Performance Evaluation of Frame Slotted-ALOHA with Succesive Interference Cancellation in Machine-to-Machine Networks

Transcription

1 Performance Evaluation of Frame Slotted-ALOHA with Succesive Interference Cancellation in Machine-to-Machine Networks F. Vázquez-Gallego, M. Rietti,J.Bas, J. Alonso-Zarate, and L. Alonso Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Castelldefels, Barcelona, Spain {francisco.vazquez, joan.bas, Universitat Politècnica de Catalunya (UPC), Castelldefels, Barcelona, Spain Abstract Machine-to-Machine (M2M) area networks connect a gateway with a huge number of energy-constrained end-devices. Therefore, energy efficiency is essential to prolong the lifetime of M2M networks. In this paper, we consider an M2M area network composed of hundreds or thousands of end-devices that periodically transmit data upon request from a gateway. We propose a Medium Access Control (MAC) protocol based on Frame Slotted- ALOHA (FSA) with Successive Interference Cancellation (SIC), also referred to as SIC-FSA, for data collection applications. By means of comprehensive computer-based simulations, we evaluate the delay and the energy consumed by the gateway and the enddevices using SIC-FSA. We have compared the delay and energy performance provided by SIC-FSA with that of conventional FSA and diversity-fsa (D-FSA). Results show that SIC-FSA can reduce the delay and energy consumption of the gateway in more than 5%, and the energy consumed by an end-device in more than %, with respect to FSA in dense M2M networks equipped with radio-transceivers in compliance with the IEEE 82. physical layer. Index Terms successive interference cancellation, frame slotted-aloha, performance evaluation, data collection. I. INTRODUCTION One of the major challenges in the design and deployment of Machine-to-Machine (M2M) networks is energy-efficiency. Future M2M networks will connect a large number of energyconstrained end-devices that must operate autonomously for years without human intervention. Therefore, communication protocols for M2M networks need to be specifically designed to reduce the energy consumed per end-device and the overall energy consumption of the network. In this paper, we focus on wireless M2M area networks for data collection applications []. Some examples of applications are asset tracking or Automatic Meter Reading (AMR), among others. In these networks, a large group of hundreds or thousands of end-devices are coordinated to periodically transmit data to an M2M gateway upon request. Although the traffic load generated by each end-device may be relatively low, the total number of end-devices that can attempt to get access This work was supported in part by the Research Projects CO2GREEN (TEC2-2823), ADVANTAGE (FP ), NEWCOM# (FP7-3836), and by the Catalan Government under grant 29SGR46. to the wireless channel simultaneously can be larger than the one manageable by traditional Medium Access Control (MAC) protocols. Consequently, an energy efficient and lowcomplexity MAC is needed to reduce collisions and manage the access to the channel. Random access protocols [2], such as Carrier Sense Multiple Access (CSMA) or ALOHA [3], are simple to implement and their distributed operation makes them ideal for low-cost end-devices with limited processing and memory resources. Indeed, many standards for wireless short-range communications typically used in M2M, such as the IEEE 82., IEEE , EN (Wireless M-bus for AMR), or ISO- 8-7 (for asset tracking) rely on random access. Unfortunately, since the end-devices must compete for the channel, random access protocols suffer from degraded performance [4] in terms of throughput and energy consumption, due to the high probability of collision, when the traffic load is high or when the number of end-devices increase, which is the case in M2M networks. Recent research works [5] [8] have adopted successive interference cancellation (SIC) techniques to resolve collisions and improve the performance of random access. The work in [9] proposes diversity frame slotted-aloha (D- FSA) as a variation of slotted-aloha (SA). In D-FSA, devices are synchronized to a common time frame pattern where every frame is divided into a number of slots in which devices contend to transmit several replicas of each data packet. Results show that D-FSA provides slight throughput enhancement with respect to SA. A more efficient use of the packet replicas was provided by Contention Resolution Diversity Slotted-ALOHA (CRDSA) [5] and Irregular Repetition Slotted-ALOHA (IRSA) [6]. In CRDSA and IRSA, similarly to D-FSA, devices also transmit two or more replicas of each data packet in every frame at random slots. In addition, both protocols implement a SIC algorithm which allows decoding collided packets, thus increasing throughput with respect to D- FSA. The works in [7] and [8] also proposed variants of FSA with SIC for M2M and Radio Frequency Identification (RFID), respectively. Up to our knowledge, previous works related to FSA with SIC have analyzed the performance enhancement

