## 4 Energy Conserving Solutions in Wireless Networking-Based IoT
### Energy Conserving Solutions for WPAN-Based IoT
#### Energy Conserving Solutions for Bluetooth Low Energy (BLE)  
Bluetooth Low Energy is a widely used WPAN technology in various IoT scenarios. [PERSON] _et al._[92] and [PERSON] _et al._[82] presented a BLE implementation and performance evaluation in comparison to ZigBee/802.15.4. Energy is spent during a master-slave discovery process when a master device and slave devices are not in a connected state and the master device scans for available slaves to connect and at the same time slave devices advertise their availability to the master. Energy spent after connection establishment is also considered in the analysis where energy consumption related parameters such as data transmission, data reception, and inter-frame spaces are examined. Using BLE with IPv6 support is considered essential in connecting a myriad of IoT devices to the Internet. Authors in [79; 80; 81] discussed energy-efficient neighbor discovery, header compression, and fragmentation to enable IPv6 on top of Bluetooth, and evaluated their impact on energy consumption. They showed the existence of a tradeoff between latency and energy consumption, which depends on device connection intervals. Particularly, a neighbor discovery mechanism for IPv6 over BLE is the major cause for energy consumption of BLE-based IoT devices. A huge number of multicast messages transmitted may result in severe energy consumption for BLE devices. To overcome this issue, the authors proposed an optimized neighbor discovery mechanism that achieves a reduced number of multicast message transmissions by using node registration and neighbor cache management. During a node registration process, a lifetime for the node is chosen in the neighbor cache. When the node's lifetime expires, it is removed from the neighbor cache. Basically, node registration entries are kept in the neighbor cache to reduce the transmission of neighbor solicitation messages from other nodes. A middleware solution to reduce the size of protocol stacks and network footprints affecting device energy consumption is proposed in [85]. For this purpose, a compressed header is formed into a frame at IoT devices, and it is restored at IoT gateways to obtain the original frame. [PERSON] _et al._[83] proposed a fuzzy logic controller on a BLE master node that calculates new values of the sleeping time for each connected BLE device. The fuzzy logic controller calculates the sleeping time of each device according to the battery level and the ratio of throughput to workload. [PERSON] _et al._[82] evaluated the performance of BLE when applying compression to sensing data before transmission to an IoT server. The electrocardiogram (ECG) data in health care monitoring is transmitted to the IoT server via BLE with enhanced techniques such as signal compression to avoid or reduce the transmission overload problem and in turn help energy conservation for devices. If raw data were transferred to the IoT server, it would cause delay and energy consumption, and thereby making a health monitoring system experience unacceptable quality. Although the previous solutions considered interesting issues such as a neighbor discovery procedure in BLE and data compression, they did not try to optimize BLE duty cycling features (e.g., BLE Sniff substring) during normal operation.