Wireless Sensor Network Management Using Satellite Communication Technologies

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5290 ◽  
Author(s):  
Jing Hu ◽  
Guangxia Li ◽  
Dongming Bian ◽  
Jingyu Tang ◽  
Shengchao Shi

This paper presents a cognitive satellite communication based wireless sensor network, which combines the wireless sensor network and the cognitive satellite terrestrial network. To address the conflict between the continuously increasing demand and the spectrum scarcity in the space network, the cognitive satellite terrestrial network becomes a promising candidate for future hybrid wireless networks. With the higher transmit capacity demand in satellite networks, explicit concerns on efficient resource allocation in the cognitive network have gained more attention. In this background, we propose a sensing-based dynamic spectrum sharing scheme for the cognitive satellite user, which is able to maximize the ergodic capacity of the satellite user with the interference of the primary terrestrial user below an acceptable average level. Firstly, the cognitive satellite user monitors the channel allocated to the terrestrial user through the wireless sensor network; then, it adjusts the transmit power based on the sensing results. If a terrestrial user is busy, the satellite user can access the channel with constrained power to avoid deteriorating the communication quality of the terrestrial user. Otherwise, if the terrestrial user is idle, the satellite user allocates the transmit power based on its benefit to enhance the capacity. Since the sensing-based dynamic spectrum sharing optimization problem can be modified into a nonlinear fraction programming problem in perfect/imperfect sensing conditions, respectively, we solve them by the Lagrange duality method. Computer simulations have shown that, compared with the opportunistic spectrum access, the proposed method can increase the channel capacity more than 20% for Pav = 10 dB in a perfect sensing scenario. In an imperfectsensing scenario, Pav = 15 dB and Qav = 5 dB, the optimal sensing time achieving the highest ergodiccapacity is about 2.34 ms when the frame duration is 10 ms.


Nowadays, several protocols and treaties coexist in the Internet world that dispenses the services to the users. But the distributed management and the control decisions make the network modest to control. Due to these problems, the behavior of the network becomes unpredictable and insufficient. Hence, there is a lack of flexibility in the conventional network architecture like in Wireless Sensor Network (WSN). Therefore, Software-Defined Networking (SDN) finds the deficiencies of the previous technologies and isolated both the planes named as control and data plane. It aims to make the network more simplified and flexible with respect to that of the traditional one. The SDN application in WSN is very commanding in terms of network configuration and network management, leading to an emerging network technology known as Software-Defined Wireless Sensor Network (SDWSN). Therefore, the paper presents the challenges in SDWSN in terms of the management and configuration of the network. The pitch is to comprehend the current challenges with an end goal to ensure more security, efficiency, and dependability. We reviewed several works of literature such as SDN, WSN, and SDWSN and present the findings in terms of architecture, challenges, and their solution. This paper shows how SDN is included in WSN to solve the existing challenges


2013 ◽  
Vol 411-414 ◽  
pp. 643-646
Author(s):  
Yong Yi Zhao ◽  
Bo Song

Wireless sensor networks with highly dynamic characteristics and limited resources, this paper presents a simple service level management protocol. It realizes service deployment management across different clusters, the request and the response of wireless sensor is simple that based on the protocol data format of XML WSNXML between the clusters. It provides an effective method for the management of shared resources of wireless sensor network and service expansion and realizes service release and deployment management difficulties between various wireless sensor network management based on different management cluster.


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