Optimized Scheduled Multiple Access Control for Wireless Sensor Networks

• Published in: IEEE transactions on automatic control Vol. 54; no. 11; pp. 2573 - 2585
• Main Authors: Paschalidis, I.C., Wei Lai, Xiangdong Song
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access control; Algorithms; Applied sciences; Channels; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Decomposition; Exact sciences and technology; Frequency; Interference constraints; Iterative methods; Large-scale systems; Mathematical analysis; Mathematical programming/optimization; multiple frequency channels; Networks; Power control; Power engineering and energy; Routing; Sensors; Software; Studies; Systems engineering and theory; Throughput; transmission scheduling; Wireless sensor networks; Access control; wireless sensor networks; Polynomial approximation; Utility function; Optimization; Mathematical programming/optimization; Information gateway; Multiple frequency; Computer security; Mathematical programming; Multiple access; Multisensor; Routing; Scheduling; Distributed system; Polynomial time; Utility theory; Power control; Smoothing; NP hard problem; Problem solving; Wireless network; Transmission rate; Large scale; Sensor array; Transport; Multiple channel; multiple frequency channels; transmission scheduling
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2009.2031210

We consider wireless sensor networks with multiple sensor modalities that capture data to be transported over multiple frequency channels to potentially multiple gateways. We study a general problem of maximizing a utility function of achievable transmission rates between communicating nodes. Decisions involve routing, transmission scheduling, power control, and channel selection, while constraints include physical communication constraints, interference constraints, and fairness constraints. Due to its structure the formulation grows exponentially with the size of the network. Drawing upon large-scale decomposition ideas in mathematical programming, we develop a cutting-plane algorithm and show that it terminates in a finite number of iterations. Every iteration requires the solution of a subproblem which is NP-hard. To solve the subproblem we i) devise a particular relaxation that is solvable in polynomial time and ii) leverage polynomial-time approximation schemes. A combination of both approaches enables an improved decomposition algorithm which is efficient for solving large problem instances.