Cross-layer medium access control protocol with quality-of-service guarantees for wireless sensor networks
© Ruiz et al; licensee Springer. 2011
Received: 28 February 2011
Accepted: 23 November 2011
Published: 23 November 2011
There is increasing demand for wireless sensor networks (WSN) to be able to carry real-time information. However, current WSN technologies are not yet capable of offering quality-of-service (QoS) guarantees, which are required to support these types of applications. Achieving QoS is especially challenging in WSNs due to their multi-hop nature and their processing-power, memory, and energy constraints. In this article, we propose a cross-layer architecture in which the medium access control (MAC) and routing protocols collaborate to organize nodes into clusters and to achieve a coordinated time-shared access to the transmission medium. The resulting protocol is called QUAlity-of-service-capable clusTer-based Time-shared ROuting (QUATTRO)-assisted MAC protocol. Our performance evaluation results show that the protocol overhead observed in terms of configuration time, transmitted control messages, and consumed energy is very reasonable and that not only QoS is achieved but also great energy savings by eliminating collisions and considerably reducing idle listening.
Keywordscross-layer protocols medium access control quality-of-service routing, wireless sensor networks
A wireless sensor network (WSN) is a self-configurable multi-hop local area network (WLAN) through which terminals, equipped with some type of sensor, transmit the measured parameters to a predetermined set of information receptacles, called sinks. The main difference between a traditional WLAN and a WSN is that, in general, the latter needs to be very energy-efficient since its nodes are powered with non-rechargeable batteries. Nodes in a WSN are also limited in processing power and memory. In addition, the routing mechanisms of a WSN must be dynamic since static routing would prematurely exhaust the energy of those nodes that participate in the packet-relaying process, in addition to the fact that some nodes can be turned off periodically to save energy or permanently as their batteries die.
There is currently great interest, both technological and economic, in the development of more efficient solutions for WSNs. These greatly-needed solutions, aimed at using the available network resources in the best possible way, include communications protocols to allow orderly and timely access to the transmission/reception medium (medium access control, MAC) as well as neighborhood discovery and intelligent routing, due to the inherent multi-hop nature of this type of networks.
Shortly after publication of the original IEEE 802.11 standard for wireless LANs, its commercial success fueled the development of improved technologies that allowed such networks to go from the initial 2 to 54 Mbps in the 802.11a/g standards, to 600 Mbps in the more recent 802.11n standard and there are even current efforts in the IEEE 802.11ac and 802.11ad study groups to generate technologies in the gigabit-per-second range. A similar evolution can be expected for WSN once their use in commercial applications intensifies. As the WSN technology evolves toward more processing power, more memory, and higher transmission rates, the implementation of systems able to sense and transmit real-time information, such as audio and video, come closer to being feasible. In fact the term wireless video sensor network has been coined (e.g., [1–3]), reflecting the relevance that video transmission in WSN has gained. Examples of potential applications include intrusion detection, object identification and tracking, suspicious behavior classification, etc. These applications need for the system to be able to offer quality-of-service (QoS) guarantees, not yet available in existing technologies. This is a research topic that has recently received a great deal of attention (see e.g. [4–15]), as explained in Section 2.
In addition, researchers have recently realized that the traditional separation of protocols in layers, through the clear definition of the services that each layer must provide and the isolation of each layer's operation, is not the best way to go. In other words, the exchange of information among layers renders a far better performance than working in isolation. This discovery has given rise to the notion of cross-layer protocol design.
With all this in mind, the goal of this study is then to propose a cross-layer architecture in which the routing and the MAC protocols collaborate to achieve an energy-efficient and QoS-aware medium access mechanism for WSN. The tasks performed by the routing protocol include path discovery, resource reservation, cluster formation, and gathering of information as to interference among clusters. The MAC protocol uses that information to create activity-window schedules for clusters to time-share the transmission medium, and uses a collision-free protocol for communication within each cluster. The resulting protocol is called QUAlity-of-service-capable clusTer-based Time-shared ROuting (QUATTRO)-assisted MAC protocol.
The proposed mechanism saves energy by avoiding collisions and by allowing nodes to safely turn off their transceivers outside their activity windows without the risk of losing any relevant frame transmission. It also ensures QoS using a bandwidth-dependence-aware resource-reservation procedure. The concept of bandwidth dependence, introduced in , means that a node will affect and be affected by the transmissions of its one- and two-hop neighboring nodes, regardless of whether or not they belong to common established routes.
It is important to note that this protocol is suitable for fixed nodes only. Assuming node mobility would slow down convergence and highly increase the need for reconfiguration, which in turn would reduce the energy efficiency of the protocol, as explained in Section 5.
The rest of the article is organized as follows: Section 2 compares previously published proposals dealing with the provision of QoS guarantees in a WSN environment. Section 3 describes our proposal in detail. Section 4 describes a typical scenario in which our protocol would be highly useful and describes the different types of guarantees that can be offered, depending on the nature of the traffic carried by the network. Section 5 presents simulation results to evaluate the overhead incurred by the protocol and its ability to actually offer QoS guarantees and save energy at the same time. Section 6 summarizes the conclusions that can be drawn from this study.
