpoints, which in turn leads to a smooth user interaction experience. We also provide a method to learn from human demonstration the score function weights to fit the task{\textquoteright}s preference.
~},
	journal = {IEEE Robotics and Automation Letters (RA-L)}
}
@article {
	title = {Near-Optimal Placement of Virtualized EPC Functions with Latency Bounds},
	author = {Dietrich, David., Papagianni, Chrysa. , Panagiotis, Papadimitriou., John S. Baras},
	abstract = {The proliferation of mobiles devices, application sprawl, and
the ever-increasing data volume generates significant stress on cellular
networks and particularly on the cellular core, also known as the
Evolved Packet Core (EPC), i.e., the cellular network component residing
between the radio access network and the Internet. This is further
exacerbated by the deployment of hardware appliances for the implementation
of a wide range of network functions (e.g., gateways, mobility
management, firewalls, network address translation), hindering any
opportunity for elastic provisioning, and eventually leading to high operational
costs and a significant degree of load imbalance across the EPC.
Network Function Virtualization (NFV) has been seen a promising
solution in order to enable elasticity in the cellular core. Applying NFV
to the EPC raises the need for network function (NF) placement, which
in turn entails significant challenges, due to the stringent delay budgets
among cellular core components and the coexistence of communicating
data and control plane elements. To address these challenges, we
present a linear programming (LP) formulation for the computation of
NF placements that strikes a balance between optimality and time complexity.
Our evaluation results show that the LP achieves significantly
better load balancing, request acceptance rate, and resource utilization
compared to a greedy algorithm that performs NF placement inline with
carriers{\textquoteright} common practice today.},
	pages = {200-222},
	volume = {10340},
	journal = {Communication Systems and Networks, Series: Computer Communication Networks and Telecommunications}
}
