The Internet of Medical Things are susceptible to Man-in-the-Middle (MitM) attack, which can identify healthcare emergency of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this article, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the raise of alarms by the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from received signal strength indication. Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%.

The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health of the monitored patient. However, the transferred data are vulnerable to various sources of interference, sensor faults, measurement faults, injection and alteration by malicious attackers, etc. In this article, we propose a change point detection model based on a Markov chain for centralized anomaly detection in WBANs. The model is derived from the Root Mean Square Error (RMSE) between the forecasted and measured values for whole attributes. The RMSE transforms the monitored attributes into a univariate times series which is divided into overlapping sliding window. The joint probability of the sequence of RMSE values in each sliding window is calculated to decide whether a change has occurred or not. When an effective change is detected over k consecutive windows, the number of deviated attributes is used to distinguish faulty measurements from a health emergency. We apply our proposed approach on real physiological data from the Physionet database and compare it with existing approaches. Our experimental results prove the effectiveness of our proposed approach, as it achieves high detection accuracy with a low false alarm rate (5.2%).

Thanks to their arising abilities to influence the human lifestyle, along with reducing the healthcare systems’ cost, wireless body area networks (WBANs) still form a strongly growing research field. Recent advances focus on the opportunities of coexistence and communication between a group of WBANs, that will forward the sensing data, using persons as network relays, until reaching a remote analysis server or cloud servers via the Internet, forming thus a body-to-body network (BBN). Such new-style networks support a range of innovative and promising applications, including ubiquitous healthcare (U-health), interactive games, and military, to cite a few. In this paper, we first present the evolution of the single WBAN concept to the cooperative network of multiple WBANs, giving rise to the BBN concept. A synopsis of the WBAN and BBN respective standards and applications is given, and the emerging BBN challenges are highlighted. Then, we present and discuss the existing WBAN proposals, especially the candidate WBAN protocols that could be adapted and used in BBNs, focusing on four intrinsically related axes of great importance for BBN design: energy efficiency, mobility prediction, quality of service (QoS) and security. Further BBN open issues are also investigated, namely, the wireless propagation between humans carrying wearable devices, the interference, storage and privacy issues as well as the heterogeneity of BBN devices and traffic.

Collisions in underwater acoustic networks can not be tolerated due to the fundamental differences between underwater acoustic propagation and terrestrial radio propagation. Thus conceiving medium access protocols that avoid collision to the most possible extent is of paramount importance. In this paper, a multi-channel MAC protocol, MC-UWMAC, especially designed for underwater acoustic sensor networks, is proposed and evaluated. MC-UWMAC is an energy efficient MAC protocol that aims at achieving a collision free communication. MC-UWMAC operates on a single slotted control channel to avoid the missing receiver problem and multiple data channels to improve the network throughput. To guarantee to the most possible extent a collision free communication, MC-UWMAC uses two key newly designed procedures: i) a grid based slot assignment procedure on the common slotted control channel that approaches the 2-hop conflict free slot assignment and ii) a quorum based data channel allocation procedure. More precisely, according to MC-UWMAC, a sender uses its own dedicated slot on the common control channel for handshaking with an intended neighbor receiver. However, data transmission takes place in a unique data channel especially reserved for this pair of neighbor nodes. In fact, MC-UWMAC reserves for each pair of neighbor nodes a unique data channel that aims at being 2-hop conflict free. As such, the probability of collision is highly reduced and even completely mitigated in some scenarios. In addition, by using multiple channels, MC-UWMAC allows multiple data communications along with handshaking on the common control channel to take place at the same time and hence the network throughput as well as energy efficiency are improved. Simulation results show that MC-UWMAC can greatly improve the network performance especially in terms of energy consumption, throughput and end-to-end delay.

In this paper, we propose a distributed multi-hop interference avoidance algorithm, namely, IAA to avoid co-channel interference inside a wireless body area network (WBAN). Our proposal adopts carrier sense multiple access with collision avoidance (CSMA/CA) between sources and relays and a flexible time division multiple access (FTDMA) between relays and coordinator. The proposed scheme enables low interfering nodes to transmit their messages using base channel. Depending on suitable situations, high interfering nodes double their contention windows (CW) and probably use switched orthogonal channel. Simulation results show that proposed scheme has far better minimum SINR (12dB improvement) and longer energy lifetime than other schemes (power control and opportunistic relaying). Additionally, we validate our proposal in a theoretical analysis and also propose a probabilistic approach to prove the outage probability can be effectively reduced to the minimal.

This paper presents a lightweight approach for early detection of nocturnal epileptic seizures through analysis of inertial data and muscle contractions. We use an overlapping sliding window to derive the variance of data acquired by the MPU9250 motion tracking device and single channel surface ElectroMyoGram. Exponentially Weighted Moving Average is used to forecast the current value of data variance. When the Kullback-Leibler divergence between forecasted and measured variances deviates from past values, a signal is transmitted to the base station to set the current counter in an alarm window. If the filling ratio of the alarm window is greater than a predefined threshold, an alarm is triggered by the base station. The proposed approach is intended to improve the performance of existing detection systems based on data analysis from Accelerometer. The MPU9250 is 9-axis motion tracking used to detect motor seizures, and it contains a 3-axis Accelerometer, Gyroscope and Magnetometer. The ElectroMyoGram is used to detect silent seizures without jerky movements. Our experimental results on a real dataset from an epileptic patient show that our approach is able to increase detection accuracy and reduce the low false alarm rate. Comparison with a Probability Density Function further demonstrates its detection efficiency of our approach.