A Cross-Layer Sleep and Rate Adaptation Mechanism for Slotted ALOHA Wireless Sensor Networks

Transcription

1 A Cross-Layer Sleep and Rate Adaptation Mechanism for Slotted ALOHA Wireless Sensor Networks Lucas D. P. Mendes, Joel J. P. C. Rodrigues, and Min Chen Instituto de Telecomunicações, University of Beira Interior, Portugal Seoul National University, Seoul, Korea Abstract The spread use of wireless sensor networks (WSNs) in a variety of fields, ranging from the military to environmental protection, has drawn the attention of researchers to this type of networks. Despite their use in interesting applications, such as healthcare and surveillance, many shortcomings derived from their small size and constrained batteries limit their use, calling for solutions to enhance the WSNs. Born in wireless networks using the transport control protocol (TCP), cross-layer proposals have been proving that there is interdependency of parameters across the layers and that collaboration among the layers of the protocol stack can improve the performance of wireless networks in terms of quality of service, throughput, and transmission fairness. Thus, cross-layer design is merging into WSNs, especially to reduce sensors energy consumption. In this work, a cross-layer sleep and rate adaptation mechanism is proposed to operate on a slotted ALOHA WSN. Through the use of sensors sleep periods, energy consumption is reduced, and throughput reduction is mitigated by the use of the rate adaptation. Keywords ALOHA, Cross-Layer Design, Frame Rate Adaptation, Sleep Time Adaptation, Wireless Sensor Networks (WSNs). I. INTRODUCTION Each day, more wireless sensor networks (WSNs) are deployed for a diversity of applications. Examples can be seen in the environmental and ecological field, such as flora and fauna study and natural disaster prevention, in the city planning field for air quality monitoring and car traffic control, in the medical field for real-time patient healthcare, in industry for machinery operation monitoring and failure prevention, and in the security field [1]. Despite the development and spread use of WSNs, some problems still limit their performance. Since sensors need to be small to be deployed in tight spaces in most applications, they need to have a small size, resulting in limited capacity battery, and from this constraint, many others can arise. Sensors energy consumption is directly affected by the used modulation, the frames scheduling, the routing protocols, the security mechanisms, and the application requirements. Thus, the utility of WSNs is a tradeoff between the network lifetime and the complexity of the functionalities provided by the layers of the network architecture stack [2]. Several medium access methods [3] and routing protocols [4] have been proposed for WSNs aiming to reduce energy consumption. However, it can also be seen in [3] and [4] that methods and protocols that consider parameters from other layers in their operation process can achieve less energy consumption than the ones operating within a single layer. The former methods and protocols are categorized as crosslayer design. For instance, a routing protocol can consider the energy available at the neighbor sensors when deciding for the next hop of a route, as proposed by Sanchez et al. [5]. It is argued in the literature that carrier sense multiple access (CSMA) is preferred over other methods because it is simpler to be implemented [6]. However, other authors defend the use of time division multiple access (TDMA) because of its transmission guarantees and simpler deterministic analysis [7]. Even though the majority of proposals consider these two medium access methods, the slotted ALOHA method is used in this work because of its lack of control messages transmission, limiting overhead to frame transmission collisions. In this paper, the behavior of slotted ALOHA in a WSN is analyzed. The effects of the frame generation rate at each sensor node on the network throughput and lifetime are also assessed, and then a cross-layer sleep and rate adaptation mechanism is proposed. It is also considered that the sensor nodes have some characteristics of commercial MICAz [8] motes, and thus future comparison with other work considering the same mote can be done. The remainder of the paper is organized as follows. In Section II, the related work on cross-layer and ALOHA proposals is presented. The system model and its characteristics are presented in Section III, also depicting the considered scenario. In Section IV, the cross-layer sleep and rate adaptation mechanism is presented, showing its motivation and guidelines used for its implementation. Section V brings the results and their analyses concerning the network throughput and lifetime. Finally, in Section VI the conclusions are drawn and insights for future work are shown /10/$ IEEE 213 ICTC 2010

