Using the node's experience from previous transmissions to improve the communication in an indoor localization system

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Contents lists available at ScienceDirect

International Journal of Electronics and Communications (AEÜ) journal homepage: www.elsevier.com/locate/aeue

Using the node’s experience from previous transmissions to improve the communication in an indoor localization system夽

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Jorge Juan Robles a,∗ , Víctor Casas Melo b,2 , Ralf Lehnert a,1 , Fredy Olarte Dussan b,2

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Chair for Telecommunications, Technische Universität Dresden, Germany Faculty of Electrical and Electronic Engineering, Universidad Nacional de Colombia, Colombia

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Article history: Received 13 September 2013 Accepted 27 May 2014

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Keywords: Wireless sensor networks Fault tolerance communication Self-organized transmission strategy Indoor localization systems

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1. Introduction

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Although originally the wireless sensor networks (WSN) were designed for transmitting environmental parameters with a low data rate, there is a growing need to use these networks for applications that require high throughput, such as real time localization systems (RTLS). However, the higher the amount of information to be transmitted through these resource-constrained networks, the more the number of communication problems (collisions, excessive delay and lack of coordination) can occur. This article presents a self-organized strategy, where the nodes exploit their own experience in previous transmissions to decide when to transmit future packets. The proposed strategy is evaluated within the protocol HCP (Highly Configurable Protocol), which was designed for indoor localization systems. Through simulations, we demonstrated that our proposal improves the performance compared with the method used originally by HCP. © 2014 Published by Elsevier GmbH.

A WSN is a set of spatially distributed sensors that measure information (such as temperature, pressure, humidity, etc.) and wirelessly exchange it among themselves [1]. One of the advantages of WSNs is its low installation and maintenance cost compared to wired technologies. However, wired networks are more robust against interference and other communication problems that can occur in a WSN [2]. To achieve a fast and reliable communication in these limited networks is a challenge issue. The development of mechanisms that improve the communication in WSN is a key point for extending these networks to new types of applications. In our case, we applied a wireless indoor sensor network for implementing a localization system, which is an application that requires transmission of larger

夽 This document is a collaborative effort. ∗ Corresponding author at: Barkhausenbau, Technische Universität Dresden, InstiQ3 tut für Nachrichtentechnik, D-01062 Dresden, Germany. Tel. +49 351 463 339457. E-mail addresses: [email protected], [email protected] (J.J. Robles), [email protected] (V.C. Melo), [email protected] (R. Lehnert), [email protected] (F.O. Dussan). 1 Barkhausenbau, Technische Universität Dresden, Institut für Nachrichtentechnik, D-01062 Dresden, Germany. Tel. +49 351 463 339457. 2 Bogotá, Ciudad Universitaria, Cra 30 No. 45-03, building 411, Colombia. Tel.: +57 1316 5000x11108.

amount of data than typical WSN applications (e.g., temperature monitoring). In our localization system there are nodes called anchors (ANs) with known positions and mobile nodes (MNs). The ANs are fixed and therefore they can be externally powered. ANs form a network with multi-hop communication that collects localization information from MNs and send it through the network to a central computer (see Fig. 1). The mobile nodes (MNs) are sensor nodes with batteries, which communicate with ANs to determine their positions and report their measurements. ANs and MNs use our HCP [3]. This protocol is designed to keep the MN’s energy consumption at low levels. The principal tasks of HCP can be summarized in three parts: the synchronization of nodes, the collection of signal strength measurements related to MNs and the transmission of these measurements to a central computer. Our work is related to this last part. We propose a method to improve the wireless communication between ANs when they have to send data to the central computer. Given the fact that ANs do not have batteries, the reduction of the AN’s energy consumption plays a secondary role here. Many communication problems in WSNs are caused by lack of coordination between the nodes. Thus, an efficient way of organizing the transmissions and the reception periods can improve the throughput of the network. In [4] two approaches are presented to establish packet delivery organization: a centralized approach and a self-organized approach. If the coordination of the transmissions between nodes is centralized, a central computer decides

http://dx.doi.org/10.1016/j.aeue.2014.05.016 1434-8411/© 2014 Published by Elsevier GmbH.

