84 Mobile Computing and Communications Review, Volume 8, Number 1 A Study of Energy Consumption and Reliability in a Multi-Hop Sensor Network I. Introduction Wireless sensor networks have enormous potential to improve how we use information from the real world. Forest fire alert systems, environmental control for the home and office, interactive toys and museums, and wildlife observation are just a few of many applications envisioned for sensor networks. Such applications are as diverse in their technological requirements as they are in their intended uses. For example, environmental control in the home might use a dozen sensor nodes with static topology, while the same application in a large office building might require thousands of nodes capable of self-configuring an ad hoc topology. To support this vast and diverse application space, researchers have placed emphasis on developing radio technologies and protocols that provide network longevity, robustness, and adaptability. The literature is rich with techniques for media-access control, routing, self-configuring topologies, location discovery, and information fusion. Most often these techniques are evaluated through simulation using a component view, rather than considering the system context. This approach often lacks the benefit of assessing the practical applicability of one’s work. To date, little effort has been placed on evaluating the performance of ad hoc sensor networks through real-world experiments. With this research we hope to inspire more effort in this area. In addition to designing protocols and radio architectures for sensor networks, we have also built a prototype system. This research is part of a larger project called PicoRadio [13][14][15], which seeks to build small, low-cost, ultra-low power, sensor network devices using a system-on-a-chip (SoC) approach. We call these devices PicoNodes. As a precursor to this custom SoC, we built a prototype PicoNode from off-the-self parts. We use this platform as a means to evaluate the real-world performance of our protocol stack. In this paper, we present a detailed empirical performance evaluation of radio energy consumption and packet reliability for our prototype PicoRadio network. We introduce a novel technique of application-aware radio duty cycling called on-demand spatial TDMA. Using this technique, we show more than an order of magnitude reduction in idle radio energy consumption as compared to the baseline system, which is the no-cycling case and further defined in Section V. We show these results in two different views: averaged over the entire network and for Jonathan M. Reason Jan M. Rabaey [email protected][email protected]Berkeley Wireless Research Center Electrical Engineering and Computer Sciences Department University of California, Berkeley, CA For a moderate-size, multi-hop, sensor network, we present experimental measurements of radio energy consumption and packet reliability. We categorize the energy measurements by energy consumed in each radio state and for each traffic type. Packet reliability results are presented from a network and link perspective, whereas prior work only considered the former. We introduce a novel technique of application-aware radio duty cycling called on-demand spatial TDMA. When compared to the non-cycling case, this technique can achieve greater than an order of magnitude reduction in idle energy consumption, while not sacrificing reliability. We show end-to-end packet loss rates as low as 0.04 when averaged over the network. Even with substantial idle energy savings, we identify radio idling as the dominate energy consumer and overhearing as the dominate traffic type.
Transcript
84 Mobile Computing and Communications Review, Volume 8, Number 1
A Study of Energy Consumption and Reliability in a Multi-Hop Sensor Network
I. Introduction Wireless sensor networks have enormous potential to improve how we use information from the real world. Forest fire alert systems, environmental control for the home and office, interactive toys and museums, and wildlife observation are just a few of many applications envisioned for sensor networks. Such applications are as diverse in their technological requirements as they are in their intended uses. For example, environmental control in the home might use a dozen sensor nodes with static topology, while the same application in a large office building might require thousands of nodes capable of self-configuring an ad hoc topology.
To support this vast and diverse application space, researchers have placed emphasis on developing radio technologies and protocols that provide network longevity, robustness, and adaptability. The literature is rich with techniques for media-access control, routing, self-configuring topologies, location discovery, and information fusion. Most often these techniques are evaluated through simulation using a component view, rather than considering the system context. This approach often lacks the benefit of assessing the practical applicability of one’s work. To date, little effort has
been placed on evaluating the performance of ad hoc sensor networks through real-world experiments. With this research we hope to inspire more effort in this area.
In addition to designing protocols and radio architectures for sensor networks, we have also built a prototype system. This research is part of a larger project called PicoRadio [13][14][15], which seeks to build small, low-cost, ultra-low power, sensor network devices using a system-on-a-chip (SoC) approach. We call these devices PicoNodes. As a precursor to this custom SoC, we built a prototype PicoNode from off-the-self parts. We use this platform as a means to evaluate the real-world performance of our protocol stack.
In this paper, we present a detailed empirical performance evaluation of radio energy consumption and packet reliability for our prototype PicoRadio network. We introduce a novel technique of application-aware radio duty cycling called on-demand spatial TDMA. Using this technique, we show more than an order of magnitude reduction in idle radio energy consumption as compared to the baseline system, which is the no-cycling case and further defined in Section V. We show these results in two different views: averaged over the entire network and for
Berkeley Wireless Research Center Electrical Engineering and Computer Sciences Department
University of California, Berkeley, CA
For a moderate-size, multi-hop, sensor network, we present experimental measurements of radio energy consumption and packet reliability. We categorize the energy measurements by energy consumed in each radio state and for each traffic type. Packet reliability results are presented from a network and link perspective, whereas prior work only considered the former. We introduce a novel technique of application-aware radio duty cycling called on-demand spatial TDMA. When compared to the non-cycling case, this technique can achieve greater than an order of magnitude reduction in idle energy consumption, while not sacrificing reliability. We show end-to-end packet loss rates as low as 0.04 when averaged over the network. Even with substantial idle energy savings, we identify radio idling as the dominate energy consumer and overhearing as the dominate traffic type.
