Security in IoMT Communications: A Survey.

The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

Towards the Definition of an Intelligent Triage and Continuous Monitoring System for Hospital Emergency Departments and Clinics.

Recent statistics have demonstrated that Emergency Departments (EDs) in Greece lack in organization and service. In most cases, patient prioritization is not automatically implemented. The main objective of this paper is to present IntelTriage, a smart triage system, that dynamically assigns priorities to patients in an ED and monitors their vital signs and location during their stay in the clinic through wearable biosensors. Initital scenarios and functional requirements are presented as preliminary results.

Collision-Free Advertisement Scheduling for IEEE 802.15.4-TSCH Networks.

IEEE802.15.4-time slotted channel hopping (TSCH) is a medium access control (MAC) protocol designed to support wireless device networking, offering high reliability and low power consumption, two features that are desirable in the industrial internet of things (IIoT). The formation of an IEEE802.15.4-TSCH network relies on the periodic transmissions of network advertising frames called enhanced beacons (EB). The scheduling of EB transmissions plays a crucial role both in the joining time and in the power consumption of the nodes. The existence of collisions between EB is an important factor that negatively affects the performance. In the worst case, all the neighboring EB transmissions of a node may collide, a phenomenon which we call a full collision. Most of the EB scheduling methods that have been proposed in the literature are fully or partially based on randomness in order to create the EB transmission schedule. In this paper, we initially show that the randomness can lead to a considerable probability of collisions, and, especially, of full collisions. Subsequently, we propose a novel autonomous EB scheduling method that eliminates collisions using a simple technique that does not increase the power consumption. To the best of our knowledge, our proposed method is the first non-centralized EB scheduling method that fully eliminates collisions, and this is guaranteed even if there are mobile nodes. To evaluate our method, we compare our proposal with recent and state-of-the-art non-centralized network-advertisement scheduling methods. Our evaluation does not consider only fixed topology networks, but also networks with mobile nodes, a scenario which has not been examined before. The results of our simulations demonstrate the superiority of our method in terms of joining time and energy consumption.

A tree-based decision rule for identifying profile groups of cases without predefined classes: application in diffuse large B-cell lymphomas.

In this paper, we examined the utility of a forward growing classification tree as a supplement to cluster analysis for deriving a decision rule for the identification of profile groups when the cases do not belong to predefined classes. The technique was applied for the identification of low and high proliferation profile groups of diffuse large B-cell lymphomas according to the immunohistochemical expression levels of proliferation proteins. In a forward growing classification tree method, the size of the tree is controlled by the improvement (threshold value) in the apparent misclassification rate after each split. The classes used in the tree were defined using k-means clustering. The decision rule consisted of the splitting points of the split variables used. The methodology was applied to the histology data from 79 cases of diffuse large B-cell lymphomas. Ten classes of individual cases were derived from k-means clustering. Then, a classification tree with a threshold of 2% was used to derive the decision rule. Branches at the left side of the tree consisted of individuals with a low proliferation profile and branches at the right side of the tree consisted of cases with a high proliferation profile. The classification tree, as a supplement method, not only identified but also provided decision rules for identifying profile groups. Finally, it also allowed for exploration of the data structure.