Enabling Heterogeneous mMTC by Energy-Efficient and Connectivity-Aware Clustering and Routing

Clustering is the fundamental of data aggregation for energy-efficient Low Power Wide Area (LPWA) networks while routing is also essential to solve the connectivity problem. In this paper, a novel clustering and routing algorithm based on a 2-level genetic framework is proposed, taking both energy-efficiency and connectivity into consideration, for massive Machine Type Communication (mMTC), where ultra-heterogeneous types of nodes may exist. The performance of the proposed algorithm is compared with the state-of-the-art and a benchmark. It doubles the network lifetime compared with the benchmark if defining it as the number of collections when the first dead node appears. The probability of successful collection is also significantly improved since connectivity is considered in the algorithm. The inter-networking gain in the heterogeneous scenario is also investigated.

[1]  Walid Saad,et al.  Toward Massive Machine Type Cellular Communications , 2017, IEEE Wireless Communications.

[2]  Jenq-Shiou Leu,et al.  Energy Efficient Clustering Scheme for Prolonging the Lifetime of Wireless Sensor Network With Isolated Nodes , 2015, IEEE Communications Letters.

[3]  Atay Ozgovde,et al.  How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions , 2017, IEEE Communications Surveys & Tutorials.

[4]  Padmalaya Nayak,et al.  Genetic algorithm based clustering approach for wireless sensor network to optimize routing techniques , 2017, 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence.

[5]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[6]  A. Barradas,et al.  GACN: Self-Clustering Genetic Algorithm for Constrained Networks , 2017, IEEE Communications Letters.

[7]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[8]  Catherine Rosenberg,et al.  Design guidelines for wireless sensor networks: communication, clustering and aggregation , 2004, Ad Hoc Networks.

[9]  Zhezhuang Xu,et al.  Joint Clustering and Routing Design for Reliable and Efficient Data Collection in Large-Scale Wireless Sensor Networks , 2016, IEEE Internet of Things Journal.

[10]  Dong-Seong Kim,et al.  Clustering algorithm of hierarchical structures in large-scale wireless sensor and actuator networks , 2015, Journal of Communications and Networks.

[11]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.