Recently, wearable devices have got increasing popularity in wide applications in medical and disaster rescue efforts to ensure the health and safety of users, which facilitates the development of the Internet of Medical Things (IoMT). Due to the posture alteration and mobility of users, the topology of the IoMT changes frequently, which increases the difficulty for resource allocation and routing strategy. In this paper, we respectively probe into the health monitoring architectures of the IoMT for both individual and group, allowing the monitored users to move at will. Furtherly, combined with the diversity of disaster rescuers, we build an IoMT-based disaster rescuer health monitoring system with searchers, doctors and porters. For each application, we point out the enabling technologies and demonstrate the existing researches. It is worth noting that the complexity of environment and high mobility of rescuers increase the probability of route breakage. Thus, this paper creatively addresses effective routing repair solutions for route breakage in IoMT-based disaster rescuer health monitoring system by exploiting the mobility of rescuers. Finally, we forecast three most likely directions in the field of IoMTs.

With the outbreak of COVID-19, people’s demand for using the Internet of Medical Things (IoMT) for physical health monitoring has increased dramatically. The considerable amount of data requires stable, reliable, and real-time transmission, which has become an urgent problem to be solved. This paper constructs a health monitoring-enabled IoMT network which is composed of several users carrying wearable devices and a coordinator. One of the important problems for the proposed network is the unstable and inefficient transmission of data packets caused by node congestion and link breakage in the routing process. Based on these, we propose a -learning-based dynamic routing selection (QDRS) algorithm. First, a mathematical model of path optimization and a solution named Global Routing selection with high Credibility and Stability (GRCS) is proposed to select the optimal path globally. However, during the data transmission through the optimal path, the node and link status may change, causing packet loss or retransmission. This is a problem not considered by standard routing algorithms. Therefore, this paper proposes a local link dynamic adjustment scheme based on GRCS, using the -learning algorithm to select the optimal next-hop node for each intermediate forwarding node. If the selected node is not the same as the original path, the chosen node replaces the downstream node in the original path and so corrects the optimal path in time. This paper considers the congestion state, remaining energy, and mobility of the node when selecting the path and considers the network state changes during packet transmission, which is the most significant innovation of this paper. The simulation results show that compared with other similar algorithms, the proposed algorithm can significantly improve the packet forwarding rate without seriously affecting the network energy consumption and delay.

Along with the development of realtime applications, the freshness of information becomes significant because the overdue information is worthless and useless and even harmful to the right judgement of system. Therefore, The Age of Information (AoI) used for marking the freshness of information is proposed. In Internet of Medical Things (IoMT), which is derived from the requirement of Internet of Thins (IoT) in medicine, high freshness of medical information should be guaranteed. In this paper, we introduce the AoI of medical information when allocating channels for users in IoMT. Due to the advantages of Deep Q‐learning Network (DQN) applied in resource management in wireless network, we propose a novel DQN‐based Channel Allocation (DQCA) algorithm to provide the strategy for channel allocation under the optimization of the system cost considering the AoI and energy consumption of coordinator nodes. Unlike the traditional centralized channel allocation methods, the DQCA algorithm is distributed as each user performs the DQN process separately. The simulation results show that our proposed DQCA algorithm is superior to Greedy algorithm and Q‐learning algorithm in terms of the average AoI, average energy consumption and system cost.

In recent years, the Wearable Wireless Sensor Network (WWSN) has become one of the most popular networks used in disaster and emergency scenarios. We propose a routing algorithm named Energy Consumption Balanced Topology Variable (ECB-TV) on the body of users in WWSN. In the ECB-TV algorithm, network topology can be variable according to the change of network state. A multi-hop topology is used in normal situation and the network will switch to single-hop topology at once when an abnormal event happens. We also design a novel multi-hop routing algorithm for multi-hop topology where we select the node with the highest energy balance factor as next hop node. Energy balance factor is innovatively designed by simultaneously taking into account both energy consumption of transmitting and receiving nodes, which can balance the energy consumption of sensor nodes well. The simulation results show that the proposed ECB-TV algorithm has better performance in terms of lifetime in normal situations and delay in abnormal situations.