Abstract

Wireless Sensor Networks (WSNs) have been a desired choice for monitoring and automatic control of remote and unreachable objects and environments due to their low cost. However, such deployment requires quality-of service (QoS) techniques to assure reliable performance. Furthermore, provision of QoS in WSNs is a challenging task due to hardware limitations. A cross-layer approach is a promising option where information from different layers can be used to make QoS decisions. In this paper, we present a routing protocol where information from the application layer is used to make differentiated routing decisions based on data packets classifications. In our case, data packets are classified into: normal, urgent, and critical. Based on this classification each data packet class is treated differently by storing each data packet class in a designated buffer. Different buffers will have different routing priority decided by the protocol designer.

Highlights

  • Recent advances in technology especially in electronics and communications allowed the emerge of Wireless Sensor Networks (WSNs)

  • Castalia simulator is a framework that can be used on top of OMET++ to simulate WSNs, Body Area Networks (BAN) and generally networks of low-power embedded devices

  • quality-of service (QoS) is a crucial feature in WSNs to ensure predictable performance in harsh environments

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Summary

Introduction

Recent advances in technology especially in electronics and communications allowed the emerge of WSNs. Besides resource limitations in WSN nodes, WSNs are usually deployed in unattended and harsh environments implementing crucial applications. These factors emphasize the importance of QoS in WSNs [4]. We are proposing a protocol that provides QoS features through implementing differentiated services, where packets are classified as critical, urgent, and normal. Based on this classification, different packets are assigned different priorities and resources.

QoS Background
QoS Approaches
Proposed Protocol
Data Propagation
Simulation and Results
Experiment One
Experiment Two
Conclusions

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