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each PicoNode. Additionally, we quantify the dominant consumers of radio energy as categorized by the state of the radio (transmitting, receiving, etc.) and the type of traffic (data query, sensor data, etc.). We show packet reliability results on an end-to-end and link basis, and use the link results to show how reliability impacts routing performance.
In this context, our paper is organized as follows. In Section II, we summarize other recent efforts to evaluate performance using real systems. Section III describes the hardware and protocol components of our prototype system. For compactness, we only cite references to well established protocol terminology and methods, while briefly elaborating on new methods. We describe on-demand spatial TDMA in Section IV. In Section V, we present the results of our performance evaluation, and then we conclude with summary remarks in Section VI.
II. Related Work Most published experimental evaluations of ad hoc networks have focused on wireless LAN implementations [5]. Such works are not directly applicable to ad hoc sensor networks because the application space and protocol requirements are different. Other evaluations have used real systems to evaluate selected layers of a protocol stack, particularly the data link layer [3][4]. To our knowledge, the most relevant published empirical evaluation considers a real-world, interactive application of ad hoc sensors nodes as voting devices [1].
In this paper, the authors evaluate the end-to-end packet reliability of moderate-scale sensor networks: 24, 48, and 91 nodes. Using a variant of the rené mote [2] for their device, they show packet loss rates that range from 20 to 90 percent over a node depth of 1 to 7. Their results highlight the challenge of providing good reliability in an ad hoc sensor network, particularly when using a device with limited resources like the rené mote. They cite congestion at the sink and overflow of limited queuing resources in intermediate nodes as the main causes of loss.
While this work is a good start, it is also limited in scope. In particular, the authors evaluated their system in a single performance space: end-to-end reliability. We believe a more comprehensive evaluation is needed, hence the impetus for this research.
III. PicoNode Prototype The PicoNode prototype emulates the protocol behavior of the PicoNode SoC implementation that is under development. We have experimented with a number of protocol options in various stack configurations, particularly at the data link and network layers. In this section, we describe the current protocol stack under evaluation and the hardware components of the PicoNode prototype.
III.A. Hardware Components
The major components of the PicoNode prototype are a StrongARM SA-1100 microprocessor, a Xilinx® XC4020XLA field programmable gate array (FPGA), an Ericsson® PBA-313-01/2 Bluetooth™ radio, 4 Mbytes each of DRAM and flash memory, and one of two custom sensor boards. Each component is mounted onto one of several stackable custom circuit boards (Figure 1). One of the sensor boards supports temperature, light, humidity, and sound measurements. The other board is populated with accelerometer and magnetometer sensors (Figure 2a).
Figure 1. An open view of the PicoNode hardware showing the custom circuit boards that comprise the system. From top to bottom the stack includes the sensor board, digital board (i.e., FPGA, microprocessor, and memory), power board, and radio board. This configuration is probably overkill for most sensor applications, but as a prototyping platform it allows us to explore a full range of complexity in applications and protocols. Our current system uses about 55 kilobytes for application and network programs, 19 kilobytes for kernel code, and 670 kilobytes for program data. Under normal operation, only about 35 kilobytes are actually used for program data; the other 635 kilobytes are only used
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to store statistics for offline processing.
III.B. Protocol Stack
Figure 2 illustrates the macro components comprising each layer in the protocol stack. Throughout this section, we will refer to this figure as appropriate.
III.B.1. Physical Layer
Following a bottom-up description, we use a Bluetooth radio for the physical layer (Figure 2d). The Bluetooth radio is a 100mW radio that supports 79 channels in the 2.4 GHz ISM frequency band and a maximum data rate of 1Mbps. The modulation is Gaussian Frequency Shift Keying (GFSK) and the channel spacing is 1MHz. We do not use any of the baseband or higher layer protocol features of the Bluetooth specification [17].
III.B.2. Data Link Layer
Figure 2c illustrates the data link layer macro components. It is comprised of the Transmit Controller and Datapath (TCD), the Receive Controller and Datapath (RCD), and the Media Access Controller (MAC). The TCD and RCD control the datapath functions: transmit and receive packet buffering, serialization and de-serialization, cyclic redundancy checking, and line balancing. Additionally, they provide the control interface between the network and physical layers.
We use a two-pronged approach for the MAC. For infrequent broadcasts of management and signaling traffic, we employ carrier sense multiple access (CSMA) with preamble sampling (PS). Of the many modes of CSMA [18], we use the non-persistent version with uniform random back off. Preamble sampling is a radio cycling method that substantially reduces idle listening energy consumption. It was originally used for paging systems [21] and has recently been proposed for sensor networks [16]. The idea is to precede each broadcast transmission with a long preamble of length Tp. A node wakes up every Tp to sense the channel. If no preamble is detected within Ts, the node goes back to sleep. If the preamble is detected, then a node continues to listen until a packet has been received or a timeout occurs. When the channel is idle, the approximate duty cycle of the radio is ps TT / .
