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Section: New Results
Routing
Routing in Wireless Sensor Networks
Participants : Emmanuel Nataf [contact] , Patrick-Olivier Kamgueu.
We deployed a wireless sensors network in the laboratory during two time period of 3 months. The first was with the legacy routing (based on expected transmission time metric) and the second was with our routing process based on a composition of several metrics (i.e. energy, transmission time and delay) by the use of fuzzy logic. We have compared these experiments by packet loss ratio and energy consumption. In all case, our routing leads to a better network [48] .
Operator calculus based routing in Wireless Sensor Networks
Participants : Evangelia Tsiontsiou, Bernardetta Addis, Ye-Qiong Song [contact] .
For supporting different QoS requirements, routing in WSN must simultaneously consider several criteria (e.g., minimizing energy consumption, hop counts or delay, packet loss probability, etc.). When multiple routing metrics are considered, the problem becomes a multi-constrained optimal path problem (MCOP), which is known as NP-complete.
Recently, Operator calculus (OC) has been developed by Schott and Staples with whom we collaborate. We make use of OC methods on graphs to solve path selection in the presence of multiple constraints. Based on OC, we developed a distributed algorithm for path selection in a graph. We also designed a new routing protocol which makes use of this algorithm: the Operator Calculus based Routing Protocol (OCRP). In OCRP, a node selects the set of eligible next hops based on the given constraints and the distance to the destination. It then sends the packet to all eligible next hops. The protocol is implemented in Contiki OS and emulated for TelosB motes using Cooja. We compared its performance against tree and directional flooding routing and show the advantages of our technique. Our ongoing work consists in its comparison with RPL to show its effective contribution to handle simultaneously several IETF ROLL routing metrics.
This work is under development as part of Lorraine AME Satelor project.
Energy-aware IP networks management
Participants : Bernardetta Addis [contact] , Giuliana Carello [DEIB, Politecnico di Milano, Italy] , Antonio Capone [DEIB, Politecnico di Milano, Italy] , Luca Gianoli [Polytecnique de Montreal, Canada] , Sara Mattia [IASI, CNR, Roma, Italy] , Brunide Sansò [Polytecnique de Montreal, Canada] .
The focus of our research is to minimize the energy consumption of the network through a management strategy that selectively switches off devices according to the traffic level. We consider a set of traffic scenarios and jointly optimize their energy consumption assuming a per-flow routing. We propose a traffic engineering mathematical programming formulation based on integer linear programming that includes constraints on the changes of the device states and routing paths to limit the impact on quality of service and the signaling overhead. We also present heuristic results to compare the optimal operational planning with online energy management operation ([3] )
Two very important issues that may be affected by green networking techniques are resilience to node and link failures, and robustness to traffic variations. We thus extended the optimization models. To guarantee network survivability we consider two different schemes, dedicated and shared protection, which assign a backup path to each traffic demand and some spare capacity on the links along the path. Robustness to traffic variations is provided by tuning the capacity margin on active links in order to accommodate load variations of different magnitude. Both exact and heuristic methods are proposed. Experimentations carried out on realistic networks operated with flow-based routing protocols (like MPLS) allow us to quantitatively analyze the trade-off between energy cost and level of protection and robustness. Results show that significant savings, up to 30%, may be achieved even when both survivability and robustness are fully guaranteed [4] .
Computational cost of proposed models can be very high when dealing with large size instances (network size and/or number of demands). For this reason, we proposed and tested different problem formulations with the aim of solving larger size instances at optimality. Preliminary results on a simplified model ([29] ) are very encouraging.
Energy-aware joint management of networks and Cloud infrastructures
Participants : Bernardetta Addis [contact] , Danilo Ardagna [DEIB, Politecnico di Milano, Italy] , Giuliana Carello [DEIB, Politecnico di Milano, Italy] , Antonio Capone [DEIB, Politecnico di Milano, Italy] .
Fueled by the massive adoption of Cloud services, overall service centers and networks account for 2–4% of global emissions and it is expected they can reach up to 10% in 5–10 years.
The geographical distribution of the computing facilities offers many opportunities for optimizing energy consumption and costs by means of a clever distribution of the computational workload exploiting different availability of renewable energy sources, but also different time zones and hourly energy pricing. Energy and cost savings can be pursued by dynamically allocating computing resources to applications at a global level, while communication networks allow to assign flexibly load requests and to move data. We propose an optimization framework able to jointly manage the use of brown and green energy in an integrated system and to guarantee quality requirements. We propose an efficient and accurate problem formulation that can be solved for real-size instances in few minutes to optimality. Numerical results, on a set of randomly generated instances and a case study representative of a large Cloud provider, show that the availability of green energy have a big impact on optimal energy management policies and that the contribution of the network is far from being negligible ([2] ).
Content centric wireless sensor networks
Participants : Abdelkader Lahmadi [contact] , Younes Abid, Olivier Festor.
During this year, we have instantiated a novel named data aggregation method [9] dedicated to wireless sensor networks . The method relies on an adaptation of the CCNx protocol implementation that we have developed in a previous work. Our method extends the CCNx protocol with in-network processing functions to aggregate named data efficiently. We have implemented and tested our solution with the Contiki operating system which is an operating system for resources-constrained embedded systems and wireless sensor networks. Our simulation and measurement results using the Cooja simulator and physical nodes show that our solution has a small overhead in terms of exchanged messages and provides acceptable data retrieval delays.