A Selective-Awakening MAC Protocol for Energy-Efficient Data Forwarding in Linear Sensor Networks

1. Introduction

Internet of Things (IoT) is a network paradigm that is playing a key role in the development of smart environments where everyday objects are augmented with computational capabilities and Internet connectivity [1, 2], so that they can interact with each other in order to share information about the environment, coordinate actions autonomously, and fulfill other specific purposes such as target tracking, medical supervision of patients, and animal monitoring [3–5].

IoT has been proposed as a core technology for establishing what it is known as Advanced Measurement Infrastructures (AMI) which are responsible for monitoring public distribution networks of water, electricity, or gas [6–8]. With this technology, it is possible to identify leaks along lines or pipelines and optimize the operation of these distribution networks.

IoT applications have also been identified as a mean for fostering smart mobility. In these scenarios, a set of sensors are located along the road infrastructure in order to coordinate urban transport, monitor events that affect mobility, or manage transit through dynamic coordination of traffic lights [9, 10]. The objective is to extend the lifetime of the road infrastructure, improve safety, decrease commuters’ travel time, and diminish air pollution in densely populated cities.

As in the previous examples, when the sensing devices are deployed along linear structures, the resulting wireless sensor network (WSN) is usually denoted as a Linear Sensor Network (LSN) [11]. In particular, when the node density is high, as it is commonly the case when a wide variety of variables are being sensed [3], the LSN is named a thick LSN [12]. As in any WSN, one of the main goals when designing an LSN is to provide high throughput in an energy-efficient way [13], which could be achieved by defining appropriate Medium Access Control (MAC) protocols.

Among the large body of work devoted to designing efficient MAC protocols that improve energy consumption, synchronized duty-cycling protocols [14] have been shown to be particularly effective in the context of MANETs [15], WSNs [16–18], and LSNs [3, 19, 20]. Building on the success of duty-cycling, complementary techniques have been proposed to further improve the performance of LSN; for instance, Tong et al. introduced the PRI-MAC protocol [19] which implements a pipelined scheduling as a complement for a duty-cycled protocol that is based on S-MAC [21].

In pipelined scheduling, nodes are classified according to their grade (distance in hops to the sink node) and nodes with the same grade share the same schedule; namely, they synchronously enter the different modes in the following sequence: one reception slot, one transmission slot, and ξ sleeping slots. Grade schedules are arranged in such a way that packets can be forwarded from one grade to another in a pipelined fashion. This way, a packet can traverse a source-to-sink path in a single cycle if there are no other packets at the queues of the nodes along its path, which is the main advantage of this strategy over other proposals.

As shown in Section 7, the first rule simultaneously reduces node energy consumption and the probability of having a packet collision, without reducing throughput or requiring extra signaling. The second rule also reduces energy consumption without affecting any of the network performance metrics, because it avoids wasting energy to receive a packet that will be dropped. To the best of our knowledge, these strategies, as well as their analysis and evaluation, have not been previously reported in the context of LSN or IoT applications.

In concordance with previous work (e.g., [16, 19, 21]), our proposed protocol is specifically designed to operate in an LSN with a single sink node at one end of the network, and the remaining nodes can both generate new packets and act as relays for other nodes without performing any data processing or aggregation. In addition, data packets are transferred between nodes located in adjacent grades by means of unicast transmissions. A detailed description of the assumptions and operation scenarios is presented in Section 3.

In order to evaluate the performance of SA-MAC, we developed a mathematical model of the nodes’ queue length based on a Discrete-Time Markov Chain (DTMC). From the steady-state probabilities of this DTMC, we developed expressions for the main system performance metrics, namely, energy consumption, throughput, packet loss probability, and source-to-sink delay. This analysis is similar to those presented in [16–19] and is validated through extensive discrete-event simulations.

Our numerical results show that SA-MAC clearly outperforms previous work in terms of energy consumption, packet loss probability, and throughput, at the cost of a moderate increase in source-to-sink delay. This is particularly true under high node density and high traffic load conditions, which are expected to be a possible scenario in the context of IoT applications [1].