Figure 2. Block diagram description of the PicoNode prototype. Each layer is shown along with its macro components. Components implemented in hardware are denoted by HW, while SW denotes software components. For unicast traffic, we use a novel variant of spatial time-division multiple-access (S-TDMA) that we call on-demand S-TDMA (Section IV). Spatial TDMA was first introduced as a collision-free, channel access protocol for packet radio networks [19]. To our knowledge, CSMA-PS combined with S-TDMA for sensor networks was first introduced in [15]. However, the author specifies no method of how to combine these diverse multiple access techniques. We offer a simple and practical means of combining these techniques when using our variant of S-TDMA.
For link layer error control, all packet headers and packet payloads are protected by an 8-bit CRC. For increased reliability of sensor data, we use a simple data/acknowledgement re-transmission scheme with timeouts. In this scheme, a sender starts a unicast data session by sending data, setting a response timer, and then waiting for an acknowledgement. If an acknowledgement is received within the timeout, then the session ends,
Media Access Controller
TransmitController
& Datapath
ReceiveController
& Datapath
c) Data Link Layer
Neighbor List Service
Energy-Aware
Routing
LocationService
b) Network LayerSW
HW
Application Drivers
Temp.Sensor
LightSensor
Humidity Sensor
Accel- erometer
SW
Mag-netometer
Sensor Board II (HW)
Queuing Service
d) Physical Layer
Bluetooth Radio
Environment
a) Application Layer
Sensor Board I (HW)
HW
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otherwise the sender resends the data. The sender will repeat this process until it gets an acknowledgement, it has attempted a maximum number of retries, or it receives a timeout.
III.B.3. Network Layer
The network layer is comprised of four macro components: the Energy Aware Routing protocol, the Location Service, the Neighbor List Service and the Queuing Service (Figure 2b). The Energy-Aware Routing (EAR) protocol is the primary function of the network layer and is described below. The Location Service is comprised of the algorithm and protocol that allows each node to dynamically discover its local position relative to anchor nodes. The Neighbor List Service performs address resolution for the Network layer, a function that is commonly found in most networks (e.g., the Address Resolution Protocol in the Internet protocol suite). The Queuing Service provides the interface between the network and data link layers.
Energy Aware Routing is a destination-initiated, reactive protocol designed by researchers in our lab and described in [6]. Whereas most other routing protocols try to find an optimal route (e.g., lowest energy or shortest path), EAR was designed to increase the survivability of sensor networks. It accomplishes this by choosing a path in a probabilistic fashion, where the probability of choosing a route is inversely proportional to the average energy cost of the route. In contrast to optimal path strategies like directed diffusion [7], EAR has been shown to extend network lifetime by providing an even depletion of network energy.
We use a geography-based addressing scheme, where location is synonymous with network address. This type of addressing requires each node to dynamically discover and configure its three-dimensional position relative to known and fixed positions throughout the network. Since many applications for sensor networks are concerned with obtaining measurements from a particular region in space, this type of addressing seemed a natural choice. For this reason, location discovery is becoming an important topic in sensor networks and a number of methods have been proposed in the literature [10][11][12].
Our approach to location discovery is called Hop- TERRAIN [8][9]. This method uses a combination of received signal strength indication (RSSI) and hop counts from reference nodes to measure range
and to perform triangulation. In simulation, Hop Terrain discovers a node’s position with accuracy between 50 to 150 percent of the radio transmission range. Because of limitations in our prototype hardware1, we have been unable to reproduce these results experimentally. Thus, the measurements presented herein are based on a static topology with a priori location settings.
Another important sub-layer of the network is our address resolution service, which we call the Neighbor List Service (NLS). The NLS maintains a table that contains a mapping between neighbor MAC IDs and network addresses. Each entry in this table also includes a link cost metric and a status indicator. The cost metric is a measure of the average energy used for a unicast session on each link. The status indicator gives the status of link quality testing. Untested links are never used as routes because the cost is unknown. Links that have been tested, but are being updated can optionally be used as routes. The NLS also manages the timing of events during the initialization process, which includes neighborhood discovery, location discovery, and dynamic MAC ID assignment.
III.B.4. Application Layer
The application layer consists of one standard sensor board (Sensor Board I), one optional sensor board (Sensor Board II), and application drivers that provide the interface between the application and network layers (Figure 2a). The initial target application for PicoRadio is in-building environment monitoring. To this end, our test bed network consists of three types of network devices: sensor, controller, and anchor nodes. Sensor nodes, or PicoNodes, gather and forward sensor measurements using one or more of the five sensors. Controller nodes primarily initiate commands to the network (e.g., requests for sensor measurements) and serve as the end destination where PicoNodes forward their responses. Anchor nodes provide static position references within the environment by periodically broadcasting their location to the network. Controller and anchor nodes have hard-wired power sources and are part of the backbone network infrastructure. Thus, controllers and anchors can communicate directly with other backbone network devices.
For collection of sensor measurements, we 1 The radios we used do not support power control and the RSSI circuitry is defective.
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support two general paradigms: request-response and event-triggered. In the request-response paradigm, a controller node broadcasts queries to the network of the form “send me N readings of sensor type M at intervals of T seconds in the region bounded by XYZ1 and XYZ2.” For our purposes, we use a cuboid region; however, other shaped regions could be constructed using the same query format. This paradigm is broad and can be used to make simple, as well as, very complex queries. Data responses to these queries are inherently correlated in time, because each PicoNode that receives the query will respond at approximately the same time.
