# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 1 Introduction  
The concept of the _Internet of Things (IoT)_ relates to the networking of devices that go beyond traditional computers, including sensors and everyday objects. The vision of IoT is to interconnect diverse entities, enabled by the proliferation of compact and cost-effective embedded devices [1]. This \"IoT revolution\", as described by [2], spans the realms of technology, policy, and engineering, with a notable presence in popular media. Notable consumer applications encompass Internet-connected appliances, energy management systems, components for home automation, as well as wearable fitness and health trackers. Clearly, the Internet of Things stands out as one of the primary topics in networking at present. Traditionally, IPv6 has served as a network protocol for connecting IoT devices. However, Information-Centric Networking, and more specifically _Named Data Networking (NDN)_, have drawn significant attention in recent research on future Internet technologies. NDN constitutes a novel networking paradigm, representing an evolution of the traditional IP architecture [3]. Numerous authors have described it as a promising technology [4; 5; 6], a key enabling paradigm for IoT [7], and some have even referred to it as revolutionary [8]. The enthusiasm for NDN is driven by its ability to address numerous classical TCP/IP-related issues such as location dependence, scalability, node mobility, intermittent connectivity, and network overhead, as documented in [9]. The motivation for the NDN protocol stems from the limitations of the existing host-centric paradigm in today's Internet. There is a clear need for an alternative approach that shifts the focus from the location of data to the data itself. Users are increasingly interested in accessing data, while possibly being unaware of its location. NDN addresses this shiftwhile also enhancing security, privacy, and efficient data retrieval. By enabling caching of information closer to consumers, it reduces network overhead and supports the growing number of devices and mobility requirements in the modern network landscape. Given the inherent constraints of IoT devices in terms of energy, memory, and computational resources [10], optimizing energy consumption in communication is a critical aspect, which is the primary focus of this work. For many IoT devices, network communication accounts for a significant portion of their energy consumption. Therefore, reducing energy consumption through communication can significantly decrease a device's overall energy footprint. This work finds the energetically best parameter combinations in a set of given parameters using hyperparameter optimization. The primary objective is to investigate the impact of network parameters, such as network size, topology, packet size, and the number of sent packets, on energy consumption within the network. Breaking down networks into a set of distinct parameters enables a precise analysis of various factors that influence energy consumption. However, for most parameters, the number of potential combinations increases exponentially. This presents a substantial challenge as the goal is to discover the optimal global energy consumption within all possible combinations. To address this issue, the problem is reframed as a _hyperparameter optimization_ problem, a concept derived from machine learning. A fundamental algorithm employed in this field is _grid search_. This algorithm conducts an exhaustive search of all parameter combinations, which, in turn, leads to an exponential increase in the number of iterations. Consequently, it becomes imperative to minimize the number of parameters and their value ranges. In addition, this work also endeavors to contribute to the advancement of NDN research through the development of an innovative framework within ndnSIM. This framework offers enhanced efficiency in simulating NDN networks, providing researchers with a valuable tool for more accurate and resource-effective evaluations of NDN-based IoT network configurations.