A lightweight forwarding strategy for Named Data Networking in low-end IoT

International audienc

A down-to-earth integration of Named Data Networking in the real-world IoT

International audienceThe IEEE802.15.4 wireless technology is one of the enablers of the Internet of Things. It allows constrained devices to communicate with a satisfactory data rate, payload size and distance range, all with reduced energy consumption. To provide IoT devices with a global Internet identity, 6LoWPAN defines the IPv6 adaptation to communicate over IEEE802.15.4. However, this integration still needs additional protocols to support other IoT requirements, which makes the IP stack in IoT devices more complex and therefore shows the limitations of the IP model to support the needs of future Internet. Named Data Networking represents an alternative that can natively support IoT constraints including mobility, security and human readable data names. This paper is a synthesis of an ongoing work that investigates the integration of NDN with IEEE802.15.4 for constrained IoT devices. The proposed design has been implemented in a real-world smart agriculture scenario, and evaluated by simulation focusing on energy consumption and network overhead in comparison to IP-based protocols

Towards evaluating Named Data Networking for the IoT: A framework for OMNeT++

International audienc

Load Balancing: An Approach Based on Clustering in Ad Hoc Networks

One of the most critical issues in wireless ad hoc networks is represented by the significant differences in term of processing and energy power between the different nodes, inducing a load imbalance between the overloaded and idle nodes. Thus, making good exploitation of the powerful nodes capacity by the overloaded nodes if a fraction of their load will be shared with other nodes is a must in ad hoc networks. In this paper, we present a new load balancing algorithm which is based on the grouping of nodes in a set of clusters and to maintain a certain balance within each cluster. The objective of our work is double. On one hand it aims at minimizing total tasks execution time and on the other at extending the overloaded nodes lifetime inducing a stability of the network. The simulation results have shown that better network performance can be reached by distributing load to idle nodes of the network

Modified reinforcement learning based- caching system for mobile edge computing

International audienceCaching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links loadand reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an importantissue. Recently, the tremendous growth ofMobile Edge Computing(MEC) empowers the edge network nodes withmore computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within themobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context informationintelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the cachingperformance, the suitable methodology is to consider both context awareness and intelligence so that the cachingstrategy is aware of the environment while caching the appropriate content by making the right decision. Inspiredby the success ofreinforcement learning(RL) that uses agents to deal with decision making problems, we presentamodified reinforcement learning(mRL) to cache contents in the network edges. Our proposed solution aims tomaximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. Themodified RL differs from other RL algorithms in the learning rate that uses the method ofstochastic gradient decent(SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.Index Terms — Caching, Reinforcement Learning, Fuzzy Logic, Mobile Edge Computing

Intelligent Routing and Flow Control In MANETs

Improving the performance of the Mobile Ad hoc NETworks (MANETs) is a challenge. MANETs present several constraints such as dynamic topology, wireless link communication and limited resources like bandwidth and energy. Routing without taking into account these constraints degrades performance and aggravates congestion problem. This paper proposes a new solution combining QoS (Quality of Service) routing protocol and flow control mechanism. This QoS routing protocol selects the routes with more resources in an intelligent manner rather than diffusion. It returns the best route offering a higher transmission rate, a less delay and a more stability. This protocol uses a new metric to compute the most stable route. To reinforce the congestion avoidance, we add a flow control mechanism to adjust the sender\u27s transmission rate for each route. The solution is modeled by the ant systems. The results obtained under the Network Simulator (NS2.31) show that our QoS routing protocol improves the network performance compared to QoS-AODV protocol. The addition of flow control mechanism produces significant improvements in network system performance

Incidences of the Improvement of the Interactions Between MAC and Routing Protocols on MANET Performance

In this paper, we present an improvement for the interactions between MAC and routing protocols to better energy consumption in MANET (Mobile Ad hoc Networks) and show its incidences on the performance of the network. We propose a new approach called IMREE (Improvement of the Interactions between MAC and Routing protocol for Energy Efficient) which exploits tow communication environment parameters. The first one is the number of nodes; our approach reduces the additional energy used to transmit the lost data by making the size of the backoff interval of MAC protocol adaptable to the nodes number in the network. The second parameter is the mobility of nodes; IMR-EE uses also the mobility of nodes to calculate a fairness threshold in order to guarantee the same level of the residual energy for each node in the network. We evaluate our IMR-EE solution with NS (Networks Simulator) and study its incidences on data lost and energy consumption in the network under varied network conditions such as load and mobility. The results showed that IMR-EE outperform MAC standard and allows significant energy saving and an increase in average lifetime of a mobiles nodes in the network

Parallel Applications Mapping onto Heterogeneous MPSoCs interconnected using Network on Chip

International audienc

PREDICTION DE MOBILITE BASEE SUR LA CLASSIFICATION SELON LE PROFIL

Les réseaux mobiles de 3èmegénération permettent aux utilisateurs une mobilité plus éte ndue et plus souple. Ceux-ci peuvent se déplacer tout en exécutant des applications multimédias et temps réel sur leurs mobiles. Toutefois, plusieur s problèmes sont à résoudre. Parmi ces problèmes nous citons : la localisation, les déconnexions fréquentes et la gestion desressources.La prédiction des déplacements peutjouer un grand rôle dans la gestion de la mobilité. Par exemple, la connaissance de la position d’un mobile par le système à l’avance, lui per mettra un gain de temps lors de sarecherche (le paging). En effet, lenombre de cellule à pager deviendra limité donc il sera plus facile au réseau de le trouver , et de lui acheminer les appels et les données. La prédiction permet également au réseau de réserver des ressources à l’avance à un mobile dans les futurescellules qu’il va traverser, réduisant ainsi la fréquence des déconnexions.Nous présentons dans ce papier une solution de prédiction des déplacements des utilisateurs basée sur le datamining, plusprécisément la classification des utilisateurs selon leurs profils