2. Related work
Since one of the main goals in WSN protocol design is energy efficiency, many protocols work with low duty cycles by including periods in which the transceiver is put to sleep. SMAC was one of the first protocols designed with this in mind and works by coordinating sleep-wake times for neighboring nodes in combination with a contention-based transmission scheme . Because of its pioneering role, SMAC has become a protocol of reference in WSN. Other examples of protocols that also use low duty cycles and coordinated activity times are T-MAC , RMAC , and DW-MAC . A different approach that has also been explored is asynchronous transmission, in which nodes, instead of agreeing on recurring activity periods, use messages to indicate when transmissions can happen. Examples of protocols using asynchronous duty cycling are B-MAC , XMAC , and Wise-MAC . These protocols succeed in achieving high energy efficiency. However, since they use contention-based transmissions, only achieve high throughput under low traffic conditions.
In addition to addressing energy efficiency, there have been recently a remarkably large number of papers dealing with the topic of QoS in WSN, which is a sign of the importance of solving this problem given its widespread applicability. As a sample, we discuss the benefits and shortcomings of only a few of these proposals.
There are several proposals that consider TDMA as a way to avoid collisions (e.g. [4–6]). TDMA is known to be costly, both in overhead and computation complexity, in addition to requiring a fine synchronization of nodes. Unfortunately, these articles do not describe in detail how nodes can exchange the necessary information to specify the TDMA schedule, which makes it difficult to estimate the overhead (both in control messages and in time) incurred by the protocol. PEDAMACS , for instance, assumes that the sink has unlimited transmission power that allows it to communicate directly with all of the nodes in the WSN, which is a condition that will seldom be satisfied in real systems.
Multipath routing, which consists of giving a node the possibility to use any of several paths to send a message to a particular destination at any given time, is also contemplated as a way for time-constrained messages to avoid congested routes (e.g. [7–10]). The authors of  propose for the sink to classify routes according to delay, reliability, and energy consumption so that nodes can send time-constrained data through the fastest route, error-constrained data through the most reliable route, and non-constrained data through least-energy routes. This is a good method for service differentiation, but it is difficult to offer QoS guarantees since, for instance, the fastest route may not be fast enough under certain conditions. The authors of  propose an adaptive technique based on the ant-colony algorithm in which routes are discovered when needed (reactive algorithm) by sending a probe message. When this message reaches the desired destination through multiple routes, messages are sent back using the reverse paths. These messages mark the nodes visited according to the quality of the route (available bandwidth, expected delay, and loss rate). Then, neighboring nodes share their quality marks so that each node is now able to select the best option when it needs to forward a frame. There is a scalability problem in this case since this algorithm has to be executed for each source-destination pair. In addition, similar to what was mentioned in the previous case, selecting the best local option does not guarantee an end-to-end acceptable performance. In , on the other hand, the emphasis is on reliability, thus the protocol is not applicable for real-time data delivery. Finally, MMSPEED  performs local estimations of the reliability and delay that packets will experience over the different paths available. Packets can then choose the best combination of service options depending on their requirements. Since both delay and reliability depend on the traffic pattern and traffic-forwarding decisions are made based on delay and reliability, this creates a feedback loop that may generate a great variability in the traffic dynamics, and therefore a demand for fast recalculation of the estimates. In addition, nodes are required to constantly keep track of deadlines and elapsed times. The algorithm therefore requires high computational resources from the nodes.
Other articles propose the introduction of priorities as a way to reduce the latency of time-constrained messages (e.g. [11, 12]). Establishing priorities for different types of traffic is again only a service-differentiation mechanism, which means that it is not possible to offer QoS guarantees since all we know is that the service for high-priority traffic will be better, but we do not know if it will be good enough.
Another alternative is the use of congestion control techniques as a reactive way to detect poor performance and to take actions to alleviate it (e.g. [13–15]). These algorithms rely in general on the continuous assessment of congestion levels to decide when to act. Acting implies dropping low-priority traffic and/or notifying upstream traffic sources to reduce their transmission rates. In the former case, there is the implicit assumption that traffic tolerates these losses, which is not always justified. In the latter case, there is the implicit assumption that traffic sources are able to reduce their traffic generation rate, which again is in general not true (e.g. for streaming applications). In addition to that, high overhead may be incurred. Moreover, there is always the risk of losing important data when congestion conditions deteriorate more rapidly than what the system can manage.
Our approach is based on reserving from the very beginning enough resources to guarantee the required QoS performance. A preliminary version of this study was published in .
3. Protocol specification
Our proposed protocol includes different components designed to carry out the following functions:
Route discovery and weight assignment.
Route selection and reservation.
Collection of cluster interference information.
Assignment of activity windows to the clusters.
As far as engineering decisions, we selected the mechanisms that compose this proposal based on the following criteria. The route discovery and weight assignment algorithm was borrowed from an existing protocol, as explained in the following section, because it is very well suited for WSN, in which information has to converge into a node with special responsibilities (the sink) and because the relevant protocol assigns weights to the different discovered paths. This notion of path weight, based on the nodes' anticipated traffic load and remaining energy, proved very helpful for our route selection and reservation phase.
Now the concept of cluster, as will be described more clearly in Section 3.2, was selected because the end result of the route discovery phase is the creation of a route tree, rooted at the sink. In this tree, when several branches converge together into a common node, it means that this common node will be in charge of forwarding traffic on behalf of all of the nodes included in those branches. So, we decided to allow the common node to be the head of a cluster and to have the next node in each branch to also be part of the same cluster; this way we can allow these nodes to communicate by scheduling them to be active at the same time as members of the same cluster.