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Fig. 2. Structure of the IEEE standard 802.15.4. Fig. 1. Indoor localization scenario.

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the instant in which the nodes can transmit. The advantage of this method is its high reliability (e.g., to avoid collisions) compared with the self-organized method. However, this approach may require the transmission of additional signaling packets to coordinate the nodes. If the generation of coordination packets is frequent, it can reduce the effective communication opportunities in the network to transmit useful data. In the self-organized approach, the nodes use local information and predefined common rules to coordinate the transmissions and avoid their collisions. A centralized mechanism for IEEE 802.15.4 network is presented in [5]. Here, three different types of traffic are identified: periodic traffic, spontaneous traffic and non real-time messages. The coordinator knows the network dynamic and is responsible for scheduling the messages. The coordinator allocates guaranteed time-slots (GTSs) for periodic data and lefts sporadic and non realtime messages for a contention access period (CAP). In order to receive a GTS, each node sends a frame request to the coordinator indicating the amount of periodic data, sporadic data and non-real time messages as well the deadline for the data. A central strategy that combines slot allocation with CSMA is presented in [6]. Here, the contention access period of 802.15.4 standard is divided in two blocks. In the first block, nodes use TDMA to transmit packets. In the second block, data is sent using CSMA/CA. The size of each block is defined by the network coordinator and depends on the network load. If the nodes have a considerable amount of packets to transmit, the coordinator assigns more time to the TDMA block, but if the packet load is low, the coordinator will assign more time to the CSMA/CA block. Note that the coordinator needs to know the amount of remaining packets in the transmission queue of each node. With this information the coordinator estimates the size of each block and the slot duration for each node in the TMDA block. In [7], the authors propose a distributed slot-based mechanism for organizing the transmission and reception of packets in a wireless industrial sensor network. For the initial communication, the nodes execute a handshaking protocol to negotiate and define the slots where they will receive and transmit packets. In addition, a special mechanism is implemented to reserve resources and create reliable end-to-end connections. The authors compare their proposal with the protocol WirelessHART, which was designed for industrial networks and uses a centralized approach. They demonstrated that the distributed protocol outperforms WirelessHART in terms of overhead and delay. However, WirelessHART shows a better performance in the evaluations related to the reliability of the network. The idea of using the nodes’ experience to improve communication can be also found in literature. For instance, the work in [8] defines the duration of a communication slot based on previous

network analysis. Nodes that are observed to send/for-ward more packets receive longer slots than nodes with less traffic. There are also routing algorithms that exploit the link quality indicator registered in previous transmissions for deciding which node will relay the packet [9]. Our work was inspired by the proposal in [10], where a MN locally defines the instant (relative to the beginning of a period) to transmit a packet. If a collision is detected, the MN changes this time (randomly) for the next transmission. In our proposal we extend this concept for ANs. Here, each AN has a local table with information of the experience in previous transmissions. This table will be used to decide when future packets will be transmitted. Principally, the table contains time stamps of when the AN had successful transmissions and their success indicators. These indicators register the transmission experience of the AN at the corresponding time stamp. The proposed strategy was tested using our protocol for localization system HCP and the results were compared with the performance evaluation of this protocol accomplished in [11]. The remainder of this article is structured as follows. First, a summary about the communication standard IEEE 802.15.4, the

Fig. 3. CSMA-CA algorithm [12].

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Fig. 4. Execution of HCP from the point of view of the MN [11].

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HCP protocol and the performance evaluation of this protocol is presented. Then, Section 3 explains the proposed strategy for improving the communication in wireless sensor networks. Next, Section 4 describes the test scenarios and simulation results. Lastly, conclusions and future work are presented in Section 5.