In contrast, the event-trigged paradigm supports data responses that may not be correlated in time. These responses are set up by queries of the form “if the reading on sensor type M reaches some threshold in the region bounded by XYZ1 and XYZ2, send me alert messages of type P at intervals of T seconds.” In this paradigm, because of variations in ambient conditions or sensor calibration, PicoNodes in the region of interest may not detect the alert condition at the same time. Both these paradigms are implemented as part of the application drivers depicted at the bottom of Figure 2a.
All the experiments performed for this paper were conducted using the request-response paradigm. As we discuss in the next section, this paradigm has synchronous properties that can be exploited to improve energy and reliability performance.
IV. On-Demand Spatial TDMA The standard TDMA protocol was designed primarily for synchronous applications that require dedicated capacity, such as telephony. To optimize capacity, such applications require tight synchronization of the TDMA frame to maintain minimal slot and frame spacing.
In a related fashion, our application is loosely synchronous. For a given PicoNode, successive data responses occur synchronously as defined by the period contained in the initial request-response query. The aggregate responses from all PicoNodes in the region of interest occur in short bursts, with long periodic gaps between bursts. Within a burst, there is not necessarily any synchronous structure to the responses, but we could impose one.
If we could duty cycle the radio synchronously with the application’s response cycle, we could conserve energy. However, a standard TDMA
MAC has no knowledge of the application-level timing. If the MAC had knowledge of this periodicity, then it could better control energy and reliability performance by imposing TDMA-like framing on each burst of traffic. This is the idea behind on-demand spatial TDMA.
We outline the on-demand S-TDMA protocol as follows:
• Upon receiving a query for data, a PicoNode will estimate a start time to measure its first sensor sample. It then sets a system timer to go off at this start time and every T seconds thereafter for N samples, where T and N are specified in the query.
• When the timer goes off, the MAC will bypass normal operation and start an S-TDMA frame. This frame will only be loosely synchronous with neighboring nodes.
• The length of the frame (Tf), the slot spacing (S), and the number of slots (Ns) within a frame are configuration parameters of the MAC. These parameters are subject to the constraint
TTSN fs ≤≤× Eq 1.
• Slot assignments are performed by using a table lookup, where the index of the table is a function of the nodes position.
• A node transmits its data packet in its designated slot. If it has packets from neighboring nodes, these packets are forwarded at any time during the frame using CSMA.
• When the frame ends, the MAC resumes normal operation until the next synchronization point.
Using this technique, we can control the radio duty cycle during unicast traffic, which in turn impacts energy and reliability performance. When the network is servicing an active request, the radio duty cycle is specified by TTf / . For fixed Ns, Tf is linearly proportional to S. Since node energy consumption is proportional to the duty cycle of its radio, we would like to make S small and Tf much smaller than T. In contrast, reliability performance should improve as S increases because larger slot spacing reduces collisions. However, increasing S means increasing the latency of each nodes’ response to the query. Thus, by varying response latency, we can empirically explore its impact on energy consumption and reliability
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V. Performance Evaluation In this section we present an evaluation of radio energy consumption, packet reliability, and traffic for our network. Throughout this discussion we will compare a baseline configuration and two sample configurations: case 1 and case 2, which we define below and summarize in Table1. The word energy will be used interchangeably with “radio energy” and reliability for “packet reliability” unless specified otherwise.
System Description
baseline CSMA On-demand S-TDMA w/ Tf =T, S=20ms and Ns=9
case 1 CSMA-PS w/ Tp=512us and Ts=5us
On-demand S-TDMA w/ Tf =256ms, S=20ms, and Ns=9
case 2 CSMA-PS w/ Tp=512us and Ts=5us
On-demand S-TDMA w/ Tf =90ms, S=10ms, and Ns=9
Table 1. Summary of system configuration parameters
Term Definition Energy/Bit
Rx Busy radio is actively receiving a packet
112 nJ
Tx Busy transmitter is on and ready to transmit
98 nJ
Rx Switch radio is switching on the receiver
112 nJ
Tx Switch radio is switching on the transmitter
98 nJ
Rx Idle 1 receiver is on and idle after an Rx Busy event
112 nJ
Rx Idle 2 receiver is on and idle after an Rx Switch event
112 nJ
Sleep low-power state 0.168 nJ
Table 2 Definition of radio states We define the baseline configuration as the system specification of Section II with preamble sampling disabled. That is, the receiver is always on when not transmitting or when the channel is idle, and we use CSMA for signaling traffic. For data traffic, the baseline case uses on-demand S-TDMA with Tf equal to T and S equal to 20 milliseconds. For a given set of configuration parameters, the
baseline case represents the best case for reliability performance, yet the worst case for energy performance. Thus, with our two sample cases, we compare how close they approach the baseline reliability and how much they improve upon the baseline energy consumption.
Traffic Type Definition Sender
Data Query broadcast request for sensor data
controller
Sensor Data response packet to a Data Query
PicoNode
Data ACK link layer ACK for unicast packets
any node
Management Data
signaling packet to change configurations
controller
Link Test Data
unicast packet used to update link cost metric
PicoNode
Overhearing any unicast packet destined for one node but overheard by another
PicoNode
Bad CRC any packet that fails the CRC check
any node
NLS Data Neighbor List Service update packets
PicoNode
Table 3 Definition of traffic types
We measure radio energy by first measuring the number of bits consumed or produced by the radio during each experiment. These measurements are indexed by radio state and traffic type. Knowing the nominal energy per bit for each radio state, it is then a simple calculation to determine the nominal radio energy consumption per traffic type, per node, or averaged over the network. Table 2 and 3 define the terms we will use to annotate the figures below.