2 II. RELATED WORK Some research work has been published on cross-layer design for WSNs, as discussed next. Tian and Ekici [9] have proposed a task and scheduling scheme that distribute the data processing among the nodes and schedule sensors transmission according to the video requirements. This method provides transmission to nodes as needed, however it requires information from all nodes in order to calculate the scheduling. Thus, in practice it will incur heavy control transmission, reducing network throughput and wasting sensors energy. The aerial platform approach proposed by Mitchell et al. [10] also considers a centralized approach. Sensor nodes gather information on their one-hop neighbors and send it to the aerial platform. Then, the latter is responsible for calculating the route to the sink and for scheduling nodes transmission in order to accomplish data forwarding towards the sink. The special aerial node introduced in this proposal can lead to an improvement over the previous proposal [9] because control messages are restricted to one-hop neighbors, and between sensors and the aerial node communication. However, the aerial platform needs to be in line-of-sight with all nodes, and it is likely to be deployed far from the nodes. Thus, in order to reach it, sensors need a higher transmission power, which also leads to high energy consumption. Working around the medium access problems, Ren and Liang [11] have proposed a method where nodes can access the medium at the same time in an ultrawideband (UWB) based WSN. The throughput maximized MAC (TM-MAC) divides the network in sets where all nodes transmit at the same time. Since the throughput is limited by the signal to interference and noise ratio (SINR), the division of the network is carried out in order to reduce this ratio to an expected value, also achieving the desired throughput level. However, in this work it is not considered if there is a sink node available for each network section, neither if the packets need to be routed towards a sink node. Despite the proposal of new MAC methods, most of crosslayer research is focused on the use of CSMA and TDMA. Demirkol and Ersoy [12] defend the use of CSMA in wireless sensor networks because it does not need synchronization. They say that synchronization adds to the complexity and the energy consumption of the nodes, and thus it should be avoided. Thus, they focus on the study of the congestion window (CW), which defines the time the nodes must wait before trying to transmit when the channel is idle. The effects of the CW sizes on energy consumption and end-to-end delays are assessed, and a method to choose its optimal value is proposed. On the other hand, Su and Zhang [13] have proposed a TDMA-based protocol for body area networks (BANs). They have thoroughly analyzed the effects of battery discharge dynamics, channel states, and queuing on the quality of service (QoS) required by a healthcare application. Although they have proven that packets delay is bounded, they have considered perfect nodes synchronization, which in fact introduces communication overhead. The ALOHA medium access method can avoid the overhead introduced by CSMA and TDMA methods, however it is rarely considered in the literature. In the work by De et al. [14], slotted ALOHA is considered in an underwater wireless network. The effectiveness of time slotting is tested in a high propagation delay environment, and a modification of the slotting scheme is proposed. Although this recent work considers ALOHA, it is not a cross-layer proposal. Thus, in this work slotted ALOHA is used to avoid contention and synchronization overhead induced by CSMA and TDMA respectively. Also, cross-layer design is considered to improve the network lifetime with minimal impact on the network throughput. III. SYSTEM MODEL The wireless sensor network (WSN) considered in this work is composed of a single cluster with one sink node. The number of sensor nodes in the cluster will vary from N = minsens =7to N = 20. They are randomly deployed around the sink node and close enough to transmit frames without relaying them through other sensor nodes, as depicted in Figure 1. All nodes transmission power are fixed at P t = [dbm] and their receiver sensitivity is considered to conform to the MICAz mote [8] (P sens = 90 [dbm]). Considering these two previous parameters, the maximum transmission range (R) has been calculated through the Friss formula, R = P t P sens G t G r ( ) λ 4π where G t and G r are the gain of the transmitter and receiver antennas (considered to be 1), and λ is the transmission wavelength, which is 125 [ηm] for the 2.4 [GHz] carrier wave used by the MICAz mote [8]. Through the presented equation and considering the given parameters, the maximum transmission range is determined to be R = 30 [m]. Thus, it is worth noting that the sensors will be deployed in a 30 [m] radius from the sink node in order to guarantee connectivity between all sensors and the sink node. Also, although some sensors will not be within the transmission range of others, simultaneous access to the medium through the slotted ALOHA method will still cause a frame collision at the sink. As in the former ALOHA networks, frame acknowledgments are considered to be done in an out-of-band transmission, and thus its effects will not be considered in this work. Also concerning the random deployment of sensors, some of them will be closer to the sink than the others. However, a constant propagation delay of (τ prop = 100) [ηs] is considered without affecting the carried out analysis. According to De et al. [14], the propagation delay can disrupt the operation of slotted ALOHA in underwater networks, but does not cause severe effects on the time slots of slotted ALOHA when the used transmission medium is the air. Notwithstanding, a guard bit has been included to the transmission time to calculate the slot time in order to (1) 214

