Abstract
In hierarchical networks, nodes are separated to play different roles such as CHs and cluster members. Each CH collects data from the cluster members within its cluster, aggregates the data and then transmits the data to the sink. Each algorithm that is used for packet routing in quality of service (QoS) based applications should be able to establish a tradeoffs between end to end delay parameter and energy consumption. Therefore, enabling QoS applications in sensor networks requires energy and QoS awareness in different layers of the protocol stack. We propose a QoS based and Energy aware Multi-path Hierarchical Routing Algorithm in wireless sensor networks namely QEMH. In this protocol, we try to satisfy the QoS requirements with the minimum energy via hierarchical methods. Our routing protocol includes two phase. In first phase, performs cluster heads election based on two parameters: node residual energy and node distance to sink. In second phase, accomplishes routes discovery using multiple criteria such as residual energy, remaining buffer size, signal-to-noise ratio and distance to sink. When each node detect an event can send data to the CH as single hop and CH to the sink along the paths. We use a weighted traffic allocation strategy to distribute the traffic amongst the available paths to improve the end to end delay and throughput. In this strategy, the CH distributes the traffic between the paths according to the end to end delay of each path. The end to end delay of each path is obtained during the paths discovery phase. QEMH maximizes the network lifetime as load balancing that causes energy consume uniformly throughout the network. Furthermore employs a queuing model to handle both real-time and non-real-time traffic. By means of simulations, we evaluate and compare the performance of our routing protocol with the MCMP and EAP protocols. Simulation results show that our proposed protocol is more efficient than those protocols in providing QoS requirements and minimizing energy consumption.
Highlights
In the recent years, the rapid advances in micro-electromechanical systems, low power and highly integrated digital electronics, small scale energy supplies, tiny microprocessors, and low power radio technologies have created low power, low cost and multifunctional wireless sensor devices, which can observe and react to changes in physical phenomena of their environments
The rapid advances in micro-electromechanical systems, low power and highly integrated digital electronics, small scale energy supplies, tiny microprocessors, and low power radio technologies have created low power, low cost and multifunctional wireless sensor devices, which can observe and react to changes in physical phenomena of their environments. These sensor devices are equipped with a small battery, a tiny microprocessor, a radio transceiver, and a set of transducers that used to gathering information that report the changes in the environment of the sensor node. The emergence of these low cost and small size wireless sensor devices has motivated intensive research in the last decade addressing the potential of collaboration among sensors in data gathering and processing, which led to the creation of Wireless Sensor Networks (WSNs)
We study the impact of changing the packet arrival rate and the node failure probability on average end to end delay, packet delivery ratio and average energy consumption impact of changing the number of nodes on lifetime and time of 100% nodes dead
Summary
The rapid advances in micro-electromechanical systems, low power and highly integrated digital electronics, small scale energy supplies, tiny microprocessors, and low power radio technologies have created low power, low cost and multifunctional wireless sensor devices, which can observe and react to changes in physical phenomena of their environments. The introduction of multimedia sensor networks along with the increasing interest in real time applications have made strict constraints on both throughput and delay in order to report the time-critical data to the sink within certain time limits and bandwidth requirements without any loss. These performance metrics (i.e. delay and bandwidth) are usually referred to as Quality of Service (QoS) requirements [3].
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