- Research Article
- Open Access
Impact of LQI-Based Routing Metrics on the Performance of a One-to-One Routing Protocol for IEEE 802.15.4 Multihop Networks
© Carles Gomez et al. 2010
Received: 13 February 2010
Accepted: 26 July 2010
Published: 9 August 2010
The quality of an IEEE 802.15.4 link can be estimated on the basis of the Link Quality Indication (LQI), which is a parameter offered by the IEEE 802.15.4 physical layer. The LQI has been recommended by organizations such as the ZigBee Alliance and the IETF as an input to routing metrics for IEEE 802.15.4 multihop networks. As these networks evolve, one-to-one communications gain relevance in many application areas. In this paper, we present an in-depth, experimental study on the impact of LQI-based routing metrics on the performance of a one-to-one routing protocol for IEEE 802.15.4 multihop networks. We conducted our experiments in a 60-node testbed. Experiments show the spectrum of performance results that using (or not) the LQI may yield. Results also highlight the importance of the additive or multiplicative nature of the routing metrics and its influence on performance.
The IEEE 802.15.4 standard [1, 2] specifies the Physical layer (PHY) and Medium Access Control (MAC) functionality of a Low-power, low-rate Wireless Personal Area Network (LoWPAN) technology conceived for a wide variety of control and monitoring applications. IEEE 802.15.4 is primarily targeted at simple and low-cost devices, including several types of embedded systems, sensors, and actuators.
IEEE 802.15.4 supports star and peer-to-peer topologies. The peer-to-peer topology is based on a multihop paradigm and is suitable for a plethora of scenarios, including industrial, agricultural, forest, urban, and vehicular environments, among others. For practical reasons, ad hoc, self-configuring, and self-healing routing functionality is commonly used in these application spaces [3–9].
The requirements for routing techniques in low-power environments are highly dependent on applications. Several routing protocols have been specifically developed for data- collection sensor networks [5–7], which are characterized by a many-to-one (or many-to-few) paradigm. Nevertheless, applications that exhibit one-to-one communication needs are gaining relevance. Some examples include interdevice communication in home automation, building automation and query and control in industrial, structural, and urban monitoring [3, 8, 9]. Many routing protocols that are currently used for this application space are descendants of the Ad hoc On-demand Distance Vector (AODV) routing protocol . Examples of these are the mesh routing functionality of the ZigBee stack , the one-to-one mechanism of the IPv6 Routing Protocol for Low-power and lossy networks (RPL), which is being specified by the IETF ROLL Working Group (WG) , and other approaches found in commercial platforms and in the literature [12–15].
One of the key factors for network performance in a wireless multihop network is the routing metric. The consideration of link quality as an input to routing has proved to be a powerful approach in IEEE 802.11-based mesh environments [16, 17]. In the IEEE 802.15.4 context, many research efforts have already been devoted to link quality estimation [18–22]. Most of these efforts have focused on the link quality indication (LQI), which is a parameter offered by the IEEE 802.15.4 PHY. The aim of the LQI is to represent the quality of a link, as perceived by the receiver of a frame at the moment of frame reception. Hence, the LQI is a good candidate for consideration as an input to routing metrics. In fact, the ZigBee standard , the IETF 6LoWPAN WG , and recent proposals within the IETF ROLL WG  recommend its use. However, this approach has received little attention, with a few exceptions which did not focus on one-to-one routing [21, 25].
In this paper, we present an in-depth, experimental study on the impact of using the LQI in routing metrics for a routing protocol based on AODV, which is called Not So Tiny-AODV (NST-AODV) . The experiments conducted show the spectrum of performance results that using (or not) LQI-based metrics may yield and allow to derive guidelines for the design of LQI-based routing metrics. While our work focuses on NST-AODV, we believe that the study will contribute to understanding the influence of LQI-based routing metrics on other routing approaches.
The remainder of the paper is organized as follows. Section 2 gives an overview of the routing protocol used in our experiments. Section 3 reviews link quality estimation techniques in low-power wireless networks. Section 4 surveys the main link quality-based routing metrics for the same environments. Section 5 describes the 60-node testbed used in this work. Section 6 presents an experimental characterization of the LQI parameter and discusses the use of LQI for routing metrics. Section 7 evaluates the performance of NST-AODV using the Hop count metric and three LQI-based routing metrics, which were selected from those examined in Section 4: (i) PATH-DR , which is aimed at choosing the paths with the maximum delivery ratio; (ii) the link quality-based metric for ZigBee mesh routing ; (iii) a metric called LETX, which aims to select the paths that require the minimum number of transmission attempts. Section 8 studies the performance of these routing metrics in the presence of background traffic. Finally, Section 9 concludes the paper with the main remarks and a discussion of future work.
2. Routing Protocol
The routing protocol we consider in our study is NST-AODV, an adaptation of AODV for IEEE 802.15.4 environments. This section first provides background on AODV. Then, it summarizes the particular features of NST-AODV.
