Phosphonium cation-containing polymers: From ionic liquids to polyelectrolytes

Phosphonium cation-based ionic liquids (ILs) are a readily available family of ILs that often offer superior properties compared to ammonium cation-based ILs. Recently investigated applications include extraction solvents, electrolytes in batteries and super-capacitors, and corrosion protection. At the same time, the range of cation–anion combinations available commercially has also increased in recent years. Polymerized ionic liquids and polyelectrolytes play major roles in a broad range of biological applications including antimicrobials, non-viral gene delivery, synthetic enzymes, metal chelation, and drug delivery. Ammonium- and phosphonium-containing macromolecules will be reviewed with a focus on structure–property relationships of these polyelectrolytes and ionic liquids. Phosphonium-containing macromolecules often display enhanced performance compared to ammonium analogs.     

Smart admission control strategy utilizing volunteer-enabled fog-cloud computing

Fog computing has become an effective platform for computing delay-sensitive IoT tasks. However, the increased scalability of IoT devices ( $\mathrm{IDs}$) makes it difficult for fog nodes to perform better. Volunteer computing (VC) has emerged as a supportive technology in which resource-capable $\mathrm{ID}$s, such as computers and laptops, share their idle resources to compute the IoT tasks. However, in VC-based approaches, improper selection of volunteer nodes (VNs) may result in an increased failure rate and delay. To address these challenges, this work proposes a Smart Admission Control strategy utilizing volunteer-enabled Fog-Cloud computing (SAC-VFC). The VNs are selected based on grey TOPSIS ranking. The incoming tasks are classified based on priority and delay and then scheduled using the Improved Jellyfish Algorithm (IJFA). Smart gateway (SGW) and fog manager (FM) act as mediators for allocating tasks among voluntary, fog, and cloud resources. FM performs a similarity-based clustering of fog nodes using the enhanced Fuzzy C Means clustering (EFCM) algorithm to manage resources. Simulation study suggests the superior performance of SAC-VFC over peers under comparison in terms of average delay, average makespan, success rate of tasks and tasks satisfying the deadline metrics.