V.A. Experimental Setup
For each experiment, we used the topology depicted by Figure 3. This network consisted of 25 PicoNodes placed in an approximate rectangular grid, where the spacing between adjacent nodes was between 3 to 7 meters. There was a glass partition separating the second and third row of nodes. Additionally, separating the first and second row of cubicles, there are square pillars with cross-sectional area of 2.25 square feet.
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Figure 3. Aerial view of the BWRC floor plan. The black circles indicate placement of PicoNodes. The black square indicates the placement of the controller node. Dimensions are 41 meters by 42 meters. Distance between adjacent PicoNodes ranges from 3 to 7 meters.
The triplet of numbers atop each node represents its three dimensional or width-depth-height (XYZ) position within the space. For readability, we use hexadecimal notation for each dimension. A unit quantity in any dimension represents a relative measure of position, not an absolute one. The controller node is depicted by the black square and is positioned at 601. The Z position for all nodes was fixed at one, since all nodes were approximately at the same height from the floor (between 1 and 2 meters).
At the beginning of each experiment, the controller node broadcasted management data to setup the appropriate configuration and to tell each node when to begin recording statistics. After this procedure, the controller broadcasted the following data query: send 200 readings of temperature measurements at intervals of 5 seconds for all nodes in the network. As the sensor measurements arrive, the controller dispatches them to a monitor application that graphs a time series of the data. Each experiment lasted approximately 17 minutes.
V.B. Energy Consumption and Survivability
In this section we show how radio cycling can improve survivability. Figure 4 depicts the network energy consumption surface for the three test cases.
The axes labeled X Position and Y Position represents the width (X) and depth (Y) positions for each node in our topology. For clarity, we disregard the height position for this presentation. Each square in the grid on the surface represents that node’s contribution to the surface.
1 3 5 6 7 9 111
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Figure 4. Network energy consumption surface for the three test cases. The energy surface is flat for the baseline case, while the surface for case 1 and case 2 has peaks. These peaks are attributed to false preamble detection. In the baseline case the surface is almost flat. This is a desired property because a flat energy surface indicates the energy is depleting uniformly across the network. Unfortunately, because there is no radio duty cycling, this case also has the
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undesirable property of quickly depleting energy. Thus, energy depletion is dominated by receiver idle energy consumption. After approximately 17 minutes, each node has consumed approximately 132 Joules of radio energy.
Figure 5. A detailed view of each node’s energy consumption for case 1 and case 2. This view clearly shows that nodes 511, 551, 571, 731, 911, and b51 experienced problems with preamble detection. Case 1 shows the impact of turning on preamble sampling. For this case, the average node energy consumption is approximately 9 Joules, which is more than an order of magnitude less than the baseline case. The energy surface is mostly flat; however there is more variation than the baseline case. Case 2 shows similar results with a slightly lower energy surface than case 1. The average node energy consumption for case 2 is approximately 6 Joules.
Figure 5 shows the variation of node energy consumption in more detail. For case 1, node energy varies between 6.4 and 27.18 Joules, and for case 2 it varies between 2.73 and 20.21 Joules. By studying the detailed energy profile for each case, we were able to isolate the cause of this behavior.
These variations are caused by false preamble detection during preamble sampling. In both cases, Figure 5 shows about six rogue nodes (511, 551, 571, 731, 911, and b51) that exhibit this behavior. We are currently exploring ways to mitigate this problem. One method would be to use a slightly longer sampling time to better resolve the preamble.
Figure 6. Detailed view of each node’s end-to-end packet reliability. For the baseline, case 1, and case 2 the network average packet loss rate is 0.04, 0.04, and 0.36, respectively.
Using these results, we can make some qualitative comments about an upper bound on survivability.
a) case 1
b) case 2
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00.020.040.060.080.1
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Each PicoNode is powered by two 3.6 volt, 1400 milliamp-hour capacity batteries. This gives us a 10.08 watt-hour capacity for each node. From our measurements, the average node radio power consumption is 132.69 milliwatts for the baseline case, 9.94 milliwatts for case 1, and 5.69 milliwatts for case 2. Let’s assume the dominate energy consumer is the radio and all other energy consumers are negligible. Using these numbers, the average baseline node will survive about 3 days compared to approximately 42 days for case 1 nodes and 74 days for case 2 nodes.
This qualitative argument is not strictly true for our prototype PicoNode, because we do not power cycle the processor or the FPGA. In addition, this calculation does not consider battery leakage as a source of energy depletion. Actual measurements for the baseline node show about 1.5 days survivability. This is because the combined processor and FPGA energy consumption is almost the same as the baseline radio energy consumption. Thus, for case 1 and case 2 nodes, the processor and FPGA are the dominate energy consumers. Therefore we would expect case 1 and case 2 nodes to survive about 3 days.
These results show modest gains in survivability when enabling the low-power features of our protocol stack on the prototype hardware. However, this is still encouraging because the prototype hardware was not designed for power performance. In our custom solution, where hardware power management is a key feature, we expect radio energy consumption to dominate. Thus, the survivability should be substantially closer to the upper bound supported by the custom SoC.