2.1. AODV Overview
AODV is a reactive routing protocol. When a node requires a route, it initiates a route discovery procedure by broadcasting Route Request (RREQ) messages. Each node rebroadcasts RREQs, unless it has a valid route entry to the destination or it is the destination itself. In this case, it sends a Route Reply (RREP) message back to the originator node and ignores any subsequent RREQs that are transmitted through alternative routes. Backward or forward next-hop routing entries are created at each node that receives an RREQ or an RREP, respectively. Route entries expire after a specified time if the route becomes inactive (i.e., it is not used for data transmission). For each route entry of a node, there is a precursor list that contains the nodes that use this one as the next hop in the path to a given destination. The loop-freedom of routes towards a destination is guaranteed by means of a destination sequence number, which is updated when new information about that destination is received.
When a link in an active path breaks, the upstream node that detects this break may try to locally repair the route if the destination is close to the node. This is an optional mechanism. If local repair cannot be completed successfully or it is not supported, the node that detects the link break creates a Route Error (RERR) message, which reports the set of unreachable destinations. This message is sent to precursor nodes. Then, the source of the active path starts a new route discovery phase if a route to the destination is still needed. Data packets waiting for a route should be buffered during route discovery.
An AODV node that belongs to an active route may periodically broadcast local Hello messages for connectivity management. However, this approach may be expensive if nodes are battery-powered. Other strategies include link layer mechanisms. For example, unsuccessful layer two transmissions may be used as an indication of a link break for AODV. This method is known as Link Layer Notification (LLN).
An LLN mechanism is enabled by default. This requires the protocol to run on top of the IEEE 802.15.4 reliable mode (where a node that correctly receives a data frame sends an acknowledgement frame to the sender).
After an unsuccessful link layer transmission, up to two additional retries triggered by layer three can be performed.
When a packet leads to link failure detection due to three consecutive, unsuccessful layer three transmission attempts, it is buffered and transmitted if a new route can be found. This may happen either if the node that detects the break is the originator itself or if it is an intermediate node that locally repairs the route.
The implementation consumes 957 bytes of RAM and 4664 bytes of ROM. For a detailed comparison of NST-AODV and other routing solutions, the reader can refer to the literature .
3. Link Quality Estimation in Low-Power Wireless Networks
Wireless communications suffer from a plethora of phenomena that make correct reception of transmitted data an uncertain event in many cases. Ideally, a routing protocol for a wireless multihop network should favor the use of good-quality links. The quality of the link between a sender and a receiver is generally modeled by the probability of successful frame transmission of that link. We denote this probability the Link Delivery Ratio (LDR). The main techniques for estimating link quality in low-power networks can be classified into (i) packet-based techniques and (ii) radio hardware-based techniques. Recent studies have experimented with combinations of both techniques .
3.1. Packet-Based Techniques
Packet-based approaches estimate the LDR (or related performance metrics) of a link by computing the ratio between the number of received and expected packets during a given time window. There are two main options for implementing this scheme: (i) active techniques, in which control packets are transmitted for this purpose [29, 30] and (ii) passive techniques, also known as snooping, in which data packets are assumed to use sequence numbers, and nodes keep track of the number of lost messages during a given time interval [4, 5, 31]. Despite their benefits, these two approaches require time and state to produce a result [19, 20, 31]. Furthermore, the first one may lead to additional energy consumption.
3.2. Radio Hardware-Based Techniques: LQI versus RSSI
To overcome the time and state limitations of existing schemes, many researchers considered the use of PHY parameters from off-the-shelf radio hardware [18–21]. Many radio chips that implement proprietary radio technologies provide the received signal strength indicator (RSSI), which is the strength of a received radiofrequency (RF) signal. Furthermore, IEEE 802.15.4-compliant radio chips, like the widely used Chipcon CC2420 , also offer the LQI. As defined by the standard, measurement of the LQI may be implemented by means of receiver energy detection, signal-to-noise ratio estimation, or a combination of these methods .
The CC2420, which has become the de facto IEEE 802.15.4 radio chip, measures the RSSI based on the average energy level of eight symbols of the incoming packet. Since the use of RSSI to calculate the LQI may lead to spurious quality indications, the CC2420 chip also provides a correlation value that is based on the first eight symbols of the incoming packet. This correlation value is in the range of 50 to 110, where 50 corresponds to the lowest quality frames detectable by the chip and 110 indicates a maximum quality frame. According to the standard, the LQI value is represented by one byte. For this reason, Chipcon suggested the use of a linear conversion of the correlation values into a range of 0 to 255, using empirical methods based on Packet Error Rate (PER) measurements. In addition, the LQI value may be obtained by combining the correlation and RSSI values. However, the LQI values have been assumed to be the correlation values in the relevant literature, without the range conversion [18–21].
These results are reasonable, as several phenomena may increase the RSSI measured by the receiver, while they may reduce the actual link quality. Some examples are the superposition of multipath components arriving from different paths  and the presence of narrowband interference . Consequently, we will use the LQI for link quality estimation.
