Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks

Computer Communications xxx (2014) xxx–xxx

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Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks Ehsan Tabatabaei Yazdi, Andreas Willig ⇑, Krzysztof Pawlikowski Department of Computer Science and Software Engineering, University of Canterbury, New Zealand

a r t i c l e

i n f o

Article history: Received 11 December 2013 Received in revised form 28 June 2014 Accepted 2 July 2014 Available online xxxx Keywords: Mobile body sensor networks IEEE 802.15.4 Frequency adaptation WiFi interference Experiments

a b s t r a c t The IEEE 802.15.4 standard is an interesting technology for use in mobile body sensor networks (MBSN), where entire networks of sensors are carried by humans. In many environments the sensor nodes experience external interference – for example, when the BSN is operated in the 2.4 GHz ISM band and the human moves in a densely populated city, it will likely experience WiFi interference, with a quickly changing ‘‘interference landscape’’. In this paper we consider whether frequency adaptation, to be carried out by the BSN, provides performance gains in such an environment. We investigate a range of adaptation schemes and assess their performance both through simulations and experimentally. We furthermore consider one particular problem caused by frequency adaptation: the problem of orphaned devices. We provide simulation results suggesting that the problem indeed is noticeable, but hard to mitigate. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction Mobile body sensor networks (MBSNs) have recently been identified as a promising technology enabling a range of applications in health and well-being [1]. The IEEE 802.15.4 standard [2] is a mature and well-established standard for low-power wireless sensor networks and has also attracted a lot of interest in the MBSN arena. We expect that due to the availability of cheap and mature components, the IEEE 802.15.4 standard will remain a serious contender for BSN applications for quite some time to come, despite the recent approval of the IEEE 802.15.6 standard for wireless body area networks [3]. On the physical layer the IEEE 802.15.4 standard specifies three different frequency bands that can be used. One of them (and the one this paper focuses on) being the 2.4 GHz ISM band, which is shared with several other technologies, including Wi-Fi. The IEEE 802.15.4 standard subdivides the available spectrum in the 2.4 GHz band into 16 different channels, and an IEEE 802.15.4 network is supposed to pick one of those channels and to stay there. A key feature of MBSNs is that they move as a whole, along with the movements of their human carrier. Having to share the spectrum with other technologies implies that data transmission in MBSNs can be harmed by external interference, as documented ⇑ Corresponding author. Address: Dept. of Computer Science and Software Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand. Tel.: +64 3 364 2987x7869. E-mail address: [email protected] (A. Willig).

in several publications addressing co-existence between Wi-Fi and IEEE 802.15.4 [4–6]. At the same time, data transmission in MBSNs is often concerned with human vital signals, hence reliable and timely data transfer is of paramount importance. However, due to their mobility, MBSNs operated in densely populated urban areas are faced with a continuously changing ‘‘landscape’’ of Wi-Fi interference, as for example shown in the measurements presented in [7]. These measurements reveal that the interference landscape can change substantially at time scales of one minute and less, so that any choice of radio parameters that was good initially will become sub-optimal quickly. Hence, it makes sense for a MBSN to adapt, i.e. to obtain measurements of the current interference situation and to set some of its operational parameters accordingly. Within the scope of the IEEE 802.15.4 standard there is a range of physical and MAC layer parameters that can be adjusted. The main candidates on the physical layer are the frequency channel and the transmit power, on the MAC layer one can pick between different operation modes (the beaconed and unbeaconed modes) and different access schemes (TDMA- versus CSMA-type schemes), one can vary the number of retransmissions, adjust backoff parameters of the CSMA MAC and so on. In this paper we concentrate on frequency adaptation and explore some of the issues and questions around it. The relatively small time scale of interference changes in urban environments presents two main difficulties. First, the time available for channel measurements is small, which poses a risk of faulty (or noisy) measurements and subsequent poor decisions. Secondly, these measurements, which may involve switching to

http://dx.doi.org/10.1016/j.comcom.2014.07.002 0140-3664/Ó 2014 Elsevier B.V. All rights reserved.

Please cite this article in press as: E.T. Yazdi et al., Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks, Comput. Commun. (2014), http://dx.doi.org/10.1016/j.comcom.2014.07.002

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various channels and listening on each one to estimate the energy or traffic load present, are an energy-consuming process by itself, and it is not a priori clear whether the energy spent for those measurements will pay off (for example by reduced numbers of retransmissions). A further difficulty with frequency adaptation appears when a change of frequency is made in a situation with severe interference. Wherever the decision point within a BSN, any decision to switch to another channel must be communicated to the other nodes, and all nodes must agree on a suitable time to switch and channel to switch to. In the presence of interference it might happen that not all members of a BSN are informed of a channel change, and these nodes (referred to as ‘‘orphans’’ in the paper) spend substantial time and energy while searching all available channels to connect back. This paper provides three main contributions: First we consider a range of different frequency adaptation schemes (including a non-adapting scheme and an idealized scheme called the genie scheme as baseline schemes) in a scenario which mimics the movement of pedestrians through densely populated urban areas with lots of WiFi interferers. We assess their performance in terms of packet loss rate and energy consumption through simulations. We keep the transmit power fixed to the smallest possible value offered by a popular IEEE 802.15.4-compliant transceiver (the ChipCon CC2420 [8]) in order to highlight the effects of frequency adaptation in isolation. Our results allow to answer the fundamental question of whether frequency adaptation can really give substantial gains over a non-adapting scheme in the affirmative. One of the considered schemes, referred to as the ‘‘lazy’’ scheme, appears to be particularly attractive, not only because it achieves excellent performance, but also because it lends itself to easy implementation on real devices. The general idea of the lazy scheme is simple: stay on the current channel as long as it is ‘‘good enough’’, but once things turn bad we scan over all channels and pick the best one. Secondly, we study the orphaning problem in more detail. Especially in the lazy scheme the times where channel switching occurs and the target channel cannot easily be predicted by nodes. Hence, an orphan does not have much information to begin with and potentially needs to spend a lot of time searching on all 16 available channels. We suggest a scheme to help orphans to shorten the search time by providing them with information that allows to come up with a correct guess of the target channel very often. Surprisingly, our results suggest that our schemes are able to significantly reduce the number of channels that an orphan scans before entering the right channel, but at the same time the average overall time spent in the orphan state is not reduced significantly in scenarios with high interference/interferer density, because the orphan often fails to receive beacons on the right channel as well. Because of this and because of our finding that in lower interference scenarios the orphan problem is not very prominent, we conclude that additional mechanisms to deal with orphans need not be included. As our third main contribution we consider the implementation of the lazy and a blind frequency-hopping scheme in a real IEEE 802.15.4 stack [9] under the TinyOS operating system [10], and we provide experimental results showing the behavior of both schemes under real WiFi interference. Our results confirm the relative performance trends already observed in our simulations and substantiate our claim that particularly the lazy scheme achieves excellent performance. Furthermore, we briefly discuss our implementations. It turns out that both schemes can be implemented with minimal changes to the given IEEE 802.15.4 MAC protocol implementation, and the actual protocol itself is not modified. This paper is an extended version of the conference papers [11,12]. It is structured as follows: In Section 2 we provide the necessary background information on IEEE 802.15.4. Section 3

