Collaboration Energy Efficiency with Mobile Edge Computing for Target Tracking in IoT

In this paper, the target tracking problem is investigated with mobile edge computing (MEC) mechanism in internet of things (IoT), where the challenge of energy efficiency is a significant issue when the target tracking event is driven. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing target tracking effects. We deign the sensor selection scheme for carrying out the tracking tasks according to energy distribution of the sensor node. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency without reducing the accuracy of target tracking. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the target state. Simulations demonstrate the efficiency for tracking performance on energy balance in terms of efficiently prolonging the IoT lifetime.

[1]  Nianxia Cao,et al.  Sensor Selection for Target Tracking in Wireless Sensor Networks With Uncertainty , 2015, IEEE Transactions on Signal Processing.

[2]  Bo Yang,et al.  Offloading Optimization in Edge Computing for Deep-Learning-Enabled Target Tracking by Internet of UAVs , 2020, IEEE Internet of Things Journal.

[3]  Hing Cheung So,et al.  SENSOR SELECTION FOR TARGET TRACKING IN SENSOR NETWORKS , 2009 .

[4]  G.B. Giannakis,et al.  Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.

[5]  Kamran Sayrafian-Pour,et al.  An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks , 2017, IEEE Transactions on Cybernetics.

[6]  Jian Zhang,et al.  Optimising rendezvous-based data collection for target tracking in WSNs with mobile elements , 2019, Int. J. Sens. Networks.

[7]  Yuanfei Tu,et al.  An Improved MDS-MAP Localization Algorithm Based on Weighted Clustering and Heuristic Merging for Anisotropic Wireless Networks with Energy Holes , 2019, Computers, Materials & Continua.

[8]  Gautam Srivastava,et al.  HUNA: A Method of Hierarchical Unsupervised Network Alignment for IoT , 2021, IEEE Internet of Things Journal.

[9]  Baoyu Ma,et al.  An Improved MDS-MAP Localization Algorithm Based on Weighted Clustering and Heuristic Merging for Anisotropic Wireless Networks with Energy Holes , 2019 .

[10]  Wen-An Zhang,et al.  Energy Efficient Distributed Filtering for a Class of Nonlinear Systems in Sensor Networks , 2015, IEEE Sensors Journal.

[11]  Pramod K. Varshney,et al.  Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems , 2013, IEEE Transactions on Signal Processing.

[12]  Pramod K. Varshney,et al.  Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation , 2014, IEEE Transactions on Signal Processing.

[13]  Jian Zhang,et al.  Load-Balancing Rendezvous Approach for Mobility-Enabled Adaptive Energy-Efficient Data Collection in WSNs , 2020, KSII Trans. Internet Inf. Syst..

[14]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[15]  Zidong Wang,et al.  Dynamic State Estimation of Power Systems With Quantization Effects: A Recursive Filter Approach , 2016, IEEE Transactions on Neural Networks and Learning Systems.