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The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC. [ABSTRACT FROM AUTHOR]

The rapid development of IoMT facilitates pervasive healthcare networks. However, numerous MUs lead to deficient wireless channel and computation resources. Edge computing based healthcare system is appreciated as a satisfactory paradigm to solve this obstacle. Based on the physical bound of WBANs, the IoMT is separated into two sub-networks, that is, intra- WBANs and beyond-WBANs. Considering the characteristics of healthcare systems, the cost of MUs relies on medical urgency, AoI and energy dissipation. In intra-WBANs, the wireless channel resource allocation problem is modeled by a cooperative game. The Nash bargaining solution is utilized to obtain the unique Pareto optimal point. In beyond-WBANs, MUs are considered to be rational and potentially selfish. Thus, a non-cooperative game is formulated to minimize the system-wide cost. To demonstrate the effectiveness of our proposed solution, performance evaluations are conducted with respect to the system-wide cost and the number of MUs benefiting from edge computing. Finally, several research challenges and open issues are discussed for further work. [ABSTRACT FROM AUTHOR]

As an application of “smart transport” for Internet of Things, Internet of Vehicle (IoV) has emerged as a new research field based on vehicular ad hoc networks (VANETs). With the development of smart vehicles and the integration of sensors, applications of traffic management and road safety in large-scale IoV systems have drawn great attentions. By sensing events occurred on roads, vehicles can broadcast messages to inform others about traffic jams or accidents. However, the store-carry-and-forward transmission pattern may cause a large transmission delay, making the implementation of large-scale traffic management difficult. In this paper, we put forward a feasible solution to minimize the response time for traffic management service, by enabling real-time content dissemination based on heterogeneous network access in IoV systems. We first design a crowdsensing-based system model for large-scale IoV systems. Then, a cluster-based optimization framework is investigated to provide timely responses for traffic management. Specifically, we estimate the message transmission delay by stochastic theory, which can provide a guideline for the next-hop relay selection in our delay-sensitive routing scheme. Furthermore, network performances are evaluated based on two city-road maps, and performance metrics, containing average delivery delay, average delivery ratio, average communication cost, and access ratio, demonstrate the superiority of our system. Finally, we conclude our work and discuss the further work. [ABSTRACT FROM AUTHOR]