4. Link Quality-Based Routing Metrics for Low-Power Wireless Networks
This section surveys the most relevant link quality-based routing metrics that are suitable for low-power wireless networks. Routing metrics based on other principles (e.g., energy-aware ones) are outside the scope of this paper. For comparison purposes, the Hop count metric is included in the survey. We are interested in selecting a set of link quality metrics that fulfil the following requirements: (i) they can be implemented easily, based on the LQI; (ii) they are appropriate for the nature of NST-AODV (i.e., they do not require transmission of additional control messages); and (iii) they take into account the qualities of all the links of a path in the computation of the path cost.
4.1. Hop Count
Hop count was the default routing metric of the first routing protocols for wireless (and wired) networks. This metric is simple, which is an interesting property for networks composed of constrained devices. If the quality of all links in the network is the same, the Hop count metric selects the best paths. Unfortunately, real networks are typically composed of links of varying quality. Hence, this metric favors the use of short paths (in hops), even if these paths may offer poorer performance than longer paths of higher quality.
4.2. Shortest Path with Link Quality Threshold
The metric defined as SP(t)  is based on a shortest path (i.e., hop count) approach that excludes links whose quality is below a threshold t. Link quality is estimated using snooping techniques. This metric avoids the use of bad quality links, but it does not distinguish the quality of the links that are considered for path selection.
4.3. Link Quality Routing
One of the first attempts at routing based on link qualities in a low-power wireless network  was carried out using the Destination Sequenced Distance Vector (DSDV) routing protocol . The quality of a link was obtained as the minimum snooped Path Delivery Ratio (PDR) in each direction between a pair of nodes. To calculate the link cost, each link quality was categorized into one of four classes. Then, it was converted into a link cost by transforming the average PDR of the corresponding category to the log scale, and then normalizing to the integer domain. The path cost was calculated as the sum of the costs of the links that compose the path. As adding link costs is equivalent to multiplying the packet delivery rates of each link, the principle behind this routing metric is to maximize the PDR. However, the computation of the link cost leads to a loss of accuracy of the metric.
The computation of the ETX metric of a link is usually based on the periodic transmission of broadcast probe messages to neighbors and a count of the related replies in defined time intervals . It is typically implemented with Hello messages [30, 37]. Low-power environments cannot afford to use periodic transmission of control messages at a certain rate, since this may lead to premature battery depletion. In some cases, ETX has been adopted as a mechanism for estimating link quality during specific training periods in many-to-one sensor network schemes . In low-power networks, the same metric has been renamed as Minimum Transmission (MT) and implemented using snooping techniques, under the assumption of a minimum data transmission rate for each node to allow for a link quality estimation .
One of the first attempts at a link quality estimator for a routing protocol based on the LQI was MultiHopLQI , which was actually an evolution of the aforementioned many-to-one scheme proposed in . A path cost metric is computed as the sum of the link costs of the path. The cost of a link is inversely proportional to the LQI.
4.6. ZigBee Metric
In effect, the ZigBee specification provides implementers with two options for computing the link cost: (i) the link cost is always equal to 7 or (ii) the link cost is related to the reciprocal of the LDR of the link. The first option is equivalent to the Hop count metric. The second one, which hereafter we will refer to as the ZigBee routing metric, was designed to reflect the number of expected transmission attempts required to get a packet through on that link, which is actually emphasized, since the exponent in the formula is 4. In this case, cost values are integer numbers in the interval between 1 and 7, in which an ideal link has a link cost value equal to 1. A drawback of this second option is that, though the quality of each link of a path is taken into account, the round() function introduces quantification error, which may preclude the metric from achieving the best performance. Note that this error grows with the path hop count. Finally, the ZigBee specification does not mandate the method for computing the LDR estimation, but it suggests two options: the first one is based on counting received beacons and data frames and observing the appropriate sequence numbers; the second one is based on the use of average LQI, which is mentioned as "the most straightforward method" in the specification .
4.7. Hop Count While Avoiding Weak Links
The hop count while avoiding weak links metric aims to select the path with the smallest number of "weak" links, that is, links whose LQI is below a certain threshold value . The metric is defined as follows. Let WL and HC denote the number of weak links and the hop count of a path, respectively. The route cost is a tuple of (WL, HC), which is ordered lexicographically. That is, the path with the minimum WL is selected by the metric. If more than one path has the same WL value, then the one with the smallest HC is chosen. This metric was proposed as an adaptation of AODV for LoWPANs.
The main drawbacks of this metric are that it does not distinguish the qualities of the good links of a path, and the fact that it may not take into consideration the hop count of a path.
4.8. MAX-LQI and RQI
This metric was defined to enhance the performance of the adaptive demand-driven multicast routing (ADMR) protocol . It was implemented using the LQI values of the control messages involved in the route discovery procedure.
