# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 1 Introduction  
Wireless communication and sensing are major enablers for Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). While considering small-to-medium scale buildings, WSNs [1] are placed at the very heart to facilitate smart operations [2; 3]. Some of the prominent application areas of WSN-based IoT for smart buildings include energy and water management, fire and smoke detection, appliance and light control, security and surveillance, and entertainment [2; 4]. IEEE 802.15.4 is one of the most popular communication standards used in WSNs. According to a recent survey, WSN deployments for which communication standards are known, more than 50% are based on IEEE 802.15.4 standards [5]. Depending on the domain of deployment and application scenarios, WSNs and IoT have different service requirements to meet including energy, reliability, delay, and Throughput (THP) [6; 7]. Recently, there has been a tremendous research effort to improve all possible performance dimensions of WSNs and IoT [6; 8]. Optimizing multiple metrics is frequently modeled as an optimization problem that is often NP-hard [9]. Optimizing one metric also tends to conflict with the other metrics, and a trade-off is needed. Mathematical programming based serialization methods and nature-inspired meta-heuristic algorithms are popular choices in this context [9]. Such methods workaround NP-hardness, compromising on accuracy. In addition, adaptivity is considered a mandatory trait for all modern communication systems. Hence, there is a growing trend of using data-driven approaches and Machine Learning (ML) [10; 11; 12] to meet the performance challenges faced by WSNs and IoT. A case study based approach to facilitate intelligence is presented in Ref. [13]. In summary, the factors that motivate the current study include:  
* Diversified QoS requirements in ever-evolving WSN and IoT infrastructure. * Intrinsic NP-hardness of the optimization problems to facilitate multi-objective and conflicting QoS goals. * Communication infrastructure that makes it viable for large-scale performance data to become available, and
* Availability of sophisticated, robust, and practical deep learning algorithms that can learn from data and promise adaptivity with near optimal accuracy. The focus in this paper is predicting reliability in the form of Packet Delivery Ratio (\(PDR\)) and Energy Consumption (\(EC\)) in IEEE 802.14.5 based networks using Deep Learning (\(DL\)). It is argued that various communication parameters (e.g., Transmission Power (\(TP\)), Packet Size (\(PS\)), Queue Size (\(QS\)), Maximum Transmissions (\(MT\)), and inter-node Distance (\(DT\)), etc.) play a vital role in defining \(PDR\) and \(EC\). Therefore, it is of primary importance to understand the dynamics of these metrics in relation to a detailed stack parameter configurations. Findings from this study can help extend the research effort encompassing a wider range of parameters including even protocols at different layers. In this paper, an adaptive system to predict \(PDR\) and \(EC\) based on different stack parameter configurations is proposed. The system used performance data and applied deep learning to make data-driven predictions for both \(PDR\) and \(EC\). In particular, the suggested design achieves an adequate prediction accuracy without having to solve any intractable optimization problem. To the best of our knowledge, this is the first work that applies deep learning to predict \(PDR\) and \(EC\) based on real data from the IEEE 802.15.4 network. The main contributions of this work can be summarized as follows:  
* Analysis of the relationship between \(PDR\) and \(EC\) in relation to more than 48,000 stacks of parameter configurations. * Development and evaluation of a deep learning model for predicting \(PDR\) and \(EC\).