Device-enabled neighborhood-slot allocation for the edge-oriented Internet of Things

Abstract

Internet of Things (IoT) has become an interesting research domain as numerous devices, preferably equipped with sensors, communication, and actuator modules, are deployed to capture real-time data in the different application areas, such as smart healthcare and industries. These devices have the built-in capacity to directly interact with the physical phenomenon and report any unusual situation within their respective coverage areas, i.e., monitoring a critical patient in the smart hospital but direct communication with a common destination module is not guaranteed and could possibly be very challenging if two or more devices, preferably those in closed proximity, are interested to transmit simultaneously. Therefore, in this manuscript, we are going to present a hybrid slot allocation approach, which is specifically designed for those devices resided in neighborhood and are eager to communication concurrently with a common destination device, i.e., server. In the beginning, the k-mean clustering algorithm is used to group these devices into clusters where server is forced to collect data from devices deployed in the respective coverage areas. Thus, every server generates dedicated slots for active devices and an additional slot for server(s). Similarly, the proposed neighborhood-enabled time division multiple access (TDMA) has the flexibility of assigning multiple slots to a requesting device if available, which is needed in scenarios, such as detection of pest in the field. Additionally, a member device is allowed to migrate (if needed and possible) from one server's coverage region to another. Simulation results confirmed that the proposed approach is better than the existing algorithms (opportunistic TDMA, hybrid TDMA, and non-orthogonal multiple access), particularly in terms of bandwidth, end-to-end delay, and empty slot utilization. The proposed scheme has improved bandwidth and empty slot utilization, which are approximately 15% and 12%, respectively, whereas it has achieved approximately 94.89% utilization of the available slots which was previously 93.4%.