Another metric, called the Route Quality Indicator (RQI), is equivalent to MAX-LQI. The RQI of a path is defined as the minimum LQI of the links of that path. The path with the greatest RQI between the sender and receiver is selected .
MAX-LQI/RQI is not an accurate metric, since it only considers the quality of the worst link of a path. It does not explicitly take into account the other characteristics of the path, such as the hop count or the LQI of the rest of the links.
was obtained as a function of the LQI values of the link l. The metric was also used for ADMR. The PATH-DR metric aims to choose the paths with the highest PDR, regardless of the number of hops. Note that the metric takes into account the quality of all the links of a path.
where is obtained as a function of the LQI of the link. The link cost is an estimate of the number of transmission attempts required for successful frame delivery in a link. The path cost is the sum of the link costs of the path. The metric takes into consideration the quality of all the links of a path.
Note that LETX has the same aim as ETX. However, ETX requires frequent (generally, periodic) transmission of control messages or data packets through all links in order to estimate the quality of those links. Hence, even if no data transmissions are carried out in a network, ETX requires a minimum amount of transmissions in the network. Instead, LETX relies on LQI-based LDR estimation, which can be done by using a single LQI value (as we argue in Section 6.3). This is adequate for a reactive routing approach (e.g., the one considered in this paper), because the LETX metric can be computed "on the fly" during route discovery, without additional transmission of packets for LDR estimation. We evaluate the performance of LETX for NST-AODV in this paper.
4.11. Summary of Link Quality Routing Metrics for Low-Power Wireless Networks
Comparison of the main characteristics of routing metrics used in low-power wireless networks.
Properties of the metric
Awareness of link quality
Quality of all links is distinguished
Link quality estimation method
Nature of the routing protocol
Shortest path with link quality threshold 
Yes, (considers only good quality links)
Link quality routing 
Proactive, one-to-one and many-to-one
ZigBee (link quality) 
Packet-based techniques/average LQI
Hop count while avoiding weak links 
Only when considered paths have the same number of weak links
5. Testbed Description
The TelosB motes use the Chipcon CC2420 radio chip, which operates in the 2.4 GHz band. The TinyOS version running in the motes for all the experiments was 2.1.1 and the IEEE 802.15.4 beaconless mode was used. The channel selected was number 26, since this minimizes interference with other systems operating in the same band (e.g., IEEE 802.11) . In order to better understand transmission performance, all motes were positioned in the same way, since the TelosB antenna is not omnidirectional.
6. LQI Experimental Characterization
In this section we present an experimental study of the use of the LQI as an estimator of the LDR, to identify the potential advantageous and adverse characteristics of the LQI for its use in routing metrics. We also present and justify our LQI-based link quality estimation solution for NST-AODV.
6.1. Relationship between the LDR and the Average LQI
We conducted a set of experiments as follows. One thousand broadcast packets were sent from the mote at one corner of the grid. The number of packets and the LQI of each received packet were obtained at each of the remaining motes. The LDR was calculated for all the receivers. The same procedure was repeated three times, and the sender was placed at each of the other three corners, producing similar results. The transmission power was set at dBm. Packets were transmitted at a rate of 3 Hz.
6.2. Variability of the LQI of a Link
Our results differ from those of a study which focused on the temporal characteristics of the LQI . Authors of the cited work concluded that the LQI was stable with time and exhibited a maximum standard deviation of 1.2. The explanation is that their experiments were carried out in very good channel conditions, since an LQI between 103.1 and 107.0 was reported.
6.3. Considerations for Routing
Ideally, a link quality estimator for a routing protocol should be accurate, agile, and stable, and should add minimum overhead to the routing protocol. Below, we discuss the trade-offs in the fulfillment of the previous requirements when an LQI-based estimator is used.
The main drawback of an LQI-based link quality estimator is the fact that it may provide spurious link quality indications in a medium quality link. If such a link appears to temporarily exhibit better quality than the steady state one, any path containing this link may experience early problems (e.g., end-to-end connectivity gaps). In the opposite case, the link quality estimation mechanism might induce the path selection algorithm to select other worse performing links. Averaging techniques could reduce the impact of LQI variations, but some of these are slow to adapt to changes [20, 31]. Furthermore, as already shown in Subsection 6.1, even the average of a large number of LQI samples does not assure accurate prediction of the LDR in medium-quality links. Hence, averaging LQI may result unnecessary in this zone of link qualities.
Finally, note that LQI-aware routing favors the use of high quality links, and hence tends to avoid the use of medium quality links (whose quality might in some cases be inaccurately estimated based on LQI). As will be shown in Section 7, adequate LQI-based routing metrics provide better performance than the Hop count routing metric.
