Opportunistic relaying protocols for human monitoring in BAN Jean-Marie Gorce∗ , Claire Goursaud∗ , Guillaume Villemaud∗ , Raffaele D’Errico† , Laurent Ouvry† ∗
Universit´e de Lyon, INRIA, INSA-Lyon, CITI, F-69621, FRANCE † CEA Leti, Grenoble, FRANCE Email:
[email protected]
Abstract—Body Area Networks (BAN) offer amazing perspectives to instrument and support humans in many aspects of their life. Among all possible applications, this paper focuses on body monitoring applications having a body equipped with a set of sensors transmitting in real-time their measures to a common sink. The underlying network topology is a star topology which is quite usual in the broad scope of wireless sensor networks. Therefore, a classical superframe structure as proposed in 802.15.3 or 802.15.4 seems to comply with the needs of such an application. Basically however, the specificities of the BAN channel can reduce the performance of such protocol. Indeed, channel time variations make the star structure unstable and temporary subject to a high packet error rate. A multi-hop mesh topology cannot counteract this problem efficiently, since the pathloss attenuation in a BAN environment is almost independent with the emitter-receiver distance. In this paper, we address this issue by considering the topology of a BAN network as a timevarying fully connected network instead of a star structure. We then show how an opportunistic cooperative mechanism based on a decode-and-forward protocol can address this issue. We derive a performance criterion based on a packet error rate outage and we discuss the implementation of this scheme in the classical superframe structure.
I. I NTRODUCTION Enabling efficient communications on, in and between human bodies is an old idea. Body Area Networks (BAN) address this issue to instrument and support humans in many aspects of their life. Possible BAN applications span a wide area, including medical, sport and leisure. Since such devices and networks are expected to taking a growing place into the market with stringent QoS constraints, flexibility and coexistence are critical issues to comply with radio resource scarcity. Different standards actually compete in this market but each solution fulfils the specifications of a single application, in the medical, sportive or entertainment fields. In the future, on body communications should gain at sharing a unique standard to manage all on-body communications (as addressed by the present 80.15.6 IEEE working group). In this context, a unique standard would have to respect very stringent but adaptable QoS constraints. Simultaneously, these equipments have to face the problem of energy consumption (especially for implanted devices). Therefore, the paradigm of a BAN can be defined as: ”offering reliable communications, at a low energy price and with variable QoS from alarm transmission up to continuous real-time high rate flows”. In this general context, this paper focuses on the important application of body monitoring. The network is made of a set
of sensors spread on the body, transmitting their measures in real-time to a common sink. At the application level, the BAN thus fits the classical star network topology, and the superframe structure already used in 802.15.3/15.4 may adapt to the BAN context. But BAN channel features may compromise the efficiency of usual protocols.The two most important features are firstly that the propagation losses are not directly proportional to the transmitter-receiver distance and secondly that all radio links exhibit high shadowing variations due to the human body motion. Therefore, the star structure becomes unstable and subject to a time-varying packet error probability (PEP). To avoid a drastic transmission power increase, cooperative mechanisms based on opportunistic relaying seem appealing. We evaluate this assertion by combining real channel measurements with an analytic formulation applied to a two-slot opportunistic relaying scheme. The main contributions of this paper are: • An analytical criterion is proposed to measure the efficiency of BAN transmissions. This criterion measures the packet error probability outage. • The efficiency of a simple Decode and Forward (DF) relay scheme is shown. • The implementation of such opportunistic relaying mechanism in a superframe structure is envisioned. II. BAN TOPOLOGY FOR MONONITORING APPLICATIONS A. BAN channel model In the scope of the IEEE 802.15.6 standardization group, a channel model has been proposed for on-body applications (namely CM3). In the final document [1] the human body mobility is to some extent addressed. Nevertheless the model presents some difficulties to extract the description of time-variant BAN channel, which is needed to evaluate the performance of each on-body node in cooperative networks. CEA-Leti performed time-variant BAN channel measurements in indoor and anechoic chamber with different human subjects. Two different star configurations have been tested with transmitter on hip or left ear, and four receiving antennas on different body positions. The results of this measurement campaign are presented in the companion paper [2] and allows to describe the simultaneous behavior of the on-body nodes in three movement conditions. For a given bandwidth B the timedependent power transfer function P(tn ) can be calculated
TABLE I FADING CHARACTERISTICS AT 2.45 GH Z (B =10 MH Z ): TX ON H IP, I NDOOR , WALKING SCENARIOS .