describes the system model used for our simulation-based performance analysis, and the considered frequency adaptation schemes are described in Section 4. The simulation-based performance evaluation results are presented in Section 5, and our experimental results are provided in Section 7. In Section 6 we investigate the issue of orphan recovery in more detail, again using simulations. Related work is discussed in Section 8 and we conclude the paper in Section 9. 2. Background on IEEE 802.15.4 2.1. Physical layer and channelization In the 2.4 GHz band the IEEE 802.15.4 standard [2] supports different physical layers. Arguably the one with the most widespread usage is the O-QPSK PHY, to which the popular ChipCon CC2420 transceiver is compliant and on which we will focus in this paper. The data rate is 250 kbps. The 2.4 GHz ISM band is sub-divided into 16 non-overlapping channels. Each channel is 2 MHz wide and the center frequencies have a spacing of 5 MHz. Obviously, these channels overlap with the WiFi spectrum, compared in Fig. 1, where we provide a view on how the channels of the two considered technologies relate to each other. One WiFi channel overlaps directly with four IEEE 802.15.4 channels, and creates adjacent-channel interference for two neighboring channels on both sides [13]. 2.2. Beaconed mode In this paper we assume that the IEEE 802.15.4 network is organized as a star network in which one coordinator serves a number of devices in single-hop distance. We furthermore assume that the network is operated in the beaconed mode.1 In this mode time is partitioned into subsequent superframes, which are further subdivided into an active period and an inactive period. Everyone, including the coordinator is allowed to sleep during the inactive period, but in some of the frequency adaptation schemes described below we assume that the coordinator spends some of the inactive period for measuring other channels. At the beginning of the active period the coordinator broadcasts a beacon packet without performing a carrier-sense operation. The length of the superframe and the relative length of the active period within a superframe are configurable. More specifically, the superframe length and therefore the beacon period is given by [2, Sec. 5.1.1.1] aBaseSuperframeDuration  2BO where aBaseSuperframeDuration ¼ 15:36 ms and BO 2 f0; 1; . . . ; 14g is the configurable beacon order. The duration of the active period is given by aBaseSuperframeDuration  2SO with 0 6 SO 6 BO 6 14 and where SO is the configurable superframe order. At the end of the active period a maximum of seven guaranteed time slots (GTS) can be allocated to nodes in an exclusive manner, transmissions in these slots do not use carrier-sensing. In the remaining slots (called contention access period, CAP) the associated nodes can send uplink packets to the coordinator or they can request pending downlink packets from the coordinator. The nodes compete for the medium using a slotted CSMA-CA or an ALOHA scheme. In this paper we restrict to the case where the sensor nodes use CSMA-CA in the CAP period to send uplink packets. A preliminary study has shown that using CSMA in the uplink direction allows to achieve much better packet success rates than 1 We do not consider any GTS transmission, because these are highly susceptible to interference due to the absence of carrier-sensing. However, even without using GTS slots the beaconed mode is still preferable when it is required that the coordinator is capable of sleeping, which in the unbeaconed mode it is not. Furthermore, in the beaconed mode it is much simpler to keep devices time-synchronized to the coordinator.

Please cite this article in press as: E.T. Yazdi et al., Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks, Comput. Commun. (2014), http://dx.doi.org/10.1016/j.comcom.2014.07.002

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Fig. 1. Spectrum usage in the 2.4 GHz band.

using GTS slots while having only marginally higher energy consumption. The GTS approach is punished here for not using carrier-sensing, with CSMA it becomes possible to avoid transmissions during interference to some extent. For similar reasons we have ignored the ALOHA scheme. 2.3. BSN startup, association and synchronization A BSN (or a PAN, we use these terms interchangeably) is started by one particular coordinator node. First, this coordinator scans all available channels, using either an energy or a passive/active MAC level scan, and then returns the result to the higher layers. These then make a decision about the channel to use, the BO/SO values and the PAN-identifier to use for the PAN (which ideally does not collide with PAN identifiers of other PANs in the vicinity – this condition can only be checked by an active or passive MAC-level scan). Once these things are fixed, the PAN coordinator starts to transmit beacons periodically. Before nodes are allowed to communicate, they must associate with the PAN coordinator. This means that they first have to discover beacons (which in turn might necessitate to search through all frequency channels and listening on them, see [14,15]), and then they send association-request packets to the coordinator during the CAP. An associated node might choose to stay synchronized with the coordinator and track its beacons [2, Section 5.1.4.1]. In this case, the node wakes up shortly before each expected beacon transmission and tries to receive the beacon. In case of successful reception the MAC informs the higher layers about key beacon parameters (BO/SO) and also hands over the beacon payload, if any. Otherwise, if a node has not received four beacons in a row, it concludes that synchronization has been lost and informs the higher layers. Throughout this paper we assume that nodes choose to remain synchronized. This is a necessity when either the higher layers adapt the BO/SO settings, for realigning a PAN, or for frequency adaptation as described below. When a node tracking beacons fails to receive four successive beacons (e.g. due to strong external interference), it informs the higher layers about loss of synchronization. These can then decide to either re-set the MAC layer completely and let the node start a new association procedure, or the node enters the orphan mode. In the orphan mode the higher layers decide on a list of channels to scan and ask the MAC sublayer to perform channel scanning (‘‘orphan channel scan’’). According to the standard [2, Sec. 5.1.2.1.3], to scan a channel, the node sends a special command frame (‘‘orphan notification command’’) and waits for an answer from its PAN coordinator. We have not adopted this approach, but instead assume that to scan a channel, the orphan listens on this channel for one beacon period.2 When the PAN coordinator 2 We have done this to circumvent a problem in the standard: in the beaconed mode there is the risk that the coordinator might sleep when the orphan sends the special command frame. By listening for an entire beacon period we are sure to catch at least one beacon (except for errors).

has been found during this scan, the node continues to track it. When in this paper referring to the ‘‘time spent in orphan state’’ we mean this to be the time between discovering four lost beacons in a row and the next received beacon. In a system with frequency adaptation the higher layers will have to instruct the MAC to run the orphan channel scan over all available channels, unless the node possesses additional information that might help him to narrow down the channel(s) onto which the PAN might have hopped (see Section 6). In a system without frequency adaptation the higher layers in the node can confine the search to the current channel.

