## 4 Discussion of the Results  
Maintaining the real-time QoS parameters in the presence of mobility has been known to be one of the key challenges of WSNs and will continue to be a massive challenge for the deployment of WSNs because progression in battery technology has been slower than the progress in data communication rates and processing power. This challenge has appealed to several researchers to introduce real-time QoS protocols to balance the bandwidth consumption, packet loss, delivery rate, packet deadline, end-to-end delay and network lifetime.  
To address these challenges, several routing protocols have been introduced at the network level. Real-time QoS provisioning protocols are of paramount significance because they provide real-time support, lower energy consumption and better mobility support than other categories of routing protocols. In this section, we discuss and compare the advantages and disadvantages of simulated real-time QoS routing protocols.  
QoSR determines the optimal path from the source node to base station consuming the minimum energy. However, QoS does not support scalability and also does not explain how to achieve reliable data reception. QoSAM solves the QoS and energy efficiency problem for forwarding the packet using dynamic programming. Furthermore, adaptive power allocation is used for obtaining the near-optimal solution. The QoSAM aims to determine the optimized route. However, energy and reliability are not fully handled.  
MIMO is proposed for large-scaled WSNs. Multi-hop Virtual MIMO was used to reduce the channel access time between each cluster head and cluster node. It also employs an equal Signal-to-Noise Ratio (SNR) policy to distribute the transmitted energy due to its spectral performance efficiency and simplicity. As a result, MIMO reduces the energy consumption, end-to-end delay, packet loss, bandwidth consumption and improves the network lifetime. However, it behaves below standard for meeting deadlines.  
The PRTR reduces the congestion and handles non-delay-sensitive flows to bypass the hotspots that distribute these unnecessary data packets for multipath transmission. The PRTR also employs calculus theory to estimate the end-to-end delay bounds for a single flow. PRTR provides scalability and satisfies the real-time routing requirements and avoid the possible congestion that causes packet loss. However, PRTR consumes additional energy while improving the congestion and packet loss.  
QEMPAR increases the network lifetime. The approach assumes that all of the nodes are randomly distributed in the intended environment. Each node was assigned a unique ID. In this protocol the energy consumption model is used to determine the suitable link, path discovery, and paths assortment. The protocol prolongs the network lifetime, but throughput is affected due to the increase of the latency. In addition, mobility is considered in this protocol and the use of GPS makes it cost ineffective.  
PT shows better performance than other QoS real-time routing protocols. The reason for this better performance is the use of a termite-based concept to establish the routes. The protocol particularly focuses on finding the shortest path by maintaining the QoS provisions. The PT introduces two new features: pheromone sensitivity and packet generation rate. The pheromone sensitivity helps determining the link capacity prior to sending the packets over the link to avoid congestion. The packet generation rate helps update the node regarding the number of generated packets. Both thesecharacteristics augment the QoS, avoiding congestion and extending the network lifetime. However, PT is not suitable for small-sized networks.  
It is confirmed based on the simulation results, that all existing real-time QoS routing protocols have some kind of limitations. There is a need to introduce robust routing protocols to meet the demands of several applications of MWSNs.