6.4. Use of LQI for NST-AODV
In view of the previous observations, we designed a simple LQI-based route selection mechanism for NST-AODV as follows. During route discovery, each node that receives an RREQ message converts the LQI of that message into the estimated LDR, by applying the piecewise linear model shown in Figure 2. The estimated LDR of each link is then used to calculate the cost of the link, according to the routing metric used. The accumulated cost of the path is written in the RREQ before being rebroadcast and the destination sends a RREP through the route with the best cost. Once a path is found, the qualities of the links of the network are not sampled again until the selected path breaks, which leads to a new route discovery process. Note that this approach neither adds a control message overhead nor adds state at the nodes, in comparison with the use of the default NST-AODV (which uses the Hop count metric).
7. Experimental Comparison of Routing Metrics
This section presents the main part of the extensive set of experiments that we conducted to evaluate the performance of NST-AODV with the Hop count, PATH-DR, ZigBee, and LETX routing metrics. Since these metrics have different objectives, we expected to obtain the spectrum of performance results that the use (or not) of LQI in the routing metric may yield. As an additional contribution of the paper, the code in nesC of NST-AODV with the four routing metrics can be found in our website .
7.1. Definition of Experiments
The experiments were performed on the testbed presented in Section 5, with low presence of people in the laboratory. We forced multihop communications by setting the transmission power so that the maximum transmission range was 2 m (recall that the TelosB antenna is not omnidirectional). We investigated the influence of each routing metric on the following performance parameters: path hop count, path lifetime, PDR, and cost of data packet delivery.
In each experiment, 1000 packets were transmitted periodically at a rate of 3 Hz from a sender to a receiver, without any other concurrent flows. Thus, the obtained results were isolated from network congestion effects (the reader may note that Section 8 is a study on the influence of background traffic on the routing metrics). All the experiments were carried out for the four routing metrics considered.
7.2. Path Hop Count
7.3. Path Lifetime
The next performance parameter we study is path lifetime. We define path lifetime as the length of each period during which an end-to-end path does not suffer link failures. This performance parameter is relevant, since a link or path failure triggers routing protocol messages in many routing techniques and may lead to route changes. Furthermore, a stable topology should make higher-level operations, such as scheduling, aggregation , and transport layer protocols easier to design and implement. Recall that NST-AODV decides that a link has failed after three consecutive unsuccessful frame transmission attempts. Note that, although the motes in our testbed are static, link failures occur due to link quality changes because mote receivers are close to the signal-to-noise threshold [5, 21].
7.4. Path Delivery Ratio
The performance of a routing metric in terms of PDR in NST-AODV can be explained by the performance of the metric in path lifetime. The reason for this is that, after a path failure, a connectivity gap takes place, during which the protocol tries to find a new route for the data. The connectivity gap ends when the first data packet reaches the receiver after the path failure by using a new path.
Remarkably, the connectivity gap duration is independent of the routing metric (we measured an average connectivity gap duration of 1.7 s, which depends on the protocol settings and the data sending rate). The reason for this is that, after route discovery, the first route obtained by the sender (via the first RREP it receives) is used for data transmission. If better routes are found later (i.e., subsequent RREPs from the same route discovery reach the sender via better paths), these routes are used for the next data packets. Nevertheless, the first data packet transmission after route discovery is always carried out through the first available path, which does not depend on the routing metric used.
In the short-path scenario, the differences between the metrics in terms of PDR are small. The lowest PDR, which is given by the Hop count metric, is equal to 94.6% whereas PATH-DR provides the highest PDR, which is equal to 97.5.
In the long-path scenario, PATH-DR also obtains the best performance, with a PDR of 95.1%, whereas the Hop count metric provides only a PDR of 81.3%. As shown in Figure 12, in this scenario the differences between the performance of the metrics under consideration become clearer than in the short-path one.
7.5. Topological and Spatial Study
We next study the influence of the location of the sender and receiver on the measured PDR and path hop count for each routing metric.
In fact, the quality of a route not only depends on the physical distance between sender and receiver, but also on how various factors affect the radio signal at the receiver of each link composing the route. One of these factors is multipath propagation (which is found in indoor and some outdoor scenarios), whereby the transmitted signal and its reflection on surfaces (e.g., walls, tables, ceiling, floor, etc.) reach the receiver by different physical paths. These signal components have different amplitudes and phases, and hence multipath propagation can lead to constructive or destructive interference. In the 2.4 GHz band, which is the one used in the experiments, the quality of the signal received by a node may vary significantly as the node's position changes by a few centimeters, because the signal wavelength is 12.5 cm . Other factors that affect the quality of a given link include obstacle attenuation; the fact that the TelosB antenna is not omnidirectional , and even differences in radio hardware manufacturing. In consequence, the PDR that can be obtained for some receivers may be greater than the PDR obtained for other receivers which are physically closer to the sender.
7.5.2. Path Hop Count
7.6. Cost of Data Packet Delivery
Finally, we study the influence of each routing metric on the cost of data packet delivery, defined as the average number of packet transmissions in the network per delivered data packet. Note that packet transmissions in the network include the transmission of AODV messages as well as data packet transmissions and retransmissions. We also test a Best-Effort (BE) approach for NST-AODV, in which only the initial route discovery takes place for a flow, and no data retransmissions are performed. Thus, we can evaluate how the routing metric affects the cost with the default and BE settings. The latter allows us to obtain a lower bound on the cost with NST-AODV, which can only be measured when the mechanisms of the protocol for connectivity maintenance and reliability are disabled. Of course, the cost benefits of this protocol variant are traded for PDR performance. With this version of the routing protocol, we measured an average PDR of between 51.9% (with the Hop count metric) and 62.9% (with the PATH-DR metric).