2.4. Implementing frequency adaptation In our framework frequency adaptation is carried out entirely by the PAN coordinator. More precisely, the PAN coordinator performs measurements on the current channel or on other channels to judge their quality, makes the decision to change the frequency, determines the frequency to hop to, and notifies the remaining nodes about the decision. In this paper we assume that frequently the superframe order SO is strictly smaller than the beacon order BO, so that there is an inactive period in each frame during which the coordinator carries out the channel measurements. For these measurements we assume that the coordinator measures the energy level without trying to de-modulate a signal. In the terminology of the IEEE 802.15.4 standard the coordinator performs RSSI (received signal strength indicator) measurements, as only this mode can detect the presence of other technologies. Since the IEEE 802.15.4-MAC does not foresee a service (or associated command frames) for channel adaptation, we have decided to use the beacon payload field for this. The 802.15.4 beacon frame can carry a variable-length payload which we will use for frequency adaptation. The precise usage depends on the adaptation scheme, but for all schemes at least two fields are included, put together into one byte: a four-bit field indicating the next channel to switch to (nextChannel), and a four-bit field (called switchCount) counting down the number of beacons to be transmitted on the current channel before switching to the channel indicated in nextChannel. The second field allows to use several beacon frames to announce the new channel before switching, which in heavy interference situations can help to notify all associated devices. Further fields might be present, depending on the scheme. When the coordinator (currently on channel co ) has made a decision to switch to a new channel cn , it writes the value cn into the nextChannel field of the next beacon and initializes its switchCount field with the value stored in the configuration parameter initialSwitchCount. The coordinator transmits initialSwitchCount beacons on channel co and then switches to channel cn . The switchCount field is counted down while transmitting the beacons on co . After switching to cn and before any new switching decision is made, the coordinator writes cn into the nextChannel field and the value 0 into the switchCount field.

Please cite this article in press as: E.T. Yazdi et al., Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks, Comput. Commun. (2014), http://dx.doi.org/10.1016/j.comcom.2014.07.002

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3.2. MBSN operation

3. System model We describe the system model for Sections 5 and 6.

3.1. Network deployment and propagation model We use a scenario in which a single MBSN moves through a field of randomly deployed WiFi interferers. In the MBSN four sensor nodes are connected to a coordinator node, forming a star topology. The sensor nodes are placed equidistantly on a circle of one meter radius around the centered coordinator. In order to focus on the impact of interference, we have disregarded other impairments such as shadowing caused by the human body. The considered playground is a 300 m by 1500 m field where the human carrier starts walking at a constant speed of 5 km/h from one end of the field (location (150 m, 150 m)) to the other end (location (1350,150)) on a straight line. The MBSN hence stays away from the boundaries of the playground. To create interference, we place a random number of stationary WiFi access points at randomly chosen locations, so that each AP’s location is chosen independently of other AP’s locations according to a uniform density. The number of interferers is drawn from a Poisson distribution with average value of D, where D represents the average number of interferers placed in our area. Thus, the deployment forms a Poisson point process, see [16, Chap. 16]. The parameter D is varied in some of our experiments. For both the sensor nodes and the interferers we use a standard log-distance model with shadowing as a path loss model [17]. The path loss at distance d for this model is

PLðdÞ ¼ PL0 þ 10  c  log10 ðd=d0 ÞX r where PL0 is the path loss at reference distance d0 (which we assume to be 1 m), and c is the path loss exponent, typically chosen between two and six. For c we have used the default value c ¼ 2:4 of the simulation package we have used, and the shadowing term X r is a zero-mean Gaussian random variable with r ¼ 4 (which is again the default value provided by the simulation package). The relatively low value for c gives very high impact of the WiFi interferers on the MBSN. The propagation parameters and other relevant parameters are summarized in Table 1.

Table 1 Simulation parameters. Main application layer parameters Packet inter-arrival time Startup delay Data payload Main IEEE 802.15.4 MAC parameters Max frame trials Max lost beacons Transaction persistence time (Expiration date) Frame order Beacon order Buffer size initialSwitchCount Buffer size

3.3. Energy consumption model In our model the power consumption of an MBSN node is considered to be only related to its transceiver, and the power consumed by other hardware components of the MBSN nodes is neglected. We have modeled the transceiver energy consumption using the characteristics of the popular IEEE 802.15.4-compliant ChipCon CC2420 transceiver [8], assuming a supply voltage of 3.3V and a fixed transmit power of 25 dBm. This is the lowest transmit power that the CC2420 transceiver supports. The transceiver has four operational states: transmit, receive, idle, and sleep, and our simulation model tracks the time a node spends in either state. This time is then multiplied with the average power consumption of this state (obtained from [8]) to compute the total energy consumption. We have also taken into account the time and power required for RSSI measurements (as they are carried out to assess channels), and the time and power needed to switch between channels after adaptation decisions. For the beacon-enabled mode we assume that the sensor nodes sleep during the inactive part of the superframe but wake up to receive beacons. Furthermore, the sensor nodes go to sleep whenever they have nothing to transmit or receive. The energy that an orphaned device spends on listening until it re-discovers its coordinator is also accounted for. 3.4. Interference traffic model

1 5 Uniform(64,102)

s s bytes

10 4 8 ⁄ 122.88 = 983.04

ms

61.44 122.88 32 4 16

Main IEEE 802.15.4 physical layer parameters TX power 25 Data rate 250 Main path loss model parameters Loss at reference distance PL0 Path loss exponent c

The network operates in beaconed mode. The devices first associate themselves to the coordinator and then generate data packets periodically with a period of one second. These packets are sent during the CAP phase, no GTS phase has been configured. The payload sizes of the MBSN data packets are chosen from a uniform distribution between 64 and 102 bytes. If the device does not receive an acknowledgement, it performs up to nine re-tries (giving ten trials per frame). The devices have to listen to the coordinators beacons to maintain synchronization. If a device has not received four successive beacons, depending on the frequency adaptation scheme, it scans through a list of channels to search for beacons and re-associates (see Section 2.3). This can for example happen when the coordinator has chosen to switch to another channel in response to excessive interference but the device has not received any of the beacons announcing this decision. The beacon order and the superframe order have been fixed to values of 3 and 2, respectively, corresponding to a beacon period of  122.88 ms and an active period of  61.44 ms duration. In Table 1 we summarize the main parameters.