3 TABLE I CONSIDERED SYSTEM PARAMETERS. Parameter (symbol) Value [unit] Minimum number of sensors (minsens) 7 Transmission power (P t) [dbm] Receiver sensitivity (P sens) -90 [dbm] Maximum transmission range (R) 30 [m] Propagation delay (τ prop) 100 [ηs] Transmission rate (R tx) 250 [kbps] Payload size (L payload ) 102 [bytes] Data frame size (L data ) 111 [bytes] Data transmission delay (τ data ) [ms] Slot time (slott ime) [ms] Sensors initial energy [ma s] Transmission energy consumption 6.2 [ma] Idle/reception energy consumption 19.7 [ma] Sleep energy consumption negligible Fig. 1. Example of the considered scenario with a sink node, N =8sensor nodes, and the transmission range of the farthest node, which is the same for all the others. avoid synchronization problems caused by the difference in the sensors propagation delay. Furthermore, some sensors characteristics are considered to be compliant with the IEEE standard [15]. For the considered frequency band, the transmission rate is 250 [kbps]. Also, the data frame is considered to contain the frame control field, the frame sequence number, the source personal area network (PAN) identifier, the source address, the maximum payload (L payload = 102 [bytes]), and the frame check sequence (FCS). Thus, the total data frame size is L data = 111 [bytes], which takes τ data = [ms] to be transmitted. As said before, one guard bit is added to the data frame transmission delay to calculate the slot time, resulting in slott ime = [ms]. The sensors initial energy is considered to be [ma s], and the nodes spend 6.2 [ma] when transmitting (interpolated from the MICAz datasheet [8]), 19.7 [ma] when receiving or in idle state, and a negligible amount of energy when in sleep mode. All the aforementioned parameters are summarized in Table I, in the same order they were commented previously. IV. CROSS-LAYER SLEEP AND RATE ADAPTIATION MECHANISM A. Optimal Frame Generation Rate The analysis of slotted ALOHA networks is well-known and it is clear that the throughput of this type of network can be calculated by [16] S slotted = G e G (2) where G is the number of transmission attempts per frame time. However, this classical analysis does not explicitly consider the number of nodes in the network, each node frame generation rate, and the transmission and propagation times all these parameters are generalized by the variable G. This more realistic analytical analysis of the slotted ALOHA protocol is out of the scope of this work, but one can still expect that the network throughput behavior given by this analysis will be similar. Thus, some simulations of a slotted ALOHA network throughput have been run with varying number of nodes (N) and frame generation rates (µ). Details of the simulation are given in the next section and the results are shown in Figure 2. Throughput [bps] N=9 N=10 N= Mean Frame Generation Rate µ [frames/s] Fig. 2. Throughput for the considered network with 9, 10, and 11 sensors as a function of the frame generation rate. As can be seen from Figure 2, the optimal mean frame generation rate varies with the number of sensors in the WSN. When the number of sensors increases, their frame generation rate must be decreased to achieve maximum network throughput. For each number of sensor nodes, the throughput decreases for µ below its optimal value because of channel idleness, while a µ value above the optimal result in throughput decrease because of the higher number of frame transmission collisions. In order to find the optimal mean frame generation for the considered range of sensors number, µ was varied in steps of 1 [arrival/s]. Further granularity was not achievable due to 215

4 TABLE II OPTIMAL FRAME GENERATION RATE AND INTERARRIVAL TIME FOR EACH NUMBER OF SENSORS IN THE NETWORK. N Optimal µ Optimal interarrival Time difference between [arrivals/s] time [ms] N and N 1 [ms] simulation time constraints, and thus results are expected to be an approximation. In Table II, the approximated optimal frame generation rate, frame interarrival time (1/µ), and the interarrival time increment are shown. B. Sleep and Rate Adaptation The mean of the interarrival time increments shown in the fourth column of Table II is [ms], which is approximately the duration of a time slot given previously. Thus, instead of adjusting the mean frame arrival according to the number of sensors in the network, only the optimal µ for the minimum number of sensors (N = 7) will be used, increasing the number of time slots spent on sleep mode in order to save energy. Hence, a cross-layer interaction can be seen between the network layer (number of sensors) and the data link layer (frame scheduling), and when a frame is generated to be transmitted, the number of sleep slots is given by sleepslots = N minsens (3) where the variables have been defined in Section III. The flowchart of each node transmission process is shown in Figure 3. Throughput [bps] Fig Sleep mechanism and frame transmission flowchart. Slotted ALOHA Slotted ALOHA with Sleep Mechanism V. THROUGHPUT AND LIFETIME ANALYSIS Previously shown results and others presented in this section have been achieved through the implementation of the sleep and rate mechanism adaptation in nodes with the slotted ALOHA as medium access method, using the OMNeT++ simulator [17]. Next, the network throughput achieved with the use of the optimal frame generation rates for each number of nodes and with the use of the sleep and rate adjustment mechanism is shown in Figure 