## 2 Research Goals, Challenges, and Contributions  
One of the key goals of introducing BN-MAC is to support the multiple application domains of WSNs. We focus on several characteristics and factors that affect the performance of existing hybrid MAC protocols and BN-MAC. Factors that affect energy consumption and scalability include idle listening, overhearing, congestion, and mobility. The key challenge is determining how to integrate all of the proposed models to work as a single unit. Mobility is also difficult to address due to limitations and constraints at the MAC layer for maintaining scalability.  
BN-MAC is proposed as a hybrid protocol involving a contention part and a scheduled part. The contention part is semi-synchronized [24] with a low duty cycle that helps to achieve faster access to the medium and manages the synchronization among nodes. The semi-synchronous feature is preferable for several application areas to reduce latency and energy consumption and maximize throughput. Second, the schedule part works with a dual message mechanism. Whenever the sensor node requires the schedule of its neighboring nodes, the sensor node uses the Anycast message mechanism because the sensor node can send a control message to only the nearest node in the group of potential receivers or may choose several nodes, depending on the situation. When the data are sent, the node uses the unicast message mechanism to forward the same data to all possible destinations. In addition, the neighbor discovery process consists of a short preamble message that consumes less energy. The dual mechanism avoids network congestion and increases the lifetime of WSNs. Third, BN-MAC discovers the presence and level of mobility of the sensor nodes within its neighbors using the received signal strength indicator (RSSI) and link quality indicator (LQI), both of which are obtained from the neighbor nodes at the time of synchronization.  
BN-MAC performs localized reuse time slot allocation without changing the slots of the nodes that already exist if the node intends to perform further communication. This feature reduces latency and control messages and increases throughput. Fourth, new energy level information (ELI) algorithm is used for the dynamic selection of the coordinator, known as the boarder node (BN). BN dynamically works as a coordinator (head or leader) on a specific position. BN stays at the position as long as it uses its sources \"energy\" for performing some specific task for a definite period inside the network region then it vacates the position when the energy is reduced for the next node to become BN. In BN-MAC, the node with the highest energy level in its region will have a large probability of becoming the BN. BN-MAC approach can handle diverse situations more effectively. Additionally, three models are included in BN-MAC: AAS, LDSNS, and IDM. AAS is a simple yet efficient model for solving an idle listening problem. With the AAS model, sensor nodes are forced to go into the sleep state after performing the events that can prolong the lifetime of the network. This model significantly outperforms the previous sleep-wake up approaches designed for controlling the idle listening time. LDSNS is used to determine the shortest distance of the sensor node to one-hop neighbor nodes. The sensor node does not have the ability to send data over long distances; thus, LDSNS finds a close one-hop neighbor node to reduce energy consumption and improve the network lifetime.  
The IDM model is used to sense the nature of the environment. This ability is critical because the sensor node is capable of obtaining energy from the Sun, which allows the sensor node to preserve its battery energy when automating the passive mode in an outdoor environment. The mode of the sensor node is typically set manually at the time of installation according to the nature of the environment; however, the IDM model automates the sensor node to reduce the energy consumption and expand the network lifetime.