55 2.4

ms ms

dBm kbps dB

Each WiFi Access Point (AP) is independent of other WiFi APs, operates at a transmit power of 20 dBm and uses no carrier-sensing. The frequency channel that a WiFi AP uses is randomly selected at the beginning of the simulation, using a uniform distribution. The packet sizes generated by an AP are a sequence of independent and identically distributed (iid) random variables, following a uniform distribution between 64 and 1500 bytes. The APs transmit their data at a data rate of 1 Mbps (IEEE 802.11b) [18]. The inter-packet gaps are an iid sequence of exponentially distributed random variables. The averages of the exponential distributions are chosen such that an AP generates prescribed traffic intensities of k ¼ 0%; 10%; 20%; . . . ; 70%. Clearly, this is an artificial traffic model, and it is a topic of future research to consider more realistic traffic and to also consider interactions between the APs that would result from running a carrier-sensing MAC protocol among them. However, this model (and especially the absence of carrier-sensing) has the advantage that any intended traffic intensity k indeed occurs faithfully on the channel.

Please cite this article in press as: E.T. Yazdi et al., Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks, Comput. Commun. (2014), http://dx.doi.org/10.1016/j.comcom.2014.07.002

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In our interference model we assume that any packet transmitted by a WiFi interferer affects only the four IEEE 802.15.4 channels that are directly overlapping with the WiFi spectrum used by the interferer. These four channels receive different levels of interference, following the spectral mask of the WiFi signal. We do not consider adjacent channel interference.

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Next we describe the frequency adaptation schemes that we have compared in the first part of our study.

measurements are noise-free and identical. However, the measurements incur an energy cost corresponding to the parallel activation of 16 transceivers for RSSI measurements. Furthermore, for each new packet both the coordinator and all sensor nodes automatically switch to the best channel (i.e. the channel with the least energy – the assumption that all measurements are identical lets the members agree on the next channel), without any signaling delay or signaling costs. This scheme approximates ideal adaptation without any of the involved risks like wrong decisions resulting from measurement noise, or failure of sensors to take notice of coordinators decisions.

4.1. Baseline schemes

4.2. Periodic schemes

There are two schemes for frequency adaptation that we consider as baseline schemes. The first of these is the no-adaptation scheme. Here the coordinator picks its initial channel randomly and never changes it. This scheme does not require any additional activities like measurements and has been included to assess the potential performance gain achievable from adaptation. When a device becomes orphaned, it stays on the chosen channel and resumes operation when it detects the next beacon. The second baseline scheme is the genie scheme. The coordinator and the sensors have the ability to measure the instantaneous RSSI levels on all channels in parallel, and we assume that all nodes

In the class of periodic adaptation schemes the coordinator decides about the next channel periodically, after a fixed number of superframes, which we call a hyperframe. In this paper we have fixed the hyperframe length to ten superframes. In the periodic-random scheme the PAN coordinator randomly selects a new channel using a uniform distribution. There are no channel measurements involved, and we only calculate the energy cost for channel switching when the previous and the next channel are different. In the periodic-measurement scheme the MBSN coordinator takes RSSI measurements of all 16 channels during the inactive

4. Frequency adaptation schemes

Fig. 2. Comparison of no-adaptation and genie schemes for D 2 f10; 100; 300g.

Please cite this article in press as: E.T. Yazdi et al., Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks, Comput. Commun. (2014), http://dx.doi.org/10.1016/j.comcom.2014.07.002

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Fig. 3. Comparison of baseline schemes with periodic schemes for D ¼ 100.

period of each superframe. At the end of the sixth superframe of a hyperperiod the coordinator makes a decision about the best channel for the following hyperperiod, based on the measurements taken in the last ten superframes. The decision is based on the observed RSSI values and is communicated to the sensors over the remaining four superframes (see initialSwitchCount parameter in Section 3.2). More specifically, we use a scheme proposed in [4]: for a single channel we use the maximum observed RSSI value out of the last ten measurements as summary statistic for this channel, and the decider chooses the channel with the smallest maximum RSSI value (in the figures the scheme is therefore denoted as periodic-measurement-max scheme). The RSSI measurements are assumed to be noise-free, but we account for the energy required to take eight RSSI samples on each channel that can be averaged. The simulation model accounts for the energy spent for scanning all 16 channels, and, if the selected channel is different from the current one, also for the cost of channel switching. 4.3. Lazy scheme The general idea for the class of lazy schemes is that the MBSN stays on the same channel as long as it is good enough. Channel switching happens only when the measured channel energy (outside own transmissions) exceeds a threshold. More specifically, in the lazy-measurement scheme the coordinator takes RSSI

measurements on all channels during the inactive periods of each superframe. Similar to the periodic-measurement scheme, the coordinator collects the last ten RSSI readings for each channel and represents the channel quality of each channel by the maximum of those readings, correspondingly the scheme is denoted as lazy-measurement-MAX in the figures. However, a channel switch is only carried out if the maximum RSSI value of the current channel exceeds a threshold of 90 dBm, and if there is another channel with a lower maximum RSSI value.

5. Frequency adaptation: simulation results We have conducted a simulation study using Castalia version 3.2 [19], an open-source network simulator specifically designed for WSN and Body Area Network (BAN) scenarios. We have evaluated the schemes described in Section 4 for varying values of the average number of interferers D and the interferer traffic intensity k. An individual interferer picks its operating channel randomly according to a uniform distribution over the allowed channels. For each combination of D and k we have performed sufficient replications to reach a maximum relative confidence interval halfwidth of 5% or less at a 95% confidence level. The confidence intervals are not shown. In each replication a new WiFi deployment is generated and the MBSN moves exactly once from its starting

Please cite this article in press as: E.T. Yazdi et al., Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks, Comput. Commun. (2014), http://dx.doi.org/10.1016/j.comcom.2014.07.002

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Fig. 4. Comparison of no-adaptation, periodic-measurement-10 and lazy scheme for D ¼ 100.

position in the playground to its end position. The performance results obtained from each replication are averaged over all replications and those averages are reported. We consider two main performance measures. The energy consumption measures the energy consumed by the transceiver as the carrier walks once from the left to the right side of the field, the reported numbers are the average energy consumption of the sensor nodes, not including the coordinator node.3 The success rate (percentage of successful acknowledged transmissions) is the average percentage of uplink packets that the coordinator has successfully received (possibly after retransmissions) and for which the sender has received an acknowledgement. 5.1. Comparison of no-adaptation and genie scheme We first compare the no-adaptation and the genie scheme to get an impression of the achievable performance gains with frequency adaptation. The genie scheme provides an upper bound on achievable performance, especially for the success rate. The results for varying values of the average number of interferers D and varying interferer traffic density k are shown in Fig. 2. It can be clearly seen 3 We focus on the sensor nodes since in many scenarios the coordinator will have more energy available than the sensor nodes.