Lastly, it is important to mention that our analysis is intended to evaluate not only the general performance of the network, but also the performance at each grade. This detailed analysis reveals that the network performance is not homogeneous and makes it possible to identify those specific grades that may compromise the whole network lifetime and reliability. This is another major contribution of this paper because it provides a guideline for the design of pipelined LSN.

The rest of the paper is organized as follows. In Section 2 we discuss a representative sample of previous work in duty-cycled MAC protocols for multihop WSNs and LSNs. In Section 3 we present a general overview of the proposed protocol. Then, in Section 4 we derive the DTMC that models our system. In Section 5 we present expressions for the network performance metrics. In Section 6 we provide criteria to select the awakening probability. Lastly, we discuss relevant numerical results and present our conclusions in Sections 7 and 8, respectively.

2. Related Work

Synchronized duty-cycled protocols for WSNs have been extensively studied because of their energy efficiency, which is achieved by reducing idle listening and overhearing. In these schemes, nodes synchronously wake up in order to transmit/receive one or several packets during a cycle. One example of this kind of protocol is Sensor MAC (S-MAC), which was proposed by Ye et al. [21]. In S-MAC, a fully connected group of nodes share the same schedule; namely, all the nodes synchronously enter the transmission/reception mode to exchange information and, then, all of them synchronously enter sleeping mode to save energy. One disadvantage of S-MAC is that it substantially increases source-to-sink delay because nodes may wait a complete cycle in order to attempt a transmission; moreover, this disadvantage is accentuated when multiple hops are required because the delay accumulates at each hop.

Yang and Heinzelman [16] provide a model, based on a DTMC, to evaluate the performance of duty-cycled protocols. In their model, the Markov chain states represent the nodes’ queue length; and the chain solution is used to evaluate throughput, delay, and power consumption. This kind of analysis has been utilized in subsequent works (e.g., [17, 18]); however, the analysis in [16] is restricted to one-hop WSNs, whereas more recent works, including [19] and our work, have extended the analysis to study LSNs.

On the other hand, in recent years, multiple works have proposed synchronized duty-cycled protocols for multihop WSNs, including [17, 18, 22–24]. In the following paragraphs, we present a brief overview of each of them.

Guntupalli and Li [17] propose another synchronized duty-cycled protocol for multiple packet transmission per cycle. In this scheme, which is called DW-MAC, multiple packet transmission is enabled by means of competition-based reservations established during a particular time interval, called the data period. The authors present a DTMC-based analysis that characterizes the energy consumption and the lifetime of the network. It must be noticed that although their study includes multiple hops, it is limited to a scenario where there is only one relay between the source node and the sink node. In [18], Guntupalli et al. extended the analysis presented in [17] to incorporate a four-dimensional DTMC; this analysis, however, does not consider multiple hops.

Singh and Chouhan [22] present a protocol called CROP-MAC that optimizes pipelined flows with the help of staggered sleep/wake scheduling, efficient synchronization, and appropriate use of routing layer information. Based on CROP-MAC, Singh et al. [23] developed a protocol that provides multipacket transmission per cycle (JRAM protocol). In this protocol, a node has more than one opportunity to transmit, and the sink is able to receive packets from multiple nodes in the same cycle. JRAM accomplishes this by dividing the network into disjoint sets of nodes, by proposing a new cycle structure, and by reducing the size of periodical synchronization packets. Their results indicate that JRAM works best in scenarios with high-rate events occurrence.

Additionally, Khaliq and Henna [24] present a protocol called HE-PRMAC that uses cross-layer information for the transmission of packets through multiple hops in a single duty cycle. As a result, packet latency is reduced. In order to decrease the overhead when traffic load is high, HE-PRMAC introduces a new message called Ready To Receive (RTR), which is used to transmit how many packets a destination node can receive from a source node.

Even though many of the above-mentioned works analyze multihop WSNs, their protocols were not designed to take advantages of particular topologies. By contrast, in [3, 19, 20] the authors proposed synchronized duty-cycled MAC protocols specifically for LSNs.

Wang et al. [3] introduce a Utility-based Adaptive Duty-Cycle (UADC) algorithm to increase energy efficiency, reduce transmission delay, and improve network lifetime of WSNs with linear topology. The main idea of this algorithm is to select the relay node that maximizes the utility of the system, which is defined as a function of the nodes’ energy consumption.

