Abstract
This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.
Highlights
Recent advances in microelectro-mechanical systems are driving the developments of low-cost and and low-power wireless sensors, with diverse applications in the physical world in areas such as environmental monitoring, disaster recovery, industrial process control, and smart environments
Two sensor scheduling protocols, Centralized Adaptive Scheduling Algorithm (CASA) and Distributed Adaptive Scheduling Algorithm (DASA), are proposed to address the application scenario of typical surveillance systems in a cluster-based network topology, where both connectivity and coverage constraint are taken into consideration to achieve performance balance
K∈Hi where MI is the memory usage for initializing the procedure of scheduling management, MR is the memory usage for updating the round ID, MS is the memory usage for broadcasting the scheduling information, and MG is the memory usage for gateway selection, NC is the number of sensors in the cluster, H1 is the set of 1-hop nodes, and NG is the number of gateway sensors for inter-cluster communication in the cluster
Summary
Recent advances in microelectro-mechanical systems are driving the developments of low-cost and and low-power wireless sensors, with diverse applications in the physical world in areas such as environmental monitoring, disaster recovery, industrial process control, and smart environments. Recent research has found that significant energy savings can be achieved by dynamic power management in sensor networks [1,2,3,4,5,6,7] To achieve this sensing process, sensors are scheduled to execute the sensing task. Two sensor scheduling protocols, Centralized Adaptive Scheduling Algorithm (CASA) and Distributed Adaptive Scheduling Algorithm (DASA), are proposed to address the application scenario of typical surveillance systems in a cluster-based network topology, where both connectivity and coverage constraint are taken into consideration to achieve performance balance.
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