Understanding the Mobility Model of Wireless Body Sensor Networks

Wireless network of wearable biomedical sensors by human body shows great potential to enhance the biometrics performance significantly. Meanwhile, it poses prominent characteristics and challenges to physicians and engineers for its particular medical application as compared to other application of wireless sensor networks (WSN). Mobility pattern plays an important role in designing the wireless body sensor networks (WBSN) and will also affect the accuracy of modeling WBSN in health care application. Much of the mobility scenarios generated in current work of wireless body sensor networks has used fairly simple models to generate the mobile topological graph, which bear little resemblance to the actual mobility patterns. This paper is the first attempt to investigate the mobility model in WBSN based on the existing mobility models in wireless data networks and ad hoc networks. We first briefly review the existing mobility models in related research areas such as wireless ad hoc network and cellular networks. Further on, we propose a dedicated and more realistic mobility model named BAMM (Body Area Mobility Model) for wireless body sensor networks by concentrating on the unique characteristics of WBSN and finally study the effects of mobility on the performance of WBSN by simulation experiments.

Significant research has been conducted for maintaining a high standard of communication and good coverage in wireless sensor networks (WSNs), but extra power consumption and mobility issues are not yet fully resolved. This paper introduces a memory-less location mobility-aware Lattice Mobility Model (LMM) for WSNs. LMM is capable of concurrently determining the node and sink mobility. LMM has a lower pause time, fewer control packets, and less node dependency (e.g., the energy consumed by each node in each cycle that is independent of the data traffic). LMM accurately determines a node’s moving location, the distance from its previous location to its current location, and the distance from its existing location to its destination. Many existing mobility models only provide a model how nodes move (e.g., to mimic pedestrian behavior), but do not actually control the next position based on properties of the underlying network topology. To determine the strength of LMM, OMNet++ was used to generate the realistic scenario to safeguard the affected area. The operation in affected area comprises searching for, detecting, and saving survivors. Currently, this process involves a time-consuming, manual search of the disaster area. This contribution aims to identify an energy efficient mobility model for a walking pattern in this particular scenario. LMM outperforms other mobility models, including the geographic-based circular mobility model (CMM), the random waypoint mobility model (RWMM) and the wind mobility model (WMM), The simulation results also demonstrate that the LMM requires the least time to change the location, has a lower drop rate, and has more residual energy savings than do the WMM, RWMM, and CMM.

<p>A wireless network composed of wearable sensing along with computing systems connected via a wireless communication channel is termed Wireless Body Sensor Network (WBSN). It enables continuous monitoring through sensors for medical and nonmedical applications. WBSN faces several security problems such as loss of information, access control, and authentication. As WBSN collects vital information and operates in an unfriendly environment, severe security mechanisms are needed in order to prevent the network from anonymous interactions. The different security threats are evaluated with the support of the data transmitted via the sensor networks amongst smart wearable devices. The whole network lifetime together with the Data Transmission (DT) quality is mitigated whilst performing DT utilizing sensor networks, which consume more energy. Hence, in this paper, an energy-efficient secure data transmission mechanism is proposed in WBSN using a novel authentication id-based group signature model and SECC technique. At first, the Group Manager (GM) is selected from the sensors in the remote body sensor system using Normalized Opposition Based Learning BAT Optimization Algorithm (NOBL-BOA). Afterward, clustering with Information Entropy induced K-Means Algorithm (IEKMA) takes place to improve energy efficiency. Next, to provide security to the WBSN, message authentication is carried out based on novel authentication ID-based group signature protocol. Finally, Secret key induced Elliptic Curve Cryptography (SECC) is used to encrypt the message for secure transmission. The simulation results reveal that in comparison with existing works, the proposed work achieves improved security and energy efficiency.</p> <p> </p>

In today’s healthcare environment, the Internet of Things technology provides suitability among physicians and patients, as it is valuable in numerous medicinal fields. Wireless body sensor network technologies are essential technologies in the growth of Internet of Things healthcare paradigm, where every patient is monitored utilising small-powered and lightweight sensor nodes. A dual-hop, inter–wireless body sensor network cooperation and an incremental inter–wireless body sensor network cooperation with energy harvesting in the Internet of Things health-based paradigm have been investigated and designed in this work. The three protocols have been named and abbreviated as follows: energy harvesting–based dual-hop cooperation, energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based incremental inter–wireless body sensor network cooperation. Outage probabilities for the three designed protocols were investigated and inspected, and mathematical expressions of the outage probabilities were derived. The simulation and numerical results showed that the energy harvesting–based incremental inter–wireless body sensor network cooperation provided superior performance over the energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based dual-hop cooperation by 1.38 times and 5.72 times, respectively; while energy harvesting–based inter–wireless body sensor network cooperation achieved better performance over energy harvesting–based dual-hop cooperation by 1.87 times.