TX on Hip, Indoor, Walking 1 0.9 0.8
Slow Fading σsS [dB] Duration (ms) 1.52 432.80 3.27 631.00 2.66 532.80 2.57 579.90
0.7 0.6 CDF
Chest Thigh R Wrist R Foot
Fast Fading Depth [dB] Duration [ms] 4.39 78.10 6.93 86.20 6.54 81.90 7,98 82,70
0.5 Chest meas. Chest model Thigh meas. Thigh model Wrist meas. Wrist model Foot meas. Foot model
0.4 0.3 0.2 0.1
from the time-variant transfer function H(tn ; f ) and expressed as follows: 1 2 P(tn ) = |H(tn ; f )| df = G0 · S(tn ) · F (tn ) (1) B B where G0 represents the mean channel gain, S(tn ) the slow fading component and F (tn ) the fast fading component. In [2] it is shown that the mean channel gain and the slow fading can be represented as two independent log-normal distributions: G0 |dB ∼ N (μ0S , σ0S )
(2)
S(tn )|dB ∼ N (0, σsS )
(3)
where S identifies the scenario, i.e. the transmitting and receiving antenna locations on the body, the environment, and the movement. In particular (2) accounts for the human variability of different bodies, and (3) accounts for the shadowing effect given by the human body movement. Here we focus on 2.45 GHz, and the bandwidth is implicitly that employed in measurements, i.e. 10 MHz. The slow fading is extracted by applying a sliding temporal window. In Tab. I we report the slow and fast fading characteristics when the human subject walks in an indoor environment. The fading duration is computed as the average time below the mean value. Measurement results show that the fade duration of F (tn ) can affect directly the single symbol transmission, while the shadowing S(tn ) can affect the packet one. The negative correlation of shadowing at different positions [2] can be exploited for packet relaying. The fast fading component can 2 be expressed as F (tn ) = |Γ(tn )| . Measurement results show that |Γ(tn )| follows a Rice distribution as depicted in Fig.1. In Tab.II we list the parameters of the Rice distribution of |Γ(tn )| obtained by Akaike method from measurement results. Results show a high K-factor when the human subject does not move, which means that there is a main path with a strong energy contribution. As a consequence the channel exhibits a small residual fast fading, put down to the involuntary breathing movements. In contrast in walking scenarios, the K-factor is low, which results in a more important fast fading. For instance in the Hip-to-Foot link the nodes are often masked by the body and propagation occurs by reflection on surrounding environments. As a consequence K-factor is close to 0 and we can approximate the fading statistic with a Rayleigh distribution. B. Network topology The BAN channel properties depicted above have a strong impact on network modeling and simulation. The first speci-
0
Fig. 1.
0
0.5
1
1.5 ⏐Γ (tn)⏐
2
2.5
3
Fast fading distribution: TX on Hip, walking movement in indoor
TABLE II FADING STATISTICS AT 2.45 GH Z (B =10 MH Z ): TX ON H IP, I NDOOR .
Chest Thigh R Wrist R Foot
ν 0,998 0,999 0,998 1,000
Still σ 0,220 0,210 0,114 0,109
K 10,266 11,328 38,424 42,030
ν 0,991 0,923 0,962 0,582
Walking σ 0,351 0,566 0,519 0,825
K 3,983 1,331 1,720 0,249
ficity of the BAN channel when compared to other Wireles Sensor Networks (WSN), holds in the independency of the path-loss with the transmitter-receiver distance, beyond few centimeters (typically > 25cm). Thus, the BAN topology is not geometric based.Secondly, due to the human motion and the surrounding environment, each pathloss is subject to time variations. We identified above variations at two time scales. Short-term variations are characterized by a fading law such as Nakagami-m, Rice or Rayleigh. Long-term variations are due to the body motion (typically with a coherence time of few hundred milliseconds) and generate shadowing effects, fitting a log-normal distribution. III. P ERFORMANCE EVALUATION MODEL In this section, we evaluate the expected performance of a classical narrow-band transmission in a BAN channel, as modeled in the previous section. The choice of a criterion for estimating the radio link quality is important. In the presence of shadowing, the symbol error rate (SER) evolves during time and the average value is not representative of the link quality. As used for mobile networks, the outage probability of the packet error rate is more relevant, since it can reflect the timevarying nature of the link. A. Packet error rate with small-scale fading The symbol error probability (SEP) is a function of the inγ) stantaneous SNR. The mean symbol error probability PS (E|¯ associated with the average SNR γ¯ is usually derived from the fading distribution.Only asymptotic formula were available [3] until recent fine analytic approximations [4], [5]. However, these formula provide the SEP when the symbols encounter independent fading states. For BAN, preliminary results show a fading state constant during a packet transmission. In this
fading model referred to as bloc fading, and without coding, the PEP is given by: γ , bf ) = PP (E|γ) · fγ (γ|¯ γ ) · dγ (4) PP (E|¯
the target SNR as a function of the target PEP Pl∗ can be obtained by inverting (5), leading to: γ¯ ∗ =
γ
with PP (E|γ) = 1−(1−PS (E|γ))NS , having NS the number of symbols per packet. Since a theoretical expression of the PEP is not available for Ricean channels, we only consider the extreme case, i.e. Rayleigh channel. We exploit the analytic approximation of the PEP proposed in [6] and given for a BPSK modulation by: −2.125 · log(NS ) + 1.1 γ , bf ) = 1 − exp PP (E|¯ (5) γ¯ Figure 2 depicts the accuracy of this approximation. It is shown that under strong fading conditions, having a success probability of about 90% needs an average SNR of about 20dB. Of course, channel coding could be added to improve this performance, but since packet losses are mostly due to fading holes, a poor benefit is anticipated.
