Research On Ant Intelligence Routing Algorithm For WSNs

Modern communication networks are becoming increasingly diverse and heterogeneous. This is the consequence of the addition of an increasing array of devices and services, especially wireless. The need for flawless interaction of numerous heterogeneous network nodes represents a formidable challenge. The need to incorporate wireless networks into the existing wire-link infrastructure renders the requirement for efficient network routing even more demanding. Current routing algorithms are not adequate in dealing with the increasing complexity of such networks. Centralized algorithms have scalability problems; static algorithms have trouble keeping up to date with network changes and on the other hand, distributed and dynamic algorithms have oscillations and stability problems.Ant colony based routing provides a promising alternative to these approaches. It utilizes mobile software agents for network management. These agents are autonomous entities, both proactive and reactive, and have the capability to adapt, cooperate and move intelligently from one location to the other in the communication network [4]. Ant colony exhibits emergent behavior whereby simple interactions of autonomous agents, with simple activities, give rise to a complex behavior that has not been specified explicitly. There is currently an increasing interest for the paradigm of autonomic computing. This is basically because networks are becoming more and more complex and larger and that it is desirable that they can self organize and self-configure, adapting to new situations in terms of traffic, services, network connectivity, and so on. To support this new paradigm, network algorithms should be robust, work in a distributed way, be able to observe changes in the network and adapt to them without compromising connectivity.Nature’s self-organizing systems like ant colony show precisely these desirable properties. Making use of a number of relatively simple biological agents (for example, the ants) a variety of different organized behaviors is generated at the system-level from the local interactions among the agents and with the environment. The robustness and effectiveness of such collective behaviors with respect to variations of environmental conditions are key-aspects of their success. Nature’s self-organizing systems have recently become a source of inspiration for the design of distributed and adaptive algorithms, and in particular routing algorithms.This thesis presents ant intelligence routing algorithm (AIRA), an adaptive, energy efficient and multiple-path protocol designed for wireless sensor networks. The primary goals of the protocol design are energy efficiency and self-organization without compromising throughput. AIRA reduces energy consumption by enabling low-duty-cycle operation and clocking neighbors to power of their radios to avoid unnecessary listening and interference during data transmission in a multihop network through adaptive sleeping technique. This greatly improves energy efficiency. It supports self-organization of individual nodes and reduces control overheads by using data packets themselves to maintain an established route for communication. Finally, AIRA applies synchronized sleeping technique to improve energy efficiency of the entire network. In an extensive set of simulations, the routing algorithm shows that it gets better performance over a range of different scenarios.