ACCESS Networked Services
Seed Project 2010-2011
Current Project Partners
Alessio De Angelis
Carlo Fischione (p. leader) carlofi@ee.kth.se
Ather Gattami gattami@kth.se
Tobias Ochtering tobias.oechtering@ee.kth.se
Ragnar Thobaben ragnar.thobaben@ee.kth.se
Associated Founding Faculty
Kar Henrik Johansson kallej@ee.kth.se
Xiaoming Hu
Peter Handel
Lars Rasmussen
Mikael Skoglund hu@kth.se
ph@ee.kth.se
Lars.Rasmussen@ee.kth.se
mikaelj@ee.kth.se
Introduction
In this project, we plan to investigate distributed and parallel optimization algorithms for networked systems. In these systems, the operations cannot rely on a central control unit. This is the typical case of wireless sensor networks, where the need of achieving maximum efficiency motivates an in-network processing. In these algorithms, a network-global function must often be optimized to ensure efficient operations of the entire network under some communication, computing, or application constraints. For instance, nodes could cooperatively exchange the measurements associated to a state of a plant or the position of some object being tracked to build an estimate with better precision. Another relevant example is when nodes cooperatively exchange side information to decode packets with better precision. A global function such as the

estimation or decoding error must be minimized by algorithms with quick computation under constraints that prevent the propagation of the error throughout the network.
Because of packet losses and hardware limitations, the solution of these optimization problems cannot be achieved efficiently with either a traditional centralized method or distributed Lagrangian message passing. A centralized computation would require collecting information associated to local nodes, and then dispatching the optimal solution to them. It is clearly inefficient for nodes to send large amount of information to a central unit and then wait for the optimal solution to further operate. Furthermore, classical distributed optimization algorithms converge with many iterations, consume significant energy in message passing transmissions, and may not scale with the network size. In this project, we aim at investigating novel distributed optimization algorithms and their application to cooperative localization, distributed estimation, and distributed source coding.
In this work package, we focus on localization for mobile sensor networks. One of the essential services in distributed networks is the self-localization of the nodes.
Localization services have significant importance when using ad-hoc networks for vehicular traffic monitoring or for recue operations. For geographic packet routing, localization allows a node to transmit a packet to the next hop node if such a next-hop node is geographically closer to the destination. Traditional localization techniques use central controllers or coordination, but such an approach is often impossible to use for large networks or for networks with high mobility of the nodes. In these cases, cooperative localization techniques can offer significant performance improvements both for indoor and outdoor situations. In this workpackage, we will develop novel techniques and algorithms for cooperative localization. We plan to investigate the fundamental performance limits offered by these techniques. Moreover, we plan to perform experimental tests using ultra wide band (UWB) positioning devices and the
IEEE 802.15.4 protocol, which supports UWB.
Deliverables:
1.
Design and Performance Analysis of Cooperative Localization Algorithms.
(Alessio, Carlo, Peter)
2.
Experimental Evaluation of Cooperative Localization Algorithms. (Alessio, Carlo,
Peter)

This workpackage is about distributed state estimation for networked control systems.
Estimation is an essential service that a network of nodes should offer, since resource limited nodes often perform noisy measurements of the state of the physical phenomena being sensed. Distributed estimation refers to the capability of network nodes to perform local estimation of the state of a plant by relying on the cooperation with neighboring nodes and without any central coordination. Distributed estimation algorithms are designed by optimization problems whose solution must be achieved via parallel computation. When considering networked systems, distributed estimation poses new challenging problems related, for instance, to the propagation of the estimation error and the robustness of the estimators to the network topology. The characteristics of the communication protocols, such as routing, medium access control, and physical communication layers, may have a significant impact on the quality of the estimates. For instance, IEEE 802.15.4 or WirelessHart pose constraints to the packet transmission, which may affect the performance of the estimators. Other idealities of the network such as packet drops and delays should be accounted for in the design of these distributed estimation algorithms.
1.
Distributed Estimation in Networked Systems, (Carlo, Kalle, Xiaoming)
In this work package, we investigate distributed algorithms for coding. Since networked systems have often limited computation and communication resources, it is important to encode data by the minimum amount of information and decrease the computational load associated to the decoding operations.
One option to reduce information to transmit over the wireless channel is to encode data by exploiting the temporal correlation of the source information. We will investigate distributed source coding schemes in which the coding is carried out over successive time slots by exploiting feedback from the sink and adjust the coding strategy in order to satisfy the quality requirements at the decoder. Our analysis will focus on the distributed optimization of the resource allocation under rate, energy, and delay constraints and the stability of the entire system.
The computation complexity of the decoding poses new problems to the design of channel coding. In general, the design of good channel codes with rates below and close to the channel capacity is a well investigated problem. However, if we constrain the complexity at the receiver, the problem of finding optimal codes and decoding strategies appears to be still open. We will combine our background on coding theory

and optimization theory in order to reformulate and analyze the underlying optimization problem from a new perspective and to develop new low-complexity decoding strategies.
Deliverables:
1.
“Distributed Source Coding with Feedback”, Carlo, Lars, Mikael, Ragnar.
2.
“Fast Iterative Channel Decoders via Parallel Optimization", Carlo, Lars, Ragnar.
3.
“On the connection between the Witsenhausen counterexample and coding for channels with channel state information at the encoder”, Ather, Carlo, Mikael S,
Tobias