V.C. Packet Reliability
Figure 6 shows the end-to-end packet loss rate (PLR) for all nodes in our network. For the baseline case, PLR values range from 0 up to 0.2, with a network average PLR of 0.04. The nodes exhibiting the best reliability performance are the nodes closest to the controller. Nodes furthest away from the controller and on the edges of the network have the worst performance. The number of hops from source to destination ranged from 1 to 8. For case 1, the variation in PLR is smaller, but the network average is the same. We interpret this result as follows. For a given slot spacing, enabling preamble sampling has negligible impact on end-to-end packet reliability. This follows by design because the two cycling strategies are
orthogonal. That is, preamble sampling is disabled whenever on-demand S-TDMA is active.
In contrast, case 2 nodes have much higher loss rates. End-to-end PLR values range from 0 to 0.88, with a network average PLR of 0.36. However, the trend is similar to the other cases, with nodes closer to the controller having better loss rates than nodes further away and on the edges. The higher PLR is caused by increased collisions, which result from a smaller frame size and slot spacing. This result highlights the trade-off between latency and reliability inherent in on-demand S-TDMA.
Figure 7. a) Sender 371’s link reliability to its neighbors. b) Sender 371’s frequency of using each neighbor as a next-hop route. The routing algorithm uses 551 as the next-hop route infrequently because its poor reliability gives it a higher cost metric. Next, we consider the impact of link reliability on next-hop routing. The energy aware routing protocol forwards unicast data to the next-hop in a probabilistic fashion. The probability of choosing the next-hop route is inversely proportional to the total cost of that route. For our prototype PicoNode cost metric, each node maintains a measure of the total average energy used for each unicast session
a) link reliability for sender 371's neighbors
b) next-hop route usage for sender 371
371 to 571 371 to 351 371 to 5510.0%
10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%
100.0%
Session Reliability
Link
571 351 5510.0%
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Next Hop Usage Rate
Next Hop
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with each of its neighbors. This cost metric is also inversely proportional to the rate of session success, which we are able to measure from our link reliability data.
Figure 7 illustrates how next-hop routing adapts to link reliability for node 371. We chose this node for illustration purposes because its link to neighbor 551 had the worst single link reliability of all nodes. The top part of the figure shows the link reliability for each of node 371’s neighbors. The bottom part of the figure shows node 371’s rate of choosing a next-hop route. The next-hops that have the best reliability (571 and 351) get used more frequently than the hop that has the worst reliability (551).
We can see this more succinctly with Figure 8, which shows the distribution for the number of transmit attempts it takes node 371 to complete a session with each of its neighbors. This is a more direct representation of cost. The more transmit attempts it takes the higher the cost of the link.
Figure 8. The distribution for the number of transmits it takes node 371 to complete a session with each of its neighbors. Node 351 is the highest cost route because it takes five or more transmits to complete most sessions. The figure shows that next hop 551 is the highest cost link as we inferred from Figure 7. Node 371 has a 0.77 probability of transmitting 5 or more unicast packets whenever it is in a session with node 551. In contrast, with probability greater than 0.9, node 371 will transmit 3 or less packets when in a session with node 571 or 351. From this graph, it is also clear why node 371 gives preference to node 571 over node 351, even though the session success rate with both nodes is almost 100 percent. With node 571, node 371 completes the session on the first attempt with probability 0.6 compared to probability 0.5 for node 351. Thus, node 571 is the lower cost route.
V.D. Energy and Traffic Profiles
Figure 9 depicts the percentage of energy consumed in each radio state for the average node, while Figure 10 depicts the average node’s energy consumption per radio state. The average baseline node consumed 132.69 Joules of radio energy. Idle listening (Rx Idle 1 and Rx Idle 2) consumed the most energy, 131.5 Joules, which amounts to 99.11 percent of the total budget. Although consuming only a small percentage (0.86 percent or 1.14 Joules), the Rx Busy state is the second most dominant energy consumer. The percentage of energy consumed by all the other states combined is only 0.03 percent or 0.05 Joules.
For case 1 and case 2, preamble sampling and on-demand S-TDMA reduce idle listening energy consumption to 9.37 Joules and 5.53 Joules, respectively. However, idle listening is still the dominant consumer, comprising 94.3 percent of the total energy budget for case 1 and 91.9 percent for case 2. In contrast to the baseline case, the idle listening energy mostly occurs during the response cycle of the on-demand S-TDMA frame.
Combing these results with the packet reliability results of SectionV.C, we see how on-demand S-TDMA can be used to trade-off energy for reliability (or vice versa). In particular, energy consumption is directly proportional to frame length, while packet loss is inversely proportional to slot spacing. Since case 1 used a frame length 2.84 times longer than case 2, it had worse energy performance. In contrast, case 1 had better reliability performance than case 2 because the slot spacing for case 1 was twice as wide.
These performance numbers suggest that reliability is a stronger function of slot spacing than energy is of frame length. For case 2, a factor of 2 reduction in slot spacing degraded average network reliability by a factor of 9 (Figure 10). Conversely, a factor of 2.84 reduction in frame length only reduced energy consumption by a factor of 1.65 (Figure 10).
Although Rx Busy and Tx Busy energy consumption represent small percentages of the total budget, it is still instructive to analyze the network traffic. Figure 11 illustrates the average node’s Rx Busy energy profile as a percentage of each traffic type. For the baseline case, the average Rx Busy energy was 1.14 Joules. Overhearing of unicast data packets is the dominate traffic, consuming 91.22 percent of active receiver energy.