Tong et al. [19] propose PRI-MAC, a synchronized duty-cycled protocol that incorporates a pipelined scheduling for LSN. The authors evaluated the performance of the network in terms of throughput, node’s radio activity per cycle, and delivered packet latency. Khanh et al. [20] introduce the RP-MAC protocol, which is based on PRI-MAC. In RP-MAC the authors define a new cycle structure and a new state, called overhearing, which eliminates the need of transmitting the Request To Send (RTS) message, hence reducing the end-to-end delay and energy consumption, in comparison with PRI-MAC.

Similar to [19, 20], we also analyze a pipelined scheduling for LSN; however, we introduce a novel MAC protocol, where nodes selectively awake, depending on node density and traffic load in the network.

From this review, we would like to highlight the main contributions of our work: (i) none of previous proposals are designed for dense LSNs, which can be widely applicable in the context of IoT; (ii) unlike previous protocols, our proposed protocol does not aggregate new control messages or node states during a cycle; (iii) even though the performance experienced by a node in an LSN is highly dependent on its distance to the sink, previous grade analyses are limited and fail to provide detailed performance models. In contrast, our model allows the analysis of both the complete LSN and each grade in the system, as it is described in the next sections.

3. Selective-Awakening MAC Protocol

In this section, we provide a general overview of the proposed protocol. We first present a brief description of one of its main components: the pipeline scheduling (Section 3.1). Then, in Section 3.2, we provide the basis of traditional synchronized duty-cycled MAC protocols ([16, 19, 21]). Later, in Section 3.3, we describe the main features of our proposed MAC protocol as well as its advantages over previous work.

3.1. Pipelined Scheduling

As in [19], we assume that the LSN has a unique sink node which is located at one end of the network. The remaining nodes generate packets with information provided by their respective sensors and can act as relays. Nodes are classified according to their grade, namely, their distance in hops to the sink node. Figure 1 illustrates this type of network. The grade of a node is denoted by i, and the total number of grades in the LSN is denoted by I. By definition, the sink node has grade zero; hence i ∈ [0, I]. As in [19, 25], we also assume that the process of determining the node’s grade and the next node on the way to the sink was previously performed during an initialization stage, where a different layer of the protocol stack fills a Next Hop table; in other words, we are assuming unicast transmissions.

Multihop transmissions are accomplished through a pipelined scheduling where newly generated packets are relayed from a node at grade i to a node at grade i − 1 until they reach the sink. During the pipelined scheduling, nodes operate according to the frame depicted in Figure 2. In this frame, the nodes at grade i + 1 with packets to transmit synchronously awake to contend for the channel, following the MAC protocol described in Section 3.2. During this same time slot, nodes at grade i also synchronously awake to be able to receive a packet from grade i + 1. In the next slot, nodes at grade i act as transmitters, while nodes at grade i − 1 act as receivers. This process continues until nodes at grade 1 become transmitters and the sink node becomes the receiver.

After the reception and transmission slots, all the nodes at a particular grade go to sleep for ξ slots (see Figure 2), in order to save energy. According to this description, the cycle duration, denoted by T_c, is equal to (ξ + 2)T, where T is the duration of every slot. Also note that since the schedules between adjacent grades are staggered, it is possible for a packet to travel through the whole LSN in only one cycle.

3.2. Synchronized Duty-Cycled MAC Protocol

Similar to PRI-MAC [19], we assume that, at the beginning of every transmission slot, awake nodes at grade i with packets to transmit synchronously initiate a backoff counter (see Figure 3), whose value w is a uniform random variable in [0, W − 1]. This backoff counter generates a contention window whose duration is equal to σw, where σ is the length of one minislot.

Contending nodes keep listening to the channel during their backoff time and if they detect a transmission from other nodes, they go to the sleeping mode. Otherwise, they initiate an RTS/CTS/DATA/ACK handshake with a receiver at grade i − 1 (recall that we assume unicast transmissions). Figure 3 illustrates the handshake process for the case where a data packet is successfully transmitted.

