The Internet of Things (IoT) devices are resource-constrained devices with limitations such as low computation power, low communication capabilities, low bandwidths, high latency, and short-lived power. Therefore, securing communication among these devices is a key challenge for various sensitive applications. However, the conventional encryption and decryption algorithms, known as ciphers, cannot be implemented because of their inherent complexities of implementation and power requirements. One of the promising options available is to implement light-weight ciphers for these resource-constrained devices. Moreover, the choice of lightweight encryption tool has a great dependency on the type of IoT devices being used in an application. In this paper, a lightweight cellular automata (CA)-based cipher, named as Lightweight CA Cipher (LCC), has been proposed for IoT applications. In the proposed method, encryption is done at the perception layer, where the sensor nodes are deployed and decryption is done at the network layer where gateway devices are installed. The experimental results show that the proposed method is efficient than some of the existing ciphers like DES, 3DES when randomness, execution time, and implementation simplicity are considered as prime requirements. This cipher passes the randomness tests as prescribed by the National Institute of Standards and Technology (NIST), and it also passes all the DIEHARD tests and it establishes the security feature of LCC. Though it is specially designed for resource-constrained environments, it can be scaled up for a large number of sensor nodes.

Due to deployment of inflated amount of sensor nodes in three dimensional space, observed data are highly correlated among sensor nodes. Since the data are highly correlated, it produces large quantity of redundant data in the network. To reduce data redundancy, we propose a clustering algorithm called Three Dimensional Event based Spatially Correlated Clustering (3D-ESCC) algorithm. Moreover, to extract more accurate data in each distributed cluster of 3D-ESCC algorithm, we propose an Event based Data Estimation (EDE) model in three dimensional space and compare it with other data estimation models. In distributed wireless sensor networks, it may be possible that due to extreme physical condition (e.g heavy rainfall, high temperature and battery discharge) the sensor nodes fails to operate. In such situation, we are able to develop a data prediction model in distributed cluster in case of node failure. Computer simulations and validations are performed to validate 3D-ESCC algorithm and EDE model.

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