4. From Figure 4, it can be seen that the maximum throughput loss caused by the sleep adjustment mechanism is less than 0.5%. It is also worth noting that the slotted ALOHA curve could be smoother if it was possible to achieve a better µ approximation by simulation in a feasible period. Anyhow, the sleep mechanism is supposed to provide energy saving, Number of Nodes Fig. 4. Network throughput as a function of the number of sensors in the network. and to assess this matter, network lifetime simulations have been carried out and their results are shown in Figure 5. Figure 5 shows that the network lifetime increases with the number of sensors in the network when the sleep adjustment mechanism is used. It is clear that it happens because the sensors will spend more slots in sleep mode to achieve the optimal frame generation rate, and consequently less energy is spent. For the slotted ALOHA case, as the number of nodes 216

5 Network Lifetime [s] Slotted ALOHA Slotted ALOHA with Sleep Mechanism Number of Nodes Fig. 5. Network lifetime as a function of the number of sensors in the network. increases, the optimal frame generation rate decreases, which means that the sensors will spend more time on the idle state. Since the energy expenditure in this state is higher than when transmitting, the network lifetime slightly decreases as the number of nodes increases. VI. CONCLUSIONS AND FUTURE WORK The impact of the chosen medium access method in a WSN lifetime is a major concern for this kind of networks. Thus, in this work a sleep and rate adaptation mechanism has been proposed to jointly increase the network throughput and lifetime. Some characteristics of real sensor nodes have been considered in the simulations. The approximated optimal frame generation rate for a set of sensors number has been found and this information has been used to propose the sleep and rate adaptation mechanism. Finally, it has been proven that the use of the proposed mechanism increases the network lifetime when compared to slotted ALOHA with cross-layer adjustment of the frame generation rate, while causing a minor reduction on the network throughput. Future work might consider the impact analysis of the proposed mechanism on end-to-end delays. Also, cross-layer solutions for other medium access mechanisms may be proposed. Then, their performance in terms of throughput, network lifetime, and delays could be compared to the performance of this proposal. ACKNOWLEDGMENTS Part of this work has been supported by Instituto de Telecomunicações, Next Generation Networks and Applications Group (NetGNA), Portugal, in the framework of the BodySens Project, and by the Euro-NF Network of Excellence from the Seventh Framework Programme of EU, in the framework of the Specific Joint Research Project PADU. REFERENCES [1] I. F. Akyildiz, S. Weilian, Y. Sankarasubramaniam, and E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, vol. 40, no. 8, pp , August [2] I. F. Akyildiz, T. Melodia, and K. R. Chowdhury, A survey on wireless multimedia sensor networks, Computer Networks, vol. 51, no. 4, pp , March [3] A. Bachir, M. Dohler, T. Watteyne, and K. K. Leung, Mac essentials for wireless sensor networks, IEEE Communications Surveys & Tutorials, vol. 12, no. 2, pp , [4] J. N. Al-Karaki, R. Ul-Mustafa, and A. E. Kamal, Data aggregation and routing in wireless sensor networks: Optimal and heuristic algorithms, Computer Networks, vol. 53, no. 7, pp , May [5] J. A. Sanchez, P. M. Ruiz, and I. Stojmenovic, Energy-efficient geographic multicast routing for sensor and actuator networks, Computer Communications, vol. 30, no. 13, pp , September [6] R. W. Ha, P.-H. Ho, and X. S. Shen, Cross-layer application-specific wireless sensor network design with single-channel CSMA MAC over sense-sleep trees, Computer Communications, vol. 29, no. 17, pp , November [7] H. Kwon, T. H. Kim, S. Choi, and B. G. Lee, A cross-layer strategy for energy-efficient reliable delivery in wireless sensor networks, IEEE Transactions on Wireless Communications, vol. 5, no. 12, pp , December [8] MICAz Wireless Measurement System, MICAz Datasheet, Crossbow, [9] Y. Tian and E. Ekici, Cross-layer collaborative in-network processing in multihop wireless sensor networks, IEEE Transactions on Mobile Computing, vol. 6, no. 3, pp , March [10] P. D. Mitchell, J. Qiu, H. Li, and D. Grace, Use of aerial platforms for energy efficient medium access control in wireless sensor networks, Computer Communications, vol. 33, no. 4, pp , March [11] Q. Ren and Q. Liang, Throughput and energy-efficiency-aware protocol for ultrawideband communication in wireless sensor networks: A crosslayer approach, IEEE Transactions on Mobile Computing, vol. 7, no. 6, pp , June [12] I. Demirkol and C. Ersoy, Energy and delay optimized contention for wireless sensor networks, Computer Networks, vol. 53, no. 12, pp , August [13] H. Su and X. Zhang, Battery-dynamics driven TDMA MAC protocols for wireless body-area monitoring networks in healthcare applications, IEEE Journal on Selected Areas in Communications, vol. 27, no. 4, pp , May [14] S. De, P. Mandal, and S. S. Chakraborty, On the characterization of Aloha in underwater wireless networks, Mathematical and Computer Modelling, 2010, doi: /j.mcm [15] IEEE , Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), IEEE Computer Society, September [16] A. S. Tanenbaum, Computer Networks, 4th ed. Prentice Hall PTR, [17] OMNeT [Online] Available: 217