In healthcare and medical applications, the energy consumption of biosensor nodes affects the collection of biomedical data packets, which are sensed and measured from the human body and then transmitted toward the sink node. Nodes that are near to the sink node consume more energy as all biomedical packets are aggregated through these nodes when communicated to the sink node. Each biosensor node in a wireless body sensor network (WBSN) such as electrocardiogram (ECG), should provide accurate biomedical data due to the paramount importance of patient information. We propose a technique to minimize energy consumed by biosensor nodes in the bottleneck zone for WBSNs, which applies the coordinated duty cycle algorithm (CDCA) to all nodes in the bottleneck zone. Superframe order selection in CDCA is based on real traffic and the priority of the nodes in the WBSN. Furthermore, we use a special case of network coding, called random linear network coding (RLNC), to encode the biomedical packets to improve reliability through calculating the probability of successful reception at the sink node. It can be concluded that CDCA outperforms other algorithms in terms of energy saving as it achieves energy savings for most biosensor nodes in WBSNs. RLNC employs relay nodes to achieve the required level of reliability in WBSNs and to guarantee that the biomedical data is delivered correctly to the sink node.

Wireless Body Sensor Networks (BSNs) represent an interesting challenge to effectively control dangerous pathology and to monitor the well being status of elderly and fragile people. However, such networks don't take into account the context that highly influences the health status. This counteracts with the notion of situated healthcare more and more claimed from both sociological and medical point of view to provide effective healthcare interventions. Following this point of view, the paper illustrates an effective architecture to control health status taking into account the user context. In particular, programmable wireless body sensor networks recently available on the market fused with environmental portable sensors are evaluated to provide a viable step towards a smart situated healthcare, i.e., monitoring and control from remote sites and in real time people in their context by using IoT devices provided with artificial intelligence, i.e., AIoT devices, and suitable web services that allow such smart bodily networks to interact with networks of other people, doctors or hospitals. Current limits and potentialities are pointed out by a case study.

This study concerns security issues of the emerging Wireless Body Sensor Network (WBSN) formed by biomedical sensors worn on or implanted in the human body for mobile healthcare applications. A novel authenticated symmetric-key establishment scheme is proposed for WBSN, which fully exploits the physiological features obtained by network entities via the body channel available in WBSN but not other wireless networks. The self-defined Intrinsic Shared Secret (ISS) is used to replace the pre-deployment of secrets among network entities, which thus eliminates centralized services or authorities essential in existing protocols, and resolves the key transport problem in the pure symmetric-key cryptosystem for WBSN as well. The security properties of the proposed scheme are demonstrated in terms of its attack complexity and the types of attacks it can resist. Besides, the scheme can be implemented under a light-weight way in WBSN systems. Due to the importance of the ISS concept, the analysis on using false acceptance/false rejection method to evaluate the performance of ISS for its usage in the scheme is also demonstrated.

Due to recent developments in pervasive and ubiquitous computing, health care systems can provide well-informed and high-quality patient care services. A care provider can receive a simplified, adaptive, and latest view of the medical data. Although the medical file is unique, the distributed record could be accessible from any place at any time by care providers. Pervasive health care information systems allow overcoming the problem of heterogeneity of technologies and services in this domain and the papers selected for this special issue represent a good panel for addressing this challenge. Of course, the selected topics and papers are not an exhaustive representation of the area of Pervasive Health Care Services and Technologies. Nonetheless, they represent the rich and many-faceted knowledge, that we have the pleasure of sharing with the readers. We would like to thank the authors for their excellent contributions and patience in assisting us. Finally, the fundamental work of all reviewers on these papers is also very warmly acknowledged.

