Effects of mobility on latency in a wsn that accommodates mobile nodes

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2020 – 2021
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Effects of mobility on latency in a WSN that accommodates mobile nodes
Abstract:
Several applications have been proposed for mobile wireless sensor networks. Some of these
applications require the transfer of a large amount of data in a short period of time. This is
challenging, since mobility can lead to a deterioration in the quality of an established link.
Frequent link disconnection may in turn require a mobile node to repeatedly establish new
links with the surrounding relay nodes to proceed with the data transfer. The new link
establishment may cause extra data communication latency and make most of the
applications delay sensitive. To evaluate the effect of mobility on latency, this paper first sets
up a mathematical model based on a hybrid medium access control (MAC) protocol in mobile
scenarios. It then uses NS2 simulation to further analyze the latency associated with mobility.
Both results show that the latency increases with an increment in the network density and the
duty cycle.

This paper proposes: 1) a traffic flow management policy, which allocates and organizes machine type communication (MTC) traffic flows network resources sharing within evolved packet system (EPS); 2) an access element as a wireless sensor network gateway for providing an overlaying access channel between the machine type devices and EPS; and 3) it addresses the effect and interaction in the heterogeneity of applications, services and terminal devices, and the related quality of service (QoS) issues among them. This paper overcomes the problems of network resource starvation by preventing deterioration of network performance. The scheme is validated through simulation, which indicates the proposed traffic flow management policy outperforms the current traffic management policy. Specifically, simulation results show that the proposed model achieves an enhancement in QoS performance for the MTC traffic flows, including a decrease of 99.45% in packet loss rate (PLR), a decrease of 99.89% in packet end to end (E2E) delay, a decrease of 99.21% in packet delay variation (PDV). Furthermore, it retains the perceived quality of experience of the real time application users within high satisfaction levels, such as the voice over long term evolution service possessing a mean opinion score (MOS) of 4.349 and enhancing the QoS of a video conference service within the standardized values of a 3GPP body, with a decrease of 85.28% in PLR, a decrease of 85% in packet E2E delay and a decrease of 88.5% in PDV. Index Terms—Application quality of service (AQoS), end to end (E2E) delay, gateway, human type communication (HTC), Internet of Things (IoT), jitter, machine type communication (MTC), network QoS (NQoS), quality of experience (QoE), QoS class identifier (QCI), traffic policy.

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