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As the popularity of wireless sensor networks (WSNs) is rapidly expanding across businesses and industries, we must make everything around us more intelligent and informative. In such networks, sensor nodes (SNs) serve as the WSN's eyes, obtaining data about various environments and conditions, while the base station (or Sink) serves as the WSN's brain, analyzing the acquired data and making decisions. However, on the one hand, the large volume of data collected by the SNs consumes the SNs' limited energy and complicates data analysis at the sink for decision making. In this article, we present a data prediction model based on the stepwise data regression method for handling large amounts of data obtained by cluster-based WSNs. The prediction model is built stepwise data regression method that is implemented at both tiers of each cluster: cluster member nodes and Cluster Heads; and is compatible with both homogeneous and heterogeneous network setups. In intracluster data transmissions, the proposed data prediction model employs a two-buffer stepwise data regression method to synchronize the sensed and predicted data intending to reduce the cumulative errors from continuous predictions. The performance of the proposed work is examined by extensive simulations on real sensor data collected from several applications and is also compared with CPMDC (Diwakaran et al. in J Supercomput 75:3302–3316, 2019) and TDPA (Sinha and Lobiyal in Wirel Pers Commun 84:1325–1343, 2015) models. The proposed model proved to be very energy efficient, with improved data prediction accuracy, increased network lifetime, and more successful data predictions while sustaining an acceptable data accuracy, and improved network lifetime when compared with CPMDC (Diwakaran et al. 2019) and TDPA (Sinha and Lobiyal 2015) models. respectively. [ABSTRACT FROM AUTHOR]

The wireless sensor networks (WSNs) have imminent constrains that makes security a crucial issue. Weak defense capability makes WSN a soft target against worm attacks. A single compromised node can spread the worm via communication in the entire network. In this paper, we propose a mathematical model that studies the epidemic behavior of such digital worms. Furthermore, we study the effect of these worms with various communication radius and node distributed density. We investigate the proposed model using the stability theory of differential equations. Basic reproduction number is found that helps us to find the threshold values for communication radius and node density distribution. The proposed model is checked and validated through extensive simulation results. Finally, we compare our scheme with the existing schemes. Comparison analysis shows that the proposed model is efficient as it has the low rate of the infectious node for different communication radius. [ABSTRACT FROM AUTHOR]

Turkanovic et al. (Ad Hoc Netw 20:96-112, 2014) proposed a user authentication and key agreement scheme based on the notion of the 'internet of things' for wireless sensor network. Authors claimed that their scheme is safe against various attacks. We found that this scheme fails against session key recovery attack. If an attacker has stolen the smartcard, he can easily obtain the session key generated between user and sensor node. In this paper, we shows that the attacker is able to compute the secret parameter $$ K_{{GW - U_{i}}} $$ , which is the used by a gateway during communication with others. Now the attacker can modify the first message that was send by the user to the sensor node. Finally, he breaks the complete system. We also provide few other insecurities and vulnerability to many attacks like offline password guessing attack, replay attack and impersonate attack etc. To remedy this, an enhanced scheme is also proposed to remove the flaws of the Turkanovic et al. scheme. The result and performance analysis of our proposed scheme shows that the new enhanced scheme provides high security with low computation, communication and storage overhead. [ABSTRACT FROM AUTHOR]