Quick Convex Hull-Based Rendezvous Planning for Delay-Harsh Mobile Data Gathering in Disjoint Sensor Networks

Sink mobility is a significant technique to improve the performance of wireless sensor networks (WSNs). Generally a mobile sink visits several rendezvous points (RPs), forming a trip tour for data collection. However, the low movement speeds of mobile sinks tend to incur serious data delivery delays. In this article, we propose a quick convex hull-based rendezvous planning (QCHBRP) scheme, which aims to not only achieve full connectivity for disjoint WSNs but also construct a shorter trip tour and minimize the data delivery latency accordingly. The trajectory formation of the mobile sink is based on a path skeleton, i.e., a near-convex hull, which is created by the quick determination of several special locations as RPs. The benefits of QCHBRP are threefold. First, it is especially designed for disjoint WSNs where sensor nodes are deployed in multiple isolated segments and the network connectivity is lost in advance. Second, it is suitable for delay-harsh applications which require short paths of the mobile sink. Third, it is of much lower computational complexity compared with existing methods. The extensive analysis and experiments validate the effectiveness and advantages of this new scheme in terms of connectivity cost and data delivery delay.

[1]  Ye Xia,et al.  Maximizing the Lifetime of Wireless Sensor Networks with Mobile Sink in Delay-Tolerant Applications , 2010, IEEE Transactions on Mobile Computing.

[2]  Suraj Sharma,et al.  Proactive data routing using controlled mobility of a mobile sink in Wireless Sensor Networks , 2018, Comput. Electr. Eng..

[3]  Adamu Murtala Zungeru,et al.  Optimizing Energy Consumption for Big Data Collection in Large-Scale Wireless Sensor Networks With Mobile Collectors , 2018, IEEE Systems Journal.

[4]  Xuxun Liu,et al.  An Optimal-Distance-Based Transmission Strategy for Lifetime Maximization of Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[5]  Hao Luo,et al.  MTES: An Intelligent Trust Evaluation Scheme in Sensor-Cloud-Enabled Industrial Internet of Things , 2020, IEEE Transactions on Industrial Informatics.

[6]  Arun Kumar Sangaiah,et al.  Energy-Efficient and Trustworthy Data Collection Protocol Based on Mobile Fog Computing in Internet of Things , 2020, IEEE Transactions on Industrial Informatics.

[7]  Xuemin Shen,et al.  Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks , 2016, IEEE Transactions on Industrial Informatics.

[8]  Anfeng Liu,et al.  Pipeline slot based fast rerouting scheme for delay optimization in duty cycle based M2M communications , 2019, Peer-to-Peer Networking and Applications.

[9]  Prasanta K. Jana,et al.  A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks , 2018, Pervasive Mob. Comput..

[10]  Chien-Fu Cheng,et al.  Data Gathering in Wireless Sensor Networks: A Combine–TSP–Reduce Approach , 2016, IEEE Transactions on Vehicular Technology.

[11]  Weifa Liang,et al.  Approximation Algorithms for Capacitated Minimum Forest Problems in Wireless Sensor Networks with a Mobile Sink , 2013, IEEE Transactions on Computers.

[12]  Jun Luo,et al.  Joint mobility and routing for lifetime elongation in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[13]  Jau-Yang Chang,et al.  An Efficient Tree-Based Power Saving Scheme for Wireless Sensor Networks With Mobile Sink , 2016, IEEE Sensors Journal.

[14]  Tie Qiu,et al.  Load-Balanced Data Dissemination for Wireless Sensor Networks: A Nature-Inspired Approach , 2019, IEEE Internet of Things Journal.

[15]  Mohamed Khalgui,et al.  On Feasibility of Multichannel Reconfigurable Wireless Sensor Networks Under Real-Time and Energy Constraints , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  Yuanyuan Yang,et al.  Dellat: Delivery Latency Minimization in Wireless Sensor Networks with Mobile Sink , 2015, J. Parallel Distributed Comput..

[17]  Sajal K. Das,et al.  Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey , 2011, TOSN.

