- freely available
J. Sens. Actuator Netw. 2019, 8(2), 27; https://doi.org/10.3390/jsan8020027
2. From WBANs to BBNs
2.1. Overview of WBAN
- Medical treatment and diagnosis: there are several cases of WBAN use in diagnostic and treatment of diseases, and many researches are conducted in this field. Use case example: cardio–vascular disease (CVD), diabetes, asthma, Parkinson’s disease, etc.
- Training schedules of professional athletes: through the use of equipments helping athletes in training and monitoring of the progress and advancement of their performances.
- Public safety and prevention of medical accidents: a large number of people dies every year due to medical accidents, then, using a sensor node to maintain a log of previous medical accidents can reduce the number of deaths.
- Safeguarding of uniformed personnel: WBAN can be used by firefighters, policeman or military to keep them monitored in hazardous environments. For example, in the case of firefighters involved in stopping a fire, the WBAN can detect the existence of a dangerous gas and its level in the air, and relay this information to these team members.
- Consumer electronics and entertainment: some WBAN devices can be integrated in electronic equipments, head-mounted displays, microphone, camera, etc.
2.2. Body-to-Body Network Concept
2.3. BBN Challenges
2.4. BBN Applications
3. BBN Design Considerations
3.1. Energy-Efficiency for BBN
- Generic: it does not depend on the type of MAC protocol and thus, it could be used in a heterogeneous WBAN network, like a BBN scenario.
- Expanded: each energy component appears clearly in the model and could be optimized to minimize the total energy consumed by a sensor node.
3.2. QoS-Aware Traffic Management for BBN
3.3. Mobility Management for BBN
3.4. Security Policies for BBN
- Intra-WBAN: the sensors measure physiological values (PVs) of the human body and then the keys are generated using those PVs to secure communications.
- Inter-WBAN: preloaded keys are used. The technique is memory-saving since it combines auto-generation and preloading key to enhance security. Any personal server (PS) can use biometric information to generate a key pool that it shares to the WBANs. The medical server (MS) assigns the responsibility of refreshing the key to any PS generator.
- Data storage:
- Confidentiality: while storing WBAN data, it should be encrypted and protected by access control lists.
- Integrity: WBAN data should be kept intact, unbroken while storage is performed.
- Dependability: WBAN data should be recovered after network incidents (link failure, deleted data, etc.).
- Privacy: unauthorized access to the stored WBAN data should be prohibited by the data storage policy.
- Data access:
- Accountability: every malicious WBAN, that would access illegally to others’ secured data, should be retrieved and held responsible of his actions.
- Revocability: malicious WBANs should be underprivileged at once.
- Non-repudiation: a source WBAN should not deny the generation of its own data.
- Other security requirements:
- Authentication: any source WBAN should be authenticated by the destination, before sending the sensing data.
- Availability: the WBAN data should be available and accessible whenever it is needed.
- Cryptography: cryptographic functions are used to ensure the safety and security of the information collected by sensor nodes (vital signs of a patient). Choosing the cryptographic system depends on the WBAN application and its energy consumption.
- Key management: cryptographic keys are generated and shared to the WBANs to ensure secure communications. Three types of key management protocols can be found: the trusted server, considering a trusty base station to validate the key agreement, the key pre-distribution or the symmetric key cryptography, and finally, the public-key infrastructure.
- Secure routing: the sensor node collects physiological information and sends this data to alternative devices. A number of routing protocols are proposed in the literature for sensor networks, but most of them suffer from security vulnerabilities that should be addressed to prevent inconsistency in the routed data.
- Resilience to node capture: this is a major issue in WBANs, especially in real-time applications. For example, the public hospital environment could be targeted by malicious behaviors from the inside or the outside. To resolve this problem, one feasible solution is to use inviolable network hardware.
- Trust management: corresponds to the partnership between two trustworthy WBANs that relay their respective data.
- Secure localization: to facilitate mobility for patients, the authors specify in  localization procedures that operate over three steps: distance estimation, angle estimation and position computation.
- Robustness to communication denial-of-services: the denial-of-service (DoS) attack can be implemented in the WBAN by broadcasting high-energy signals. Therefore, an energy-based security mechanism should be implemented to deal with this vulnerability.
4. BBN Design Challenges and Open Issues
4.1. Wireless Channel Propagation
4.1.1. BBN Users in Stationary Positions
- Involuntary slight movements of two persons in LOS position, may result in noticeable variations of the body-to-body signal level, in comparison to the log-distance pathloss model prediction.
- In outdoor NLOS situation, when one person turns his body back to the other person, the direct signal path is shadowed and the received signal level decreases consequently.
4.1.2. BBN Users in Movement
- Rotation movements significantly alter the average signal level (up to 50 dB attenuation), especially when the person turns on the spot and obstructs the LOS position with the other person.
- During tilt movements, the received signal is as much impacted as the vicinity between the two persons is reduced.
- Finally, in LOS and NLOS walking situations, when one person starts moving during the body-to-body signal propagation, body shadowing and path loss components increase in comparison to stationary scenarios.
4.2. Interference and Coexistence
- Beacon shifting: by using a beacon shifting sequence not used by his neighbors, a WBAN could avoid co-channel allocation conflicts. Yet, each WBAN will periodically transmit within a scheduled interval and is characterized by its beacon shifting sequence index and the length of its beacon period.
- Channel hopping: the WBAN is allocated a wireless channel for a fixed number of superframes. Then, it should hop to another channel according to his channel hopping sequence, which is different from his neighbors. Thus, WBANs could not reserve indefinitely a wireless channel.