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2. Preliminaries

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2.1. IEEE standard 802.15.4

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The sensor nodes used in our reference network are IEEE 802.15.4-compliant. The standard IEEE 802.15.4 was designed for low-power, low-rate networks [12]. It defines the principal characteristics of the PHY and MAC layer. IEEE 802.15.4 sensor nodes can operate as either Full Function Device (FFD) or Reduced Function Device (RFD). A node configured as FDD is able to route packets from other nodes. On the contrary, this is not possible for nodes operating as RFD, which are configured to sleep a long time in order to save energy. In our network, the ANs are FFD and the MNs are RFD. The MAC layer proposes two configurations for the network: the beacon enabled and the non-beacon enabled mode. In the first configuration, the nodes periodically transmit beacons, which are very short packets with synchronization information. Between two consecutive beacons, the nodes can communicate and also sleep a defined time. In the non-beacon enabled mode, the MAC layer does not define the transmission of beacons. If beacons are necessary,

the upper layers have to coordinate their transmissions. The same occurs for the sleep periods. The beacon enabled mode is appropriate for fixed low-power networks that support low latencies. The main disadvantage of this configuration is its low flexibility. Due to this last point and the dynamics of our network, we used the non-beacon mode for developing HCP. The layered structure is presented in Fig. 2. The user defines the upper layers according to the application. The different layers interact using two kind of services: data service and management service. Data service refers to the information that is received/transmitted by the node, meanwhile management services are a set of internal control messages between layers. For example, an upper layer knows whether its packet has been sent through a confirmation control message from the management service of the MAC layer. The MAC layer handles acknowledgements and the way the channel is accessed. The standard’s mechanism for accessing the channel is: the Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA). This mechanism is described in Fig. 3. When a transmission is requested from the upper layers, the MAC layer waits for a random time between 0 and 2(BE−1) unit backoff periods, where BE is the backoff exponent. The duration of an unit backoff period is 320 ␮s in our case. Afterwards, the node performs the Channel Clear Assessment (CCA) to check if the channel is idle. If so, the MAC attempts to send the message. If not, the MAC calculates a new BE as described in Fig. 3 and waits another random time to attempt again. The parameters macMinBE and macMaxBE define the minimum and maximum value of BE, respectively. The parameter NB is a counter used to register the number of transmission attempts.

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Fig. 6. Average number of packets received at the central computer per period, when the MNs operated in mode three (figure taken from [11]).

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Anchor 3: Avg. Nº of frames

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Finally, if the node could not transmit the packet after a maximum number of attempts (defined by the parameter macMaxCSMABackoffs), the MAC layer sends a control message to the upper layer with the error Channel Access Failure (CAF). The upper layer can discard the packet or try a retransmission. Another error notification produced by the MAC layer is related to the lack of ACKs reception. If ACKs are enabled, the sensor node waits for an ACK from the other side, after the transmission of its frame. If this ACK is lost or not transmitted, the MAC will try to transmit the frame again. The parameter macMaxFrameRetries (three by default) limits the number of MAC retransmissions. After this point, the MAC sends a error message to the upper layer. 2.2. Highly configurable protocol (HCP) The protocol HCP proposed in [3] was designed to enable the localization of MNs by using the Received Signal Strength Indicator (RSSI) between AN and MN. This protocol indicates the way the RSSI measurements are collected and the transmission of this information to the place where the position is estimated, e.g., a central computer. We assume that the ANs are installed forming a tree topology, where the coordinator represents the highest hierarchy level. The coordinator is connected to a central computer.