2 in terms of throughput with respect to FSA. Nevertheless, the energy efficiency provided by FSA with SIC remains an open research question. This is the main motivation of this paper, where we focus on identifying the conditions when a SIC- FSA protocol improves the energy efficiency with respect to FSA in M2M networks for data collection. Towards this end, we present the performance evaluation of a SIC-FSA protocol, and compare the performance of SIC-FSA, D-FSA, and FSA in terms of delay and energy consumption. For this purpose, we consider low power radio transceivers in compliance with the IEEE 82. standard []. The remainder of this paper is organized as follows. In Section II and Section III, we describe the system model and summarize the operation of the FSA, D-FSA, and SIC- FSA protocols considered in this work. In Section IV, we formulate the delay and energy consumption models. Section V is devoted to evaluate the performance of the protocols through comprehensive computer-based simulations. Finally, Section VI concludes the paper. II. SYSTEM MODEL We consider a wireless M2M area network composed of gateway (also referred to as coordinator) and n end-devices. The coordinator gathers data from the end-devices via periodic data collection rounds (DCR). A DCR is initiated when the coordinator broadcasts a Request for Data (RFD) packet. In every DCR each end-device has exactly one data packet ready to transmit to the coordinator. We assume that all enddevices are listening to the channel when the coordinator transmits an RFD. After decoding an RFD, the end-devices are synchronized to a common time frame pattern and contend for the channel to transmit their data packet according to the rules of the adopted MAC protocol described in Section III. Every RFD is followed by a sequence of time frames further divided into m slots. The size of the data packets is fixed and the duration of a time slot is equal to the duration of a data packet. The end-devices transmit their data packet in randomly selected slots and without performing carrier sensing, thus, each slot of a frame can be in one of three states: (i) empty, i.e., no end-device has transmitted in the slot; (ii) success, i.e., just one end-device has transmitted successfully in the slot; or (iii) failure, i.e., one or more end-devices have transmitted in the slot but none data packet can be decoded by the coordinator due to channel error or collision. Since we focus on the MAC layer, we assume that all data packets are always transmitted without errors induced by the wireless channel and there is no capture effect, i.e., none data packet involved in a collision can be decoded by the coordinator. The inclusion of capture effect and transmission errors constitutes part of our future work. In addition, we assume that the coordinator has perfect knowledge of the wireless channel between the coordinator and each end-device. The coordinator broadcasts a feedback packet (FBP) at the end of each frame in order to inform about the data packets successfully decoded in every frame. An end-device succeeds when its data packet has been decoded by the coordinator. The contention process of a DCR finishes when the coordinator has decoded one data packet from each end-device. The coordinator and the end-devices can be in four different modes of operation: i) transmitting a packet, ii) receiving, iii) idle listening, iv) standby, or v) sleeping. The associated power consumptions are ρ tx, ρ rx, ρ σ, ρ stby, and ρ sleep, respectively. We assume that the energy and time required to switch between inactive (i.e., standby, sleep) and active modes (i.e., transmitting, receiving, idle listening) are negligible, being ρ σ = ρ rx. In order to save energy, when an end-device succeeds in a given frame, it enters into sleep mode for the remaining of the DCR. III. MEDIUM ACCESS CONTROL PROTOCOLS In this section, we describe the MAC protocols that we have evaluated and compared in this paper: FSA, D-FSA, and SIC- FSA. In FSA, each end-device transmits its data packet in only one randomly selected slot of every frame until it succeeds. In D-FSA, each end-device transmits K replicas of the same data-packet, being K >, in K different slots of a frame. It is worth noting that D-FSA becomes FSA for K =, i.e., a data packet is only transmitted once in a frame. In both protocols FSA and D-FSA, those end-devices that do not succeed in a given frame will contend again in the following frames. Figure.a shows an example of a DCR using D-FSA with 4 end-devices, 5 slots per frame, and K =2replicas. In the example, end-device succeeds in transmitting one replica of its data packet in frame. All replicas transmitted by enddevices 2, 3 and 4 collide in frame and have to be retransmitted in frame 2. Finally, end-devices 2, 3, and 4 succeed in frame 2. SIC-FSA also relies on the transmission of K replicas of the data packet in K random slots within a frame. The key of SIC-FSA is that the information from a successfully decoded replica of a data packet is used to cancel the interference that other replicas of the same data packet may cause in other slots of the frame. An example of SIC-FSA is depicted in Figure.b. Each replica of a data packet includes in the MAC header a pointer to the position of the other K replicas in the frame. Thus, when the coordinator successfully decodes one replica of a data packet, it can determine the slots where the other replicas have been transmitted. Then, in the slots where a replica has collided, the coordinator can subtract the interference signal of the replica to the original signal received in the corresponding slot. This IC process may allow decoding data packets that were initially lost due to collision. Since every recovered replica may allow decoding other collided packets, the IC process is iterated in every frame until the coordinator is unable to decode more packets. At this point there are only empty slots or slots with collisions that cannot be cancelled. The coordinator broadcasts in the FBP the identification of the end-devices that have succeeded in each frame. The end-devices that do not succeed in a frame will transmit again in subsequent frames.