(Fig. 2b) that the genie scheme achieves a substantially higher success rate for increasing interference intensity k, and the difference becomes larger as the interferer node density D increases. This is one of our key results: frequency adaptation alone can have substantial advantages in terms of success rate, which for healthrelated applications is a prime performance measure. At the same time, the genie scheme requires much more energy at the sensor nodes (Fig. 2a), which results from the energy expenditure of the (assumed) 16 transceivers on each sensor node, carrying out measurements and potentially switching for each new packet. Interestingly, the energy consumed by the sensor nodes decreases as the interference traffic intensity k increases. This finding can likely be explained by the contribution of the energy to switch channels: as k increases, it becomes less and less likely that another channel is better than the current one, so no switching costs are incurred. The decreasing number of channel hops is shown in Fig. 2c. 5.2. Periodic schemes We next analyze the performance of the periodic schemes for the case D ¼ 100. The trends identified for this value of D are similar to the trends for other values of D. We have simulated two periodic-random schemes, with periods of one and ten superframes, respectively, and the periodic-measurement scheme with

Please cite this article in press as: E.T. Yazdi et al., Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks, Comput. Commun. (2014), http://dx.doi.org/10.1016/j.comcom.2014.07.002

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Fig. 5. Comparison of no-adaptation, periodic-measurement-10 and lazy scheme for D ¼ 300.

a period of ten superframes. In Fig. 3 we compare these schemes to each other, to the no-adaptation scheme and the genie scheme. There are some surprising findings: (i) The periodic-measurement scheme has clearly the best success rate of all periodic schemes (see Fig. 3b), it even outperforms the genie scheme in terms of success rate (see below). At the same time, the periodic-measurement scheme has, for interferer traffic density k > 20% the lowest sensor energy consumption, even lower than the no-adaptation scheme (while at the same time having more channel hops, compare Figs. 3a and 3c). One explanation of this finding is the periodicmeasurement schemes ability to reduce the number of retransmissions. (ii) The periodic-random-1 scheme has a significantly better success probability than the periodic-random-10 scheme, perhaps due to its ability to leave poor channels more quickly. A likely explanation for the periodic-measurement schemes advantage over the genie scheme in terms of success rate is the lack of ‘‘history’’ for the genie scheme: the latter considers only instantaneous channel samples, and it might happen that it samples the channel at a time when a close interferer has an interpacket gap. In this case the next MBSN packet would be hit by the next packet of the WiFi interferer. The measurement scheme makes ten observations of the same channel (with a spacing of one superframe) and has a much better chance to detect interferer activities and to avoid the channel.

5.3. Lazy scheme We finally look at the lazy scheme and compare it against the periodic-measurement-10 scheme and the no-adaptation scheme for average numbers of interferers of D ¼ 100 and D ¼ 300. The results are shown in Figs. 4 and 5. For both values of D the lazy scheme achieves almost exactly the same success rate as the periodic-measurement scheme, but with lower consumed energy at the sensor nodes for smaller values of k and generally with significantly fewer channel switches. 5.4. Shadowing by the human body In all results presented so far we have made the simplifying assumption that there is no additional shadowing between the BSN nodes. This assumption allows us to cleanly see the effects of interference in isolation (which is the main goal of this paper). However, it is well-known that the human body can introduce substantial additional path loss in the order of 30–35 dB and that, for IEEE 802.15.4 networks, it is often required to use the largest possible transmit power (see [1]). To study these effects, we have performed another set of simulations in which we have added additional path loss between the coordinator and the attached nodes, and have used a transmit power of 0 dBm. More specifically,

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Fig. 6. Comparison of no-adaptation and lazy scheme for D ¼ 300 and with additional node-dependent path loss.

there was no additional path loss for the first sensor node (just the 55 dB path loss at the reference distance PL0 ), 11 dB additional path loss for the second node, 20 dB for the third node and 30 dB for the fourth node. We have considered a scenario with an average number of D 2 f100; 200; 300g interferers and have again varied the interference intensity k. To save space, in Fig. 6 we show selected results for the case D ¼ 300 and for the lazy and the no-adaptation schemes, which we compare in the absence and presence of additional path loss (both using 0 dBm transmit power). It can be observed that performance differences between the cases with and without additional path loss are modest, and the relative performance gains of the lazy scheme over the no-adaptation scheme remain. 5.5. Discussion The results show first of all that frequency adaptation offers a significant potential for performance improvements over the case of no-adaptation, both in terms of achieved success rate and in terms of energy required at the sensors. Furthermore, the effort required for measurement-based adaptation pays out, presumably by saving sensors from excessive retransmissions on interferenceprone channels.

However, as the results presented in Fig. 5 for D ¼ 300 show, the success rate decreases substantially when the interference density k increases. Analysis of these simulation results has shown that the devices spend more and more time in the orphan state as k increases. We will consider this problem in more detail in the next section. 6. Orphan recovery: schemes and results As already stated in the introduction, the lazy adaptation scheme, while showing very promising performance, still suffers from the problem that orphans need to scan other channels than the last one (because the coordinator might have switched channels), which costs time and leads to losses of periodic data packets during the time a sensor is orphaned. In this section we look at ways to improve the lazy scheme by reducing the overall time the sensor nodes spend in the orphan state. Specifically, we propose three different methods to reduce the number of channels that the orphan has to scan before it re-discovers its coordinator. We compare these three schemes against two other schemes in which the channels are scanned sequentially or in a random order. The main comparison metrics are the number of channels that the orphan scans before finding the right channel, and the overall

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Fig. 7. Comparison of the different proposed coordinator discovery schemes added to the lazy Scheme where D ¼ 300.

percentage of time spent in the orphan state. We first describe the considered schemes and then present simulation results.

6.1. Considered schemes We consider three baseline schemes. The first one is the no-adaptation scheme described in Section 4.1. Remember that in this scheme the coordinator picks one frequency channel at the beginning and never changes it throughout its lifetime. When a device becomes an orphan, it does not scan any other channels than the one it was operating on all the time.4 The other two baseline schemes are based on the lazy adaptation scheme. In the lazy-ordered scheme, when a sensor has lost synchronization while operating on channel f, it scans through all 16 available channels, starting with channel f, then channel ðf þ 1Þmod16, then channel ðf þ 2Þmod16 and so forth. The device listens on each channel for one entire beacon period (which is possible as it knows the beacon order). In the lazy-random scheme, the orphan scans the 16 channels in random order. Please note that in these baseline schemes 4 To ensure that the device knows this channel initially, in our simulation settings we have ensured that interference only sets in after a few seconds so that a device can successfully find the coordinator and associate with it.

