Abstract: We present a 40 Gb/s asynchronous self-routing network and node architecture that exploits bit
and packet level optical signal processing to perform synchronization, forwarding and
switching. Optical packets are self-routed on a hop-by-hop basis through the network by using
stacked optical tags, each representing a specific optical node. Each tag contains control signals
for configuring the switching matrix and forwarding each packet to the appropriate outgoing
link and onto the next hop. Physical layer simulations are performed, modeling each optical subsystem
of the node showing acceptable signal quality and Bit Error Rates. Resource reservationbased
signaling algorithms are theoretically modeled for the control plane capable of providing
high performance in terms of blocking probability and holding time.

Abstract: In this work we present a platform-agnostic framework for intergrating heterogeneous Smart Objects in the Web of Things. Our framework, consists of 4 different hardware platforms, Arduino, SunSPOT, TelosB, iSense. These hardware platforms are the most representative ones, as used by the relevant research community. A first contribution of our work is a careful description of the necessary steps to make such a heterogeneous network interoperate and the implementation of a networkstack, in the form of a software library, named mkSense, which enables their intercommunication. Moreover, we describe the design and implementation of software library which can be used for building “intelligent software” for the Web of Things.

Abstract: In this work, we expanded the Arduino's
capabilities by adding an 802.15.4 wireless module, in order to
expose its functionality as a Web of Things node. The second
contribution of our work is a careful description of the necessary
steps to make a heterogeneous network interoperate and the
implementation of a networkstack for the 4 most representative
hardware platforms, as used by the relevant research community
(Arduino, SunSPOT, TelosB, iSense), in the form of a software
library, named mkSense, which enables their
intercommunication. Moreover, we describe the design and
implementation of a software library which can be used for
building “intelligent software” for the Web of Things.

Abstract: In large scale networks users often behave selfishly trying to minimize their routing cost. Modelling this as a noncooperative game, may yield a Nash equilibrium with unboundedly poor network performance. To measure this inefficacy, the Coordination Ratio or Price of Anarchy (PoA) was introduced. It equals the ratio of the cost induced by the worst Nash equilibrium, to the corresponding one induced by the overall optimum assignment of the jobs to the network. On improving the PoA of a given network, a series of papers model this selfish behavior as a Stackelberg or Leader-Followers game.
We consider random tuples of machines, with either linear or M/M/1 latency functions, and PoA at least a tuning parameter c. We validate a variant (NLS) of the Largest Latency First (LLF) Leaderrsquos strategy on tuples with PoA ge c. NLS experimentally improves on LLF for systems with inherently high PoA, where the Leader is constrained to control low portion agr of jobs. This suggests even better performance for systems with arbitrary PoA. Also, we bounded experimentally the least Leaderrsquos portion agr0 needed to induce optimum cost. Unexpectedly, as parameter c increases the corresponding agr0 decreases, for M/M/1 latency functions. All these are implemented in an extensive Matlab toolbox.

Abstract: Let M be a single s-t network of parallel links with load dependent latency functions shared by an infinite number of selfish users. This may yield a Nash equilibrium with unbounded Coordination Ratio [E. Koutsoupias, C. Papadimitriou, Worst-case equilibria, in: 16th Annual Symposium on Theoretical Aspects of Computer Science, STACS, vol. 1563, 1999, pp. 404-413; T. Roughgarden, E. Tardos, How bad is selfish routing? in: 41st IEEE Annual Symposium of Foundations of Computer Science, FOCS, 2000, pp. 93-102]. A Leader can decrease the coordination ratio by assigning flow {\'a}r on M, and then all Followers assign selfishly the (1-{\'a})r remaining flow. This is a Stackelberg Scheduling Instance(M,r,{\'a}),0≤{\'a}≤1. It was shown [T. Roughgarden, Stackelberg scheduling strategies, in: 33rd Annual Symposium on Theory of Computing, STOC, 2001, pp. 104-113] that it is weakly NP-hard to compute the optimal Leader's strategy. For any such network M we efficiently compute the minimum portion @b"M of flow r>0 needed by a Leader to induce M's optimum cost, as well as her optimal strategy. This shows that the optimal Leader's strategy on instances (M,r,@a>=@b"M) is in P. Unfortunately, Stackelberg routing in more general nets can be arbitrarily hard. Roughgarden presented a modification of Braess's Paradox graph, such that no strategy controlling {\'a}r flow can induce ≤1/{\'a} times the optimum cost. However, we show that our main result also applies to any s-t net G. We take care of the Braess's graph explicitly, as a convincing example. Finally, we extend this result to k commodities. A conference version of this paper has appeared in [A. Kaporis, P. Spirakis, The price of optimum in stackelberg games on arbitrary single commodity networks and latency functions, in: 18th annual ACM symposium on Parallelism in Algorithms and Architectures, SPAA, 2006, pp. 19-28]. Some preliminary results have also appeared as technical report in [A.C. Kaporis, E. Politopoulou, P.G. Spirakis, The price of optimum in stackelberg games, in: Electronic Colloquium on Computational Complexity, ECCC, (056), 2005].

Abstract: In this paper we describe a new simulation platform for complex wireless sensor networks that operate a collection of distributed algorithms and network protocols. Simulating such systems is complicated because of the need to coordinate different network layers and debug protocol stacks, often with very different interfaces, options, and fidelities. Our platform (which we call WSNGE) is a flexible and extensible environment that provides a highly scalable simulator with unique characteristics. It focuses on user friendliness, providing every function in both scriptable and visual way, allowing the researcher to define simulations and view results in an easy to use graphical environment. Unlike other solutions, WSNGE does not distinguish between different scenario types, allowing multiple different protocols to run at the same time. It enables rich online interaction with running simulations, allowing parameters, topologies or the whole scenario to be altered at any point in time.