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| Paper | Approach | Parameter | Drawback |
|
| ICN in the IoT-NDN Wild [12] | Random | No parameter considered | Does not consider any parameters |
| Consumer-driven information freshness [29] | Random | Freshness | Other essential IoT parameters are not considered |
| Cache aware NDN in IoT [30] | Probabilistic | Freshness with cache time | Not taking content’s popularity into account |
| Probabilistic caching strategy for the internet of things [31] | Probabilistic | content freshness, energy level, and storage capability | Caching utility function may return a value even if one or more of its parameters are obsolete |
| Caching transient data [32] | Probability-based caching | Freshness and content popularity | Disregard content size |
| Lifetime-based cooperative caching (LCC) [33] | Cooperative caching scheme | Content popularity and freshness | Ignore the content and cache size parameters |
| DRL [34] | Deep neural network based | Content popularity and freshness | Cache size and content size parameters not considered |
| PUC [35] | Clustering | Content popularity and freshness | Content size not taken into account |
| CCS/CES [14] | Exponential weighted moving average (EWMA) and probabilistic | Freshness and popularity | Disregard essential IoT parameters like limited cache and content size not taken into account |
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