3 Coordinator End-Device End-Device 2 End-Device 3 Success RFD IFS Frame 2 FBP IFS (a) Diversity Frame Slotted-ALOHA (b) SIC Frame Slotted-ALOHA 2 Failure due to collision Frame End-Device Coordinator End-Device End-Device 2 End-Device 3 End-Device 4 Slot Slot RFD End-Device Frame Slot FBP slot Time+ FBP Decoded after IC Figure. Example of a data collection round using K =2replicas: (a) D-FSA with n =4devices and m =5slots per frame, and (b) SIC-FSA with n =5devices and m =6slots per frame. Figure.b shows an example of a DCR using SIC-FSA with n =5end-devices, m =6slots per frame, and K =2replicas of the data packet. In the first iteration of the IC process, the coordinator decodes successfully one replica transmitted by end-device in slot 2 (packet ) and one replica transmitted by end-device 2 in slot 5 (packet 2). In the second iteration, packet is subtracted to the signal received in slot 3, and thus one of the replicas transmitted by end-device 5 is decoded (packet 5). In the third iteration, packet 2 is subtracted to the signal received in slot 4, and thus one of the replicas transmitted by end-device 4 is decoded (packet 4). In the fourth and last iteration, packet 5 is subtracted to the signal received in slot, and thus one of the replicas transmitted by end-device 3 is decoded (packet 3). In this example, the coordinator is able to decode all data packets in the first frame of the DCR by executing 4 successive iterations of interference cancellation. As shown in Figure, a guard time called Inter Frame Space (IFS) is left between reception and transmission modes in order to compensate propagation, processing, and turn-around times to switch the radio transceivers between reception and transmission. IV. DELAY AND ENERGY MODELS In this section, we formulate the delay and energy models of FSA, D-FSA, and SIC-FSA. These models are later used in Section V to compute the delay and energy performance results by means of computer-based simulations. The average delay to successfully transmit to the coordinator one data packet from each of the n end-devices can be formulated as T CR = CF (mt SLOT +2T IFS +T FBP ), () where T SLOT, T IFS, and T FBP are the duration of a slot, an IFS, and the time of transmission of a FBP, respectively, and CF is the average number of frames needed to complete a DCR. The average energy consumed by the coordinator to successfully receive one data packet from each of the n enddevices in a DCR is denoted by E coord. The coordinator goes through the following steps in every frame: (i) it listens to the channel for the m slots and stores in memory the signals received in each slot, (ii) it performs a number of iterations of the successive IC process, and (iii) it transmits a FBP at the end of the frame. Assuming that the energy required to execute the SIC algorithm is negligible, the average energy consumed by the coordinator in a DCR can be formulated as E coord = CF (mρ rx T SLOT +2ρ σ T IFS + ρ tx T FBP ). (2) The average energy consumed per end-device is denoted by E device. An end-device that has not succeeded yet to transmit its data packet goes through the following steps in every frame: (i) it transmits K replicas of the data packet in K of the m slots, (ii) it keeps in standby mode for the other m K slots, and (iii) it receives a FBP. The energy consumed by an end-device in a frame where it contends can be formulated as E frame tx = Kρ tx T SLOT +(m K) ρ stby T SLOT +2ρ σ T IFS + ρ rx T FBP. (3) An end-device that succeeds in transmitting its data packet in a given frame will not contend in subsequent frames. Then, the end-device sleeps for the remaining of the DCR and its energy consumption in a frame can be formulated as E frame sleep = ρ sleep (mt SLOT +2T IFS + T FBP ). (4) Finally, the average energy consumed per end-device can be formulated as E device = CF tx E frame tx + CF sleep E frame sleep, (5) where CF tx is the average number of frames for which the end-device has to contend, and CF sleep is the average number of frames for which it sleeps until the DCR terminates. All the parameters in the expressions of T CR (), E coord (2), and E device (5) have deterministic and known values, except for the values of CF, CF tx, and CF sleep. In the next section, we compute these values through computer-based simulation in order to evaluate the delay and energy performance of SIC- FSA and compare it to the performance of D-FSA and FSA.

