An analysis of software-defined routing approach for wireless sensor networks

In emerging wireless sensor applications, sensor nodes are equipped with low battery and limited capacity when transmitting the sensed data to the sink.To prolong the lifetime of sensor networks, the energy consumption has to be reduced and the bandwidth channel utilization has to be improved. To accomplish these requirements, we have proposed a new scheme as software based multi-flow in wireless sensor network. This scheme is developed based on the Software Defined Network (SDN) and allocates separated channels for data plane and control plane. It also generates inbuilt software module in every sensor nodeto increase the speed of the network. The proposed scheme is used to maintain the topology and also manages the limited battery power usage. Experimental results demonstrate the performance of the proposed software based multi flow in terms of indispensible network parameters including throughput, delay and energy consumption.

[1]  Hiroshi Saito,et al.  A Novel FPGA-Based Multi-Channel Multi-Interface Wireless Node: Implementation and Preliminary Test , 2016 .

[2]  Kenneth N. Brown,et al.  Evaluation of available bandwidth as a routing metric for delay-sensitive IEEE 802.15.4-based ad-hoc networks , 2016, Ad Hoc Networks.

[3]  Hyuntae Cho,et al.  Time Synchronization for Multi-hop Surveillance Camera Systems , 2016 .

[4]  Peng Guo,et al.  The Research of Reducing Energy Consumption Method on WSN Based on the Data Correlation , 2016 .

[5]  Jaspreet Singh,et al.  An optimized prioritized load balancing approach to scalable routing (OPLBA) , 2016, Wirel. Networks.

[6]  Satish Vadlamani,et al.  Jamming attacks on wireless networks: A taxonomic survey , 2016 .

[7]  Elham Rezaei,et al.  Multi-hop Routing Algorithm Using Steiner Points for Reducing Energy Consumption in Wireless Sensor Networks , 2016, Wirel. Pers. Commun..

[8]  S. Prabhavathi,et al.  Energy Efficient Dynamic Reconfiguration of Routing Agents for WSN Data Aggregation , 2016 .

[9]  Donggyu Yun,et al.  Distributed Medium Access for Camera Sensor Networks: Theory and Practice , 2016 .

[10]  Mounir Tahar Abbes,et al.  Adaptation of a routing algorithm in wireless video sensor network for disaster scenarios using JPEG 2000 , 2016, Wirel. Networks.

[11]  Hyung Won Kim Wireless Sensor Network for Video Sensors , 2016 .

[12]  Byung-Seo Kim,et al.  Adaptive Window Size-Based Medium Access Control Protocol for Cognitive Radio Wireless Sensor Networks , 2016, J. Sensors.

[13]  Min-Jie Xin,et al.  Joint Game Algorithm of Power Control and Channel Allocation Considering Channel Interval and Relay Transmission Obstacle for WSN , 2016, Wirel. Pers. Commun..

[14]  Ali Bohlooli,et al.  Medium access control layer management for saving energy in wireless sensor networks routing algorithms , 2016 .

[15]  Ibrahim Korpeoglu,et al.  Distributed joint flow-radio and channel assignment using partially overlapping channels in multi-radio wireless mesh networks , 2016, Wirel. Networks.

[16]  Ji-Yan Zou,et al.  An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks , 2016, Wirel. Networks.

[17]  Yasunori Mitani,et al.  Future strategic plan analysis for integrating distributed renewable generation to smart grid through wireless sensor network: Malaysia prospect , 2016 .

[18]  Young-Bae Ko,et al.  Efficient topology construction and routing for IEEE 802.15.4m-based smart grid networks , 2017, Wirel. Networks.

[19]  Victor C. M. Leung,et al.  Scheduling and routing methods for cognitive radio sensor networks in regular topology , 2016, Wirel. Commun. Mob. Comput..

[20]  Yukikazu Nakamoto,et al.  Topology Management for Reducing Energy Consumption and Tolerating Failures in Wireless Sensor Networks , 2016, Int. J. Netw. Comput..