A New Method for Multi-objective TDMA Scheduling in Wireless Sensor Networks Using Pareto-Based PSO and Fuzzy Comprehensive Judgement

Review of nature-inspired methods for wake-up scheduling in wireless sensor networks

“…The two major groups of nature-inspired methods utilized to address wake-up scheduling are swarm intelligence and evolutionary computing. The algorithms based on swarm intelligence include methods inspired by Ant Colony Optimization [57,[79][80][81][82][83][84][85][86], Particle Swarm Optimization [58,64,83,[87][88][89][90][91], and Pulse-coupled Biological Oscillators [19,59,92,93]. Evolutionary methods for WSN scheduling are mostly based on Genetic Algorithms [94][95][96][97][98][99][100][101] but other evolutionary approaches have been considered as well [102].…”

“…A hybrid method combining PSO with simulated annealing is designed to solve the problem. Simulation experiments show that the hybrid natureinspired meta-heuristic outperforms two methods based on graph colouring and a hybrid between PSO and GA. Another PSO-based TDMA scheduling method has been proposed in [88]. In comparison to [87], the new approach can accommodate conflicting optimization objectives.…”

“…The main reasons for using bio-inspired methods to solve wake-up scheduling problems have been their optimization capability [143,80,[87][88][89][90]94,20,14,17,105,95,129,104,98,102,100,84,79,58,74,57] that facilitates the discovery of efficient wake-up schedules; distributed nature [113,18,92,80,[87][88][89]94,14,[103][104][105]136,138,17,66,69,74,78,93,57] that well matches the topology of WSNs and allows optimization based on readily available local information; and various self-n properties, that endow the networks with greater autonomy; they include ○ self-organization [ ○ self-assembly (of services and schedules) [138,14], ○ self-healing …”

Review of Optimization Problems in Wireless Sensor Networks

“…Notice that a link can be scheduled more than once in a time frame to satisfy the node data rates, which is the main difference with the basic version. Wang et al (2007) formulate a multi-objective optimization problem. The question is to find a time slot allocation that satisfies i) the data delivery delay and ii) the node energy constraint.…”

Optimization Problems in Wireless Sensor Networks

“…The difference with the aforementioned works stands in considering the interference graph 6 and scheduling the edges more than once in a time frame. The work of [53] transforms the problem in a multi-objective optimization problem. The first objective is to minimize the time delay of data delivery and the second one is to minimize the energy while taking not only the transmission and receiving energy into account but also the energy consumed for transition between sleep and active mode.…”