- Active superframe interleaving: two neighboring WBANs could share the same channel by interleaving their active superframes, i.e., alternating between them during their inactive periods.
4.3. Storage and Privacy of Health Data in IoT/Cloud Environment
4.4. Heterogeneous Devices and Traffic
4.5. Ethical Challenges
Conflicts of Interest
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|EAAODV ||Network||Single-hop||On-demand Routing|
|Results only for ZigBee|
|Mobility management |
|Disconnection during sensor node mobility|
|SIMPLE ||Network||Multi-hop||Reliability, High throughput |
On-demand intra-WBAN routing
|No mechanism to handle mobility|
|EERS ||Network||Multi-hop||Joint Routing and Adaptive power control. |
Prolong the lifetime of the overall network.
|Only focus on the data transmission energy. Sensing and reception energy are assumed constant.|
|EAWD ||Network||Multi-hop||Topology-aware energy-efficiency. |
Minimizing the number of relay nodes.
Minimizing the energy consumption.
Generic (separate energy components).
|Absence of retransmission energy, MAC access energy and signaling energy components.|
|CLDO ||MAC |
|Multi-hop||Transmission reliability |
Energy efficiency with suitable packet size
Prolong WBAN lifetime.
|Do not handle topology changes.|
|Two-hop||Adaptive power control |
Channel link estimation
|Handle only two-hop topology relaying.|
|Duty-cycle ||MAC||Single-hop||Guard time: no overlaps, data reliability. |
Reduce idle listening.
|Movement History-Based Mobility Prediction|
|Exploit the regularity in human movement behavior within a defined period of time.||Unpredictable changes in user’s behavior. |
Limited feasibility for supporting high quality services.
|U-health monitoring for some sports/Athletes|
|Physical Topology-Based Mobility Prediction|
|Link expiration time estimation||Estimate the expiration time of the wireless link. |
Routes are reconfigured before disconnecting.
|Support simple mobility pattern, no sudden changes in velocity and moving directions||U-health in indoor environments (hospital, rest home)|
|Link availability estimation||Immediate rerouting in link failure case. |
Select more reliable neighbors to form more stable clusters.
|Difficulties in learning the changes in link status due to nodes movements. |
In highly volatile environments, increase of the control overhead.
|U-health in accidents and emergencies. (Patient transportation, sharing emergency information, and vital signs monitoring.)|
|Group mobility and network partition prediction||Prevent disruptions caused by the network partitioning. |
Low-complexity clustering algorithm accurately determines the mobility groups and their mobility
|Assume that the velocity of the mobile group of nodes is invariant over time. Non realistic assumption.||U-Health for a rescue team in a disaster area|
|Cluster change based prediction||The mobile node position and the direction of its movement in the cluster are used to predict future cluster switching.||This prediction process needs an accurate location. |
The use of GPS is needed to locate the mobile nodes.
|U-health monitoring of a group of soldiers. The position and mobility of each soldier are function of those of his neighbors.|
|Logical Topology-Based Mobility Prediction|
|Neighboring Nodes Relative Mobility Based Prediction||Based on past measurements. |
A linear model is implemented by mobile nodes to estimate the distance from their next cluster head (CH).
|Do not consider node mobility during CH election.||U-health in indoor environments (hospitals), or u-health monitoring of a sport team or a rescue team in outdoor/indoor environment.|
|Information theory based mobility prediction||Assume virtual clusters, no fixed geographical area. |
Online learning to predict the next cluster.
|Recurrent CH switching due to node mobility. |
Node’s mobility is deduced from the frequency of its neighborhood changeability over time.
|U-health in indoor environments, or U-health monitoring of a sport team or a rescue team in a limited geographical area.|
|Evidence based mobility prediction||No GPS use is required. |
Accurate prediction of the user trajectory.
The distance between mobile nodes is estimated by the signal strength.
Applied to the Zone Routing Protocol.
|Only applied by the border nodes to predict the next cluster.||Feasible in outdoor environments, for U-health monitoring of freely moving patients or any group of persons in an outdoor/indoor environment.|
|sec. mechanism |
|BBN Scenario||Description||Human Body Effect||n|
|Stationary LOS||Person A and person B are facing and stationary at a distance of 15 m||Increased variability in path loss caused by slight body movements.|
|Stationary NLOS||Person A and person B have their bodies rotated through and keep stationary||The path loss significantly increases with an extra 40 dB attenuation compared to the LOS state, due to the spread of the component.|
|Rotation||Person A performs a full rotation, turning from LOS to NLOS position||Received signal under the noise threshold of the receiver. The magnitude of the increases due to the varying propagation links transmitting the wireless signal.||-||-||-|
|Tilt||Person A tilts his position through a angle||At 1 m, a variation of 10 dB in the signal level is recorded due to the distance between the two persons.||-||-||-|
|Walking LOS||Person A walks at a normal pace (0.88 m/s) towards person B until reaching a distance of 1 m.||When person A is walking, the path loss exponent n increases compared to the stationary scenario, because of the body shadowing.|
|Walking NLOS||Person A turned from person B and walks from an initial distance of 1 m to 15 m.||The constantly changing propagation paths impact the received signal level.The spread of the standard deviation and s parameter noticeably increase compared to previous scenarios.|
|BBN Scenario||Description||n||Nakagami Fading||Indoor Shadowing|
|Stationary LOS||Person A stands at a distance m from person B and performs two forward walkings and two backward walkings, into the average speed of 0.8 m/s, reproducing in that way LOS and NLOS conditions.||-|
|Rotation||Person B realizes full rotations beside person A, starting from to , turning from LOS to NLOS position, and inversely.||-||-||-|
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