An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks

The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements

Experimental validation of a reinforcement learning based approach for a service-wise optimisation of heterogeneous wireless sensor networks

Due to their constrained nature, wireless sensor networks (WSNs) are often optimised for a specific application domain, for example by designing a custom medium access control protocol. However, when several WSNs 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 %

Elaboration of Cognitive Decision Making Methods in the Context of Symbiotic Networking

Abstract-Recently, the concept of 'cognitive networking' has been introduced, in which reconfigurable radio networks rely on self-awareness and artificial intelligence to optimize their network performance. These cognitive networks are able to perceive current network conditions and then plan, learn and act according to end-to-end goals. This paper elaborates on different methods (network solutions) that can be used by cognitive networks for deciding on how to optimize the performance of a large number of co-located devices with different characteristics and network requirements. To this end, a negotiation based networking methodology ('symbiotic networking') is used that supports efficient network cooperation between heterogeneous devices in order to optimize their network performance. In this paper, the advantages and disadvantages of different reasoning techniques that can be used during the decision making phase are discussed