Table 2 Lines of nesC code for implementation of different frequency adaptation schemes. Scheme/node

 Lines of code

Lazy/coordinator Lazy/sensor Periodic-random/coordinator Periodic-random/sensor No-adaptation/coordinator No-adaptation/sensor

420 560 280 590 195 480

the orphan does not try to come up with any kind of guess on the channel on which the coordinator could be. In the lazy-energy-scan scheme the orphan device starts by performing a quick scan on all available channels for their energy (RSSI). The orphan then sorts the channels according to the RSSI levels in increasing order, and it starts to listen on the channel with the lowest energy level (which is supposedly the ‘‘cleanest’’ channel, to which the coordinator might have changed), then followed by the second-lowest-energy channel and so forth. The idea behind this scheme is that if a node becomes orphan, it is more likely that its coordinator is operating on channels with less noise. We assume that the measurements are noise-free, so that we can

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Fig. 8. Scenario one.

Fig. 9. Scenario two.

Fig. 10. Summary experimental results for both scenarios.

observe the performance of this scheme under ideal conditions. In the lazy-sequence-scan scheme the device does not perform own measurements, but utilizes the measurement results of the coordinator, which the latter includes in its beacon packets as payload. More specifically, the coordinator includes a ranking of the 16 channels in terms of its own energy measurements, so that the first channel listed in this ranking is the one the coordinator would switch to when it is forced to change channels now. In the moment a device becomes orphaned, it listens on all the channels in the order established in the last received beacon. Finally, the lazyheuristic-sequence-scan scheme operates by the same principle, but the orphan scans the first two channels in the provided sequence from the coordinator twice as long as other channels. The rationale for this scheme is to increase the probability of finding the coordinator, provided it is indeed on one of the first two channels. 6.2. Performance evaluation and discussion The results shown here are obtained using the same simulation scenario as in Section 5. The schemes described in Section 6.1 are

evaluated for an average number D ¼ 300 of WiFi interferers (because in this scenario the time spent in orphan state is highest, so the effectiveness of the different schemes can be inspected most clearly – however, the trends observed below are also true for smaller average numbers of interferers, for which we do not show results due to lack of space) and varying values of the interference intensity k. Again we ran sufficient replications to reach a relative confidence interval half-width of 5% at a 95% confidence level. Besides the success rate and the time spent in orphan state we have also looked at the average channel offset. If at time t0 a device becomes an orphan, it will compute a sequence of channels on which to look for the coordinator (this sequence clearly depends on the scheme), so that the first channel in this sequence is considered first, the second channel is considered next and so forth. The average channel offset specifies the position (0-based) of the actual channel of the coordinator at time t 0 on this list – smaller values indicate that the coordinator is potentially found earlier. This calculation is done right after the node becomes orphan and just before the device starts listening, only in the case of lazyenergy-scan scheme the offset calculation is made after the sensor has gathered RSSI measurements from all 16 channels. The results are shown in Fig. 7. The main finding is that, although the proposed lazy-heuristic-sequence and lazy-sequence scheme are very effective in predicting the channel where the coordinator could be found (due to the good ‘‘average channel offset performance’’, see Fig. 7c), the overall time spent in the orphan state is not affected substantially by this, and all the lazy schemes show approximately the same performance (Fig. 7b). One likely explanation for this is that the orphan, after switching to the right channel, still fails to receive the beacons because of high interference levels – after all, the average number D ¼ 300 of interferers chosen is relatively high. In other words: these two approaches ‘‘do the right thing’’, but it does not help, at least not in highinterference situations. While this appears to be a negative result, it is nonetheless useful for designers as it might save implementation efforts. The other schemes do much less well in terms of channel offset performance but still spent on average the same in the orphan state (with exception of the no-adaptation scheme).5

5 The reader might wonder about the shape of the curves for the other schemes in Fig. 7c, for example why the lazy-random scheme does not remain constant at around seven. This is due to our assumption that channel measurements are not noisy, and due to an implementation choice in the coordinator which, when being faced to several channels of the same minimal energy, prefers the one with the numerically lowest number. This biases channel selection in the lazy scheme, but our conclusion remains valid.

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Fig. 11. No-adaptation scheme for one interferer, first scenario.

7. Experimental results In this section we report on experimental work that we have carried out with implementations of the standard IEEE 802.15.4 MAC (no-adaptation), of our lazy scheme (more precisely, the lazy-measurement scheme described in Section 4.3) and and the periodic-random-1 scheme hopping after each beacon period. We first give a brief overview of our implementation, then describe the experimental setups used and finally present our measurement results. We have conducted these experiments to see whether we can confirm qualitatively the performance trends observed in Section 5, and to get insights into the behavior of our schemes under realistic conditions. We have used the TKN154 implementation [9] of the IEEE 802.15.4 MAC protocol under the TinyOS operating system in version 2.1.1 [10] on MicaZ motes [20]. The code is written in the nesC programming language.

7.1. Brief overview of implementation TinyOS is a component-based operating system for embedded platforms. In TinyOS components interact through interfaces. Interfaces have a provider and a user. The user of an interface

can call so-called commands implemented by the provider. In the other direction, the interface provider can signal so-called events to the interface user, which in practical terms means that the interface provider calls a function specified by the interface user. The TKN154 implementation of the IEEE 802.15.4 protocol is organized as a set of components, interacting with each other and with higher layers through well-defined interfaces. We have decided to place the bulk of the implementations of the lazy and periodic-random scheme outside the TKN154 components into our application. The TKN154 implementation remains almost unmodified, however, we have added three events that are signaled by the TKN154 MAC to higher layers:  An event signaling loss of one individual beacon: this event is generated within a sensor node when, after waking up for receiving a beacon from its coordinator, such a beacon is not received.  An event signaling the end of the active period: this event is generated within the coordinator when the active period of a beacon period ends and the inactive period starts.  An event immediately before the start of the next beacon: this event is generated both within the coordinator and the sensors a short time before the beacon is transmitted (coordinator) or before the beacon is expected (sensors).

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Fig. 12. Periodic-random-1 scheme for one interferer, first scenario.

Besides the generation of these three events no other changes to the TKN154 MAC have been made. Most importantly, we have not changed the protocol as such. The actual implementations of the lazy and periodic-random schemes are contained in the application code (but could be placed into a separate component). They are driven by receiving the three events from the MAC implementation. For example, on receiving the event signaling the end of the active period, the coordinator in the lazy scheme starts sampling the energy levels on all available channels. In Table 2 we display the number of code lines required for the lazy and the periodic-random scheme on the coordinator and devices, respectively – please note that this does not count the code of the TKN154 implementation, but includes debugging/logging code. Furthermore, the code includes generic parts that also occur in the implementation of the no-adaptation scheme. Thus, for reference we have also added the lines of application code for the latter. These numbers show that the implementation of our frequency adaptation schemes requires just a few hundred lines of code, for example about 230 lines of code for the lazy scheme on the coordinator.