−2.125 log(NS ) + 1.1 log(Pl∗ )
(7)
Then, (6) can be written: PP (O|Γ; bf, shad) = P (¯ γ ≥ γ¯ ∗ |sh)
(8)
Since the shadowing is assumed log-normally distributed, one obtain: ∗ ΓdB − γ¯dB PP (O|Γ; bf, shad) = Q (9) σdB Figure 3 plots the outage probability for two P EP ∗ values (2%, 10%), and two shadowing strengths (3dB, 6B).
Fig. 3. Outage probability as a function of the average SNR over a radio link. The arrows indicate the points at which Γ = γ ¯ ∗ , i.e. the outage is equal to 0.5.
Fig. 2. Analytic approximation of the link probability (Lp = 1 − P EP ) in Rayleigh bloc fading channels.
B. Packet error rate outage probability Evaluating the long-term performance requires integrating the shadowing in the PEP computation [7]. However, the resulting PEP would be neither significant nor representative of the time-varying channel features. Considering real-time monitoring scenarii, the most relevant information is the PEP over a time window lower than the latency criterion (typically < 125ms).Since shadowing duration was observed up to 500ms (see section II), an outage formulation is more appropriate. Let us keep the PEP approximation in (5), and define P EP ∗ as required by the upper network layers. The shadowing effects are then introduced by computing the probability of having a local PEP lower than this threshold, i.e. the outage probability: PP (O|Γ; bf, sh) = P (PP (E|¯ γ , bf ) ≥ P EP ∗ |sh)
(6)
where Γ stands for the long term averaged SNR; bf and sh refer resp. to bloc fading and shadowing conditions. Further,
These results show that the SNR margin required to achieve P EP ∗ with a low outage is high (beyond 20dB). To ensure low energy consumption and to prevent from high interference between adjacent BANs, it seems necessary to reduce this margin. The most promising approach relies on compensating simultaneously for short term fading and shadowing thanks to a diversity mechanism. Interleaving and convolutional coding over several packets could be seducing if the shadowing variations were fast enough. Nevertheless, these mechanisms could only compensate for shadowing at the price of increasing the latency to achieve independent channel states. Therefore, cooperative relaying seems appealing. IV. O PPORTUNISTIC RELAYING Cooperative mechanisms were subject to numerous studies over past ten years; see [8] for a recent overview. A. Relaying scheme Usual cooperative mechanisms investigated for WSN communications are relay channel, multi-hop and virtual MIMO. Virtual MIMO consists in using clusters of nodes to behave like multi-antennas systems.This approach offers the highest capacity but requires a fine synchronization and complex distributed coding methods. The multi-hop approach consists in organizing the network to route information. This is not
appropriate to the case of BAN since all paths support the same path-loss. The relay channel consists in using a relay (or more) to retransmit a packet to a destination.We focus on this late mechanism by considering a half-duplex relaying with a decode-and-forward (DF) protocol. B. Performance evaluation principle To evaluate the performance of the DF protocol in terms of outage probability, the following basic scheme is considered: 1) A source sends its packet to the sink. 2) If the relay succeeds in decoding this packet in the same time, it sends a copy of it in a further slot. Note that the relay has to be listening. The exact framing of this approach will be described in the next section. 3) The sink decodes a packet with the two received copies. Further, two receiving policies are possible: (i) the sink decodes each packet independently and succeeds in receiving the packet if at least one packet is successfully decoded or (ii) the sink performs a soft combination. For the sake of simplicity, we will treat the first policy only. However, we claim that this approach can achieve most of the diversity gain. In the following, the subscripts .SD , .SR−RD , and .SRD refer respectively to the direct, the relayed and the combined paths. 1) Packet error probability with the relay node: The packet error probability associated with the combined path can be obviously expressed as: γi ) = PSD (E|¯ γ1 ) PSRD (E|¯ · [1 − (1 − PSR (E|¯ γ2 )) · (1 − PRD (E|¯ γ3 )]
(10)