Regarding the assignment of activity windows to the clusters, described in Section 3.4, we chose a staggered approach according to the depth of the cluster heads (CHs), so that information can move forward in every activity window to a node that has not had yet a chance to transmit in the current cycle. The end result is that all the information that is in the nodes' buffers at the beginning of a cycle will be able to reach the sink by the end of the same cycle, thus reducing delay.
We also selected polling as the method to send traffic from cluster members to the CH during the normal operation of the protocol (after the initial configuration) due to the fact that it is a simple protocol that incurs low and deterministic overhead. Moreover, it is perfectly suited for the master-slave relationship that exists between the CH and the other cluster members.
The idea of having periodic reconfigurations has two goals. One of them is to make sure that routing information remains up to date, and the other is to distribute more evenly the energy consumption among nodes. This idea is also borrowed from the LEACH protocol .
These functions and the proposed methods to carry them out will be described in the following sections. The names of the different messages used within these methods are composed of a prefix that identifies the protocol phase to which they correspond and a suffix that indicates the specific role played by the message. The prefixes used are R (route discovery), W (weight assignment), RS (reservation), CI (cluster interference information), AW (activity window assignment), and RC (reconfiguration).
3.1. Route discovery and weight assignment
The second phase of the relevant routing protocol consists of the transmission of messages of type RALT, used by each sensing node to share with its neighbors the set of alternative routes discovered, either by hearing the route-update message from a node different from its parent or by receiving a RALT message from one of its neighbors. See Figure 1 for a list of fields included in these messages. When the second phase ends, the sensing nodes have discovered all the available minimum-cost disjoint routes to the sink. In our case, the cost is given by the number of hops in the route, but other metrics such as the received power, estimated BER, or level of node conglomeration (anticipated collision rate) could be used as well.
In phase three, each sensing node will send a WPRB (probe) message through each of the routes discovered, including the primary and the alternative ones. When a sensing node receives one of these messages, it will increase a counter of the number of routes that will potentially go through it (num_routes) and will forward the message. The message will eventually reach the sink, which will maintain a table of the paths contained in the received probe messages.
where ε i is energy bottleneck of route i received in related probe response message (or zero if none received), λ i is load bottleneck of route i received in related probe response message (or 1 if none received), h i number of hops in route i, β ∈ (0, 1) is the factor defining the desired impact of the number of hops on the weight.
3.2. Route selection and reservation
The second step is the reservation of resources over one of the existing routes between each sensing node and the currently active sink. Every sensing node will become a traffic source during the normal operation of the system, and the selected route has to be capable of satisfying the QoS requirements of the traffic that will be generated. Routes are reserved in a link-by-link fashion. When a link reservation attempt is successful, both nodes become part of a cluster and the node that agrees to forward the requesting node's traffic becomes the CH. The cluster will grow as more nodes select the same CH to be the next node en route to the sink. As we can see, in this proposal a cluster is defined as a set of one-hop neighboring nodes that need to communicate directly, and the goal is to coordinate the sleeping and waking patterns of all the nodes in a cluster. The cluster ID will be the same as that of the CH. We assume that there is no data traffic flowing from the sink to the sensing nodes, as is typical in WSNs; there may be control traffic flowing in this direction (from sink to sensing node), but it will be in general less intense and non-real-time, thus no reservation is necessary for this traffic.
Notice that a node that agrees to forward other nodes' traffic will in fact be a member of two clusters, one in which it is the head (in charge of collecting data from its members) and another one in which it is just another member (its function in this case is to forward to the new CH the data collected previously plus that generated locally).
We argue that guaranteeing a certain amount of bandwidth is enough for QoS assurance, i.e., throughput, delay, jitter, and losses due to buffer overflow will be bounded as a consequence, as explained in . This will be discussed in more detail later.
The bandwidth management procedure will be as follows. All nodes start with an available bandwidth equal to the wireless channel's achievable effective data rate R, which depends on the underlying physical layer and medium access policy. For instance, in a polling-based system such as the one assumed in this work, the achievable throughput is about 85% of the channel gross bit rate . In addition, R can be reduced further to account for the expected channel errors, which are present in any transmission medium but are especially common in wireless environments.
In the previous equation Bavail is the bandwidth still available for new reservations, Bcommitted is the total bandwidth that the relevant node has committed to forward on behalf of other nodes, Bown is the traffic that the node itself will generate, and Boverheard is the amount of bandwidth committed in reservations that the node has overheard. Bcommitted is counted twice since the same channel will be used by the node to receive and forward the traffic.
Let us denote by Breq the amount of bandwidth to be reserved by a node, which in general will be equal to Bcommitted + Bown. The formats of the reservation-associated messages are shown in Figure 4. If the reservation-requesting node is only one-hop away from the sink, it will need no more verifications than Equation 2 before sending a RSRQ (reservation request) message to the sink asking if it is able to reserve the necessary bandwidth, and will wait for a response. If, on the other hand, the node is more than one-hop away from the sink, it will only send the reservation request to the next node in the selected route if its own available bandwidth is at least equal to Breq, in anticipation of the reservation that the next node in the path will have to make to further forward the traffic. All the nodes that can overhear the message will also check if their available bandwidth is at least equal to Breq; if not, they will send back a response message indicating that the new reservation is not possible. In turn, the node for which the reservation request message was addressed will check if its own available bandwidth is at least one, two or three times Breq, depending on whether it is the sink or a node one-hop or at least two-hops away from the sink, respectively, again anticipating the reservations that it itself and the next node in the path will have to make to further forward the traffic; if everything goes well, it will send a positive response as expected and will start over to reserve the next link in the route. If a node that had not heard the original reservation request hears the response, it will also verify its available bandwidth and, if it finds that the new reservation represents a problem, it will send a response message to indicate it. When a node sends a negative response, it will include in the Balt field the amount of bandwidth it has available as a way to help make an alternative reservation.