1 2 3 4 5 6 7 8 9 11371 to 351371 to 551371 to 571
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This result is largely explained by a limitation in our radio hardware, namely no power control. Without power control, most nodes in our network can potentially overhear each other. If power control were available, we would expect most of this overhearing energy to actually be idle listening energy.
Figure 9. The average node’s radio energy profile as a percentage of each radio state. Idle listening is the dominant consumer of energy for all cases. Packets that fail the CRC check are the second dominant traffic type, consuming 7.59 percent of active receiver energy. Since the end-to-end and link packet loss rates are low, we conclude that
most of the lost packets caused by corrupt CRCs were overheard packets. Sensor Data only represents 1.08 percent of the total active receiver energy budget.
Figure 10. Detailed view of average node’s radio energy per radio state. For the baseline case, case 1, and case 2, the total energy consumed by the average node was 132.69, 9.94, and 6.02 Joules, respectively.
For case 1, the average Rx Busy energy was 0.188 Joules. Although there is a substantial reduction in energy consumption, the energy profile shows only a slight improvement in efficiency. Sensor Data accounts for 7.31 percent of the Rx Busy energy. Overhearing is still the dominate traffic type, consuming 78.64 percent of the active receiver energy, while traffic with corrupt CRCs account for
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11.69 percent.
Figure 11. The average node’s Rx Busy energy profile as a percentage of traffic type. Overhearing is the dominant traffic type.
The contribution of the other traffic types to the
Rx Busy energy profile is small. During the experiment there was no need to send Management Data. Similarly, Data Query traffic is also infrequent. Once there is an active query, subsequent queries are sent only to re-synchronize the response cycle or to setup a new query. These experiments were not long enough for any significant phase drift in the response cycle to require re-synchronization.
These results already reflect a data link layer optimization that turns off the receiver for the duration of an overheard packet after reading the
header. Therefore, no energy is wasted receiving payloads of overheard packets. We are still exploring other means to reduce Overhearing traffic. Data fusion is one obvious technique that we hope to employ in the future.
Figure 12. The average node’s Tx Busy energy profile as a percentage of traffic type. Sensor Data is the dominant traffic type.
Figure 12 illustrates the average node’s Tx Busy energy profile as a percentage of each traffic type. For each case, Sensor Data is the dominate traffic type. The average baseline node spends 59.05 percent of its active transmitter energy sending sensor measurements, and 35.93 percent of it energy sending Data Acknowledgements. Thus, in total, it spends about 95 percent of its transmitter energy engaged in a unicast session, either as a
a) baseline
c) case 2
Data Query Sensor Data
Data ACK Link Test Data
Management Data Bad CRC
Overhearing b) case 1
Overhearing
Bad CRC
Sensor DataData ACK
Link Test Data Data Query
Management Data
1.084%
7.591%
0.071% 0.036%
0.002% 0.001%
91.216%
7.306%
11.691%
1.729% 0.544%
0.051% 0.038%
78.641%
8.763%
17.862%
1.496%0.915% 0.143%
0.046%
70.772%
0.003%
Overhearing
Bad CRC
Sensor Data
Data ACKLink Test Data Data Query
Management Data NLS Data
a) baseline
c) case 2
Data QuerySensor Data
Data ACK
Link Test Data
Management Data
b) case 1
Sensor Data
Data ACK
Link Test Data
Data Query
Management Data
59.054%0.007% 0.216%
35.938%
4.786%
15.767%
29.837%
0.280% 0.043% 54.073%
13.621%
23.591%
0.174% 0.140%
0.006% 62.468%
Sensor Data
Management Data Data Query
NLS Data
Data ACK
Link Test Data
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sender or a receiver responding with a Data Acknowledgement.
The percentage of link quality testing traffic is small in all cases. This is because link cost metrics are updated every time a node sends or forwards sensor data. Therefore, nodes rarely send these special test packets in an active network. Interestingly, the percentage of Link Test Data is smaller for the baseline case than the other cases. This is because the other cases were configured to send link test packets outside of the on-demand S-TDMA frame, where all packets are sent with the longer preamble. In contrast, the baseline case sends all unicast packets with short preambles.
VI. Conclusion In this paper, we presented a performance evaluation of energy consumption, packet reliability, and network traffic for an environmental monitoring application of sensor networks. While this research is only an initial step, these results demonstrated a protocol stack that achieves good reliability and energy conservation in a moderate-scale network with static topology. However, further work is needed to evaluate the performance of our protocol stack in other scenarios. We enumerate some of these alternatives below, as well as summarize some of our results.
We did not consider dynamic location discovery in this study because of limitations in our radio that prevent us from measuring reasonable location estimates. It would be interesting to see how robust geography-based routing is to inaccurate location estimates. Noisy location estimates will undoubtedly impact reliability performance.
Although many applications envisaged for sensor networks do not require mobility, there are clearly some that do. We are currently exploring ways to support mobility in our protocol stack.
On-demand S-TDMA shows promise as a MAC strategy that provides a means to control reliability and energy consumption. However, more study is needed to assess its robustness to errors in frame synchronization. In addition, analysis is needed to provide a means for determining optimal parameter settings.