The tree topology is supported by the IEEE 802.15.4 standard and is quite used in WSN applications because of the simplicity of the protocols that operate on this topology. The inherent hierarchical level of each node can be exploited for instance by synchronization mechanisms and routing protocols by assuming that nodes placed at higher hierarchical levels have more accurate timing information and are closer to the coordinator. We used the routing algorithm designed for tree-topologies described in [13,14], which is based on [15,16]. Please refer to [17–20] for more information about communication protocols that are able to work on tree-topologies. In HCP, nodes execute a cycle periodically. This cycle is called a “period”, which contains different phases: SYNC, REPORT, VIP and COM SINK. The time required for executing each phase is fixed and known by all nodes in the network. In the SYNC phase, starting with the coordinator, all ANs broadcast timing information in order to synchronize other ANs and MNs. The synchronization phase allows each node to determine, when the next phase starts. Furthermore, it is possible that MNs measure the RSSI from the synchronization packets. In the REPORT phase, MNs can inform a certain AN about its RSSI measurements taken in the previous SYNC phases. It is also possible that the MNs report their sensors’ status. The VIP phase also serves to collect measurements. But here, the MNs broadcast short packets, and the ANs take RSSI measurements.

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Table 1 Example of a time list. me

Fig. 8. Division into time slots according to the hop number. Tx: transmission – Rx: reception.

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The COM SINK phase is used by ANs to send the measurements related to MNs to the central computer. Furthermore, the computer can transmit MN localization data to ANs. Specifically, there are two COM SINK phases in the protocol: COM SINK 1 and COM SINK 2. In the former, all the information is sent from the ANs to the central computer through the ANs network (multihop communication). That is why many ANs also have to route packets from other ANs. In the COM SINK 1 phase, an AN is independently aware of the number of packets to be generated, which depends on the number of MNs that it attended in the previous phases. Nevertheless, it is problematic for the AN to know how many packets have to route, because it does not know the number of MNs heard by other ANs. This characteristic makes it difficult to coordinate the communication between ANs. In the COM SINK 2 phase, the information goes from the computer to ANs. For example, the coordinator sends a previously estimated position or configuration information to a MN. In HCP, the MNs can operate with different operational modes, please refer to [3] for further information. In this paper, we will describe the most frequently used operational modes: mode 1 and mode 3. The other modes are a combination of these. In mode 1, the MNs take RSSI measurements from different ANs in SYNC phases. For instance, in Fig. 4 the MN takes RSSI samples for four SYNC phases to average these values and minimize the dispersion effect of the RSSI. Afterwards, the MN sends a packet containing all the measurements to the AN that registered the strongest RSSI, in the REPORT phase. This AN generates a packet with all the RSSI information to the central computer in the COM SINK 1 phase.

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If MNs operate in mode 3, they broadcast a few short packets in the VIP phase. Here, each AN in the transmission range of the MN takes RSSI samples. This information is also sent to the central computer in the next COM SINK 1 phase. The principal advantage of mode 3 compared to mode 1 is its low energy consumption, since the MNs can sleep during long periods, and only wake up to transmit their packets. On the other hand, the disadvantage of mode 3 is the traffic load generated. Whereas in mode 1, a single AN sends all the RSSI information about the MN, in mode 3 each AN that heard the MN generates a packet for the central computer[3]. Summarizing, the HCP protocol provides synchronization (SYNC phase), a strategy to collect RSSI measurements regarding the MN (REPORT and VIP phase) and a time scheme to send data through the network (COM SINK 1 and COM SINK 2 phase).

2.3. Performance evaluation

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In [11], HCP was implemented in a real sensor network to evaluate its performance. The reference network used consists of 10 ANs forming a tree topology and a different number of MNs operating either in mode 1 or mode 3. In Fig. 5 the logical connection between ANs is shown. In this figure, the white circles represent the fixed nodes, the circle marked with the capital letter C represents the coordinator and the black smaller points represent the mobile nodes. During the experiment, the MNs and ANs were located in an area, where each node is in the transmission range of the other nodes. We decided to simulate MNs working in mode 3 in our investigations, given the fact that the ANs generate more packets in the network than in mode 1. Hence, it was possible to test different congestion levels in the network more easily. As described, the MNs operating in mode 3 broadcast packets in the VIP phase and each AN tries to generate one packet per MN in the COM SINK 1 phase. Therefore, in the reference scenario, if there are 6 MNs, ANs must generate a total of 60 packets. Consequently, ANs route 174 packets to the coordinator during the COM SINK 1 phase.

