Energy-Efficient Task Scheduling and Data Aggregation Techniques in Wireless Sensor Networks for Information Explosion Era

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

“…f À 1 ðtÞ ¼ e t . Due to the close analogy between WSNs, networks of PBO, and the RFA in particular, this algorithm and its variants have been used to tackle WSN synchronization and scheduling problems several times [59,66,92,93,113]. Another example of a PBO-based synchronization algorithm is the self-organization inspired by the behaviour of Japanese tree frogs [19].…”

“…Other often cited attributes of nature-inspired methods important for the wake-up scheduling and synchronization tasks in WSNs are (in alphabetical order) adaptability [18,92,107,127,80,14,103,17,136,66,108,69,19,59], effectiveness [14,20,87,17,97,91,99,85,79,57], fault tolerance [103,138] (also error-tolerance), [122,69] (also delay-tolerance, error-tolerance), [19] (also accuracy-tolerance, delay-tolerance, error-tolerance), flexibility [18,[95][96][97]108,102], interactivity [18,129,19,74,57,78], reliability [14,17,104,79,86], robustness [18,…”

“…Data aggregation, as for example in [66], where two natureinspired task scheduling and data aggregation approaches were proposed and integrated into a complex energy-efficient network management strategy.…”

“…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 …”

“…Additionally, data is propagated following a travelling wave pattern formed using a PBO-based synchronization and sleep scheduling approach. A self-adaptive task scheduling for WSN was developed by Kanzaki et al [66]. The authors used a PBO-based model inspired by the synchronization behaviour of groups of flashing fireflies for data gathering, in connection with a response threshold model for adaptive sensing.…”