Performance Improvement Strategies of Edge-Enabled Social Impact Applications

In recent years, social relationships have been rooted in a blend with technological advancements to eradicate emerging challenges, such as loneliness, poverty, pollution, climate change, health issues, and so forth. IoT-enabled social impact applications, accordingly, have emerged in various dimensions. In fact, those developing IoT-enabled social impact applications have to diligently consider the efficiency of under-lying computational infrastructures. This article explores the performance improvement (PI) strategies of edge intelligence techniques that apply to social impact applications. It highlights the most commonly practiced PI methods such as edge caching, model partitioning, offloading, and so forth; it lists the near-future research perspectives of edge-enabled solutions, including collaborative edge-level learning methods. The article will be beneficial to several researchers/practitioners who prefer to address social causes using edge-enabled efficient intelligent techniques.