Table 1.

Overview of previous related surveys and comparison with our study with respect to their contributions and discussed intelligent techniques (classical AI, ML and DL).

Reference	Year	Main Focus	AI Techniques
[40]	2015	Evaluation of different available resources,	×
		communication mediums, and frameworks
		for industrial market perspective.
[41]	2016	A context-aware review for recognizing emerging	×
		fields from a software development
		point of view.
[42]	2019	A survey study about context-aware crowd	×
		sensing systems for urban environments.
[43]	2022	A survey on the use of ML methods in context-	AI, ML and DL
		aware middlewares for HAR.
[44]	2018	A survey on context awareness for IoT big data analysis.	AI, ML and DL
[45]	2018	A comprehensive survey on the utilization of AI	AI, ML and DL
		integrating ML, data analytics, and NLP techniques
		for enhancing the efficiency of wireless networks.
[46]	2019	A literature analysis of various context-aware systems	×
		(modelling, organization, and middleware).
[47]	2019	A short survey of the latest development	AI, ML and DL
		of context-aware systems.
[48]	2019	A survey of recent advances in intelligent sensing,	AI, ML and DL
		computation, communication, and energy
		management for resource-constrained
		IoT sensor nodes.
[49]	2021	An extensive survey of AI-based mobile context-	AI, ML and DL
		aware recommender systems.
Our Paper	2022	A broad study of the adoption	AI, ML and DL
		of edge-based AI solutions for context-
		awareness in WSNs.