If a reservation fails, the requesting node will try the same reservation request on another route, selected again based on their weights. If all routes fail, it will reduce the amount of bandwidth needed by canceling the agreements previously made with some of its cluster members, if any, and try again on the route with the most available bandwidth. When a node has to select another route because a reservation attempt failed, it will send again an intention message before the actual reservation request. If the next node already sent its own reservation request, it will forward the intention message to the next node in the relevant route. The idea here is that, even if some reservations take longer than expected, the sink should be aware that it has to wait a little longer before passing to the next step in the setup process, which would be the collection of interference information.
The actual reservation procedure will be started at the sensor nodes when a timer expires indicating that a sufficiently long time has passed without overhearing new RSINT messages, as explained above. Those nodes that did not receive intention messages (so-called leaf nodes) will send their RSRQ message immediately and will set Breq to Bown since they do not need to forward any data on behalf of other nodes. If, on the other hand, a node did receive one or more intention messages, which turned it into a potential CH, it will start a new timer to know how long it should wait for the RSRQ messages indicated by the intention messages previously received. In other words, it will not start the reservation of its own resources until it has received all of the expected RSRQ messages or the relevant timer expires. When that happens, it will send a RSRQ message to the next node in its selected route asking for enough resources to send its own traffic plus that of its cluster members; that is, it will set the field Breq to Bcommitted + Bown.
In addition to listening to these messages to keep track of the bandwidth usage in their neighborhood, nodes will also extract from RSRP and RSACK messages information as to the creation of new clusters that can potentially interfere with them, which is needed in the next protocol step.
3.3. Collection of cluster interference information
Information relative to the potential interference among neighboring clusters has to be collected to enable the scheduling of waking times of the different clusters without damaging overlaps. To do that, at the end of the route reservation phase, the sink will broadcast a CISTART (start of cluster-interference information collection) message that will be flooded over the network so that all nodes know that this new phase has started. These messages only have two fields, mtype set to CISTART and mid set to the current route update cycle number.
As the sink receives the CIINFO messages, it will create a temporary array called temp_sched in which clusters will be sorted by their depth. Each array element will contain the cluster ID, its depth, the path to reach it, the bandwidth required by its members and a list of interfering clusters resulting from merging the local interf and cluster interf fields.
3.4. Assignment of activity windows to the clusters
At the end of the process described in the previous section, the sink will have enough information to assign sufficiently long activity windows to the different clusters in such a way that they do not interfere with each other. The assignment of activity windows to the clusters is the last stage of what will be referred to as the setup process. It is important to mention that the setup process will be repeated periodically, with a relatively low frequency (possibly in the order of hours or days) that depends on the size and density of the network, on the residual energy of nodes, on the traffic intensity, and/or other factors, with the goal of reassigning the responsibility of frame forwarding, hence redistributing the energy consumption to extend the system lifetime.
The process to assign activity windows to the clusters starts when the sink receives interference information messages from all of the CHs in the network or a timer expires. It will then create a two-dimensional structure called cluster_sched in which elements in the same column are such that they have the same depth and do not interfere with one another, and for that reason they can be scheduled to be active simultaneously; similarly, the clusters in the leftmost column will be scheduled first in each cycle and those in the rightmost column will be scheduled last. To achieve this, the sink removes one by one the clusters from the temporary array temp_sched it created before. If there are no columns in cluster_sched containing clusters of the same depth as the one just removed from temp_sched, the sink will insert it into the next empty column. If, on the other hand, there is already at least one column in cluster_sched containing clusters of the same depth as the cluster just removed from temp_sched, the sink will try to add this new cluster into one of such columns verifying first if there is no potential interference; if the cluster can be accommodated in more than one column, it will be included in one in which the difference between its required bandwidth and the maximum required bandwidth of clusters already in the column is either negative (if any) or as small as possible (if all of them are positive); if the cluster cannot be accommodated in any of the columns already in use due to potential interference, it will be inserted into the next empty column.
where R is the wireless channel's achievable effective data rate, Bcommitted(j) is the bandwidth needed by CH j to collect data from its children, and Tcycle is the duration of each cycle. Tcycle should be small enough so that the maximum delay that a frame will experience from the time it is generated to the time it is received by the sink is within acceptable values, and large enough to allow as many frame transmissions as needed by the members of all clusters. Notice that the maximum delay that a frame can experience is upper-bounded by twice the duration of a cycle. The reason for that is the fact that a frame can be generated after the activity window of the node's cluster has already passed within the current cycle, which means that it will reach the sink by the end of the next cycle. From here, the maximum delay tolerated by the application being served should be less than 2 Tcycle.
it means that the system does not have enough bandwidth to satisfy the QoS requirements of its nodes. When this happens, a topology-control mechanism may optionally be run  to decide if a reduced number of nodes (hence a smaller amount of traffic) would be enough to satisfy coverage and connectivity requirements. Topology-control mechanisms are not required for the proper operation of QUATTRO and are therefore out of the scope of this article.