In contrast to results presented in [1], our results showed much better end-to-end reliability performance. There are undoubtedly many reasons for this better performance and a detailed
comparison is beyond the scope of this paper. However, we can draw some insight into future directions by highlighting one obvious reason: PicoNode prototypes have more resources than rene motes. Most notably, the theoretical network bandwidth for the prototype PicoNode is 100 times that of rene motes (1 Mbps vs. 10 kbps). Thus, for a given network size and offered load, more network bandwidth should lead to better reliability performance. This suggests that a good cost-performance configuration for a network sensor device might ultimately lie somewhere between a very low-bandwidth device and a high-bandwidth one. Consistent with this statement, future PicoNode SoC implementations will target a 100 kbps radio.
Acknowledgments We acknowledge the contributions of the PicoRadio Group of the Berkeley Wireless Research Center, especially the Test Bed and NAMP subgroups. This research has been sponsored by DARPA as part of the PAC/C program, and by the MARCO GSRC research consortium.
References [1] M. Yarvis, et al, "Real-World Experiences with
an Interactive Ad Hoc Sensor Network," Proceedings of the International Workshop on Ad Hoc Networking, Vancouver, Canada, August, 2002.
[2] J. Hill, R. Szewcyk, A. Woo, D. Culler, S. Hollar, K. Pister, “System Architecture Directions for Networked Sensors”, Proc. of the 9th Intl. Conf. on Architectural Support for Programming Languages and Operating Systems, pp. 93-104, November 2000.
[3] W. Ye, J Heidemann, D. Estrin, “An Energy-Efficient MAC Protocol for Wireless Sensor Networks”, IEEE Infocom, June 2002.
[4] A. Woo, D. Culler, “A Transmission Control Scheme for Media Access in Sensor Networks”, ACM/IEEE Mobicom, 221-2, July, 2001.
[5] D. Maltz, J. Broch, D. Johnson, “Lessons from a Full-Scale Multihop Wireless Ad Hoc Network Testbed”, IEEE Personal Comm., Vol. 8, No. 1, pp. 8-15, February, 2001.
[6] R. Shah and J. Rabaey, "Energy Aware Routing for Low Energy Ad Hoc Sensor Networks", IEEE Wireless Communications and Networking Conference, March 17-21, 2002, Orlando, FL.
Mobile Computing and Communications Review, Volume 8, Number 1 97
[7] C. Intanagonwiwat, R. Govindan, D. Estrin, “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks”, IEEE/ACM Mobicom, pp. 56-67, 2000.
[8] C. Savarese, J. Rabaey, J. Beutel, "Locationing in Distributed Ad-Hoc Wireless Sensor Networks," ICASSP 2001.
[9] C. Savarese, "Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks", M.S. Thesis, University of California, Berkeley, 2002.
[10] N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less Low-cost Outdoor Localization for Very Small Devices,” IEEE Personal Communications, 7(5):28–34, Oct 2000.
[11] A. Savvides, C.-C. Han, and M. Srivastava, “Dynamic Fine-Grained Localization in Ad-hoc Networks of Sensors,” in 7th ACM Int. Conf. on Mobile Computing and Networking (Mobicom), pages 166–179, Rome, Italy, July 2001.
[12] L. Doherty, K. Pister, and L. El Ghaoui, “Convex Position Estimation in Wireless Sensor Networks,” in IEEE Infocom 2001, Anchorage, AK, April 2001.
[13] J. Rabaey, M. J. Ammer, J. da Silva Jr., D. Patel, S. Roundy, "PicoRadio Supports Ad Hoc Ultra-Low Power Wireless Networking", IEEE Computer Magazine, pp. 42-48, July, 2000.
[14] J. Rabaey, M. J. Ammer, T. Karalar, S. Li, B. Otis, M. Sheets, T. Tuan, "PicoRadios for Wireless Sensor Networks: The Next Challenge in Ultra-Low-Power Design", Proceedings of the International Solid-State Circuits Conference, San Francisco, CA, February 3-7, 2002.
[15] B. Otis and J. Rabaey, "A 300µW 1.9GHz CMOS Oscillator Utilizing Micromachined Resonators", Proceedings of the European Solid-State Circuits Conference, Florence, Italy, September 24-26, 2002.
[16] A. El-Hoiydi, “Spatial TDMA and CSMA with Preamble Sampling for Low Power Ad Hoc Wireless Sensor Networks,” Proc. of the Intl. Symp. on Comp. and Comm., 2002.
[17] A. El-Hoiydi, “Aloha with Preamble Sampling for Sporadic Traffic in Ad Hoc Wireless Sensor Networks”, Proc. IEEE International Conference on Communications, 2002.
[18] Bluetooth Special Interest Group, Bluetooth Specification v1.1, http://www.bluetooth.com, February, 2001.
[19] L. Kleinrock and F. Tobagi, “Packet Switching in Radio Channels: Part I--Carrier Sense Multiple-Access Modes and Their
[20] R. Nelson and L. Kleinrock, “Spatial TDMA: A Collision-Free Multihop Channel Access Protocol”, IEEE Trans. Comm., Vol. 33 No. 9, pp. 934-944, September, 1985.
[21] K. Sohrabi, J. Gao, V. Ailawadhi,and G. J. Pottie, “Protocols for Sel-Organization of a Wireless Sensor Network”, IEEE Personal Communications, pp. 16-27, October, 2000.
[22] International Telecommunication Union, “Codes and formats for radio paging”, ITU-R Rec. M.584-2 (11/97), http://www.itu.int, November, 1997.