7.2. Experimental setup We have created an experimental setup in which a BSN with one coordinator and one sensor moves through an outdoor field with varying numbers of WiFi access points. To remove unwanted WiFi interference we have used a place far away from other buildings. We have considered two different scenarios for the placement of the WiFi interferers. In the first scenario (see Fig. 8) we placed WiFi access points (AP) in the middle of the field very close to each other, with a few centimeters distance between them. The BSN was carried by a person and the person moved along a straight line of length 140 m, in the middle of which the APs were placed. The path taken by the BSN goes directly over the APs, so that in the middle of the path the BSN experiences maximum interference. The person has moved at constant pedestrian speed of approximately 5 km/h (measured). In this scenario we have varied the number of APs from zero to five, and these APs are tuned to channels 1, 3, 6, 9, and 11, respectively. The BSN was allowed to work on all 16 available channels, and even with all WiFi interferers present there was always one IEEE 802.15.4 channel available without interference (channel 26).

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Fig. 13. Lazy scheme for one interferer, first scenario.

In the second scenario (Fig. 9) the APs were not placed on the same spot. Instead, we have place three APs on a line with a spacing of 30 m between them. The person carrying the BSN again moves along a 140 m long line which coincides with the line where the APs are placed. We have used frequency channels 1, 4, and 7 for the WiFi APs. The BSN was only allowed to work on channels 11 to 20, so that there is no channel without any interference. The goal of this setup was to see how each scheme performs in an environment where interference is on all channels but interferers are spread over distance, and how the adaptation process behaves over time. The BSN uses a transmit power of 25 dBm and the sensor generates data packets with a period of one second. The beacon order has been chosen as 6 and the superframe order as 4. The distance between the coordinator and the sensor was one meter. The channel for the no-adaptation scheme has, when interferers are present, always been chosen to operate on one of the interfered channels. The person carrying out the experiments carried both motes in front of him, so they had a line-of-sight to each other. When the person has passed the access points, its body provides partial shielding from interference. This explains the somewhat asymmetric appearance of the spectrum utilization over time (see below). For the WiFi interferers we have used Cisco Aironet 1130AG Series

WiFi access points. Each AP operated at full transmit power (20 dBm) using 802.11b modulation and the lowest data rate of 1 Mbit/s, otherwise we have used their default configuration (which includes that they perform carrier-sensing). We have used an open-source packet traffic generator and analyzer called ‘‘Ostinato’’6 to generate a continuous stream of UDP packets. The packet length has been set to 1518 bytes. We have also used the USB-based Wi-Spy 2.4 GHz spectrum analyzer. This device is able to track all radio activities from WiFi, Cordless Phones, Microwaves, ZigBee, Bluetooth, and other devices operating in the 2.4 GHz frequency band. It takes 419 samples between frequencies 2400 MHz and 2483 MHz at a spacing of 199 kHz, the time resolution is 2 Hz. We used a number of laptops for different purposes. One laptop was used as the master laptop, running a DHCP server and a NTP server to synchronize the clock between the laptops. Furthermore, the master laptop is responsible for initial configuration of all WiFi access points (disabling and enabling, setting transmit power, operating channel, etc.), and for running the Ostinato load generator. The master laptop was connected to the APs through Ethernet. Next, for each AP there was a separate laptop configured 6

http://code.google.com/p/ostinato/.

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Fig. 14. No-adaptation scheme with three interferers, second scenario.

as a wireless client – without this, the intelligent Cisco APs would not have forwarded the UDP packets generated by the master laptop. Finally, the data logger laptop connected to the Wi-Spy spectrum analyzer and the two Micaz motes making up the BSN. It logs the information being generated by them and sent via USB. For each scheme and each number of APs we repeated the experiment ten times. The speed of the BSN movement in the field was controlled with a stop watch timer. 7.3. Results We now present the results of our measurements. In Fig. 10 we show the packet success rate, the percentage of time spent in the orphan state, the number of channel hops made by the sensor while searching for beacons, and the number of channel switching decisions executed by the sensor for varying number of WiFi access points in the first scenario (marked ‘‘S1’’ in the figure). We have also included the corresponding results for the second scenario (marked ‘‘S2’’). Each bar in this figure is averaged over all ten repetitions of the corresponding experiment, and the outcome of each repetition is averaged over the entire time it takes to move along the path. While not directly comparable, the results for the packet success rate confirm the relative performance trends we have

observed in our simulations, see Section 5.3. The lazy scheme has an almost perfect packet success rate and outperforms both the periodic-random-1 scheme and the no-adaptation scheme significantly in both scenarios. In the first scenario, this is due to the ability of the lazy scheme to find the free channel and to leverage it. In the second scenario, the lazy scheme uses its ability to pick the instantaneously best channel to ‘‘move around’’ the interferers. Furthermore, in the first scenario the periodic-random-1 scheme outperforms the no-adaptation scheme, whereas in the second scenario the latter scheme has a slight advantage. The fact that the no-adaptation scheme still has an overall success rate of P 40% is because a substantial part of the path taken by the BSN is outside the interference range. The percentage time spent in the orphan state is almost constant and very low for the lazy scheme. The finding that it starts at a nonzero value even without interference is related to the initialization of the system where the sensor node tries to find its coordinator for the first time for subsequent association – this time is included in our statistics. However, the fact that there are no changes for the lazy scheme for increasing number of access points in scenario one can again be explained by the lazy scheme’s ability to find the free channel and to avoid the orphan state. Even in the second scenario this scheme’s time spent in the orphan state is very small. The two other schemes show generally worse performance as the

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Fig. 15. Periodic-random-1 scheme with three interferers, second scenario.

number of access points is increased. The performance of the periodic-random-1 scheme appears to be constant for scenario one, the no-adaptation scheme varies inconsistently with the number of interferers. This is probably due to having only ten repetitions. In order to get a more detailed insight into the behavior of our schemes, in Figs. 11–13 we show for the first scenario and the three implemented schemes how various important performance figures evolve over time. More specifically, these figures show results for one representative repetition. In the topmost graph of each figure the time axis is partitioned into bins of ten seconds, and for each bin we show the number of beacon packets received by the sensor node. In the next-to-topmost graph of each figure we show the number of packets that are successfully received after the first trial, again in bins of ten seconds size. The third graph of each figure shows the number of packets successfully received after at least one retransmission in bins of ten seconds size. The fourth graph shows for each ten-second-bin how much of these ten seconds have been spent in the orphan state (by the sensor). The fifth graph indicates the reception of beacons by the sensor: a received beacon is indicated by a value of one, a missed beacon by a value of zero. The sixth graph shows the current channel the BSN operates on, and the last graph shows the spectrum utilization over time. Considering the no-adaptation scheme (Fig. 11) we can see that adding one interferer already impacts many performance