When there is a tie in the smallest backoff counter generated by the contending nodes, an RTS collision occurs, and none of the winners receive the corresponding CTS message. When these nodes detect the lack of this message, they go to sleep; consequently, no data packet is transmitted from grade i during that particular cycle. From now on, we will refer to the smallest backoff time generated by a contending node as the winner backoff time.

We also assume that time is slotted with a slot duration T, that is given by

$$ (1)

where τ_difs is the duration of a DCF Interframe Space (DIFS), σW is the maximum allowed backoff duration, τ_sifs is the duration of a Short Interframe Space (SIFS), and τ_rts, τ_cts, τ_data, and τ_ack are the duration of the messages defined by the protocol.

Note that it is possible to have a sleeping period after the exchange of frames within a transmission slot because the winner backoff time can be smaller than W (see Figure 3). However, the slot duration is always equal to T in order to maintain synchronization. Also, note that, in this protocol, at most one packet can be transmitted per cycle from one grade to the following one. Thus, since this is also true for the communication between grade 1 and the sink node, the maximum throughput in the LSN is 1/T_c packets/sec.

4. Analytical Model

As it has been proposed in several works (e.g., [16–19]), we model the behavior of the nodes by means of a unidimensional DTMC whose states represent the length of a node’s queue at the beginning of the transmission slot. Let K be the maximum queues’ length (in packets) and m any of the possible states for this chain; hence, m ∈ [0, K]. In Figure 4, we illustrate the possible transitions from one of these states.

In addition, we denote by $$ the transition probabilities from state m to state n in one cycle, for a node in grade i. As we show in Figure 4, the chain cannot transit from state m to a state n ≤ m − 2 because the nodes can transmit (eliminate from its queue) at most one packet during one cycle. By contrast, transitions to any higher state are possible as a result of the new-packet generation process in each node.

As it was mentioned before, a node at grade i has to relay packets from nodes belonging to higher grades (besides transmitting the locally generated packets); hence, the DTMC steady-state probabilities at grade i depend on the behavior of the higher grades.

In order to obtain these steady-state probabilities at each grade, we adapted the method described in [19] to our proposal. This method consists in solving the DTMC for the grade i and then using these results to calculate the probability of having a successful transmission at this grade, which in turn is used to solve the DTMC at grade i − 1, and so on. Notice that the steady-state probabilities at grade I do not depend on other grades (since nodes at this grade do not relay packets), and hence, the method proceeds by first solving the Markov chain for this latter case.

The presentation of the analysis is organized as follows. In Section 4.1 we calculate the probabilities of transmitting or receiving a packet in each cycle. These probabilities are required to establish the transition probabilities for the DTMC. Since the DTMC sampling points are the beginnings of the transmission slots, all the probabilities in Section 4.1 refer to such points. In Section 4.2, we establish the transition probabilities for the DTMC and we provide a detailed description of the method mentioned in the previous paragraph. Considering that the evaluation of some performance parameters depends on the probability of having a saturated queue at the beginning of the reception slot, we obtain such probability in Section 4.3.

5. Performance Metrics

Based on the steady-state probabilities of the previously described Markov chain, in this section, we determine expressions for the following performance metrics: energy consumption, packet loss probability, throughput, and source-to-sink delay. Initially, all the expressions are formulated in terms of the nodes’ grade and, subsequently, the general performance of the network is obtained.

6. Criteria for Selecting the Awakening Probabilities

As we mentioned in Section 3, if an excessively small value of p(i) is selected, it is probable that none of the nodes wakes up in some of the transmission slots, hence reducing the network throughput. On the other hand, if we simply select p(i) = 1 (or select a large value), possibly, a lot of collisions will occur and the network throughput will be also diminished. On the basis of these considerations, we propose to select p(i) in such a way that the throughput in the corresponding grade is maximized.

By solving (49) in terms of p(i), we can obtain the awakening probability that maximizes Th(i). However, this cannot be directly calculated, since p_e(i) depends on p(i) (see Section 4.2). In order to overcome this problem, we propose to calculate an approximation for p_e(i), which will only be used to solve (49). This approximation is denoted by $$ and must be substituted into (49) instead of p_e(i).

