Novel Scheduling and Optimization Methods to Wireless Sensor Networks
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 3296
Special Issue Editors
Interests: Intelligent optimization; system optimization and decision making
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent optimization theory; modeling, optimization and scheduling for complex industry process
Special Issues, Collections and Topics in MDPI journals
Interests: swarm intelligence; evolutionary algorithms; big data analytics; particle swarm optimization; brain storm optimization
Special Issues, Collections and Topics in MDPI journals
Interests: swarm intelligence; natural computing; evolutionary computation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Wireless sensor networks are one of the first real-world examples of pervasive computing—small, smart, and cheap sensing and computing systems that can monitor and control with unsurpassed efficiency and accuracy. They have supported a variety of military services, industry, smart house, intelligent transportation, healthcare, etc.
To provide a higher quality of experience (QoE) in various fields, the development of wireless sensor networks presents the following trends: 1) the network scale expands rapidly, and one network may contain tremendous heterogeneous sensors, each of which has different service capabilities and requirements; 2) the mobility of sensors results in a complex time-variant network structure; 3) the network environment and system requirements change with the space and time domains; 4) the network flexibility and efficiency will be greatly improved using software-based network management.
To handle these challenging characteristics imposed by wireless sensor networks, more research efforts should be conducted focusing on novel scheduling and optimization methods to ensure high-rate, low-latency, low-cost, and reliable services.
Prof. Dr. Lining Xing
Prof. Dr. Ling Wang
Prof. Dr. Shi Cheng
Prof. Dr. Gaige Wang
Guest Editors
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Keywords
- bio-inspired scheduling methods
- multiobjective evolutionary optimization
- scalable optimization methods
- constraint handling methods
- reinforcement learning based optimization methods
- mathematical programming methods
- game theory
