A Hybrid Transmission Based Data Collection Scheme With Delay and Reliability Guaranteed for Lossy WSNs

Mobile computing provides new ways of data collection on the Internet of Things (IoT). The sensed environment information by various sensing devices could make access to a wireless sensor network (WSN) through access points and vice versa. Improving the energy efficiency for a prolonged network lifetime while meeting the adaptive performance requirements under the guarantees of transport delay and reliability is an important problem to be solved in data collection for lossy and large-scale wireless sensor networks (WSN). Although many previous studies devoted to improving the energy efficiency, reliability, or network delay, they mainly focus on one of the performance metrics of the WSN. In this paper, a hybrid transmission scheme integrating the packet reproduction (PR) and Hop-by-Hop Automatic Repeat-reQuest (HBH ARQ) schemes are proposed to improve energy efficiency and reduce delay under guarantee of reliability for the lossy WSN. In the proposed scheme, the network is divided into two parts by a hybrid boundary where the location information is used to help determine the routing changes between the PR and HBH ARQ schemes. Some key parameters involved in the scheme are discussed and analyzed. The effectiveness of the proposed scheme is evaluated via extensive simulation results.

[1]  Naixue Xiong,et al.  A novel code data dissemination scheme for Internet of Things through mobile vehicle of smart cities , 2019, Future Gener. Comput. Syst..

[2]  Yaw-Yauan Tyan,et al.  Applying ZigBee wireless sensor and control network for bridge safety monitoring , 2018, Advances in Mechanical Engineering.

[3]  Pi-Cheng Hsiu,et al.  A Data Parasitizing Scheme for Effective Health Monitoring in Wireless Body Area Networks , 2019, IEEE Transactions on Mobile Computing.

[4]  Ridha Ouni,et al.  Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks , 2018, Sustain. Comput. Informatics Syst..

[5]  B. B. Zaidan,et al.  Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review , 2018, Journal of Medical Systems.

[6]  Albert Mo Kim Cheng,et al.  Technological advances in wireless sensor networks enabling diverse Internet of Things applications , 2018, Int. J. Distributed Sens. Networks.

[7]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[8]  Xuejun Yue,et al.  Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs , 2017, Sensors.

[9]  Vivek Kumar Singh,et al.  In-Network Data Processing Based on Compressed Sensing in WSN: A Survey , 2017, Wireless Personal Communications.

[10]  Pinyi Ren,et al.  Secure ARQ Protocol for Wireless Communications: Performance Analysis and Packet Coding Design , 2018, IEEE Transactions on Vehicular Technology.

[11]  Jiaheng Wang,et al.  Design of a Smart Sensor Network System for Real-Time Air Quality Monitoring on Green Roof , 2018, J. Sensors.

[12]  Nima Jafari Navimipour,et al.  Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research , 2017, J. Netw. Comput. Appl..

[13]  Xiong Li,et al.  Optimizing the Coverage via the UAVs With Lower Costs for Information-Centric Internet of Things , 2019, IEEE Access.

[14]  Rong Du,et al.  On Maximizing Sensor Network Lifetime by Energy Balancing , 2017, IEEE Transactions on Control of Network Systems.

[15]  F. Liu,et al.  Battery-Friendly Relay Selection Scheme for Prolonging the Lifetimes of Sensor Nodes in the Internet of Things , 2019, IEEE Access.

[16]  Prasun Sinha,et al.  Optimal sleep-wakeup algorithms for barriers of wireless sensors , 2007, 2007 Fourth International Conference on Broadband Communications, Networks and Systems (BROADNETS '07).

[17]  Xiaobo Min,et al.  Study on the Ecological Farming Control System Based on the Internet of Things , 2018, Wirel. Pers. Commun..

[18]  Xin Jin,et al.  Deployment guidelines for achieving maximum lifetime and avoiding energy holes in sensor network , 2013, Inf. Sci..

[19]  Geetika Dhand,et al.  Data Aggregation Techniques in WSN:Survey , 2016 .

[20]  Tiago M. Fernández-Caramés,et al.  A Cost-Effective IoT System for Monitoring Indoor Radon Gas Concentration , 2018, Sensors.

[21]  Hanbin Luo,et al.  Cyber-physical-system-based safety monitoring for blind hoisting with the internet of things: A case study , 2019, Automation in Construction.

[22]  Shumaila Javaid,et al.  Traffic-aware congestion control (TACC) for wireless multimedia sensor networks , 2018, Multimedia Tools and Applications.

[23]  Manas Ranjan Kabat,et al.  Energy Efficient Reliable Multi-path Data Transmission in WSN for Healthcare Application , 2016, Int. J. Wirel. Inf. Networks.

[24]  Raphaël Couturier,et al.  Health risk assessment and decision-making for patient monitoring and decision-support using Wireless Body Sensor Networks , 2019, Inf. Fusion.

[25]  Xuxun Liu,et al.  Data Drainage: A Novel Load Balancing Strategy for Wireless Sensor Networks , 2018, IEEE Communications Letters.

[26]  Hanan Elazhary,et al.  Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions , 2019, J. Netw. Comput. Appl..

[27]  Xuxun Liu.  Node Deployment Based on Extra Path Creation for Wireless Sensor Networks on Mountain Roads , 2017, IEEE Communications Letters.

[28]  Wei Liu,et al.  A low redundancy data collection scheme to maximize lifetime using matrix completion technique , 2019, EURASIP J. Wirel. Commun. Netw..

[29]  Dayong Ye,et al.  A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks , 2018, IEEE Transactions on Cybernetics.