As it was mentioned above, p(i) must be selected in order to avoid collisions. Hence, our approximation considers each node at grade i as a K-length queue with one server, where packets arrive at rate λTc + p_r(i) packets per cycle, and the mean service time is N cycles. Notice that the latter of these assumptions is a direct consequence of considering that the probability of collision is very close to zero.

Lastly, it is important to notice that $$ depends on the behavior of nodes in higher grades, since p_r(i) = p_a(i + 1)p(i + 1). Therefore, for the purpose of calculating $$ and consequently p(i), the higher grades must be previously solved, according to the method described in the end of Section 4.2.

7. Numerical Results and Discussion

The DTMC evaluation is carried out through the numerical solution explained in Section 4.2. As we have mentioned before, this solution involves the application of the Gauss-Seidel method; we have evaluated it with 5 × 10⁴ iterations, with an approximation error of 5 × 10⁻⁴.

In addition, we have developed a discrete-event simulation for SA-MAC to validate our analyses, which was implemented in Matlab and executed over 1 × 10⁵ cycles. In every cycle, we simulated all the events that are included in the staggered schedules of the grades, and we provide a brief description of them in the following paragraph.

At the beginning of a transmission slot, we identify all nodes that have packets to transmit, and these nodes wake up with probability p(i). Subsequently, we generate a backoff counter for every awake node and, then, we carry on the contention process. Once this process is over, we update statistics related to the packet transmission. Simultaneously, we simulate the reception slot for the next grade. In this case, we identify which nodes have nonsaturated queues and we execute for them the reception events, and we also update statistics about the packet reception. We simulate the transmission/reception between every couple of adjacent nodes, including the process between grade 1 and the sink node. It is important to mention that, along these processes, nodes are generating new packets at a rate λ. In all the transmission/reception events, we consider an ideal channel.

In this section, we first present numerical evaluations for the awakening probability selection: in terms of traffic load, from low to high load with λ ∈ {0.001,0.003,0.01,0.03}, in a dense network (N = 20), and in terms of network density, from low to high node density with N ∈ {10,20,30}, and λ = 0.003.

Second, we evaluate SA-MAC performance in terms of throughput, power consumption, average source-to-sink delay, and new-packet drop probability. In order to highlight the advantages of SA-MAC over previous proposals, we compare its performance with PRI-MAC under different scenarios. This helps us to recognize how the number of nodes per grade and traffic load affect these performance parameters.

Finally, we present results on the grade analysis, where we can highlight that packet loss probability and source-to-sink delay are not the same for every grade, whereas average power consumption is different for remote grades.

In all our numerical evaluations, we select I = 7 (number of grades in the network) and K = 15 (queue’s length), and we take from [16, 19] the rest of the network parameter values, which are shown in Table 1.

7.1. Awakening Probability Evaluation

In Figure 5, we show the selected awakening probability for a node with packets to transmit, p(i), as a function of the grade number i and for different values of the packet generation rate (λ) when N = 20. In Figure 6, we also show this probability, but in terms of the number of nodes per grade (N) for the case when λ = 0.003. These results were obtained through the approximation method described in Section 6.

From Figure 5, we can observe that if λ increases, the selected p(i) must decrease because the higher the value of λ, the higher the probability that multiple nodes have packets to transmit; therefore, it is necessary to avoid collisions by reducing the number of contenders. Similarly, large values of N require the selection of small values of p(i), as it can be observed in Figure 6.

Additionally, it is interesting to point out that, in both Figures, p(i) is smaller for nodes closer to the sink (small values of i). This is a consequence of the fact that these nodes accumulate packets from higher grades. As a result, their probability of having packets to transmit is higher, and collisions must be avoided by awaking a small proportion of them in each cycle, that is, selecting a small value of p(i).

We also analyze the effect of using the proposed approximation method for determining p(i) on the system throughput by exhaustively evaluating Th_i over the whole domain of p(i). These results are shown in Figure 7, where the triangles indicate the points (p(i), Th_i) computed by the approximation method. In this scenario (λ = 0.003 and N = 30), Th_i exhibits the same performance for i ∈ [3,6] and, hence, we only show the results for Th₆.

