This paper proposes a NASH Q-learning (NashQ) algorithm in a packet forwarding game in overlay noncooperative multi-agent wireless sensor networks (WSNs). The objective is to achieve the best mutual response between two agents. The results show that NashQ can obtain the best mutual response by learning online, as opposed to the offline exhaustive search in an existing non-cooperative game theoretic approach. Therefore, NashQ is more adaptive to topological changes yet less computationally demanding in the long run. Furthermore, NashQ also appears to be more robust to the non-uniqueness of Nash equilibrium as results show a consistent cooperative behavior trend when compared with the existing approach.

Coverage control is crucial for the deployment of wireless sensor networks (WSNs). However, most coverage control schemes are based on single objective optimization such as coverage area only, which do not consider other contradicting objectives such as energy consumption, the number of working nodes, wasteful overlapping areas. This paper proposes on a Multi-Objective Optimization (MOO) coverage control called Scalarized Q Multi-Objective Reinforcement Learning (SQMORL). The two objectives are to achieve the maximize area coverage and to minimize the overlapping area to reduce energy consumption. Performance evaluation is conducted for both simulation and multi-agent lighting control testbed experiments. Simulation results show that SQMORL can obtain more efficient area coverage with fewer working nodes than other existing schemes.  The hardware testbed results show that SQMORL algorithm can find the optimal policy with good accuracy from the repeated runs.

An important use of multi-domain wireless sensor networks (WSNs) is resource sharing between different networks co-existing in the same area to prolong the network lifetime. The challenge of resource allocation in such scenario is how to determine packet forwarding strategies which are beneficial to all networks under constrained resources in non-cooperative multi-domain WSNs. Therefore, this paper proposes the Non-cooperative game algorithm based on Lemke Howson method (NCG-LH) for a packet forwarding game in non-cooperative multi-domain WSNs. The objective is to achieve the best mutual strategy and improve the network performance between two different network authorities in non-cooperative multi-domain WSNs in separate sink scenario. Results show that NCG-LH can obtain longer network lifetime than the existing routing algorithms particularly in presence of failed nodes and high path loss. NCG-LH also outperforms the other routing algorithms in terms of fair route selection by attaining the lowest average difference in energy consumption.