Notice that the reservation procedure is in charge of a first assessment as to whether the system has enough resources to satisfy QoS requirements. However, allocating different activity windows to the clusters means that, when a given node is transmitting, all members of every neighboring cluster have to be silent, and not only those that can experience a collision with the relevant transmitting node. In other words, allocating activity windows is more resource-demanding than local bandwidth reservation, meaning that the latter may be successful and still the former can fail.
The leaf nodes will acknowledge receipt of the AWLN message by sending an AWACK message. A CH will wait to receive AWACK messages from all of its cluster members to generate its own AWACK message. This process continues until the sink eventually receives the corresponding acknowledgments from its own cluster members.
The system is now ready to start working in a QoS-aware, collision-free, safe-sleep fashion. To make that stage start, the sink sends out a GOAHEAD message that is flooded throughout the network to make every node aware of it. This message contains the time at which the first cycle will begin, as shown in Figure 11, which is what nodes still need to know in order to calculate their sleep and wake times. The duty cycle is included so that nodes can know what fraction of time during each cycle the whole system is idle; this information will be useful when the network has to be reconfigured, as explained in Section 3.7.
Up to this point, communication among nodes takes place using a contention-based MAC protocol, such as CSMA/CA in its IEEE 802.15.4 or 802.11 versions. If IEEE 802.11 (DCF) is used, it would be advisable to force nodes to backoff before every transmission, even when the channel is initially idle, to reduce collisions.
3.5. Normal operation phase
During the normal operation phase, the only active nodes will be those corresponding to the clusters whose activity window includes the current time. Access to the channel is not through contention anymore, but cluster members will wait to receive a poll from the CH indicating that the receiving node is allowed to send. The polls may allow the transmission of one frame at a time, or all frames corresponding to a single upper-layer packet (MSDU), or as many frames as possible during a pre-specified amount of time (TXOP), proportional to the amount of bandwidth requested during the reservation phase. In our simulations, we adopted the TXOP approach.
A node that receives a poll will respond with a Data frame if it has information to send, indicating respectively with the More_Frag and More_Data bits if there are more frames still to send corresponding to the packet being transmitted and if there are more packets to forward in addition to the one currently in service. If the node does not have information to send, it will respond with a Null frame. Either way, the CH can detect when a node has finished sending the information it stored in its buffer and stop polling it during the current cycle. The member node can go to sleep at this time to further save energy. If all the member nodes go to sleep before the end of the activity window, the CH can follow suit. This approach is designed to save energy by avoiding collisions and idle listening as much as possible.
If a node fails repeatedly to respond to polls, an error message will be generated by the CH to alert the sink.
3.6. Network-wide synchronization
Synchronization is very important in time-shared access protocols, such as this one, to keep activity windows from overlapping because of clock drifts. To achieve synchronization, a mechanism such as the one described in [30, 31] can be used from the beginning of the initial setup process. As soon as a node identifies its parent node through the process described in Section 3.1, it can start exchanging synchronization messages with its parent and adopting its clock. This will eventually cause for all nodes to be synchronized to the sink's clock. After a successful reservation procedure, the node can start exchanging synchronization messages with its selected CH and again adopting its clock. This process can continue during the data exchange within the normal operation phase to avoid considerable drifts. Poll, Data and Null frames can carry time stamps to achieve this task.
Notice that even though this is a time-shared access technique, there is no need for nodes to be very precisely synchronized as in TDMA-based systems. The reason for this is the fact that, even during the polling-based data transmission, this remains to be a random-access technique in the sense that every transmission includes a preamble that indicates when a frame is about to begin. Dividing time into activity windows is only used as a reference to specify, with relatively low accuracy, when attempts to access the medium are allowed to be made.
Being a CH can be energy consuming because they have to be awake during two activity windows and because, in addition to sending their own collected data, they have to forward those of their cluster members. That is why nodes will alternate taking the role as CHs by rerunning periodically the setup process. The redistribution of energy consumption relies on the fact that nodes select a route based, among other aspects, on the available energy of the weakest node in that path, as explained in Sections 3.1 and 3.2. If there is only one sink in the system, the redistribution of energy consumption may not be as effective as we would like since nodes closer to the sink will always carry more traffic than those farther away. Hence, having several sinks and alternating their activity is advisable.
In preparation for the beginning of the new setup procedure, after some time of working in the normal operation phase, the current sink will broadcast a RCWK (wake up) message that will be repeatedly rebroadcast by CHs, at the beginning of each subsequent cycle, with the goal of eventually reaching the sink that has to be active during the next superframe. When the sought-after sink receives the RCWK message, it will immediately respond with a RCWRP (wake-up response) message to confirm that it is ready to play its role. The CHs that hear the RCWRP message will forward it until it reaches the currently-active sink; they will also stop rebroadcasting the RCWK message previously received. It is clear that sleeping sinks will have to turn on their receivers periodically to listen for wake-up messages. It is also recommended for them to use overheard synchronization-related messages to remain synchronized with the currently active sink. If no answer to the RCWK is received during a certain number of cycles, the current sink will try to wake up another sink or, if none responds, it itself will remain active during one more superframe.
