@conference {
	title = {"State-dependent Data Queuing in Shared-resource Networked Control Systems"},
	author = {Mamduhi, Mohammad H., Baras, John S., Johansson, Karl Henrik., Hirche, Sandra.},
	year = {2018},
	month = {12/2018},
	abstract = {In the design of shared resource networked control systems (NCSs), resource managers play an important role to
appropriately allocate limited resources across the distributedsystem. They are often used to fairly distribute the limited bandwidth among the medium-sharing entities at the expense of delaying or discarding unnecessary data samples. Considering the rapidly growing volume of information that need to be exchanged, a relevant scenario for efficient resource management is state-dependent data buffering through network queues. In this paper, we propose state-dependent data buffering for shared-resource NCSs, such that both buffer input and output can be adjusted depending on the real-time conditions of the control systems and the communication network. We consider that the transmission decisions at the sensor side are taken by event-based schedulers, and that those data eventually sent for transmission are queued and processed, depending on the available communication resource. To improve the buffer service, we propose a state-dependent buffer discharge method, based on real-time feedback from the communication network. We derive sufficient stability conditions under which the overall NCS with the proposed cross-layer transmission scheme is stable in mean-square sense. Moreover, we show performance improvements resulting from our proposed design in comparison with its state-independent counterpart.},
	address = {Miami Beach, Florida},
	booktitle = {Conference on Decision and Control (CDC)2018}
}
@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 = {Stochastic Control Formulation of The Car Overtake Problem},
	author = {Raghavan, Aneesh., Wei, Jieqiang., Baras, John S., Johansson, Karl Henrik.},
	year = {2018},
	month = {06/2018},
	abstract = {~In this paper, we consider the classic car overtake problem. There are three cars, two moving along the same direction in the same lane while the third car moves in the direction opposite to that of the first two cars in the adjacent lane. The objective of the trailing car is to overtake the car in front of it avoiding collision with the other cars in the scenario. The information available to the trailing car is the relative position, relative velocities with respect to other cars and its position and past actions. The relative position and relative velocity information is corrupted by noise. Given this information, the car needs to make a decision as to whether it wants to overtake or not. We present a control algorithm for the car which minimizes the probability of collision with both the cars. We also present the results obtained by simulating the above scenario with the control algorithm. Through simulations we study the effect of the variance of the measurement noise and the time at which the decision is made on the probability of collision.},
	address = {Savona, Italy},
	booktitle = {15th IFAC Symposium on Control in Transportation Systems},
	pages = {pp. 124-129}
}
@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 = {The Evolution of Beliefs over Signed Social Networks},
	author = {Shi, Guodong., Proutiere, Alexandre., Johansson, Mikael., Baras, John S., Johansson, Karl Henrik.},
	year = {2016},
	month = {10/2016},
	abstract = {We study the evolution of opinions (or beliefs) over a social network modeled as a signed graph. The sign attached to an edge in this graph characterizes whether the corresponding individuals or end nodes are friends (positive links) or enemies (negative links). Pairs of nodes are randomly selected to interact over time, and when two nodes interact, each of them updates its opinion based on the opinion of the other node and the sign of the corresponding link. This model generalizes the DeGroot model to account for negative links: when two adversaries interact, their opinions go in opposite directions. We provide conditions for convergence and divergence in expectation, in mean-square, and in almost sure sense and exhibit phase transition phenomena for these notions of convergence depending on the parameters of the opinion update model and on the structure of the underlying graph. We establish a no-survivor theorem, stating that the difference in opinions of any two nodes diverges whenever opinions in the network diverge as a whole. We also prove a live-or-die lemma, indicating that almost surely, the opinions either converge to an agreement or diverge. Finally, we extend our analysis to cases where opinions have hard lower and upper limits. In these cases, we study when and how opinions may become asymptotically clustered to the belief boundaries and highlight the crucial influence of (strong or weak) structural balance of the underlying network on this clustering phenomenon},
	pages = {pp. 585-604},
	volume = {Vol. 64},
	number = {Issue 3},
	journal = {Operations Research}
}
@article {
	title = {Emergent Behaviors over Signed Random Dynamical Networks: Relative-State-Flipping Model},
	author = {Shi, Guodong., Proutiere, Alexandre., Johansson, Mikael., Baras, John S., Johansson, Karl Henrik.},
	year = {2016},
	month = {06/2016},
	abstract = {We study asymptotic dynamical patterns that emerge among a set of nodes interacting in a dynamically evolving signed random network, where positive links carry out standard consensus and negative links induce relative-state flipping. A sequence of deterministic signed graphs defines potential node interactions that take place independently. Each node receives a positive recommendation consistent with the standard consensus algorithm from its positive neighbors, and a negative recommendation defined by relative-state flipping from its negative neighbors. After receiving these recommendations, each node puts a deterministic weight to each recommendation and then encodes these weighted recommendations in its state update through stochastic attentions defined by two Bernoulli random variables. We establish a number of conditions regarding almost sure convergence and divergence of the node states. We also propose a condition for almost sure state clustering for essentially weakly balanced graphs, with the help of several martingale convergence lemmas. Some fundamental differences on the impact of the deterministic weights and stochastic attentions to the node state evolution are highlighted between the current relative-state- flipping model and the state-flipping model considered in Shi et al., IEEE Transaction on Control of Network Systems, 2015.},
	pages = {pp. 1-11},
	volume = {Vol. 3},
	number = {Issue 2},
	journal = {IEEE transactions on control of network systems}
}
@article {
	title = {Emergent Behaviors over Signed Random Dynamical Networks: State-Flipping Model},
	author = {Shi, Guodong., Proutiere, Alexandre., Johansson, Mikael., Baras, John S., Johansson, Karl Henrik.},
	year = {2015},
	month = {06/2015},
	abstract = {Recent studies from social, biological, and engineering network systems have drawn attention to the dynamics over signed networks, where each link is associated with a positive/negative sign indicating trustful/mistrustful, activator/ inhibitor, or secure/malicious interactions. We study asymptotic dynamical patterns that emerge among a set of nodes that interact in a dynamically evolving signed random network. Node interactions take place at random on a sequence of deterministic signed graphs. Each node receives positive or negative recommendations from its neighbors depending on the sign of the interaction arcs, and updates its state accordingly. Recommendations along a positive arc follow the standard consensus update. As in the work by Altafini, negative recommendations use an update where the sign of the neighbor state is flipped. Nodes may weight positive and negative recommendations differently, and random processes are introduced to model the time-varying attention that nodes pay to these recommendations. Conditions for almost sure convergence and divergence of the node states are established. We show that under this so-called state-flipping model, all links contribute to a consensus of the absolute values of the nodes, even under switching sign patterns and a dynamically changing environment. A no-survivor property is established, indicating that every node state diverges almost surely if the maximum network state diverges.},
	pages = {pp. 142-153},
	volume = {Vol. 2},
	number = {No. 2},
	journal = {IEEE transactions on control of network systems}
}