8. Performance of Routing Metrics in the Presence of Background Traffic
This section presents an experimental evaluation of the routing metrics under consideration in the presence of background (BG) traffic. First, we present a study on the sensitivity of the LQI to BG traffic. Then, we evaluate the impact of BG traffic on the performance of default NST-AODV with the routing metrics considered in Section 7. The radio chip settings for the experiments were those used in the previous section. In order to make sure that data transmissions were affected by BG traffic, the BG transmitters were set to broadcast packets continuously, that is, these transmission attempts could only be delayed by medium access contention. Note that these are severe background traffic conditions, which are unlikely to be found in real deployments, but which allow us to derive conclusions in a worst case scenario.
8.1. Sensitivity of the LQI to Background Traffic
As shown in Figures 21 and 22 (for Link 1), the LQI is sensitive to background traffic, but the decrease of average LQI, and the increase of LQI standard deviation with background traffic are low. However, LQI-based routing metrics may yield good performance, as the sensitivity of the LQI to background traffic accumulates over all the hops of a path (see Section 8.2). Note that, in Scenario B, Link 2 is severely affected by BG traffic and no packet is correctly delivered (and hence, no LQI values are obtained).
If the RSSI measured by the sender during Clear Channel Assessment (CCA) is greater than the energy detection threshold, after the random backoff, the sender will wait for another random period before trying to access the channel again . This procedure will be repeated up to a maximum number of times before a channel access failure is declared.
Otherwise, a background transmission will appear as interference at the receiver, which can corrupt the received data signal.
Whereas both phenomena may contribute to data packet loss, LQI is only sensitive to the second one. Nevertheless, the TinyOS 2.1.1 IEEE 802.15.4 implementation for the CC2420 radio chip does not limit the number of backoff periods for a sender in a transmission attempt. Due to this reason, the packet losses occurred during our experiments were only due to the second phenomenon indicated.
8.2. Impact of Background Traffic on the Performance of Routing Metrics
As shown in Figure 24, all LQI-based routing metrics outperform the Hop count metric in terms of PDR under all BG traffic conditions. The PDR decreases and the path hop count increases as the BG traffic transmitters are closer to the sender/receiver pair and as the interference level at the receiving end of each link becomes greater. PATH-DR yields the largest paths because it aims at maximizing the PDR, as we also observed in Section 7. The Hop count metric minimizes the path length. However, this metric selects paths that may not include good quality links and may be affected by BG traffic. LETX and ZigBee do not yield the same PDR as PATH-DR, but select shorter paths than those chosen by PATH-DR (see Figure 25).
As shown in Figure 26, in the absence of BG traffic, the PATH-DR metric gives the highest delivery cost. This happens because route failures do not happen often, and hence the number of control packet messages transmitted is low, which benefits the Hop count metric. However, under BG traffic conditions, LQI-based metrics, and in particular PATH-DR, outperform the Hop count metric. In fact, in these conditions, control packets due to route failure and discovery dominate the delivery cost, which benefits the metrics that provide good PDR.
9. Conclusions and Future Work
This paper presents an in-depth, experimental evaluation of LQI-based routing metrics for NST-AODV, which is a one-to-one routing protocol for IEEE 802.15.4 multihop networks.
From a characterization of the LQI, we conclude that a single LQI sample per link is sufficient for route discovery, since high-quality links provide stable LQI values and averaging the LQI for medium quality links does not assure reliable link estimation. The LQI values of route discovery messages are used to estimate link qualities, which are in turn the input for various routing metrics. The metrics considered are the Hop count (which does not take into account link quality), PATH-DR, ZigBee (link quality), and LETX metrics. The measurements were carried out in a 60-node test-bed over 52 different sender/receiver pairs. The influence of background traffic on the routing metrics was also tested.
Results show that PATH-DR obtains the highest PDR and maximizes path lifetime. The good performance of PATH-DR is due to the fact that it tends to select long paths composed of many robust links. LETX and ZigBee metrics are also sensitive to link quality and give a better performance than the Hop count metric, which selects the shortest paths regardless of their quality and suffers frequent path failures. However, both LETX and ZigBee are additive metrics, and therefore tend to select short paths, which may be composed of links that are not as robust as those used by PATH-DR.
With regard to minimizing the number of network transmissions per delivered packet, the best metric depends on the routing protocol settings. If a path failure triggers a route discovery procedure, then PATH-DR compensates its large paths with good stability. Otherwise, under a low rate of routing protocol messages, PATH-DR trades path stability for energy consumption.
