## 4 Energy Conserving Solutions in Wireless Networking-Based IoT
### Energy Conserving Solutions for WWAN-Based IoT
#### 4.1.1 Solutions for Overload Control and Radio Resource Allocation  
In this subsection, we will discuss the solutions for the overload or congestion issue. [PERSON] _et al._[30] discussed push and pull based methods to overcome the overload problem in the radio access network. They also proposed methods to alleviate the congestion problem in the core network, including the use of extended access barring, extended wait timer, and delay tolerant indicator. [PERSON] _et al._[31] presented random access techniques to avoid the congestion and overloading problem that massive IoT/MTC devices face in the 3 GPP network environment. They developed more energy efficient mechanisms for the existing baseline techniques, e.g., access class barring, back-off adjustment schemes, dynamic allocation of Random Access Channel (RACH) resources, prioritized random access, _etc._ [PERSON] _et al._[32] provided a solution to signaling message storms like RACH messages in the presence of both Human Type Communications (HTC) and MTC. The solution is to split HTC and MTC devices into two groups, and an evolved NodeB (eNB) makes prioritization decisions to grant access according to a type of a device. For example, if a collision occurs because of MTC devices, then a priority will be given to HTC devices. [PERSON] _et al._[37] proposed that available radio resources in terms of time and frequency, called Resource Blocks (RBs), should be divided into two groups, _i.e._, a UE specific set dedicated to UEs and an M2M specific set dedicated to M2M devices. Separate Quality of Service Class Indicators (QCIs) are defined for both types of devices so that user quality of experience should not be affected in the presence of different types of devices.  
The proper resource allocation of Modulation and Coding Scheme (MCS) and transmit power is a key factor to energy efficient communications. [PERSON] _et al._[33] proposed an energy efficient solution for optimal MCS determination and LTE uplink power control, and implemented it to transfer MTC data over an LTE network. [PERSON] _et al._[36] noted that devices in an LTE network send signaling messages for downlink or uplink channel condition assessment to their eNB, which has to make a scheduling decision in every 1 ms. To resolve the problem, they suggested that when an eNB notices any performance degradation by employing statistical and machine learning techniques, it can instruct devices to stop reporting downlink or uplink related channel information to save their power. [PERSON] _et al._[34] proposed the usage of LTE-Advanced relays and focused on uplink resource and transmit power allocation to save the energy of MTC devices. In the presence of LTE-Advanced relays that can enhance radio coverage and data rates, and increase the throughput of cell edge devices, they proposed optimal MCS and transmit power allocation for energy efficient communication through the relays.  
In the discussed solutions, we notice that decisions for overload control and resource allocation are only determined by a single eNB. More optimal decisions can be possible if a group of eNBs share their information about camping-on MTC devices to make global information.