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Fig. 9. Example of the use of the time list during consecutive COM SINK 1 phases.

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Fig. 10. Algorithm to find earlier transmission times.

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The strategy used by ANs to send packets is the following: at the beginning of the COM SINK 1 phase, each AN divides this phase into the same number of time slots as packets to be generated. In each time slot, the upper layer of the AN waits for a random delay and then sends a packet to the MAC layer to be transmitted. ANs may receive a packet to be routed. In this case, the upper layer of the AN sends the packet to the MAC layer immediately. During the measurements, the ACK in the MAC level and in the upper layer were enabled. This means that when the MAC layer gives an error notification, the upper layer retransmits the packet (sending a new transmission request to the MAC layer) up to three times. The above mentioned strategy tries to distribute the transmissions across the COM SINK phase avoiding the concentration of transmissions in one sector of the phase. Another advantage lies in the fact that no coordination between the ANs is required. However, the AN network performance decreases as the number of MNs increases; this condition may be observed by the number of packets that reach the computer, as showed in Fig. 6.

Fig. 11. Number of packets received by the coordinator using the experience-based strategy after a certain number of periods (scenario with 10 ANs).

This figure presents the amount of received packets at the central computer with different number of MNs (from one to eight). The information of more than 300 periods were averaged. Here, the ideal curve refers to the total number of expected packets that the central computer should receive if there were no limitation in the network capacity. Thus, as mentioned, for 6 MNs the central computer should receive 60 packets. The total curve shows the total number of packets received by the central computer. The useful curve refers to the good put at the coordinator and represents the total number of packets received, without counting the repeated packets. These may occur because the ACKs are active. If an AN received a packet and transmitted an ACK, but this ACK was not received, then the transmitter will send the same packet again. This can be avoided by using a filter at the receiver that detects two similar consecutive packets. Naturally, if more than 10 ANs had been in the transmission range of the MNs, more traffic would have been generated and the saturation point of the network would have been reached earlier (with fewer MNs).

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Fig. 12. Number of MAC errors using the experience-based strategy after a certain number of periods (scenario with 10 ANs).

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Fig. 13. Average number of packets received at the central computer over time, when the experience-based strategy is used (scenario with 10 ANs).

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Fig. 7 presents the average number of MAC errors compared with the successful transmissions, in four different ANs of the network (see AN 1–4 in Fig. 8). Overall, the ANs that required more hops to achieve the coordinator, registered a greater number of errors. Note, that in some cases there are more MAC errors than effective transmissions. For instance, AN 4 with any number of MNs. The performance evaluation of the HCP protocol raises the necessity of developing a better transmission strategy to improve the reliability and throughput in wireless sensor networks. The next section describes our new proposal based on the ANs experience.

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Fig. 15. Geographical deployment of the network with 25 ANs [22].

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Our new strategy coordinates the transmissions between transmitter and receiver reducing network communication problems. The strategy is composed of two mechanisms: group the transmissions based on the hierarchical level of the AN and the inclusion of a local list at each AN containing the times when it was able to transmit. These mechanisms are described below in detail. 3.1. Group the AN’s transmissions according to their hierarchical level In a tree topology, the node can act as a parent node and/or child node. A problem occurs when a child node wants to transmit a packet to its parent node, but this last node is busy trying to transmit a packet to its own parent node. In order to minimize this problem, we propose to define two kind of time slots (S 0 and S 1), which are located across the COM SINK 1 phase alternately. The ANs belonging to even hierarchical levels are able to transmit their packets in the slots S 0, while the ANs located in odd hierarchical levels have to listen. Then, the process is inverted for the slots S 1, where the ANs of the odd hierarchical levels are able to transmit data and the rest of ANs have to listen.

Fig. 14. Reference network with 25 ANs.