The next step is for the currently active sink to broadcast an reconfiguration notification (RCN) message indicating that the setup phase has to start again. Receiving an RCN message indicates a CH to stop rebroadcasting the RCWK message if it has not stopped yet. If there is only one sink in the whole network, the RCWK and RCWRP messages will not be used and the sink will proceed immediately to send the RCN message.
The reconfiguration procedure can also be initiated if a severe problem is detected in the system, such as the loss of a CH, which would isolate a set of nodes from the sink. If this happens, an error message will be generated by the node detecting the problem and forwarded until it reaches the sink, as mentioned in Section 3.5. Depending on the extent of the damage (e.g. number of nodes that have failed) the sink can decide if a complete setup procedure is needed, or if a fast reconfiguration might be enough. This will again be indicated in the reconfig_type field of the RCN message transmitted by the sink. In a fast reconfiguration, nodes do not go through the route discovery and weight assignment phases, described in Section 3.1, but use the information collected during the most recent setup process and go straight into the reservation phase described in Section 3.2. In fact, nodes that have not been disconnected can send the RSRQ message to the same node currently acting as its CH, which will have a high probability of accomplishing a successful reservation, thus saving time and energy. Regardless of whether the reconfiguration is complete or fast, it can be carried out using the unused portion of time (normal) or stopping data transmission to use the whole bandwidth (exclusive).
A node will know that it has become isolated from the rest of the network if polls are not received from its CH during several consecutive cycles. When that happens, if the node is itself a CH, it will stop polling its cluster members to indicate them that their data cannot be forwarded all the way up to the sink. To be reconnected to the rest of the network, isolated nodes will remain awake (during the time that the system would otherwise be idle, if the duty cycle is sufficiently less than 1, or constantly if not) waiting for the RCN message that will start a new reconfiguration process, allowing them to select a new CH.
4. Applicability of our proposal
The following is a typical scenario in which our protocol would be highly useful. Assume a security surveillance system either for home or for office, commercial or government buildings. Assume that sensor nodes are equipped with at least two types of sensors, one to detect the presence of intruders (motion sensors) and another to collect video once suspicious activity has been identified. Data collected by the motion sensors may not be continuously transmitted to the sink, but can be used locally by sensor nodes to know when to alert the sink and when to activate the video sensors. Video information, on the other hand, will be transmitted continuously to the sink once a video sensor is activated. If there are intruders in the building, suspicious activity will eventually be detected by several motion sensors located within a certain region, and the corresponding video streams will have to start flowing concurrently toward the sink. Even if not all video sensors become active at the same time, the fact that a set of close neighbors become active concurrently makes reservation of enough resources, as proposed in this study, to be necessary and pertinent.
A similar scenario would be a surveillance system for the battlefield. Sensor nodes may be equipped to detect the presence of enemy forces and, once identified, to start collecting video so that military personnel analyzing the data collected at the sink in real time can estimate the number of rival troops that they are faced with and identify the type of equipment they have.
Our proposed protocol can work with different types of traffics. If the maximum amount of data that can be generated by a node during a cycle can be known beforehand, such as in constant-bit-rate or leaky-bucket-constrained streams , then deterministic (hard) QoS guarantees can be offered. If, on the other hand, traffic presents a higher variability so that reserving enough resources to process the maximum amount of data that can be generated during a cycle is not practical, then stochastic (soft) guarantees can still be offered, in the sense that QoS requirements can be violated but with a quantifiable low probability. It is out of the scope of this study to design a method to calculate the necessary amount of bandwidth that has to be reserved so that, when the offered traffic varies according to a certain random behavior, the probability of violation of QoS guarantees is upped-bounded by a desired value.
It might be a little premature to think of implementing this proposal in real devices, especially because video transmission requires high bandwidth capabilities that, to the best of our knowledge, are not yet available in currently commercially available motes. The protocol itself, however, is not as demanding in terms of computer power as it may seem. The protocol operation is divided into well-defined states, as shown in Figure 13, and in each state only a small subset of messages and events are expected; similarly, the corresponding actions only involve the transmission of new messages and the starting or stopping of some timers. No complicated calculations are required from the nodes. Nonetheless, there is currently a fair amount of research in this direction because, as mentioned in the introduction, technology advances very rapidly, especially when it is driven by commercial success.
5. Performance evaluation
We first evaluate setup costs measured in terms of time and energy, which can be considered as the price to pay in overhead for the possibility to work not only with QoS guarantees, but also without collisions and with minimum idle listening, thus achieving high energy savings.
The second evaluation focuses on the performance of our proposed protocol during data transmissions in the normal operation phase. In this case, performance is measured in terms of achieved throughput, delay, and energy consumption. To put QUATTRO into perspective, we compare it with SMAC  which, as mentioned in Section 2, is one of the most important protocols of reference in WSN. Even though SMAC was not designed to provide QoS, the fact that its main objective is to save energy allows us to assess how energy-efficient our proposal is in addition to providing the other already mentioned benefits.