[18]  Lei Shu,et al.  Cache-Aware Query Optimization in Multiapplication Sharing Wireless Sensor Networks , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[19]  Cem Ersoy,et al.  Distributed Mobile Sink Routing for Wireless Sensor Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[20]  Chien-Fu Cheng,et al.  Data Gathering With Minimum Number of Relay Packets in Wireless Sensor Networks , 2017, IEEE Sensors Journal.

[21]  Tao Gu,et al.  A Mixed Transmission Strategy to Achieve Energy Balancing in Wireless Sensor Networks , 2017, IEEE Transactions on Wireless Communications.

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

[23]  Jian Li,et al.  An analytical model for the energy hole problem in many-to-one sensor networks , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[24]  Yuanyuan Yang,et al.  Efficient Data Gathering with Mobile Collectors and Space-Division Multiple Access Technique in Wireless Sensor Networks , 2011, IEEE Transactions on Computers.

[25]  Yuanyuan Yang,et al.  Tour Planning for Mobile Data-Gathering Mechanisms in Wireless Sensor Networks , 2013, IEEE Transactions on Vehicular Technology.

[26]  Vinton G. Cerf,et al.  Delay-tolerant networking: an approach to interplanetary Internet , 2003, IEEE Commun. Mag..

[27]  Nabanita Das,et al.  Load Balanced Coverage with Graded Node Deployment in Wireless Sensor Networks , 2017, IEEE Transactions on Multi-Scale Computing Systems.

[28]  Cem Ersoy,et al.  Ring Routing: An Energy-Efficient Routing Protocol for Wireless Sensor Networks with a Mobile Sink , 2012, IEEE Transactions on Mobile Computing.

[29]  Anfeng Liu,et al.  Content Propagation for Content-Centric Networking Systems From Location-Based Social Networks , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[30]  Zhili Sun,et al.  Time Efficient Data Collection With Mobile Sink and vMIMO Technique in Wireless Sensor Networks , 2018, IEEE Systems Journal.

[31]  Yu-Chee Tseng,et al.  Energy-Balanced Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Network , 2010, IEEE Transactions on Parallel and Distributed Systems.

[32]  D. PraveenKumar,et al.  ACO-based mobile sink path determination for wireless sensor networks under non-uniform data constraints , 2018, Appl. Soft Comput..

[33]  Jiming Chen,et al.  Cooperative and active sensing in mobile sensor networks for scalar field mapping , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[34]  G. C. Shephard,et al.  Convex Polytopes , 1969, The Mathematical Gazette.

[35]  H. T. Mouftah,et al.  Routing protocols for wireless sensor networks with mobile sinks: a survey , 2014, IEEE Communications Magazine.

[36]  Oliver Nelles,et al.  Interpolation and extrapolation: Comparison of definitions and survey of algorithms for convex and concave hulls , 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[37]  Kamalrulnizam Abu Bakar,et al.  Inter- and intra-cluster movement of mobile sink algorithms for cluster-based networks to enhance the network lifetime , 2019, Ad Hoc Networks.

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

[39]  Naixue Xiong,et al.  Design and Analysis of Probing Route to Defense Sink-Hole Attacks for Internet of Things Security , 2020, IEEE Transactions on Network Science and Engineering.

[40]  Shenghui Zhao,et al.  EAPC: Energy-Aware Path Construction for Data Collection Using Mobile Sink in Wireless Sensor Networks , 2018, IEEE Sensors Journal.

[41]  Xuxun Liu,et al.  A novel transmission range adjustment strategy for energy hole avoiding in wireless sensor networks , 2016, J. Netw. Comput. Appl..

[42]  Xi Zheng,et al.  Crowdsourcing Mechanism for Trust Evaluation in CPCS Based on Intelligent Mobile Edge Computing , 2019, ACM Trans. Intell. Syst. Technol..

[43]  Khaled Almiani,et al.  Energy-efficient data gathering with tour length-constrained mobile elements in wireless sensor networks , 2010, IEEE Local Computer Network Conference.