In our implementation, the phase COM SINK 1 phase is divided into as many slots as hierarchical levels presented in the network. Fig. 8 shows a network with four hierarchical levels. Thus, the transmission phase is divided in four slots. In this example, the grey ANs are transmitting (hop 2 and 4 to the coordinator) while the other ANs are listening in the first slot. In the next slot, those ANs that were able to transmit in the last period have to listen to the packets from ANs located in the hop 1 and 3. Note that the ANs that share the same slot for transmitting their packets can also have communication problems (e.g., collisions). The next section describes a strategy, which is locally executed at each AN, to minimize this problem. 3.2. Time list The time list is a mechanism to register the experience of the ANs in their transmissions in previous COM SINK 1 phases. Here, each AN saves a table with two columns (see e.g., Table 1). The first column contains timestamps. These timestamps are relative to the beginning of the phase and indicate the instant in which the

Fig. 16. Number of packets received at the coordinator with the original transmission strategy described in Section 2.3 (scenario with 25 ANs).

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Fig. 17. Number of MAC errors using the original transmission strategy presented in Section 2.3 (scenario with 25 ANs).

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AN may transmit. Each timestamp has a success indicator (column 2). The success indicator increases when a packet is successfully transmitted and decreases when a communication problem occurs. The success indicator can also decrease if its associated timestamp is not used during a defined time. By using the time list, if an AN successfully transmitted in particular moments in previous phases, it can use the same moments for its transmissions in the next phases. In Fig. 9, we show an example where the transmission attempts of two nearby nodes (A and B) are illustrated during five consecutive COM SINK 1 phases. The elements of the time list and their respective success indicators are also showed. In the figure, the black boxes represent the timestamps used to transmit data, white boxes represent timestamps that are not used and dotted boxes represent deleted timestamps. Note that for simplicity, the slots described in the previous section are not considered in the figure. Therefore, we assume that anchor A and B can use the whole phase to transmit their packets. The time list is empty at the beginning of the process. At this moment, the ANs have not yet transmitted any packet and thus, they do not have any experience. Assume that each node (A and B) has two packets to send in the first period. Thus, each node randomly generates a possible time to transmit. If the transmission is successful, the corresponding timestamp is included in the time list with a success indicator equal to one. On the contrary, if the transmission fails, the timestamp is discarded and the AN generates a new time to transmit data (randomly) in the remaining slots and tries again. In this example, all transmissions were successful in the first period and therefore, each node stores two timestamps in the list (ta1 and ta2 for node A and tb1 and tb2 for node B). In the next periods, the ANs will use the instant of times stored in its list to transmit packets. In the second period node A needs to transmit three packets and node B just one. Since node A already has two transmission times in the list, it only looks for one extra transmission time randomly. Oppositely, node B only uses one of its transmission times (the first one). Note that in our example the third timestamp of node

A coincides with the second timestamp of node B. However, since node B is not using this time in the second period, node A transmits without problems. As mentioned, the success indicator will be increased or decreased depending on if the AN could transmit the packet at the indicated time. Unused timestamps (e.g., tb2 in the third period) are decreased until they achieve a minimum value of one. In the next periods, this timestamp (with success indicator of 1) will be deleted if the AN tries to transmit at this time without success. This situation is illustrated in period four. In periods two and three, node B sends one packet, therefore tb2 is not used, but in period four this node sends two packets again. Hence, node B uses its second timestamp again, although without success because there is a collision with node A. Both nodes update their respective success indicators and try to transmit their packets again. If there is one available time in the list, it will be used for the retransmission. Otherwise, a new time is generated within the remaining time in the current period. In our example, nodes A and B randomly look for new times and transmit with success. Note that the initial second timestamp of node B (tb2) reached the value of zero and therefore it was removed from the list in the fourth period. Thus, now the second timestamp of node B is the new one generated randomly. The other timestamps will increase their success indicators as long they are successfully used. In our implementation, we limit the maximal value of this success indicator to five. If some slots have been used, the time list remains the same, but the nodes can still search possible transmission times within the remaining slots. The packets are sent using CSMA/CA. In the communication, the MAC acknowledgements are enabled. We disabled the random backoff time (by changing some internal parameters of CSMA/CA as described in [21]). Consequently, the CSMA only performs CCA before the transmissions. This allowed us to control the transmission time from the upper layer. The packets to be routed are handled in a similar way as the retransmissions. When a packet arrives or a retransmission is necessary, the node checks if there are remaining transmission times in

