Wireless sensor networks have a wide range of applications because they can be adapted for various environments. They can operate independently in harsh places where a human presence is risky or even impossible. Since their life time is dependent on their batteries and replacing or recharging their batteries is impossible in rough places, it is necessary to find energy efficient routing protocols for them. In this paper, a number of well-know energy efficient routing algorithms for WSNs have been classified and presented based on their attributes.

Measuring pairwise document similarity is an essential operation in various text mining tasks. Most of the similarity measures judge the similarity between two documents based on the term weights and the information content that two documents share in common. However, they are insufficient when there exist several documents with an identical degree of similarity to a particular document. This paper introduces a novel text document similarity measure based on the term weights and the number of terms appeared in at least one of the two documents. The effectiveness of our measure is evaluated on two real-world document collections for a variety of text mining tasks, such as text document classification, clustering, and near-duplicates detection. The performance of our measure is compared with that of some popular measures. The experimental results showed that our proposed similarity measure yields more accurate results.

The adaptable and distributed nature of wireless sensor networks has made them popular in a broad range of applications. Clustering is a widely accepted approach for organising nodes in sensor networks to address the network congestion and energy efficiency concerns. In clustering, the number and uniform distribution of the cluster heads are crucial for the effectiveness of an algorithm. In this paper, we propose a new clustering algorithm for wireless sensor networks that reduces the networks energy consumption and significantly prolongs its lifetime. This is achieved by optimising the distribution of cluster heads across the network. The results of our extensive simulation study show considerable reduction in network energy consumption and therefore prolonging network lifetime.

SUMMARY: This paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HFOPDSMC is proposed. The bounded model uncertainties are considered in the dynamics of the robot, and then the robustness of the controller is verified. The Lyapunov theory is utilized in order to show the stability of the proposed controller. In this paper, the EGWO is developed by adding the emphasis coefficients to the typical grey wolf optimizer (GWO). The GWO and EGWO, then, are applied to optimize the proposed control parameters which result in the optimized GWO-HFOPDSMC and EGWO-HFOPDSMC, respectively. The effectivenesses of the optimized controllers (GWO-HFOPDSMC and EGWO-HFOPDSMC) are completely verified by comparing the simulation results of the optimized controllers with the typical FOSMC and HFOPDSMC.