## 4 Content Analysis and Data Mining
### Group 2: Power and Energy  
The second group of the AISH dataset includes [PERSON] [74], [PERSON] [75], [PERSON] [76], [PERSON] et al. [77], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [49], [PERSON] et al. [78]. As shown previously, these papers have utilized IoT and AI algorithms (see Figure 8), where they have also referred to energy efficiency in different contexts and power saving, network energy consumption, and network utility cost. The papers of this group focus on network development and measuring energy consumptions (refer to Figures 6 and 8). Figure 8 shows that the highest reference number is 321 for 'network' with a weighted presentation of 1.12. This word is used in different stems, such as networked, networking, and networks. For example, \"... self-organizing networks (for machine to machine)... \", \"... network nodes... \", \"... autonomous network management... \", \"... the  
Figure 7: The word cloud based on the frequency of exact words, and the frequency of words within Group 1 of the AISH database considering word stems. network energy consumption... \", and \"... Autonomous networks... \". Another high referenced word is 'power', which is used in this group and refers to 'power in cellular networks', 'power and rate allocations', 'power allocation of neighboring nodes', 'computational power', and 'power control'. This helps to point out an important direction in a smart home, such as developing efficient network technology and power networks. Figure 8 provides an insight into the group papers and also shows other highly used words in this group. [PERSON] [75] presented asynchronous weak commitment search (AWCS) that introduces a decentralized framework of AWCS for independent quality of service (QoS) provision for networks that reach end nodes at IoT. AWCS is a method for independent network management and recovery, which is lightweight and strong in scheduling and power control. This method is based on exchanging local messages and evades the need for knowledge of channel gains. For AWCS study, cognitive spatial reuse time division access (STDMA) and code division multiple access (CDMA) communication networks are elaborated as case studies. Cognitive STDMA networks include two stages: (1) cognitive radios (CRs) interact with primary, in a repetitive manner, to ensure they do not force interference on the basis of primary users and secondary users coexisting, and (2) CRs use AWCS among themselves to reach optimal resource allocations. For cognitive CDMA networks, AWCS can be applied for allocating optimal power satisfying QoS needs of nodes, regarding their constraints on each other. AWCS with multiple local variables is used to allocate optimal rate and powers to CR nodes, without central management. Numerical observations are carried out for the evaluation. The results showed that extensions of this approach could be used in self-organizing networks, e.g., home automation and networking and vehicular technology. The application of this AI in wireless and mobile communications can be applied in home automation to provide quality of service. The application of this method is suggested to be studied for self-organizing networks of vehicular communications and capillary networks that realize IoT. [PERSON] [76] used machine learning for adaptive interference suppression in wireless sensor networks (WSN) [76], and they aimed to examine the level of WSN interferences and adjust transmitted power. A machine-learning algorithm was used to predict the needed amount of power for transmitting. The WSN nodes were used to measure parameters, such as temperature, humidity, people present, etc. The evaluation results showed that the system is qualified to save battery power from 42% to 82%, and it is able to get 86% (19% power savings) score in training, with five samples in training, and supports online learning. [PERSON], [PERSON] and [PERSON] [77] introduced an efficient and private traffic blockage (EPIC) framework for smart homes in order to protect against traffic analysis attacks.