The sensitivity of LQI to background traffic is low but sufficient, since the LQI-based routing metrics considered also perform well in the presence of background transmitters.
Although this study has been carried out using NST-AODV as the routing protocol, we believe that it will contribute to understanding the influence of LQI-based routing metrics for other routing paradigms for IEEE 802.15.4 multihop networks.
In future studies, we plan to evaluate the performance of LQI-based routing metrics in a network of battery-powered motes. According to preliminary results, the LQI values measured at a receiver decrease with the remaining energy level of the sender. In consequence, LQI-based routing metrics are also power-aware and can improve network lifetime.
This work is supported in part by the Spanish Government through project TEC2009-11453 and by the i2cat Foundation through the TRILOGY project. The authors would like to thank Sara Berzosa, Raúl Giménez, Tomás García, and Omar García for their contributions, and the anonymous reviewers for their valuable comments, which helped to improve the quality of the paper.
- IEEE 802.15.4-2003 : Wireless Medium Access Control (MAC) and Physical Layer(PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). IEEE Computer Society, October 2003Google Scholar
- IEEE 802.15.4-2006 : Wireless Medium Access Control (MAC) and Physical Layer(PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). IEEE Computer Society, September 2006Google Scholar
- Kim E, Chevrollier N, Kaspar D, Vasseur JP: Design and Application Spaces for 6LoWPANs. draft-ietf-6lowpan-usecases-05, IETF Internet Draft (Work in Progress),November 2009Google Scholar
- ZigBee specification, version r17, ZigBee Alliance, January 2008Google Scholar
- Woo A, Tong T, Culler D: Taming the underlying challenges of reliable multihop routing in sensor networks. Proceedings of the 1st ACM International Conference on Embedded Networked Sensor Systems (SenSys '03), November 2003, Los Angeles, Calif, USAGoogle Scholar
- MultiHopLQI source code, http://www.tinyos.net/tinyos-1.x/tos/lib/MultiHopLQI
- MultiHopLQI source code, http://www.tinyos.net/tinyos-2.x/doc/txt/tep123
- Dohler M, Watteyne T, Winter T, Barthel D: Urban WSNs Routing Requirements in Low Power and Lossy Networks. RFC 5548, May 2009Google Scholar
- Martocci J, de Mil P, Vermeylen W, Riou NBuilding Automation Routing Requirements in Low Power and Lossy Networks, Internet Draft (work in progress), January 2010Google Scholar
- Perkins C, Belding-Royer EM, Das S: Ad hoc On Demand Distance Vector Routing (AODV). RFC 3561, July 2003Google Scholar
- Goyal M: Mechanisms to Support Point-to-Point Routing in Low Power and Lossy Networks. Internet Draft (work in progress), April 2010Google Scholar
- Project Sun SPOT documentation, http://www.sunspotworld.com/docs/
- Stathopoulos T, Girod L, Heidemann J, Estrin D: Mote herding for tiered wirelesssensor networks. Tech. Rep. 58, CENS; December 2005.Google Scholar
- Kim HJ, Kim E, Song WJ, Lee WJ: Architectural design and optimization ofIPv6-based sensor protocols for wireless personal area networks. Proceedings of the 23rd International Conference on Circuits/Systems, Computers and Communications (ITC-CSCC '08), July 2008, Yamaguchi JapanGoogle Scholar
- Chen C, Ma J: Simulation study of AODV performance over IEEE 802.15.4 MAC in WSN with mobile sinks. Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops/Symposia (AINAW '07), May 2007, Niagara Falls, Canada 1: 159-164.View ArticleGoogle Scholar
- Draves R, Padhye J, Zill B: Routing in multi-radio, multi-hop wireless mesh networks. Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MobiCom '04), October 2004, Philadelphia, Pa, USA 114-128.View ArticleGoogle Scholar
- de Couto SJ, Aguayo D, Bicket J, Morris R: A high-throughput path metric formulti-hop wireless routing. Proceedings of the 9th Annual International Conference on Mobile Computing and Networking (MobiCom '03), September 2003, San Diego, Calif, USAGoogle Scholar
- Polastre J, Szewczyk R, Culler D: Telos: enabling ultra-low power wireless research. Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN '05), April 2005, Los Angeles, Calif, USA 364-369.Google Scholar
- Tang L, Wang K-C, Huang Y, Gu F: Channel characterization and link quality assessment of IEEE 802.15.4-compliant radio for factory environments. IEEE Transactions on Industrial Informatics 2007, 3(2):99-110.View ArticleGoogle Scholar
- Srinivasan K, Levis P: RSSI is under appreciated. Proceedings of the 3rd ACM Workshop on Embedded Networked Sensors (EmNets '06), May 2006, Cambridge, Mass, USAGoogle Scholar