where γ¯i ; i ∈ 1, 2, 3 stand respectively for the SNR of the SD, SR and RD links. 2) Outage probability of relayed channels: Computing the outage probability relies on integrating (10) with respect to the three SNR variables γ¯i ; i ∈ 1, 2, 3. This is tricky and finding a threshold γ¯ ∗ as a function of P EP ∗ does not make sense since we have three independent SNR variables. We instead propose a formal approach to deriving the outage by computing the probability density functions (pdf) of the different PEP. Deriving analytically the final pdf is cumbersome, and we rather propose a numerical computationally efficient approach, using two classical theorems of random variables. Theorem 1 relates the pdf of y = g(x) to the pdf of x, and theorem 2 provides the pdf of w = u + v, as a convolution f (w) = f (u) ∗ f (v). We also need to exploit alternately the pdf of the success probability Px (S|.) or the error probability Px (E|.). Having Px (S|.) = 1 − Px (E|.) the pdf are linked to each other with: fsuc (Px (S|.)) = ferr (Px (E|.)). Then, computing the PEP pdf associated with the combined path relies on the following steps: 1) The successful transmission probabilty of the relayed path is equal to the product of the two successful transmissions: PSR−RD (S) = PSR (S) · PRD (S). The pdf fSR−RD (P (S)), is firstly computed in the logarimthic space. First, the pdf of log(PX (S)), ∀X ∈ SR, RF are assessed using theorem 1. Second, the pdf of their sum
is computed with theorem 2. Lastly, we are back to the linear space with theorem 1. 2) The combination at the sink according to (10), is still done in the logarithmic space with the pdf of Px (E|.). C. Performance evaluation results Figure 4(a) depicts the gain of using a simple opportunistic relaying scheme in terms of packet error rate, with the following conditions: the transmission power and the path loss are assumed identical for the three links. A shadowing of σdB = 3dB is superposed. The pdf associated with the SD link, with the SR − RD link alone (multi-hop routing) and with the proposed combination are plotted. The SRD link alone is worse because two successive transmissions are needed on a channel having the same conditions. Nevertheless, the final gain is significant. The cumulative density functions (cdf) represented in Fig.4(b) depict the outage for different target P EP ∗ . For instance, P EP ∗ = 0.1 is achieved only for 30% (outage=70%) when a direct transmission is used. The multi-hop transmission achieves P EP ∗ for less than 2% (outage = 98%) but the combined approach allows to decrease the outage down to 10%. V. R ELAYING IN THE SUPERFRAME STRUCTURE A. Superframe structure The classical MAC layer needs to be adapted to specific BAN requirements. A superframe format based on IEEE 802.15.4 (or 15.3) is suggested in [9] with enhancements to meet QoS requirements. In the proposed scheme, the beacon entirely specifies the superframe, divided in several flexible periods. Neighborhood discovery and network management are done during the Control portion which includes a beacon period, a GTS Request period and a Topology Management (TM) period. The Data portion is used for data frames and other command frames. The contention access period (CAP) is used to transport command or association frames or any frame sent in a distributed scheme. The contention free period (CFP) is composed of guaranteed slots, reserved by the coordinator. In our proposed scheme, the beacon describes the whole superframe with a GTS descriptor which is convenient to manage a variable superframe size and the flexibility of the CFP portion size. In addition energy needs are reduced because, thanks to the information included in the beacon, each device knows when it has to listen to and transmit (efficient awake/asleep scheduling). B. Implementation issues The relaying retransmission can be either deterministic or opportunistic. Deterministic is more effective in a stationary channel. Indeed, once the best relay is elected, the others do not have to listen for potential packets to retransmit, and thus can save energy. Besides, the channel allocation is simplified and optimized. Thus, the packet loss during the election of the relay is negligible. However, when the channel is time varying, the relay election must be done frequently. Consequently, the
(a) PEP pdf Fig. 4.