An energy-saving routing architecture with a uniform clustering algorithm for wireless body sensor networks

The Wireless Sensors Network (WSN) is an emergent technology resulting from progress of various fields. Many applications of networks WSN are born. One of the applications which have an operational effectiveness relates to the field of health and allows a medical remote support. Miniature wireless sensors, strategically placed on the human body, create a Wireless Body Sensor Network (WBSN) which allows supervising various essential biological signals (rate of heartbeat, pressure, etc). The sensitivity of medical information requires mechanisms of safety. This performance constitutes a challenge for WBSN because of their limitation in resources energy and data-processing. In this paper we propose a new approach to symmetric cryptographic key establishment, based on biometrics physiology. This approach takes into account WBSN constraints and its topology.

Personal health status is becoming a matter of concern while pressure of work growing gradually in everyday life, in the meantime, the ageing population needs more attention. Thanks to many challenging problems of wireless sensor networks (WSNs) solved, exploring work of human body monitoring based on WSNs achieved great progress, we called it wireless body sensor networks(BSNs). In this paper, we propose a novel health-monitoring system based on BSNs; we named it SENS-U. It consists of wireless multi-parameters monitoring terminals and two medical care centers; with human body, they form a remote human in loop 4 vital signs and ECG monitoring system.

Wireless body sensor network (WBSN) allows remote monitoring for different types of applications in security, healthcare and medical domains. Medical applications involve monitoring a large number of patients in real-time environments. The WBSNs in such environments have to be efficient and reliable in terms of data transfer rate, accuracy, latency, and power consumption. This work focuses on studying the slotted access protocol variables in the Contention Access Period (CAP) with the acknowledged uplink traffic (nodes-to-coordinator) under the WBSN channel. This paper proposes a Markov Chain model in WBSN (MC-WBSN) for improving the efficiency and reliability of patients’ remote monitoring systems. The application of the model includes propagating human arm sensory data and analyzing the latency, power consumption, throughput, and higher path loss channel of the WBSN. The results show that the hidden nodes have a great impact on WBSNs performance and throughput. This issue is highly associated with the capacity of the transmitted power.

SENET: A novel architecture for IoT-based body sensor networks

A four-levels hierarchical wireless body sensor network (WBSN) system is designed for biometrics and healthcare applications. It also separates pathways for communication and control. In order to improve performance, a communication cycle is constructed for synchronizing the WBSN system with the pipeline. A low-power adaptive process is a necessity for long-time healthcare monitoring. It includes a data encoder and an adaptive power conserving algorithm within each sensor node along with an accurate control switch system for adaptive power control. The thermal sensor node consists of a micro control unit (MCU), a thermal bipolar junction transistor sensor, an analog-to-digital converter (ADC), a calibrator, a data encoder, a 2.4-GHz radio frequency transceiver, and an antenna. When detecting ten body temperature or 240 electrocardiogram (ECG) signals per second, the power consumption is either 106.3 ?W or 220.4 ?W. By switching circuits, multi sharing wireless protocol, and reducing transmission data by data encoder, it achieves a reduction of 99.573% or 99.164% in power consumption compared to those without using adaptive and encoding modules. Compared with published research reports and industrial works, the proposed method is 69.6% or 98% lower than the power consumption in thermal sensor nodes which consist only of a sensor and ADC (without MCU, 2.4-GHz transceiver, modulator, demodulator, and data encoder) or wireless ECG sensor nodes which selected Bluetooth, 2.4-GHz transceiver, and Zigbee as wireless protocols.

Securing an individual’s privacy in a proficient manner is an important task for critical infrastructures like Wireless Body Sensor Networks (WBSN). WBSNs provide real-time monitoring of patients. By protecting the WBSN, lives of patients or soldiers on battleground is secured. This paper gives a comparative study of electrocardiogram (ECG)-based key agreement schemes in WBSNs. The usage of ECG aims to bring plug-and-play capability in WBSNs, i.e., the deployment of sensors on the human body will enable wireless and secure communication for exchanging information. The schemes use Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) for feature extraction. The process is made secure by adding watermark. The watermarking technique does the locking and unlocking technique at the sender and receiver’s side improving the security while not affecting the plug-and-play paradigm. The simulation and analysis is done by using ECG signal taken from MIT PhysioBank database.