Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Ant Colonies for Adaptive Routing in Packet-Switched Communications Networks
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
Energy and QoS Aware Routing in Wireless Sensor Networks
Cluster Computing
Fluid models for large-scale wireless sensor networks
Performance Evaluation
Energy-aware routing in sensor networks: A large system approach
Ad Hoc Networks
Computer
Analysis of On-off policies in Sensor Networks Using Interacting Markovian Agents
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
IEEE Communications Magazine
Dependability analysis of wireless sensor networks with active-sleep cycles and redundant nodes
Proceedings of the First Workshop on DYnamic Aspects in DEpendability Models for Fault-Tolerant Systems
A Markovian agent model for fire propagation in outdoor environments
EPEW'10 Proceedings of the 7th European performance engineering conference on Computer performance engineering
Markovian agent modeling swarm intelligence algorithms in wireless sensor networks
Performance Evaluation
| Hi-index | 0.02 |
Wireless Sensor Networks (WSN) consist of a large number of tiny sensor nodes that are usually randomly distributed over a geographical region. In order to reduce power consumption, battery operated sensors undergo cycles of sleeping - active periods; furthermore, sensors may be located in hostile environments increasing their attitude to failure. As a result, the topology of the WSN may be varying in time in an unpredictable manner. For this reason multi-hop routing algorithms to carry messages from a sensor node to a sink should be rapidly adaptable to the changing topology. Swarm intelligence has been proposed for this purpose, since it allows to emerge a single global behavior from the interaction of many simple local agents. Swarm intelligent routing has been traditionally studied by resorting to simulation. The present paper is aimed to show that the recently proposed modeling technique, known as Markovian Agents, is suited to implement swarm intelligent algorithms for large networks of interacting sensors. Various experimental results and quantitative performance indices are evaluated to support the previous claim.