4 V. PERFORMANCE EVALUATION The delay and energy performance of D-FSA and SIC-FSA in M2M networks for data collection is evaluated in this section by means of computer simulations using MATLAB. The operations of the protocols have been implemented without any simplification, thus representing the actual operation of a single-hop network using D-FSA or SIC-FSA. We have averaged the results of simulation samples for each test case. The parameters used to run the simulations are summarized in Table I. They have been selected according to the IEEE 82. Standard and from the specification of a low power Wi-Fi device (RN-3 from Roving Networks []). The length of the FBP payload has been set to attach 6 bits per slot to inform about the status of the slots and the identification of the end-device whose data packet has been decoded in each slot. The length of the data packet payload has been set to 24 bytes. In the following sections, we first evaluate the average delay and the average energy consumed by the coordinator and per end-device as a function of the number K of replicas using different numbers of slots per frame. Results show that there exists an optimum value of K which minimizes both average delay and energy consumption. In all tested cases, this value is approximately equal to K =3replicas when m =2n slots per frame. Finally, using this optimum value, we evaluate the performance of FSA, D-FSA, and SIC-FSA when the number of end-devices increases in dense M2M networks. A. Optimum Number of Replicas and Slots per Frame The average delay to terminate a DCR, the average energy consumption of the coordinator, and the average energy consumed per end-device are represented in Figure 2, Figure 3, and Figure 4, respectively. They have been evaluated as a function of the number K of replicas for D-FSA and SIC- FSA considering a fixed number of end-devices (n = ) and using three different frame lengths (m = n, m =2n, and m =3n slots). As it could be expected from the analytical expressions formulated in Section IV, both delay () and coordinator energy consumption (2) show a similar trend. Recall that in FSA a data packet is only transmitted once in a frame. Thus, the average delay and energy consumption using FSA correspond to the results at K =. Indeed, the optimum m that minimizes delay and energy consumption in FSA is found when m = n, as it was demonstrated in [2]. As it can be observed in Figure 2, Figure 3, and Figure 4, SIC-FSA improves the average delay and energy performance with respect to that of D-FSA in all tested cases. Indeed, this Table I SYSTEM PARAMETERS Parameter Value Parameter Value MAC header 3 bytes Data-rate 54 Mbps Data payload 24 bytes T SLOT μs FBP payload 6 bits/slot T FBP FBP 8/data-rate CRC 4 bytes T IFS 6 μs ρ tx 2 ma 3V ρ stby 5 ma 3V ρ rx = ρ σ 4 ma 3V ρ sleep 4 μa 3V Average Delay [s] Delay (n = end devices) D FSA, m = n SIC FSA, m = n D FSA, m = 2n SIC FSA, m = 2n D FSA, m = 3n SIC FSA, m = 3n Number of replicas (K) Figure 2. Average delay to finish a data collection round using D-FSA and SIC-FSA over the number K of replicas. Average Energy Consumption of the Coordinator [J] Coordinator Energy Consumption (n = end devices) D FSA, m = n SIC FSA, m = n D FSA, m = 2n SIC FSA, m = 2n D FSA, m = 3n SIC FSA, m = 3n Number of replicas (K) Figure 3. Average energy consumed by the coordinator in a data collection round using D-FSA and SIC-FSA over the number K of replicas. is due to the fact that a successfully decoded replica in SIC- FSA can be used to cancel the interference in other slots of the same frame, and this allows decoding other data packets that collided. As it could be expected, the average delay and the average energy consumption have a convex trend over the number of replicas in D-FSA and SIC-FSA. Indeed, when K is within its optimal range, the average number of frames required to solve the contention in D-FSA and SIC-FSA is much lower than in FSA. In SIC-FSA, the average delay and energy consumption of the coordinator reach a minimum value for K [2, 3] when m = n; K [3, 7] when m =2n; and K [3, 3] when m = 3n slots. In D-FSA, the delay and the energy consumption of the coordinator are minimum for K =when m = n; K [2, 3] when m =2n; and K [3, 4] when m =3n.