In this scenario, the throughput in remote grades (e.g., i = 7) reaches its maximum over a wide range of values of p(i). This is because the arrival packet rate is so low that small values of p(i) do not create bottlenecks and large values do not generate collisions. By contrast, in grades where the traffic load is much higher, the throughput reaches its maximum only if p(i) is adequately selected.

These results show that, independently of the traffic load per grade, the proposed method is capable of finding the values of p(i) that maximize the throughput. On the basis of this observation, in the remaining numerical evaluations, we use the approximation method for selecting the values of p(i).

Additionally, it is important to point out that the wide range of values of p(i) that provide the maximum throughput will allow, in future work, to select this probability in such a way as to optimize not only the throughput, but also other performance parameters. For example, further energy savings could be achieved, but they must be constrained by target levels of source-to-sink delay or packet loss probability. Another approximation may include the selection of the awakening probabilities as a function of the queue’s length of nodes, so that the packet loss probability or the source-to-sink delay can also be optimized, while satisfying energy consumption constraints.

These new proposals may require redefining equations in Section 6; however, the DTMC and performance parameters definitions would suffer small changes.

7.2. SA-MAC Protocol Evaluation

In Figure 8 we show the network throughput attained by SA-MAC and PRI-MAC. In SA-MAC, the throughput is almost constant in spite of the increase in N. The explanation for this performance is that p(i) is selected to maximize the throughput (it must be noticed that, given the network parameters in Table 1, the maximum throughput, 1/T_c, is 0.31 packets/sec). By contrast, since PRI-MAC does not consider a selective awakening, that is, p(i) = 1 regardless of the network parameters, a lot of collisions occur for large values of N in the grades which are closer to the sink, and, as a result, the throughput performance is degraded. Consequently, it can be concluded that SA-MAC is better suited for high-density networks than PRI-MAC.

In Figure 9 we show the average power consumption for both protocols. It can be observed that a significant reduction of power consumption can be achieved with SA-MAC, in comparison with that of PRI-MAC; and it must be highlighted that this reduction is quite notorious for large values of N. This performance is achieved in SA-MAC because the energy wasted in collisions is very small; moreover, further savings are achieved by avoiding the awakening of multiple nodes in a network that is able to transmit only one packet per contention. Besides, in SA-MAC, the wasted energy is also reduced because the nodes with a saturated queue do not awake in the reception mode.

Notice that the joint analysis of throughput and power consumption reveals the main advantage of SA-MAC: its adaptation to different levels of traffic load allows it to save a lot of energy (hence, extending the network lifetime), without sacrificing the throughput performance.

It is also important to notice that, in some scenarios, this advantage comes with a moderate degradation of the source-to-sink delay, as it is shown in Figure 10. This was expected because our proposal reduces collisions by probabilistically turning off nodes with packets to transmit, which increments the waiting time experienced by the packets in the sleeping nodes. This degradation is more apparent for large values of N, as we can observe in Figure 10. This is because the larger the value of N, the smaller the value of p(i). Therefore, on average, nodes will have to wait for longer periods of time to attempt a transmission.

This increase in delay also affects the new-packet drop probability, p_ND(i), because large delays reduce the space in the buffers to admit new generated packets. Since delay is larger for SA-MAC in comparison with PRI-MAC, p_ND(i) is also slightly larger for the former, as it is shown in Figure 11. A similar situation occurs in terms of N: when this parameter increases, p_ND(i) also increases in both schemes.

When p_ND(i) is analyzed in terms of the grade i, a special case arises for the last grade (i = 7 in our experiments), where buffers are dedicated only to admit the locally generated packets; therefore, p_ND(7) is lower in comparison with other grades.

From this analysis, it is possible to identify network conditions where both protocols exhibit similar performance for throughput, source-to-sink delay, and drop probability, but where SA-MAC significantly outperforms PRI-MAC in terms of energy consumption; for instance, when N = 10 the power consumption incurred by SA-MAC is around 63% of that of PRI-MAC.

