Routing Wireless Sensor Networks Based on Soft Computing Paradigms: Survey

Abstract

Wireless Sensor Networks (WSNs) are defined as dynamic, self-deployed, highly constrained structured network. It`s high computational environment with limited and controlled transmission range, processing, as well as limited energy sources. The sever power constraints strongly affect the existence of active nodes and hence the network lifetime. In order to prolong the network life time we have to overcome the scarcity in energy resources and preserve the processing of the sensor nodes as long as possible. Power management approaches efficiently reduce the sensor nodes energy consumption individually in each sensor node and the adaptive efficient routing technique has greatly appeals a great attention in research. The potential paradigms of soft-computing (SC) highly addressed their adaptability and compatibility to overwhelm the complex challenges in WSNs. This paper is introducing and surveying some of the Soft Computing proposed routing models for WSNs that optimally prolongs its life time.

Conceptual Design of Multi-agent System for Suramadu Bridge Structural Health Monitoring System

“…RL is on the top survey that the most appropriate technique for WSN optimization [8]. Therefore, RL is the technique that will be our main focus to be implemented in MA, MgrA, DA, and DPCS agent.…”

“…Combination of genetic algorithm and reinforcement learning probably will give better result in mobile agent implementation and they are our main research focus. Genetic algorithm has been proved to solve combinatory problems, whereas reinforcement learning, which is sufficient to be implemented on a sensor node [8], empowers agent to make their own decision. We will also leverage Markov Decission Process (MDP), as a part of reinforcement learning model, and Belief-Desire-Intention (BDI) as two approaches that can be mapped each other [9] to create the intelligent agent deployed on a sensor node.…”

Multiagent System In-network Processing In Wireless Sensor Network

“…The mobile agent migration plan was also described in [8], which proposes the itinerary energy minimum for first-source-selection algorithm and the itinerary energy minimum algorithm. Other algorithms that can be considered for routing in WSN, but not specific to mobile agent migration, include reinforcement learning, swarm intelligence, evolutionary algorithm, and neural network [9].…”

A fuzzy metric for Collection Tree Protocol in Wireless Sensor Networks

“…The classical routing methods used for data collection in WSNs face a huge challenge of uncertainty involved in sensing dynamic events and routing data. These methods are unable to handle problems with imprecise and incomplete data; they exclude vagueness and contradictory conditions [2] [3]. In this respect, Fuzzy Logic [4], a flexible computational model that deals with fuzziness and uncertainty of data, promises to be a good solution for these environments.…”