In the previous two analyses, we use random deployment of nodes in the selected area. When this is the case, it is common that some clusters do not interfere with others, which allows for some bandwidth and time gains by scheduling non-interfering clusters to be active simultaneously. To analyze the limit case in which there are no such bandwidth or time gains, we also include in this study the case in which nodes are located in a row, in the sense that each node has connectivity with at most two nodes, one that becomes its parent and the other, if any, that becomes its child. This scheme is more restrictive because of the fact that a CH has to wait to receive data from all of its children before starting its own transmission.
Finally, we evaluate reconfiguration costs, which allows us to analyze in detail the tradeoff between the time and energy invested and the benefits achieved in return when the different options are considered: normal or exclusive, and complete or fast reconfigurations. We also examine how far apart reconfiguration periods should be for the protocol to remain efficient.
We evaluated the performance of our proposal through simulations using OPNET . Ten simulations are run for every case, each with a different seed for the random numbers, and the results shown correspond to the average values and the associated 95% confidence intervals. We assume an underlying IEEE 802.11-compatible physical layer running at 1 Mbps. This assumption is based again on the fact that high bandwidth is required from nodes. In our models, no frames are lost due to channel errors, but all losses are due to collisions. If the system is to be analyzed with channel errors, then this fact has to be taken into consideration when the wireless channel's effective data rate R is estimated, as mentioned in Section 3.2. It is worth emphasizing that, even though channel errors were not considered in our simulations, provisions are made to recover from them in the protocol.
The coverage area or transmission range of each node is assumed to be 10 m. Frames containing control messages are 100 bits long and data frames are 1 kbits long.
5.1. Setup cost evaluation
One in which the number of nodes is fixed to 100 and the total deployment area is varied, and
Another in which the deployment area is fixed to 25 × 25 m2 and the number of nodes is varied.
The goal in both cases is to analyze the system under different node densities. The difference between them lies in the fact that, as node density is increased in the first set of simulations by reducing the deployment area, the maximum number of hops is eventually reduced to 1, which does not happen in the second set of simulations. The sink is always assumed to be in the center of the deployment area.
We can see in both scenarios that the time needed for the entire setup phase is less than a minute. We can also see that the price to pay in terms of extra transmissions is about 70 control messages in the worst case. We believe that the overhead incurred by our proposed protocol, both in time and control messages, is very reasonable given the great benefits obtained when the system enters the collision-free transmission phase.
The energy consumption is based on Atheros energy model for WLAN products . The power consumption assumed is as follows: Transmit = 2 W, Receive = 0.9 W, and Listen = 0.8 W. WLAN devices are usually expected to have coverage areas much greater than the 10 m assumed in our simulations for a WSN. These results are presented for illustrative purposes only, but sensor nodes in a real system may consume far less energy.
5.2. Performance during data transmission in random deployment
To evaluate the performance of QUATTRO during data transmission, we reproduced the second set of simulations in Section 5.1, in which an increasing number of nodes are randomly deployed in a fixed-size area. In each simulation, sensor nodes spend 60 s transmitting data to the sink.
Each sensing node generates data traffic at a constant bit rate (CBR) of 4 kbps. Notice that handling CBR traffic can be considered the worst-case scenario if we are dealing with hard QoS guarantees. The reason for this is the fact that, if traffic were generated with variable rates, we would have to reserve enough bandwidth to transmit the maximum amount of data that may accumulate during a cycle, which means that the offered traffic would be on average less than what the network can handle. If the traffic is CBR, on the other hand, the amount of traffic generated by the nodes during each cycle is comparable to the maximum that the network can handle.
We use the following metrics to evaluate our proposal and compare it to SMAC:
Average throughput, defined as the fraction of the frames generated by the sensor nodes that successfully arrive at the sink.
Average frame delay, defined as the average time elapsed from the generation of a data frame at a sensor node to its arrival at the sink.
Average amount of energy consumed to transmit data frames, per sensor node over the entire simulation time.
Fraction of time on, defined as the percentage of time that sensor nodes remain awake on average, regardless of whether they are transmitting, receiving or listening idly.
We compare QUATTRO and SMAC using the same scenarios, routes, and data transmission rates. The cycle duration in both QUATTRO and SMAC is 0.25 s and the selected duty cycle for SMAC was 10%. Note that, according to the way SMAC was designed, a node will remain awake more than the designated duty cycle if there are data frames pending to be transmitted in which it itself is the transmitter or the intended receiver. We include results for SMAC using 7 and 255 as the maximum number of retransmission attempts.
Notice that, as explained in Section 3.4, we designed our protocol in such a way that the maximum delay tolerated by the application being served is equal to twice the duration of a cycle. Therefore, we are implicitly assuming that the maximum tolerated delay is 0.5 s. With the information provided by Figures 19 and 20 we can conclude that QUATTRO succeeds at delivering every frame generated by the sensor nodes within the required timeframe, while SMAC in most cases exceeds the maximum delay or even ultimately fails to deliver some of the frames (or most of them, in some cases) after attempting all of the allowed retransmissions.
From the results presented here we can conclude that our proposed protocol improves transmission efficiency, thus being able to provide QoS guarantees without having to pay a price in terms of increased energy consumption; on the contrary, energy efficiency is greatly improved as well.
5.3. Performance during data transmission when nodes are deployed in a row
We ran the simulations with topologies including from 2 to 18 sensor nodes plus the sink. We assumed again that the traffic generated by each node is CBR at a rate of 4 kbps. In each simulation, nodes are active generating traffic during 60 s. The metrics used to evaluate and compare both protocols here are the same as those described in the previous section.