7.3. Per-Grade Network Performance

In Figures 12, 13, and 14 we show the analysis by grade for packet loss probability p_PL(i), average power consumption P(i), and source-to-sink delay D_i, respectively, under different scenarios.

As it was expected, the packet loss probability increases as the distance from the sink also increases, because a packet generated in a remote node needs to overcome a large number of hops. This observation leads to the conclusion that prioritization schemes must be proposed in future works for packets generated in remote nodes. In addition, it is interesting to observe that this probability exhibits smooth variations in terms of i for small traffic loads (e.g., λ = 0.003).

In Figure 13 we can observe that, in general, power consumption increases as the traffic load decreases. This effect is a direct consequence of the strategy used to turn off nodes during the reception slot: since a node goes to sleep when its queue is full, under low traffic loads, nodes stay awake most of the time. On account of these observations, it is clear that in future works a node must go to sleep (during the transmission slot) not only when its queue is saturated, but also when the traffic load is very low.

In order to interpret the power consumption in each grade, we highlight that as i increases, the transmission power consumption decreases (since the nodes have less packets to relay), while the reception power consumption increases (since the nodes keep awake because their queues are not saturated). For λ = 0.003, the decreasing component (i.e., the transmission power consumption) is dominant when i ≥ 3, whereas the opposite occurs otherwise; as a result, grade 3 is the least power efficient in the whole network (see Figure 13). Notice that the less efficient grade will depend on traffic load; it can even be grade I when the traffic load is high (e.g., λ = 0.01), or grade 1 when the traffic load is very low.

The previous observations also lead us to identify that the network lifetime is not determined by the average power consumption in the whole network, but by the average power consumption in the less efficient grade.

Lastly, in Figure 14, it is possible to appreciate that, under low traffic loads, the source-to-sink delay is almost the same, regardless of the packet source (e.g., λ = 0.003). This phenomenon is a consequence of the pipelined scheduling, which makes possible for a packet to go through the whole network in less than one cycle, when such packet finds an empty queue in every node (which is only possible under very low traffic loads). Under moderate traffic loads (e.g., λ = 0.01), the previously described phenomenon occurs only in the most remote grades (where multiple hops are possible during one cycle), but in the closest grades to the sink, a significant extra delay is accumulated for each accomplished hop. When the traffic load is large, this significant extra delay is accumulated in all the hops; therefore, the average delay is directly proportional to the number of hops to reach the sink.

8. Conclusions and Future Work

In this paper, we have introduced SA-MAC, an energy-efficient MAC protocol for dense LSN with applications to IoT. SA-MAC is a synchronized duty-cycle MAC protocol with a pipelined scheduling where nodes selectively awake based on the node density per grade and on the state of their data queues.

As part of SA-MAC, we introduced a set of awakening probabilities that maximizes the throughput per grade by reducing the number of packet collisions. Our numerical evaluations showed that, in order to select appropriate values for these probabilities, the network designer should also consider the grade, the data packet generation rate, and the number of nodes per grade.

Additionally, we have developed a DTMC-based analytical model for the performance of SA-MAC that was validated by means of extensive discrete-event simulations. The results obtained from simulations and mathematical analysis show that SA-MAC outperforms previous work in terms of energy efficiency, packet loss probability, and throughput. The latter is particularly true under certain IoT-like conditions, where both high node density and high traffic load are possible scenarios.

Another contribution of this work is the first per-grade detailed analysis of the performance of an LSN. This deeper analysis revealed, among other things, that there is a set of specific grades that have a larger impact over the network lifetime and reliability.

We believe that the analysis per grade deserves further investigation, for instance, to include other objective functions besides throughput, such as packet drop probability, source-to-sink delay, or energy consumption. It is even possible to formulate the problem as a multiobjective optimization problem where two or more performance metrics are jointly optimized. Other extensions of this work also include prioritization schemes for QoS provisioning and new schemes for selecting the awakening probabilities as a function of the queue’s length that also optimize the main network performance parameters. This new scheme may lead to further reductions in the number of contending nodes and to the design of new mechanisms for assigning priorities to data packets that can also help reducing the packet loss probability and the source-to-sink delay, all this while preserving energy in order to increase the network lifetime.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.