In a future internet of things, an increasing number of every-day objects becomes interconnected with each other. Current network solutions are not designed to connect a large number of co-located devices with different characteristics and network requirements. To cope with increasingly large and heterogeneous networks, this paper presents an 'incentive driven' networking approach that optimizes the network performance by taking into account the network goals ('incentives') of all individual devices. Incentive driven networking consists of the following steps. First, devices dynamically search for co-located devices with similar network preferences and hardware and/or software capabilities. Next, if such devices are found, communities consisting of interconnected objects with similar network expectations are formed on an ad-hoc basis. Due to the similarities between the involved devices, it is easier to optimize the network performance of each individual community. Finally, different communities can cooperate with each other by activating and sharing (software or hardware) network resources. The paper describes which (future) research is needed to realize this vision and illustrates the concepts with a number of simple algorithms. Through an experimental proof-of-concept implementation with two networks of resource-constrained embedded devices, it is shown that even these simple algorithms already result in improved network performance. Finally, the paper describes a large number of example use cases that can potentially benefit from our innovative networking methodology.

Due to a drastic increase of the number of wireless communication devices, these devices are forced to interfere or interact with each other. This raises the issue of possible effects this coexistence might have on the performance of each of the networks. Negative effects are a consequence of contention for network resources (such as free wireless communication frequencies) between different devices. On the other hand, a possible cooperation between co-located networks could also improve certain aspects of networking for each one of them. This paper presents a self-learning, cognitive cooperation approach for heterogeneous co-located networks. Enabling cooperation is performed by activating or deactivating services that influence the interaction between wireless devices, such as an interference avoidance service, a packet sharing service, etc. Activation of a cooperative service might have both positive and negative effects on network's performance, regarding its high level goals. Such a cooperation approach has to incorporate a reasoning mechanism, centralized or distributed, able to determine the influence of each symbiotic service on the performance of all the participating sub-networks, taking into consideration their requirements. Coupled with the concept of enabling symbiotic services, a machine learning technique known as the Least Squares Policy Iteration (LSPI), is presented in this paper as a novel network cooperation paradigm.

Abstract-Although Internet on the train and train to wayside communication in general becomes more and more available for train operators, there are still a lot of challenges for future research. We previously developed a network platform that is responsible for an uninterrupted and seamless connectivity from the train to the wayside through heterogeneous wireless access technologies. This paper mainly focuses on the concept for providing sufficient Quality of Service (QoS) guarantees in a dynamic train environment. Within this network platform, IPv6 strategies are adopted for QoS, exploiting multi-homing and intelligent aggregation techniques. The implementation that has been done in the Click Modular Router programming environment will also be presented in details.

Due to their constrained nature, wireless sensor networks are often optimised for a specific application domain, for example by designing a custom MAC protocol. However, when several wireless sensor networks are located in close proximity to one another, the performance of the individual networks can be negatively affected as a result of unexpected protocol interactions. The performance impact of this 'protocol interference' depends on the exact set of protocols and (network) services used. This paper therefore proposes an optimisation approach that uses self-learning techniques to automatically learn the optimal combination of services and/or protocols in each individual network. We introduce tools capable of discovering this optimal set of services and protocols for any given set of co-located heterogeneous sensor networks. These tools eliminate the need for manual reconfiguration while only requiring minimal a priori knowledge about the network. A continuous re-evaluation of the decision process provides resilience to volatile networking conditions in case of highly dynamic environments. The methodology is experimentally evaluated in a large scale testbed using both single-and multihop scenarios, showing a clear decrease in end-to-end delay and an increase in reliability of almost 25 percent.