5.4. Recovery of setup cost
We consider the energy spent during setup as an investment that will bring about, among others, the benefit of saving energy during the normal operation phase. In this section we analyze how long it takes for the energy invested during setup to start paying off.
In the above equation, E0 is the average energy spent per node during the entire setup period, E nb is the average energy spent to transfer each data bit from its origin to the sink during the normal operation phase of the protocol, and Nb (t) is the number of data bits that have been received at the sink up to time t. It is clear that, for very large values of t, E b (t) will approach E nb , meaning that the initial investment E0 has been diluted to the point that it is insignificant.
For this analysis, we consider the set of simulations described in Section 5.2 for the specific case in which the node density is 70 n/CA.
5.5. Cost of the different reconfiguration options
As mentioned in Section 3.7, when a reconfiguration is needed, nodes can use the time in which the system would otherwise be idle to start exchanging control messages (normal) or they can instead stop temporarily the data transfer phase to use the entire time for control messages (exclusive). It was also explained that the reconfiguration procedure does not necessarily have to be complete, but it can be sped up using the routing information collected previously and by going straight into the reservation phase (fast). Given the expected time to carry it out and the amount of data that may be lost if reconfiguration is due to node failures, we analyze the conditions under which it is better to perform a complete or a fast reconfiguration, using only the otherwise unused time or stopping data to run an exclusive reconfiguration.
Fast reconfiguration using only idle time (labeled fast)
Complete reconfiguration using only idle time (labeled complete)
Complete reconfiguration using the entire time (labeled exclusive)
To recover from node failures, assuming that only a few nodes break down simultaneously (e.g. 1 or 2), we can see that it clearly makes sense to run fast reconfigurations when node densities are less than 50 n/CA since it is faster than running an exclusive complete reconfiguration and there is no need to stop the data transfer of nodes that are still connected.
It even makes sense to run a fast reconfiguration for higher node densities if the percentage of nodes that become disconnected is below a certain threshold. The criterion used to decide here is the amount of data that would be lost as a consequence of the time taken to complete the reconfiguration. In other words, if a small number of nodes are disconnected during the relatively long time needed to carry out a normal fast reconfiguration, we might lose less data than if we stop data transmission from all nodes during the shorter period of time needed to run an exclusive complete reconfiguration.
If several nodes (e.g. 3 or more) fail roughly at the same time, it may not be prudent to run a fast reconfiguration since the routing information stored at the sensor nodes may need to be updated to reflect the new topology. If that is the case, a complete reconfiguration is advisable, but the sink still has to decide if stopping data transmission from all nodes during the time needed to run an exclusive reconfiguration would cause more/less data losses than running a normal reconfiguration losing the data generated only by the disconnected nodes during a longer time.
When the reason for reconfiguration is simply that it is time to run the setup process again to redistribute energy consumption, as opposed to node failures, then it is advisable to select a complete normal reconfiguration. The reason to select the normal option is that it is better not to stop collecting data, and the reason to choose the complete option is the fact that there is no urgency to finish precisely because of the fact that the data generated by the sensor nodes is not being lost.
When nodes fail, it is possible that some other nodes will become disconnected from the rest of the network. Figure 30 also shows with a doted line the percentage of nodes that need to be disabled on average for the network to become disconnected, as a function of node density. It is to be expected that networks with lower node densities may become disconnected with fewer disabled nodes since they do not provide as many alternative routes. However, even for the lower densities examined in this study, we can see that when 75% of nodes fail the network will still remain connected with a high probability.
We have proposed an architecture in which the MAC and routing protocols collaborate to discover routes and reserve resources, to organize nodes into clusters and to schedule the access to the transmission medium in a coordinated time-shared fashion. As a consequence, we are able to offer QoS guarantees in addition to achieving great energy savings by eliminating collisions and considerably reducing idle listening. We evaluate our proposal using simulations and our results show that the protocol overhead observed in terms of configuration time, transmitted control messages, and consumed energy is very reasonable. When data transmission begins, we can see that the energy invested during setup starts paying off immediately in terms of QoS and soon after in terms of energy efficiency.
In addition to that, we also evaluate the performance of our protocol QUATTRO during data transmission under different node densities and topologies. We conclude that the QoS guarantees promised during setup in terms of delay and throughput are satisfied in all cases. We compare the performance of QUATTRO to that of SMAC, which is one of the first protocols designed specifically for WSN and which, precisely because of its pioneering role, has become a protocol of reference in sensor networks. Our results show that QUATTRO outperforms SMAC in its ability to provide QoS guarantees and also because of the fact that it uses energy more efficiently.
In addition, we propose a fast reconfiguration alternative that can be used when only a few nodes fail at about the same time. Also, depending on the number of nodes that become disconnected when one or more CHs break down, we offer the option of stopping data transmission during reconfiguration or continuing to gather the data generated by the sensor nodes that remain connected. We analyze these different reconfiguration alternatives and provide guidelines that can be used for the sink to decide when to choose one option over the others.
Overall, we can conclude that QUATTRO possesses all of the characteristics required in a QoS-oriented protocol designed to be used in a WSN environment, in the sense that it is able to satisfy QoS guarantees and achieve great energy efficiency.
The authors would also like to thank the anonymous reviewers for their valuable contributions to improve and enrich the presentation of the material included in this article.
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