5 Average Energy Consumption per End Device [J] Energy Consumption per End Device (n = end devices) D FSA, m = n SIC FSA, m = n D FSA, m = 2n SIC FSA, m = 2n D FSA, m = 3n SIC FSA, m = 3n Average Delay [s].5.5 Delay D FSA (m = 2n, K = 6) D FSA (m = 2n, K = 3) FSA (m = n) SIC FSA (m = 2n, K = 3) SIC FSA (m = 2n, K = 6) Reduction SIC FSA wrt FSA Delay Reduction [%] Number of replicas (K) Number of end devices Figure 4. Average energy consumed per end-device in a data collection round using D-FSA and SIC-FSA over the number K of replicas. Regarding the average energy consumed per end-device, in SIC-FSA it reaches a minimum value for K =2when m = n; K [2, 3] when m =2n; and K =2when m =3n slots. In D-FSA, the energy consumption of an end-device is minimum for K =. Therefore, D-FSA does not improve the energy consumed per end-device with respect to FSA. When the number K of replicas is not within the optimal range that leads to the lowest average delay and energy consumption, the performance of the protocols differs. When the value of K is below the optimum, the delay and energy performance of D-FSA and SIC-FSA tend to that of FSA. However, when the value of K increases above the optimum, the average delay and energy consumption increase exponentially with K in both protocols. Indeed, when the number of replicas transmitted in one frame increases considerably, the probability of collision may increase dramatically and thus the number of frames may also increase, even degrading the average delay and energy consumption with respect to FSA. In addition, the higher the number of transmitted replicas, the higher the energy consumed by an end-device in a frame. Therefore, the value of m and K need to be optimized in D- FSA and SIC-FSA according to the expected number n of enddevices in order to minimize delay and energy consumption. B. Delay and Energy Performance in Dense M2M Networks In this section, we aim at understanding the performance of D-FSA and SIC-FSA as a function of the number n of end-devices. The average delay and energy consumed by the coordinator are represented in Figure 5 and Figure 6 (left vertical axis), respectively, as a function of the number n of end-devices (from 5 to ). In all cases, we have used the optimum values of K and m that minimize delay and energy consumption. According to the discussion presented in the previous section, this corresponds to m = n in FSA (i.e., K =), and K =3 Figure 5. Average delay to finish a data collection round using FSA, D-FSA and SIC-FSA over the number of end-devices. Average Energy consumption [J] Energy Consumption of the Coordinator D FSA (m = 2n, K = 6) D FSA (m = 2n, K = 3) FSA (m = n) SIC FSA (m = 2n, K = 3) SIC FSA (m = 2n, K = 6) SIC FSA wrt FSA Number of end devices Figure 6. Average energy consumed by the coordinator in a data collection round using FSA, D-FSA and SIC-FSA over the number of end-devices. with m =2n in D-FSA and SIC-FSA. It is worth noting that the average delay and energy consumption of the coordinator in D-FSA and SIC-FSA increase linearly with the increasing value of n. This fact was also demonstrated in [2] for FSA. As it could be expected, SIC- FSA outperforms FSA and D-FSA in terms of average delay and energy consumption of the coordinator. As it can be observed, SIC-FSA provides delay reductions and energy savings (shown on the right vertical axis of Figure 5 and Figure 6, respectively) of up to 6% with respect to FSA. These delay reductions and energy savings increase with the number of end-devices. In addition, it is worth noting that when n<5 the delay and the energy consumption of the coordinator are very similar with either FSA or D-FSA. Therefore, the use of SIC-FSA can improve considerably the energy efficiency in dense M2M networks, compared to that 6 4 Energy Saving [%]

