Generally, sensors do not come with on-board address except for specialized protocols like RFID, 6LowPAN and some typical location aware sensing applications. Identity establishment with id-less sensor devices is often a centralized activity. Manual assignment of the addresses will be a costly alternative, also would result in long setup time for a large network. Effectiveness of provisioning additional hardware, like GPS, will be subject to weather conditions, nature of deployment, etc. The performance of conventional addressing schemes relies on prior knowledge of network topology. This assumption fails for typical applications, especially in the era of Internet of Things (IoT), that demands massive deployment of sensor nodes with a nearly unstructured topology. IPv6 could be an alternative, having ability to support large address spaces. However, inherent assumption of IPv6 is the presence of identity and reliability at link layer hence, its adaptation is doubtful for sensor networks with massive deployment and unknown topology. Moreover, applications having inherent security concern, require to identify the malicious node rather than zone from where the node belongs. This paper presents dynamic address allocation scheme for applications like Smart-Cities having massive sensor node deployment with an indefinite topology. Algorithm works over a flat network and can even build a clusters among the nodes in the process. Proposed algorithm is simulated over Omnet++ for large sensor network having upto 5000 nodes. The simulated algorithm has given approx 99.7% resolved address irrespective of number of nodes in the network. Proposed distributed address allocation approach reduces the task load of base stations with a small number of packet exchanges hence conserves energy dissipation within the network.
CITATION STYLE
Mishra, R. K., Chaki, N., & Choudhury, S. (2019). An addressing scheme for massive sensor networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11703 LNCS, pp. 481–492). Springer Verlag. https://doi.org/10.1007/978-3-030-28957-7_40
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