Optimisation-based time slot assignment and synchronisation for TDMA MAC in industrial wireless sensor network

被引：9

作者：

Chang, Ching-Lung ^{[1
]}

Chang, Chuan-Yu ^{[1
,2
]}

Chen, Shuo-Tsung ^{[3
]}

Tu, Shu-Yi ^{[4
]}

Ho, Kuan-Yi ^{[1
]}

机构：

[1] Natl Yunlin Univ Sci & Technol, Dept Comp Sci & Informat Engn, Touliu 64002, Yunlin, Taiwan

[2] Natl Yunlin Univ Sci & Technol, IR IS Res Ctr, Touliu 64002, Yunlin, Taiwan

[3] Fu Jen Catholic Univ, Coll Management, Dept Informat Management, New Taipei, Taiwan

[4] Univ Michigan, Dept Math, Flint, MI 48502 USA

来源：

IET COMMUNICATIONS | 2019年 / 13卷 / 18期

关键词：

wireless sensor networks; linear programming; simulated annealing; particle swarm optimisation; time division multiple access; telecommunication scheduling; telecommunication network routing; optimisation-based time slot assignment; objective function; fixed routing; spatial reuse; industrial environment; data collision avoidance; industrial wireless sensor network; time division multiple access MAC; transmission collision; dynamic routing; data delivery time constraint; synchronisation; simulated annealing algorithm; network bandwidth; time slot usage; IWSN; TDMA schedules; linear programming model; bounded transmission delay;

D O I：

10.1049/iet-com.2018.6065

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Wireless sensor network in the industrial environment [industrial wireless sensor network (IWSN)] has data delivery time constraint. Due to the dynamic routing and transmission collision, the data delivery time is unpredictable. The authors utilised time division multiple access (TDMA) MAC to avoid data collision and to provide bounded transmission delay. Moreover, a linear programming model is proposed to construct the TDMA schedules, which is focused on spatial reuse and fixed routing in IWSN. The objective function of the model is to minimise the time slot usage to increase the overall network bandwidth. Finally, both simulated annealing algorithm and particle swarm optimisation are applied to approximate the optimal solution of time slot usage.

引用

页码：2932 / 2940

页数：9

共 22 条

[1]

Addisu A., 2013, P 2013 19 INT C EMB, P55

[2] Schedulability analysis of sporadic tasks with multiple criticality specifications [J].

Baruah, Sanjoy ;

Vestal, Steve .

ECRTS 2008: PROCEEDINGS OF THE 20TH EUROMICRO CONFERENCE ON REAL-TIME SYSTEMS, 2008, :147-+

[3] Cyclic Executives, Multi-Core Platforms and Mixed Criticality Applications [J].

Burns, A. ;

Fleming, T. ;

Baruah, S. .

PROCEEDINGS OF THE 2015 27TH EUROMICRO CONFERENCE ON REAL-TIME SYSTEMS (ECRTS 2015), 2015, :3-12

[4] A Wormhole NoC Protocol for Mixed Criticality Systems [J].

Burns, A. ;

Harbin, J. ;

Indrusiak, L. S. .

2014 IEEE 35TH REAL-TIME SYSTEMS SYMPOSIUM (RTSS 2014), 2014, :184-195

[5] Mixed Criticality on Controller Area Network [J].

Burns, A. ;

Davis, R. I. .

PROCEEDINGS OF THE 2013 25TH EUROMICRO CONFERENCE ON REAL-TIME SYSTEMS (ECRTS 2013), 2013, :125-134

[6]

Carvajal G, 2013, DES AUT TEST EUROPE, P153

[7] Real-Time Query Scheduling for Wireless Sensor Networks [J].

Chipara, Octav ;

Lu, Chenyang ;

Roman, Gruia-Catalin .

IEEE TRANSACTIONS ON COMPUTERS, 2013, 62 (09) :1850-1865

[8]

Cros Olivier, 2014, Ada User Journal, V35, P138

[9] SIMULATED ANNEALING - A PROOF OF CONVERGENCE [J].

GRANVILLE, V ;

KRIVANEK, M ;

RASSON, JP .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1994, 16 (06) :652-656

[10] Mixed Criticality Scheduling for Industrial Wireless Sensor Networks [J].

Jin, Xi ;

Xia, Changqing ;

Xu, Huiting ;

Wang, Jintao ;

Zeng, Peng .

SENSORS, 2016, 16 (09)

← 1 2 3 →