6 Average Energy consumption [J] Energy Consumption per End Device D FSA (m = 2n, K = 6) D FSA (m = 2n, K = 3) SIC FSA (m = 2n, K = 6) SIC FSA (m = 2n, K = 3) FSA (m = n) SIC FSA (m =.5n, K = 3) Saving SIC FSA wrt FSA Number of end devices Figure 7. Average energy consumed per end-device in a data collection round using FSA, D-FSA and SIC-FSA over the number of end-devices. of FSA, when the number of devices is very high. The average energy consumed per end-device is represented in Figure 7 as a function of the number n of end-devices (from 5 to ). We have used the optimum values of K and m that minimize the energy consumed per end-device. According to the discussion presented in the previous section for n = end-devices, this corresponds to m = n in FSA and K =3 with m = 2n in D-FSA and SIC-FSA. It is worth noting that the average energy consumption of an end-device using FSA, D-FSA, and SIC-FSA also increases linearly with the increasing number of end-devices. As it can be observed in Figure 7, the energy consumed per end-device with SIC-FSA (using K = 3 replicas and m =2n slots) becomes higher than with FSA when n>3 end-devices. Therefore, we have also evaluated the energy performance of SIC-FSA using K =3and m =.5n slots per frame. With these values, SIC-FSA outperforms FSA and D-FSA in terms of average energy consumed per end-device. SIC-FSA provides energy savings (shown on the right vertical axis of Figure 7) of a % with respect to FSA Energy Saving [%] similarly to FSA, the delay and the energy consumption using SIC-FSA increase linearly with the number of end-devices when we use the optimal number of replicas and slots. SIC- FSA provides delay reductions and energy savings of more than 5% in the energy consumption of the coordinator with respect to FSA, and of more than % in the energy consumed per end-device. Therefore, the use of SIC-FSA can improve considerably the energy efficiency of M2M networks when the number of devices is huge. Future work aims at evaluating the performance of SIC- FSA analytically and at reducing the energy consumed by the end-devices implementing inter-frame SIC. REFERENCES [] Technical Specification, Machine-to-Machine communications (M2M); Functional Architecture. TS 2 69, v.., ETSI, October 2. [2] P. Huang, L. Xiao, S. Soltani, M. Mutka, and N. Xi, The Evolution of MAC Protocols in Wireless Sensor Networks: A Survey, IEEE Communications Surveys Tutorials, vol. 5, no., pp. 2, 23. [3] L. Roberts, Aloha packet system with and without slots and capture, in ACM SIGCOMM Comput. Commun. Rev., vol. 5, no. 2, 975. [4] L. Kleinrock and F. A. Tobagi, Packet switching in radio channels:part : CSMA modes and their throughput-delay characteristics, IEEE Trans. Commun., vol. 23, pp. 4 46, 975. [5] E. Casini, R. De Gaudenzi, and O. Herrero, Contention resolution diversity slotted aloha (CRDSA): An enhanced random access scheme for satellite access packet networks, IEEE Transactions on Wireless Communications, vol. 6, no. 4, pp , April 27. [6] G. Liva, Graph-based analysis and optimization of contention resolution diversity slotted ALOHA, IEEE Transactions on Communications, vol. 59, no. 2, pp , February 2. [7] C. Stefanovic, K. Trilingsgaard, N. Pratas, and P. Popovski, Joint estimation and contention-resolution protocol for wireless random access, in IEEE International Conference on Communications (ICC), June 23, pp [8] F. Ricciato and P. Castiglione, Pseudo-random ALOHA for enhanced collision-recovery in RFID, IEEE Communications Letters, vol. 7, no. 3, pp. 68 6, March 23. [9] L. Choudhury and S. Rappaport, Diversity ALOHA - a random access scheme for satellite communications, IEEE Transactions on Communications, vol. 3, no. 3, pp , Mar 983. [] IEEE 82. Standard: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Local and Metropolitan Area Networks, IEEE Std., 27. [] RN3 datasheet. [Online]. Available: products/rn 3C RN3C RM [2] F. Vazquez-Gallego, L. Alonso, and J. Alonso-Zarate, Energy and Delay Analysis of Contention Resolution Mechanisms for Machine-to-Machine Networks based on Low-Power WiFi, in IEEE International Conference on Communications (ICC), June 23, pp VI. CONCLUSION In this paper, we have evaluated the delay and the energy consumed by the gateway and end-devices of dense M2M area networks that use a Frame Slotted-ALOHA (FSA) protocol with Successive Interference Cancellation (SIC-FSA) to periodically transmit data to a coordinator. We have compared the performance of SIC-FSA with respect to that of conventional FSA. Results show that in SIC-FSA there is an optimal number of packet replicas and slots per frame, which minimize delay and energy consumption, and must be optimized as a function of the number of end-devices. The delay and the energy consumption increase exponentially when the number of replicas increases above its optimal value. In addition,