- Chen B, Muniswamy-Reddy K-K, Welsh M: Ad-hoc multicast routing on resource limited sensor nodes. Proceedings of the 2nd International Workshop on Multi-Hop Adhoc Networks: From Theory to Reality (REALMAN '06), May 2006, Florence, ItalyGoogle Scholar
- Fonseca R, Gnawali O, Jamieson K, Levis P: Four-bit wireless link estimation. Proceedings of the 6th Workshop on Hot Topics in Networks (HotNets '06), November 2007, Atlanta, Ga, USAGoogle Scholar
- Kim E, Kaspar D, Gomez C, Bormann C: Problem Statement and Requirements for 6LoWPAN routing. draft-ietf-6lowpan-routing-requirements-04, IETF Internet Draft(work in progress), July 2009Google Scholar
- Tavakoli A, Dawson-Haggerty S, Hui J, Culler D: HYDRO: A Hybrid Routing Protocol for Lossy and Low Power Networks. Internet Draft (work in progress), March 2009Google Scholar
- Gungor VC, Sastry C, Song Z, Integlia R: Resource-aware and link quality based routing metric for wireless sensor and actor networks. Proceedings of the IEEE International Conference on Communications (ICC '07), June 2007, Glasgow, Scotland 3364-3369.Google Scholar
- Gomez C, Salvatella P, Alonso O, Paradells J: Adapting AODV for IEEE 802.15.4 mesh sensor networks: theoretical discussion and performance evaluation in a real environment. Proceedings of the 7th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM '06), June 2006, Niagara Falls, NY, USA 2006: 159-167.Google Scholar
- TinyOS Community Forum, http://www.tinyos.net
- TinyAODV implementation, TinyOS source code repository, http://cvs.sourcefourge.net/viewcvs.py/tinyos/tinyos-1.x/contrib/hsn
- Cao Q, He T, Fang L, Abdelzaher T, Stankovic J, Son S: Efficiency centric communication model for wireless sensor networks. Proceedings of the 25th Conference on ComputerCommunications (INFOCOM '06), April 2006, Barcelona, Spain 1-12.Google Scholar
- OLSRD implementation, http://www.olsr.org
- Woo A, Culler D: Evaluation of efficient link reliability estimators for low-power wireless networks. University of California, Berkeley, Calif, USA; November 2003.Google Scholar
- 2.4 GHz IEEE 802.15.4 compliant and ZigBee ready RF transceiver CC2420 Datasheet, March 2007Google Scholar
- TelosB Datasheet, http://www.willow.co.uk/TelosB_Datasheet.pdf
- Chun BN, et al.: Mirage: a microeconomic resource allocation system for sensornet testbeds. Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors (EmNetS '05), May 2005, Sydney, AustraliaGoogle Scholar
- Yarvis MD, Conner WS, Krishnamurthy L, Mainwaring A, Chhabra J, Elliott B: Real-world experiences with an interactive ad hoc sensor network. Proceedings of the International Conference on Parallel Processing Workshops, August 2002, Vancouver, CanadaGoogle Scholar
- Perkins CE, Bhagwat P: Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. Proceedings of the ACM Conference onCommunications Architectures, Protocols and Applications (SIGCOMM '94), September 1994, London, UKGoogle Scholar
- Ramachandran K, Buddhikot M, Chandranmenon G, Miller S, Belding-Royer E, Almeroth K: On the design and implementation of infrastructure mesh networks. Proceedings of the IEEE Workshop on Wireless Mesh Networks (WiMesh '05), September 2005, Santa Clara, Calif, USAGoogle Scholar
- Kim K, Daniel Park S, Montenegro G, Yoo S, Kushalnagar N: 6LoWPAN Ad HocOn-Demand Distance Vector Routing (LOAD). draft-daniel-6lowpan-load-adhocrouting-03.txt, IETF Internet Draft (work in progress), June 2007Google Scholar
- Jetcheva JG, Johnson DB: Adaptive demand-driven multicast routing in multi-hop wireless ad hoc networks. Proceedings of the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '01), October 2001, Long Beach, Calif, USA 33-44.View ArticleGoogle Scholar
- Chen B, Pompili D, Marsic I: Continuous vital sign monitoring via wireless sensor network. Proceedings of Malignant Spaghetti—Wireless Technologies in Hospital Healthcare, November 2008, NewYork, NY, USAGoogle Scholar
- Werner-Allen G, Swieskowski P, Welsh M: MoteLab: a wireless sensor network testbed. Proceedings of 4th International Symposium on Information Processing in Sensor Networks (IPSN '05), April 2005 2005: 483-488. Special Track on Platform Tools and Design Methods for Network Embedded Sensors (SPOTS)Google Scholar
- Avoiding RF interference between WiFi and ZigBee http://www.mobiusconsulting.com/papers/ZigBeeandWiFiInterference.pdf
- URL of the implementation of NST-AODV with Hop Count, PATH-DR, ZigBee (link quality) and LETX http://www-entel.upc.edu/carles.gomez
- Farahani S: ZigBee Wireless Networks and Transceivers. Elsevier, Amsterdam, The Netherlands; 2008.Google Scholar
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