The use of advanced communication technologies such as Internet of Things (IoT) in the domain of Ambient Assisted Living (AAL) tends to promote the quality of living for elderly staying independently. However, the state of the art IoT based solutions for AAL systems have not fully expressed the importance of building social connections between smart devices. This paper attempts to study the significance of deploying socially enabled IoT systems in AAL environment by proposing a robust Social IoT based AAL system for elderly named FriendCare-AAL. In addition, it presents a schematic approach to establish a partnership among smart devices and introduces the concept of responsibility offloading between devices. The proposed system is capable of providing assistance to the elderly staying in smart home environment. In case of emergency, the system automatically generates alerts intimating about the situation to the concerned entities. To experimentally evaluate the system’s performance, a smart home AAL environment for an elderly person is simulated using human activity simulator namely ‘Home Sensor Simulator’ and person’s routine dataset is generated. Further, two machine learning models; Naive Bayes (NB) and Random Forest (RF) are employed to analyze the data in order to predict the well being of the elderly person. The performance of the two classifiers is assessed using metrics such as sensitivity, specificity, detection rate and accuracy. Experimental results revealed that RF classifier outperforms NB classifier in terms of overall accuracy, detection rate and balanced accuracy. The overall accuracy is observed to be 89.2% for RF and 83.9% for NB classifier. Furthermore, a performance comparison of the proposed model is performed with two baseline approaches. A system prototype is also developed using Node-Red simulation tool to determine the performance of the proposed system in real-world and failure-prone environments. It turns out that the system performs well in critical situations with a tolerable response time of less than 1.2 s for a high failure rate of upto 50%.

在环境辅助生活（AAL）领域中使用诸如物联网（IoT）之类的先进通信技术往往可以提高老年人独立居住的生活质量。但是，用于AAL系统的基于IoT的最新解决方案尚未完全表达在智能设备之间建立社交联系的重要性。本文试图通过为老人朋友FriendCare-AAL提出一个基于健壮的基于IoT的AAL系统，研究在AAL环境中部署具有社交功能的IoT系统的重要性。此外，它提供了一种在智能设备之间建立合作关系的示意方法，并介绍了设备之间责任分担的概念。所提出的系统能够为住在智能家居环境中的老年人提供帮助。在紧急情况下，系统会自动生成警报，告知有关实体有关情况。为了通过实验评估系统的性能，使用人类活动模拟器“ Home Sensor Simulator”模拟了老年人的智能家居AAL环境，并生成了人的常规数据集。此外，还有两个机器学习模型；朴素贝叶斯（NB）和随机森林（RF）用于分析数据，以预测老年人的健康状况。使用灵敏度，特异性，检测率和准确性等指标评估这两个分类器的性能。实验结果表明，RF分类器在整体准确度，检测率和平衡准确度方面均优于NB分类器。对于RF分类器和NB分类器，整体精度为89.2％。此外，所提出模型的性能比较是通过两种基准方法进行的。还使用Node-Red仿真工具开发了系统原型，以确定拟议系统在现实世界和容易出现故障的环境中的性能。事实证明，该系统在紧急情况下表现良好，可响应时间小于1.2秒，故障率高达50％。