TY - JOUR
T1 - Energy-Efficient Relay-Selection-Based Dynamic Routing Algorithm for IoT-Oriented Software-Defined WSNs
AU - Ding, Zhaoming
AU - Shen, Lianfeng
AU - Chen, Hongyang
AU - Yan, Feng
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received January 13, 2020; revised March 9, 2020 and May 28, 2020; accepted June 7, 2020. Date of publication June 15, 2020; date of current version September 15, 2020. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1500800, in part by the Specialized Development Foundation for the Achievement Transformation of Jiangsu Province under Grant BA2019025, in part by the National Natural Science Foundation of China under Grant 61601122, and in part by the Open Project of Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences under Grant 20190907. This article was presented in part at the IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA, Aug. 2018. (Corresponding author: Lianfeng Shen.) Zhaoming Ding, Lianfeng Shen, and Feng Yan are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (e-mail: zhmding@seu.edu.cn; lfshen@seu.edu.cn; feng.yan@seu.edu.cn).
Publisher Copyright:
© 2014 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - In this article, a dynamic routing algorithm based on energy-efficient relay selection (RS), referred to as DRA-EERS, is proposed to adapt to the higher dynamics in time-varying software-defined wireless sensor networks (SDWSNs) for the Internet-of-Things (IoT) applications. First, the time-varying features of SDWSNs are investigated from which the state-transition probability (STP) of the node is calculated based on a Markov chain. Second, a dynamic link weight is designed for DRA-EERS by incorporating both the link reward and the link cost, where the link reward is related to the link energy efficiency (EE) and the node STP, while the link cost is affected by the locations of nodes. Moreover, one adjustable coefficient is used to balance the link reward and the link cost. Finally, the energy-efficient routing problem can be formulated as an optimization problem, and DRA-EERS is performed to find the best relay according to the energy-efficient RS criteria derived from the designed link weight. The simulation results demonstrate that the path EE obtained by DRA-EERS through an available coefficient adjustment outperforms that by Dijkstra's shortest path algorithm. Again, a tradeoff between the EE and the throughput can be achieved by adjusting the coefficient of the link weight, i.e., increasing the impact of the link reward to improve the EE, and otherwise, to improve the throughput.
AB - In this article, a dynamic routing algorithm based on energy-efficient relay selection (RS), referred to as DRA-EERS, is proposed to adapt to the higher dynamics in time-varying software-defined wireless sensor networks (SDWSNs) for the Internet-of-Things (IoT) applications. First, the time-varying features of SDWSNs are investigated from which the state-transition probability (STP) of the node is calculated based on a Markov chain. Second, a dynamic link weight is designed for DRA-EERS by incorporating both the link reward and the link cost, where the link reward is related to the link energy efficiency (EE) and the node STP, while the link cost is affected by the locations of nodes. Moreover, one adjustable coefficient is used to balance the link reward and the link cost. Finally, the energy-efficient routing problem can be formulated as an optimization problem, and DRA-EERS is performed to find the best relay according to the energy-efficient RS criteria derived from the designed link weight. The simulation results demonstrate that the path EE obtained by DRA-EERS through an available coefficient adjustment outperforms that by Dijkstra's shortest path algorithm. Again, a tradeoff between the EE and the throughput can be achieved by adjusting the coefficient of the link weight, i.e., increasing the impact of the link reward to improve the EE, and otherwise, to improve the throughput.
KW - Dynamic routing
KW - Internet of Things (IoT)
KW - energy efficiency (EE)
KW - relay selection (RS)
KW - software-defined wireless sensor networks (SDWSNs)
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U2 - 10.1109/JIOT.2020.3002233
DO - 10.1109/JIOT.2020.3002233
M3 - Article
AN - SCOPUS:85092178376
SN - 2327-4662
VL - 7
SP - 9050
EP - 9065
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 9
M1 - 9116974
ER -