In order to improve the performance of PSO algorithm, number of its variants has been made. This paper presents detail overview of the basic concepts of PSO and its variants. Many variants of PSO have been developed due to improved speed of convergence and quality of solution found by Researchers. The Applications of PSO in Complex Environments is discussed. Modifications, both those already developed, and promising future application areas are reviewed. Observation and review of 117 related studies in the period between 1995 and 2015 on different variants of PSO algorithms are discussed along with their advantages and disadvantages.

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Security has been a primary concern in almost all areas of computing and for the devices that are low on computing power it becomes more important. In this paper, a new class of computing device termed as Low Computing Power Device (LCPD) has been defined conceptually. The paper brings out common attributes, security requirements and security challenges of all kinds of low computing power devices in one place so that common security solutions for these can be designed and implemented rather than doing this for each individual device type. A survey of existing recent security solutions for different LCPDs hasve been presented here. This paper has also provided possible security solutions for LCPDs which include identification of countermeasures against different threats and attacks on these devices, and choosing appropriate cryptographic mechanism for implementing the countermeasures efficiently.

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The sensors deployed inside a wireless sensor network (WSN) have limited energy sources, significantly impacting the network throughput. This article's research objective is to increase network lifetime by developing an energy-efficient layered-based routing algorithm for WSNs using the grey wolf optimization (LBR-GWO) algorithm. In this, the grey wolf's leadership hierarchy has followed, which improves the network's energy capability. The entire region of the deployed nodes divides into four layers. In these nodes, layer one is chosen as cluster heads. If more than two nodes are present in layer one, then the cluster head is selected based on the game theory model; otherwise, the decision is made based on the node's residual energy. While the existing algorithm has several complex control parameter points, the current algorithm has fewer complex parameters. Therefore, in comparison to other algorithms, this algorithm is easy to apply in cluster-based sensor networks. Simulation findings prove the LBR-GWO algorithm supremacy for balancing energy consumption across the nodes and improving the network's lifetime compared to the LEACH, HEED, and PSO protocols.