parameters significantly: when the BSN approaches the interferer, fewer beacons are received, more data packets are lost, and more time is spent in the orphan state. For the periodic-random-1 scheme we can see in Fig. 12 how the system jumps over all channels and how, after the sensor becomes orphan, it scans through all channels to find the coordinator (see curve for channel hops). Finally, in the lazy scheme, shown in Fig. 13, channel search occurs only once and aside from this initial search no time is spent in the orphan state. We have created similar graphs for the second scenario, shown in Figs. 14–16, respectively. The no-adaptation scheme is only affected by one of the interferers (the middle one) and the figure looks similar to the one for the first scenario. The periodicrandom-1 scheme is much more badly affected than in the first scenario (compare the time spent in the orphan state), because effectively it suffers from interference for a much larger proportion of its path. Finally, when looking at the operating channel of the lazy scheme, it can be seen that it manages to change its channel according to its relative position with respect to the interferers: up to 45 m it chooses a channel that avoids the first interferer well but then runs into the second interferer. At 45 m it changes channel to avoid the second (and the first) interferer as well, jumping to the lowest channel. Later it collides with the third interferer on this channel and changes again. Overall, the lazy scheme manages to maintain a very good packet success rate.

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Fig. 16. Lazy scheme with three interferers, second scenario.

8. Related work Co-existence between different wireless standards operating in the ISM band has received significant attention. Many researchers have concentrated on the impact of IEEE 802.15.1 and IEEE 802.15.4 on WiFi [21,6,22–24]. In the other direction, the IEEE 802.15.4 standard provides mechanisms to cope with other devices operating in the 2.4 GHz, for example the carrier-sensing ability. Nonetheless, as shown by our results and in [23,24] IEEE 802.15.4 networks suffer from very high packet loss rates under interference. For mobile body sensor networks, since the human carrier moves in urban environments the interference level changes rapidly, as shown experimentally by Hauer et al using real IEEE 802.15.4 body sensor networks nodes. The interference power varies over relatively short timescales of tens of seconds to few minutes [7]. Several papers address the choice of a frequency channel in static interference environments. In [4] the impact of interference from different technologies is assessed and interference estimators are considered. Then a scheme for per-source choice of frequency channel in a multi-hop network is proposed, which involves substantial signaling packets. In [23] a scheme for classifying different types of interferers is proposed. To deal with WiFi interferers, the authors proposed a two stage algorithmic framework. First the

network is scanned to find interference patterns and in stage two this information is cross-referenced with stored reference shapes of known interference patterns. Based on this the sensor nodes decide their transmission channel, the inter arrival time between their data packets and the time schedule of their sleep-wake cycles. In [24] distributed channel selection strategies are considered. The first strategy is based on channel scanning, the other applies techniques from machine learning. Clearly, there are also other approaches to interference mitigation than just frequency adaptation. The authors of [25] suggest a scheme in which redundant headers are added to transmitted packets to increase resilience against interference. Initial coordinator discovery has received significant attention from the research community, while the issue of recovery from the orphan state is less well considered. Many researchers have concentrated on the impact of IEEE 802.15.4 coordinator discovery and association on packet loss rate, energy consumption, and latency [26–29,14]. However, the discovery problem is harder than the orphan recovery problem, since in discovery the searching node knows neither the frequency channel nor the major operational parameters, especially the beacon order, and the search procedure must accommodate this. An orphan device, however, does know the beacon order and can search much more efficiently.

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9. Conclusions We have considered frequency adaptation schemes for IEEE 802.15.4-based mobile body sensor networks under various interference scenarios. Frequency adaptation pays out and in fact can be implemented with little effort. Furthermore, we have considered one problem incurred by frequency adaptation approaches, namely the orphaning problem. However, here our results indicate that there is not much to be gained by implementing countermeasures, even if they manage to find the new channel of the coordinator quickly. There is considerable scope for future work. One important item is the joint consideration of frequency and power adaptation, another one is the extension of the proposed schemes to multi-hop body sensor networks and to networks operating in the unbeaconed mode. Acknowledgements We wish to acknowledge the contribution of BlueFernÒ. BlueFernÒ is a high performance computing facility funded jointly by the New Zealand eScience Infrastructure and the University of Canterbury through the Ministry of Business, Innovation and Employment’s Research Infrastructure program. URLs: http:// www.nesi.org.nz and http://www.bluefern.canterbury.ac.nz. We also wish to thank the reviewers for their useful comments. References [1] B. Latre, B. Braem, I. Moerman, C. Blondia, P. Demeester, A survey on wireless body area networks, Wireless Netw. 17 (2011) 1–18. [2] LAN/MAN Standards Committee of the IEEE Computer Society, IEEE Standard for Local and metropolitan area networks – Part 15.4: Low Rate Wireless Personal Area Networks (LR-WPANs), revision of 2011, June 2011. [3] LAN/MAN Standards Committee of the IEEE Computer Society, IEEE Standard for Local and metropolitan area networks – Part 15.6: Wireless Body Area Networks, February 2012. [4] R. Musaloiu-E, A. Terzis, Minimising the effect of WiFi interference in 802.15. 4 wireless sensor networks, Int. J. Sensor Netw. 3 (1) (2008) 43–54. [5] S. Pollin, M. Ergen, M. Timmers, A. Dejonghe, L. van der Perre, F. Catthoor, I. Moerman, A. Bahai, Distributed cognitive coexistence of 802.15.4 with 802.11, 2006, pp. 1–5. [6] I. Howitt, J. Gutierrez, IEEE 802.15.4 Low rate – wireless personal area network coexistence issues, in: Wireless Communications and Networking Conference, 2003 (WCNC 2003), vol. 3, 2003, pp. 1481–1486. [7] J.-H. Hauer, V. Handziski, A. Wolisz, Experimental study of the impact of WLAN interference on IEEE 802.15.4 body area networks, in: U. Roedig, C. Sreenan (Eds.), Wireless Sensor Networks, Lecture Notes in Computer Science, vol. 5432, Springer, Berlin/Heidelberg, 2009, pp. 17–32. [8] Chipcon, 2.4 GHz IEEE 802.15.4/ZigBee-ready RF Transceiver, Chipcon Products from Texas Instruments, 2004. [9] J. Hauer, TKN15.4: an IEEE 802.15.4 MAC implementation for TinyOS 2, TKN Technical Report Series TKN-08-003, Telecommunication Networks Group, Technical University of Berlin, March 2009.

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Please cite this article in press as: E.T. Yazdi et al., Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks, Comput. Commun. (2014), http://dx.doi.org/10.1016/j.comcom.2014.07.002