NQAR: Network Quality Aware Routing in Error-Prone Wireless Sensor Networks
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We propose a network quality aware routing (NQAR) mechanism to provide an enabling method of the delay-sensitive data delivery over error-prone wireless sensor networks. Unlike the existing routing methods that select routes with the shortest arrival latency or the minimum hop count, the proposed scheme adaptively selects the route based on the network qualities including link errors and collisions with minimum additional complexity. It is designed to avoid the paths with potential noise and collision that may cause many non-deterministic backoffs and retransmissions. We propose a generic framework to select a minimum cost route that takes the packet loss rate and collision history into account. NQAR uses a data centric approach to estimate a single-hop delay based on processing time, propagation delay, packet loss rate, number of backoffs, and the retransmission timeout between two neighboring nodes. This enables a source node to choose the shortest expected end-to-end delay path to send a delay-sensitive data. The experiment results show that NQAR reduces the end-to-end transfer delay up to approximately 50% in comparison with the latency-based directed diffusion and the hop count-based directed diffusion under the error-prone network environments. Moreover, NQAR shows better performance than those routing methods in terms of jitter, reachability, and network lifetime.
KeywordsSensor Node Packet Loss Network Lifetime Sink Node Packet Loss Rate
Wireless Sensor Networks (WSNs) consist of a large number of battery-powered, low-cost, and tiny sensor nodes, which have the capability of sensing, data processing, and wireless communication. The sensor nodes can be deployed randomly close to or inside the terrain of interest to create a cooperative and self-organizing wireless ad hoc network with minimal provisioning. Unlike the traditional high cost and fixed array of sensor systems, the WSN technology enables countless new applications including environmental hazard monitoring, military surveillance and reconnaissance, and health monitoring applications to name a few.
In WSNs, the sensed data and control messages are exchanged between sensor nodes and the control (sink) nodes relayed by the neighbor sensor nodes via a multihop routing protocol. To build practical services over WSNs, especially considering sensors' limitations in power, computation, and local storage, it is both critical and challenging to support efficient network layer multihop routing protocols. To cope with the characteristics of sensor nodes, various new routing protocols have been proposed in WSNs [1, 2, 3]. These protocols are mainly designed ( Open image in new window ) to reduce redundant data (data aggregation) and unnecessary controls by using on-demand data centric approaches [4, 5, 6, 7], ( Open image in new window ) to limit the network scale by using structured approaches such as clustering and hierarchical architectures [8, 9], and ( Open image in new window ) to decrease distributed state overheads by using location based approaches [10, 11]. To achieve an efficient resource usage, those routing protocols commonly select routes based upon the static quality factors such as maximum power availability, minimum energy usage, maximum position progress, minimum hop count, or the shortest arrival latency. However, those static quality based parameters have limitations in case of the error-prone and densely deployed WSNs, because they do not take retransmissions due to packet losses and backoffs due to collisions into consideration.
In this paper, we propose a network quality aware routing (NQAR) mechanism to provide an enabling method of the delay-sensitive data delivery over error-prone and densely deployed WSNs. The proposed scheme adaptively utilizes the dynamic network quality factors including link error rates and collision histories. It is designed to avoid the paths with potential noise and collision, which may cause many nondeterministic retransmissions and backoffs. NQAR uses a data centric on-demand method based on a directed diffusion to estimate the minimum cost end-to-end routing path. The NQAR operation steps are as follows. First, each sensor node maintains its network quality information including the packet loss rate, the retransmission rate, and the backoff rate for a certain period. Second, during the interest dissemination period, each node relays interest with its network quality information to its neighbors. Third, each node estimates a single-hop delay based on processing time, propagation delay, packet loss rate, number of backoffs, and retransmission timeouts between two neighboring nodes, which in turn enables the calculations of expected end-to-end delays during the interest dissemination period. Finally, a source node can send delay-sensitive data to a sink node along the shortest expected end-to-end delay path. It is clearly noted that the proposed scheme simultaneously considers the dynamic qualities of wireless network links in addition to the overall static parameters including per-hop processing time and power in the routing decision process.
To the best of our knowledge, NQAR is the first work to simultaneously consider the qualities of wireless links as well as processing time in the routing decision process. We perform extensive simulations under the various qualities of links and show that the NQAR reduces the end-to-end transfer delay up to 50% in comparison with the latency-based directed diffusion  and the hop count-based directed diffusion  under the error-prone (link error and collision) network environments. Moreover, NQAR performs better than other routing methods in terms of jitter. Since NQAR inherently avoids error-prone links, the reachability (reliability) is improved as well if no packet retransmission is assumed. We also find that NQAR prolongs the network lifetime as it prevents unnecessary energy consumption, resulting from the relative reductions of packet losses and retransmissions.
The remainder of this paper is organized as follows. Section 2 summarizes a background and the related work to this research. Section 3 explains the details of the NQAR algorithm and Section 4 presents the evaluation results. Finally, we conclude our work in Section 5.
2. Background and Related Work
Energy-efficient Differentiated Directed Diffusion (EDDD)  is an extension of the directed diffusion protocol to establish a path between a source and a sink with the minimum hop count and the minimum available energy to enhance the shortcomings of the original directed diffusion. However, both directed diffusion and EDDD do not reflect the error-prone (noise and collision) network link characteristics of WSNs . It causes nondeterministic additional delays due to retransmission and/or reprocessing in the MAC layer.
