The Hamilton spaces of Cayley graphs on abelian groups
Discrete Mathematics
Ants: agents on networks, trees, and subgraphs
Future Generation Computer Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
MARP: A Multi-Agent Routing Protocol for Mobile Wireless Ad Hoc Networks
Autonomous Agents and Multi-Agent Systems
Mobile agent middleware for sensor networks: an application case study
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
On balancing network traffic in path-based multicast communication
Future Generation Computer Systems - Systems performance analysis and evaluation
Exploring sensor networks using mobile agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Multi-Agent System for Directed Diffusion in Wireless Sensor Networks
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Mobile agent-based directed diffusion in wireless sensor networks
EURASIP Journal on Applied Signal Processing
Cyber Physical Systems: Design Challenges
ISORC '08 Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing
Adaptive Data Collection Strategies for Lifetime-Constrained Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
A scalable key management and clustering scheme for wireless ad hoc and sensor networks
Future Generation Computer Systems
On Computing Mobile Agent Routes for Data Fusion in Distributed Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Journal of Network and Computer Applications
Cross-layer design and optimisation for wireless sensor networks
International Journal of Sensor Networks
Balancing Energy Consumption to Maximize Network Lifetime in Data-Gathering Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Multi-agent-based clustering approach to wireless sensor networks
International Journal of Wireless and Mobile Computing
Expert Systems with Applications: An International Journal
Balancing energy consumption with mobile agents in wireless sensor networks
Future Generation Computer Systems
EBRP: Energy-Balanced Routing Protocol for Data Gathering in Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Future Generation Computer Systems
Multiresolution data integration using mobile agents in distributedsensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Communications Magazine
| Hi-index | 0.00 |
Recently, the cyber physical system has emerged as a promising direction to enrich the interactions between physical and virtual worlds. Meanwhile, a lot of research is dedicated to wireless sensor networks as an integral part of cyber physical systems. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensors to monitor physical or environmental conditions. These autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. Shrinking size and increasing deployment density of wireless sensor nodes implies the smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization to increase the WSN lifetime. The network lifetime is defined as the time duration until the first sensor node in a network fails due to battery depletion. One solution for enhancing the lifetime of WSN is to utilize mobile agents. In this paper, we propose an agent-based approach that performs data processing and data aggregation decisions locally i.e., at nodes rather than bringing data back to a central processor (sink). Our proposed approach increases the network lifetime by generating an optimal routing path for mobile agents to transverse the network. The proposed approach consists of two phases. In the first phase, Dijkstra's algorithm is used to generate a complete graph to connect all source nodes in a WSN. In the second phase, a genetic algorithm is used to generate the best-approximated route for mobile agents in a radio harsh environment to route the sensory data to the base-station. To demonstrate the feasibility of our approach, a formal analysis and experimental results are presented.