(b) PEP cdf
PEP’s pdf and cdf associated with direct transmission, multi-hop and combining paths.
relay election either induces higher packet losses, or introduces delays (due to buffering, during the relay election). Who should relay ? In a well topology, the coordinator is the final receptor. [10] proposes a protocol that classifies potential relays by the receptor. The coordinator listens to all packets and determines the classification as a function of channel quality between node and coordinator. This list is sent in a common packet (ack or beacon).If the first relay (in the list) doesn’t relay, the next relay can do it. When relaying ? Only the CAP and CFP sections are eligible for retransmission. Some recent papers proposed a mechanism to retransmit in the superframe [11]. In [12], the inactive period mutes to a CSMA/CA period for retransmission. We think that the CFP is more adapted for retransmission to not overloading the CAP. In this case, a GTS is not allocated to a node but to the transmission of a given packet (or amount of packets). When a transmission fails, the relays try to re-send the packet during the allocated GTS. How listening ? Whatever the chosen scheme, an important problem is the way of deciding which node will relay the information, and by the way when nodes have to listen to other GTS slots. Indeed, the great interest of dedicated GTS for each node is that all nodes do not have to listen the medium during the entire GTS period. VI. C ONCLUSION AND PERSPECTIVES This paper focused on opportunistic mechanisms for improving the reliability of transmissions in BAN monitoring applications. In this kind of applications, the constraints are relative to managing several continuous flows at a constant real-time rate. Three criteria are competing for evaluating a solution: latency, outage probability and energy consumption. Considering current results on the BAN channel, we analyzed the statistics and show that the BAN channel is described by a constant mean path-loss whatever the transmitter-receiver distance, a log-normal shadowing and a strong Rician bloc fading. With this channel, we derived an appropriate packet error rate outage criterion, and we used it to measure the benefit of opportunistic cooperation.
In future work we will first provide extended outage evaluation in various conditions with multiple nodes within our framework. Secondly, we will detail and evaluate specific protocols in the multi-frame model to exploit this diversity while minimizing latency and energy consumption. ACKNOWLEDGMENT This work has been carried out with the support of the project BANET (”Body Area Networks and Technologies”) supported by the French National Research Agency (ANR). R EFERENCES [1] “Channel Model for Body Area Network (BAN),” IEEE P802.15-080780-07-0006, March, 2008. [2] R. D’Errico and L. Ouvry, “Time-variant BAN channel characterization,” in to appear in PIMRC 2009, 2009. [3] Z. Wang and G. B. Giannakis, “A Simple and General Parameterization Quantifying Performance in Fading Channels,” IEEE Trans on Communications, vol. 51, no. 8, pp. 1389–1398, August 2003. [4] A. Conti, M. Z. Win, M. Chiani, and J. H. Winters, “Bit Error Outage for Diversity Reception in Shadowing Environment,” IEEE Communications Letters, vol. 7, no. 1, pp. 15–17, January 2003. [5] P. Mary, M. Dohler, J.-M. Gorce, and G. Villemaud, “Symbol error outage analysis of mimo stbc systems over fading channels in shadowing environments,” to appear in IEEE Trans. on Wireless Communications, 2009. [6] R. Zhang, J.-M. Gorce, and K. Jaffrs-Runser, “Energy-delay bounds analysis in wireless multi-hop networks with unreliable radio links,” Technical report 6598, INRIA, July, 2008. [7] D. Miorandi and E. Altman, “Coverage and Connectivity of Ad Hoc Networks Presence of Channel Randomness,” in INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 1, March 2005, pp. 491–502. [8] K. Ray, A. Liu, K. Sadek, W. Su, and A. Kwasinski, cooperative communications and networking, C. U. Press, Ed., 2008. [9] J.-M. Gorce, C. Goursaud, C. Savigny, G. Villemaud, R. d’Errico, F. Dehmas, M. Maman, L. Ouvry, B. Miscopein, and J. Schwoerer, “Cooperation mechanisms in BANs,” in COST2100, 8th management meeting, Valencia, Spain, May 2009, p. 24. [10] K. Chin and D. Lowe, “MiniMesh: an opportunistic transmission protocol for the IEEE 802.15. 3 MAC,” IEEE 80.15.6, March, 2007. [11] N. Karowski, A. Willig, and J. Hauer, “Passive discovery schemes for opportunistic message relaying schemes based on IEEE 802.15. 4,” TKN Technical Report TKN-08-008, Berlin, August 2008. [12] G. Ahn, M. Lee, and S. Joo, “Versatile MAC for Body Area Network,” TG6 Call for Proposals (15-08-0811-03-0006-tg6-call-proposals), May, 2009.