Low Energy Adaptive Clustering Hierarchy (LEACH)  is introduced to achieve an energy efficiency to arrange a structured traffic path by forming clusters. Only a few representative nodes (cluster heads) are involved in the cluster control (assigning transmission time for each sensor node: TDMA) and data transmission (including data aggregation). To support equal energy dissipation, the cluster head roles are evenly alternated among the sensor nodes. Power-Efficient Gathering in Sensor Information Systems (PEGASIS)  is a network lifetime enhancement work over LEACH protocol. It reduces communication overhead by arranging local coordination among the neighboring sensor nodes and by chaining the communication path to the sinks instead of the cluster formation. Nodes need only to communicate with their neighbors and they take turns in communicating with the sink.
Greedy Perimeter Stateless Routing (GPSR)  is an earlier version of the location-based geographic routing protocol. It decreases the number of distributed states and the maintenance overheads by calculating the next forwarding node based only upon the destination location information on each forwarding node. It chooses a routing path according to the best position progression towards the destination. However, it will need an additional location service to map positions and node IDs. Geographic and Energy Aware Routing (GEAR)  adds an energy parameter to the geographical progress parameter in calculation of the best destination path. It refines the next estimated progression cost with the learned cost, which is the feedback information of the previously propagated packet cost to the destination. It also reduces interest dissemination to a certain region to conserve more energy.
All of the above methods, however, use only static factors, and nondeterministic delays due to retransmissions and backoffs that have not been taken into consideration. NQAR is unique in that it uses dynamic network quality parameters as well as static delays in order to estimate the expected event-to-sink path delay.
3. Network Quality Aware Routing (NQAR) Algorithms
An important observation is that packets may be lost due to channel problems such as interference and collision. Then the link layer retransmission is performed after a packet loss is detected, and the time necessary to detect a packet loss is at least twice as much as one-way propagation delay. Furthermore, there can be repeated packet loss and retransmission attempts. Therefore, the problem of selecting a path with the shortest end-to-end propagation delay or minimum hop count is that the performance is significantly affected by packet loss rates of links. In such a case, the existing methods will undergo the additional delays that are not presupposed, and may fail to transmit time-critical data successfully.
We first describe our approach of a path selection that is based on directed diffusion, but takes packet loss rate into account in link costs. We then discuss how the cost can be used to meet various parameters such as delay and energy consumption.
By (5)–(7), we can calculate an expected transfer delay of a link. Open image in new window in (5) then means the sum of the expected delays in successfully transferring data over 1 hop considering channel assessment time, transmission time, propagation delay, packet loss rate, and retransmission timeout. This in turn enables us to compute the expected end-to-end delay of a path via (2). Note that NQAR requires that each sensor should estimate and maintain its network quality information such as link loss rate and channel loss rate. Exponentially weighted moving average algorithm can be used for the estimations, and the memory and computation overhead involved is small. In summary, our route selection approach effectively takes into account of dynamic as well as static qualities of link and channel for the performance of delay, jitter, and energy consumption that are analyzed in Section 4.
4. Validation and Performance Study
In this section, we first validate the computation of expected delay with a simple network topology with three paths. We then conduct more extensive simulations to compare the performance results of NQAR with the ones from the original latency-based directed diffusion  and the hop count-based directed diffusion EDDD . The performance metrics used include the end-to-end delays, jitters, reachability (thus reliability), and network lifetime.
4.2. Simulation Setup
Configuring of experiment parameters.
Number of nodes
Initial energy of each node
4.3. Evaluation Results
According to both Figures 8 and 9, for the given similar traffic distribution trends, it is clear that NQAR prolongs the network lifetime in error-prone networks by reducing the chance of retransmissions.
We have proposed a network quality aware routing (NQAR) protocol for error-prone and densely deployed WSNs. In addition to the existing routing methods that select routes with the least energy cost, the shortest arrival latency, or the minimum hop count, NQAR adaptively utilizes the network qualities including link error rates and collision histories in the route selection. It is designed to avoid the paths with potential noise and collision that may cause many nondeterministic delays due to backoffs and retransmissions. NQAR uses a data centric approach to estimate a single-hop delay based on processing time, propagation delay, packet loss rate, the number of backoffs, and retransmission timeouts between two neighboring nodes. This in turn enables the source to select the shortest expected end-to-end delay path to send data. NQAR is unique in that it holistically considers the qualities of wireless links as well as processing time in the routing decision process. Through extensive simulations, we have validated that NQAR improves the end-to-end transfer delay performance and decreases jitter significantly under the error-prone (link error and collision) network environments. We have shown that NQAR increases end-to-end reachability (reliability) in case of no data retransmission, because of its inherent nature of avoiding error-prone links. We have also found that NQAR prolongs the network lifetime, as it prevents unnecessary energy consumption resulting from the relative reductions of packet losses and retransmissions.
This work was supported in part by the US National Science Foundation under Grant no. 0729197. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US National Science Foundation.
- 8.Heinzelman W, Chandrakasan A, Balakrishnan H: Energy-efficient communication protocol for wireless microsensor networks. Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS '00), January 2000, Maui, Hawaii, USA-223.Google Scholar
- 9.Lindsey S, Raghavendra CS: Pegasis: power-efficient gathering in sensor information systems. Proceedings of the IEEE Aerospace Conference, March 2002, Big Sky, Mont, USA 3: 1125-1130.Google Scholar
- 11.Yu Y, Estrin D, Govindan R: Geographical and energy-aware routing: a recursive data dissemination protocol for wireless sensor networks. UCLA Computer Science Department; May 2001.Google Scholar
- 12.Felemban E, Lee C-G, Ekici E, Boder R, Vural S: Probabilistic QoS guarantee in reliability and timeliness domains in wireless sensor networks. Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '05), March 2005, Miami, Fla, USA 4: 2646-2657.CrossRefGoogle Scholar
- 13.Texas Instruments : Cc2520 datasheet. http://focus.ti.com/docs/prod/folders/print/cc2520.html
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