An efficient network mobility management for a 6LoWPAN mobile network in hospital environments

In recent times, the hospital wireless sensor network (HWSN) has become one of the most important IPv6 over low-power personal area network (6LoWPAN) applications because the patients are attached with tiny 6LoWPAN sensors. One of the application scenarios is the monitoring of patients’ vital signals while the patients are on the move within the hospital premise. Among the different WSN mobility protocols, NEMO is the most common for 6LoWPAN mobile network in buildings equipped with wireless network infrastructure, such as with the case at modern hospitals and clinics. Current network mobility solutions do not perform well in terms of end-to-end delay, packet loss, handover signalling delay and signalling cost in 6LoWPAN area, to keep continuous connectivity for transmitting patients’ vital signals. To this date, only a few works on NEMO for 6LoWPAN mobile networks have been reported. The foremost demand of medical application is the need to ensure quality of service (QoS) for data transfer due to its criticalness in medical context. This thesis aims to present an efficient mobility management protocol for HWSNs to reduce patient’s data packet loss rate, signalling cost, handover delay, end-to-end delay, and optimize the energy consumption to maintain continued connectivity with the remote care giver. The effects of three design parameters, namely number of mobile network nodes (MNNs), number of handovers, and MNN packet generation rate in NEMO are evaluated. It is shown that the mobile router (MR) suffers from high energy consumption and traffic congestion which result in a bottleneck. If the MR drains its energy entirely, the connectivity with home network will be lost. Hence, in this thesis, we propose a number of schemes for NEMO based on 6LoWPAN MNNs. The first scheme improves the NEMO handover process on HWSN based on 6LoWPAN called HWSN6 mobility solution. This is extended to MR to offer a fast handover mechanism with low handover signalling cost, handover delay and packet loss, respectively. The second scheme is a message-scheduling algorithm based on route optimization in tunnelling process between the MNNs and HA to decrease the traffic congestion at MR and packet end-toend delay. The third scheme considers on the remaining energy of MR to optimize energy consumption to prolong the connectivity between MNNs and HA, this is called selective optimal MR algorithm. The results are drawn from analytical models and OMNeT++ simulator running on Contiki to perform the 6LoWPAN adaptation layer tasks. An analytical model of the proposed scheme is derived for handover signalling cost, handover signalling delay, and tunnelling cost. Simulation results show that the proposed solution reduces traffic congestion at MR by using the HWSN6 handover solution and the message-scheduling algorithm in the tunnelling process. The number of handover signalling messages is reduced from 6 stages in MIPv6 to 3 stages, this is achieved by exploiting other network elements such as border router (BR). The handover signalling costs and packet loss in the proposed scheme, NEMO-HWSN, are optimized around 13% and 31% respectively and compared to NEMO. Then, by using the proposed message-scheduling algorithm, end-to-end delay in NEMO-HWSN is reduced by approximately 20%. Finally, by using the selective optimal algorithm, the MR energy consumption in NEMO-HWSN is minimized by approximately 15.5%. Our proposed scheme also has a good performance for intra-PAN mobility like hospital environments because it skips the “Binding” and “Challenge” process in intra-PAN mobility. The above results proved that the NEMOHWSN is more efficient than other schemes in the HWSN environment, with low handover signalling delay, handover signalling cost, packet loss, packet end-to-end delay and energy consumption. Analytical models and simulation have been conducted to show the validation of work to verify that simulation results correspond to reference values (MIPv6, NEMO and HWSN6).

Preview Text FK 2017 124 IR.pdf Download (900kB) | Preview

Actions (login required)

View Item