Optimal Network Coding-Based In-Network Data Storage and Data Retrieval for IoT/WSNs

Abstract

The evolution of the Internet of Things (IoT) allows the development of new services and applications but triggers as well a full set of new issues to be solved. Among them, there are the problems related to the integration of the WSNs in the IoT realm including those related to data access. In this paper, we address more precisely the in-network data storage and data retrieval performed in a WSN integrated in the IoT realm. In order to develop an adequate data storage scheme for this scenario, we first design a system that integrates the Virtual Broking Coding (VBC) data storage scheme in the IoT realm. Then, we propose an algorithm called Dynamic Adaptive Virtual Broking Coding (DA-VBC) that adapts dynamically the packet redundancy level adopted in VBC to the optimal redundancy level, regarding the actual condition of the network, in order to ensure a reliable data storage and data retrieval. To do so, we model the choice of the optimal redundancy level as a Markov Decision Process (MDP) problem. Using the optimal policy found by the MDP, DA-VBC always performs with the minimum costbenefit for the network which means allowing more packet to be retrieved without overload the energy consumption. The simulation results confirm that the dynamic adaptation of the redundancy level improves the reliability of the data storage scheme while achieving an energy consumption comparable to the solution that does not use any redundancy. Besides, they show that the optimization of the cost-benefit metric is far more efficient than optimizing only one metric (for instance the cost or the packet delivery ratio), or using a fixed redundancy level.

Attack Detection Using Network Coding in IoT Environment

“…The study to ensure the security of network coding discussed in chapter 2 includes the method of applying cryptographic techniques such as encryption algorithms and digital signatures in [1,4,[8][9][10][11], the method of secure network coding proposed in [7,9,17], and the prevention of attacks by distinguishing the trusted nodes in [13,15]. In the method of applying a cryptographic technique, there is overhead such as key exchange, and if an internal attacker modifies the packet and encrypts it with a correct key, the destination decrypts it and trusts the packet and executes the recovery for network coding.…”

“…In the IoT, when a lot of data is stored in a data collection center, network coding may be applied to the data for retrieval efficiency or security of information. Oliveira et al applied network coding to the storage of data and optimized the added redundancy to ensure reliable data storage and to retrieve more packets at minimal cost [17]. Lei et al showed that the network coding can be efficiently applied between data producers and consumers to handle large amounts of data transmission in the named data networking (NDN) model to provide IoT applications [18].…”

Semantic network coding for WSAN in IoT Applications

“…C.HS. Oliveira et al from our lab have also presented a proposal for network coding in wireless network [12], but the implementation of their solution is too much a burden for the limited processing capabilities of our devices (as shown above by G. Peralta et al in [14]).…”

A Novel Redundant Data Storage Algorithm Based on Minimum Spanning Tree and Quasi-randomized Matrix

“…In order to develop an efficient data storage scheme, authors in [16] first design a system that integrates the Virtual Broking Coding (VBC) data storage scheme in the IoT realm. Then, they propose an algorithm called Dynamic Adaptive Virtual Broking Coding (DA-VBC) that adapts dynamically the packet redundancy level adopted in VBC to the optimal redundancy level, regarding the actual condition of the network, in order to ensure a reliable data storage and data retrieval.…”

“…But they don't take the harsh environments into account. Compared to [16], we consider that in the harsh environment, efficient storage and rapid collection are two key issues. Based on the combination of the minimum spanning tree and the semi-random matrix, a new energy efficient and fast decoding algorithm, QRNCDS, is proposed to simplify the coding process and improve the recovery rate of the source packet.…”