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This paper describes AntHocNet, an algorithm for routing in mobile ad-hoc networks based on ideas from the ant colony optimisation framework. In AntHocNet a source node reactively sets up a path to a destination node at the start of each communication session. During the course of the session, the source node uses ant agents to proactively search for alternatives and improvements of the original path. This allows to adapt to changes in the network, and to construct a mesh of alternative paths between source and destination. The proactive behaviour is supported by a lightweight information bootstrapping process. Paths are represented in the form of distance-vector routing tables called pheromone tables. An entry of a pheromone table contains the estimated goodness of going over a certain neighbour to reach a certain destination. Data are routed stochastically over the different paths of the mesh according to these goodness estimates. In an extensive set of simulation tests, we compare AntHocNet to AODV, a reactive algorithm which is an important reference in this research area. We show that AntHocNet can outperform AODV for different evaluation criteria in a wide range of different scenarios. AntHocNet is also shown to scale well with respect to the number of nodes.

Multicasting protocols deliver data packets from a source node to multiple receivers, and serve a very important function in mobile ad-hoc networks (MANETs). In this paper, a novel receiver-initiated soft-state probabilistic multicasting protocol (RISP) for MANETs is proposed. RISP is inspired by the ant colony's route-seeking mechanism, in which an individual ant chooses the optimal path to its destination through cooperation with others in a totally distributed manner. Imitating the behaviour of ants in nature, RISP introduces probabilistic forwarding and soft-state for making relay decisions that are automatically adaptive to node mobility in MANETs. Compared with other protocols, we show by computer simulations that RISP has lower delivery redundancy, while achieving higher delivery ratio at all mobility scenarios. Furthermore, RISP has lower control overhead.

In this paper, we present a novel multiuser detection (MUD) technique based on ant colony optimisation (ACO), for synchronous direct sequence code division multiple access systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO-based MUD converges to the optimal bit-error-rate performance in relatively few iterations providing 95% savings in computational complexity. This reduction in complexity is retained even when considering users with unequal received powers.