# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 2 Related Work  
This section highlights the importance of the IoT-NDN research field and identifies several pertinent articles. It also introduces research that employs ndnSIM to evaluate application fields related to NDN. A primary focus in reviewing IoT-NDN networks is on energy consumption. Numerous studies provide insights into various aspects of IoT-NDN. In the following, we will present papers related to the fundamental concepts of IoT-NDN, energy efficiency in real-world IoT-NDN environments, and performance analysis using ndnSIM. _Fundamental Concepts_: The study by [10] addresses challenges in IoT and proposes an NDN-based IoT system architecture, establishing foundational concepts in this field. NDN, initially designed to enhance the Internet's communication structure, is now extended to wireless networks such as ad-hoc and wireless sensor networks [11; 12; 13; 14]. In these scenarios, NDN offers numerous benefits, including mobility support, low-cost configuration, delay tolerance, and opportunistic networking. Furthermore, integrating NDN into IoT is argued to enhance system efficiency, flexibility, and robustness [15]. _Real-World IoT-NDN and Energy Efficiency_: Here, we explore research within the realm of Named Data Networking (NDN), with a particular focus on energy efficiency achieved through caching strategies. The work conducted by [9; 14; 16] collectively examines the influence of caching parameters on IoT energy consumption using NDN. Specifically, Ref. [14] provides a detailed comparison between the energy efficiencies of NDN and IPv6 within IoT nodes, investigating a variety of forwarding strategies and caching scenarios. These articles delve into practical network implementations, highlighting the role of caching strategies, data production, and energy-saving measures. However, they do not fully explore the various parameters and combinations that could significantly impact the outcomes of energy consumption studies. Unfortunately, this comprehensive approach to analyzing parameter combinations related to energy efficiency in IoT-NDN has not been addressed in the works mentioned above. Our contribution lies in our thorough consideration of all relevant parameter combinations to optimize energy consumption in this field, providing practical steps for researchers to enhance energy efficiency in IoT-NDN deployments. _Performance Analysis using ndnSIM:_ Our research paper examines how different caching methods and network designs impact the efficiency of Named Data Networking (NDN), focussing on aspects such as server workload, data access rates, and the performance of various caching rules like FIFO, LRU, and Universal Caching, as explored in studies [17; 18]. It also discusses the optimization of caching for energy savings in vehicular networks through ndnSIM [19]. Refs. [20; 21] explore the deployment of Named Data Networking (NDN) in Mobile Ad Hoc Networks (MANETs) and the utilization of simulation tools like ndnSIM for research purposes. Through our extension of ndnSIM, we have enhanced the efficiency and flexibility of NDN within MANET environments. This advancement supports and simplifies conducting experiments using ndnSIM for MANET-related research. Additionally, we build upon the fundamental analysis of ndnSIM's development and its role in NDN research as presented in [22]. While [21; 23] utilize ndnSIM for experiments described in these articles, they do not explore the vast array of combinations that could be tested during experiments due to the complexity and the time-consuming nature of such configurations in ndnSIM simulations. Our work extends ndnSIM's capabilitiesby incorporating novel features such as optimal parameter combinations and presenting simple steps to build, test, and evaluate experiments, which simplifies the presentation of all experimental results. Furthermore, the framework presented in this article automates experiments using ndnSIM, a capability not currently considered or present in previously mentioned articles related to our work.