Ant Colony Optimization
A Minimum Cost Heterogeneous Sensor Network with a Lifetime Constraint
IEEE Transactions on Mobile Computing
Integrated coverage and connectivity configuration for energy conservation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Maximizing the Lifetime of Wireless Sensor Networks through Optimal Single-Session Flow Routing
IEEE Transactions on Mobile Computing
Preprocessing in a tiered sensor network for habitat monitoring
EURASIP Journal on Applied Signal Processing
A Tabu Search Algorithm for Cluster Building in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
IEEE Transactions on Parallel and Distributed Systems
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The design space of wireless sensor networks
IEEE Wireless Communications
Wireless multimedia sensor networks: A survey
IEEE Wireless Communications
On energy provisioning and relay node placement for wireless sensor networks
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Two Tier Secure Routing Protocol for Heterogeneous Sensor Networks
IEEE Transactions on Wireless Communications
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Scheduling activities of network devices is an important and promising methodology for prolonging the lifetime of wireless sensor networks (WSNs). The existing scheduling methods in the literature are mostly designed for homogeneous WSNs. Heterogeneous WSNs, despite their wide applications, have received few research attentions. This paper proposes an ant-colony-system-based scheduling method (ACS-SM) for maximizing the lifetime of a typical type of heterogeneous WSNs. First, the lifetime maximization problem is formulated as finding the maximum number of disjoint sets of devices, with each set fulfilling sensing coverage and network connectivity simultaneously. Then ACS-SM adapts the incremental solution construction mechanism in ACS for building disjoint connected cover sets on the basis of a well-designed construction graph. Pheromone trails that record search experience and heuristic information that combines domain knowledge are utilized to guide the set building procedure. A local search process is also developed to further enhance the efficiency of the method. ACS-SM is applied to fifteen WSN cases in three series. Experimental results show that the proposed method can find high-quality solutions at a fast speed for WSNs with different characteristics.