Heterogeneous Dynamic Priority Scheduling in Time Critical Applications: Mobile Wireless Sensor Networks

Heterogeneous Dynamic Priority Scheduling in Time Critical Applications: Mobile Wireless Sensor Networks

Arvind Viswanathan, Garimella Rama Murthy, Naveen Chilamkurti
DOI: 10.4018/ijwnbt.2012040104
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In the unlicensed band, the notion of primary user and secondary user (To implement cognitive radio) is not explicit. By dynamic priority assignment the authors propose to implement cognitive radio in the unlicensed band. In time critical events, the data which is most important, has to be given the time slots. Wireless Sensor nodes in the authors’ case are considered to be mobile, and hence make it difficult to prioritize one over another. A node may be out of the reach of the cluster head or base station by the time it is allotted a time slot and hence mobility is a constraint. With the data changing dynamically and factors such as energy and mobility, which are major constraints, assigning priority to the nodes becomes difficult. In this paper, the authors have discussed about how Wireless Sensor Networks are able to allocate priorities to nodes in the unlicensed band with multiple parameters being posed. They have done simulations on NS-2 and have shown the implementation results.
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1. Introduction

Wireless Technologies play an integral role in our day to day lives. Though the channels are reusable, due to increasing number of users, we have to turn to the unlicensed spectrum as there is a limited licensed spectrum. The unlicensed spectrum (Akyildiz, Lee, Vuran, & Mohanty, 2006) is one where any user is allowed to access as there is no priority mechanism to allow the more important events to be given higher priorities (Yang & Vaidya, 2002) for communication. This can be disastrous in the long run as the events of higher importance are being starved of the spectrum due to lack of a priority mechanism. We can assume that there are multiple networks competing for the spectrum and hence there is a need to assign a priority for the networks for higher importance during the occurrence of a critical event. Each network itself will have multiple nodes which will also be competing for the channels (Sriporamanont & Liming, 2006). These nodes will also have to be allotted a certain priority.

To decide how networks are given different priorities and how nodes in each network are allocated priorities we can resort to a combined TDMA – FDMA access mechanism.

To reach our goal we need to follow the following steps:

  • Select a network

  • Select a node

Here we are taking into consideration the following assumptions:

  • Mobile base stations, Cluster Heads and sensors (all are mobile)

  • Controlled Mobility versus Uncontrolled Mobility

This research paper is organized as follows. First, we shall give a brief background on cognitive radio. Then we had a discussion on the work previously done is reported. Afterwards, we shall discuss about the improvements and the modifications to the existing algorithm. In the following sections, we will talk about the new proposed algorithm and the implementations of the algorithm. Finally, we will conclude the paper.


3. Previous Work Done

Modified Distributed Laxity-based Priority Scheduling scheme (MDLPS) (RamMurthy, Naveen Reddy, & Ravi Shankar Varma, 2011) is a packet scheduling scheme that improves the average end to end delay and the packet delivery ratio within the deadline when compared with the Distributed Laxity-based Priority Scheduling scheme (DLPS) and the Distributed Priority Scheduling (DPS). The DLPS is a scheduling scheme where the state of the neighboring nodes and the feedback regarding the packet loss from the nodes nearby are taken into consideration.

Previously, priority was given with the help of parameters such as battery power, battery threshold level and mobility in the MDLPS scheme. The Priority Index (PI) was computed with the help of the Packet Delivery Ratio (PDR), Uniform Laxity Budget (ULB), mobility of the node (v), and desired PDR for the flow defined by the parameter M.


We have also seen that priority index is high when the priority is low and the priority index is low when the priority is high. Hence, it has an inverse relation with the priority.


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