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the list. If that is the case, the packet will be transmitted in the next available time. If not, the node generates a new time to transmit the packet. It is possible for two ANs to transmit simultaneously. In this case, both ANs will have a communication problem decreasing the corresponding success indicator of this time in the list. In this competition, the AN that has a higher success indicator will keep this time in its list for future periods. On the contrary, the other AN will remove this time from its list when the success indicator achieves zero. In the first simulation results of the described approach, we detected that the AN’s time list had a high number of timestamps concentrated in the last part of the transmission phase (COM SINK 1). This is because the nodes had to dynamically include new times in the list when they retransmitted or routed packets. For instance, assume that an AN is exactly in the middle of the COM SINK phase and receives a packet to route, but there are no transmission times available in the list. Then, the AN generates a new transmission time but, obviously, it is located in the second part of the transmission phase. In order to distribute the transmission time of the list across the transmission phase, the time list strategy is complemented with the mechanism presented in Fig. 10 that periodically looks for earlier transmission times. Here, before the COM SINK 1 phase starts, each AN generates a random number between 0 and 1. If this number is smaller than a threshold prob x, it executes the mechanism to create new times to transmit data. If not, the time list remains the same. This mechanism tries to find (X) new (earlier) times to be included in the list. X is relative to the actual number of elements of the list. After simulating several iterations with different values, we obtained the highest throughput results when prob x = 0.3 and C = 5% of the length of the list. Naturally, if X=0 the algorithm ends. If X >0, then the algorithm checks whether it is possible to generate a new transmission time in the first part of the transmission phase. If this part is saturated with already existing transmission times, no new transmission time is generated. If there is place to locate a new one, this is included to the list with a success indicator equal to one. As described in Fig. 10, this process is repeated up to X times.

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The performance of the proposed strategy is evaluated in the COM SINK 1 phase of the HCP protocol. For this purpose two scenarios were used. One has the same conditions and characteristics as the scenario used in [11]. Remember that this scenario has 10 ANs and each node (MN or AN) is in the transmission range of the other nodes. The tree topology used by the 10 ANs is the same as the one depicted in Fig. 8. The second scenario is the one proposed in [22], which consists of 25 fixed nodes distributed in a grid. In both cases, MNs are working in mode 3 and the duration of the COM SINK 1 was set to 1 s. The self-organized strategy and our two test scenarios were implemented in the network simulator OMNet++using the MiXiM framework. In this situation, we modeled the sensor node used in [11], which has the radio transceiver AT86RF230 [23]. For both scenarios, we assumed that the coordinator can send all received information to the central computer without loss of information. Results are presented below. 4.1. Scenario with 10 ANs For this scenario the simulation contains 80 consecutive periods. Each simulation was performed 100 times to estimate confidence

Fig. 18. Number of packets received at the coordinator using the experience-based strategy after a certain number of periods (scenario with 25 ANs).

intervals with a 95% confidence level. Fig. 11(a) shows the number of received packets by the coordinator in the first period. The ideal curve depicts the case of a network without errors. Here, if there are 8 nodes operating in mode 3, the computer will receive 80 packets (because each one of the 10 ANs generates a packet per MN). The simulation curve refers to the effective average number of packets received by the central computer in our reference network. The problem of the occurrence of repeated packets described in Section 2.3 was solved by implementing filters in the different layers, which detect and discard duplicated packets. At the beginning of the first period, the time lists of the ANs are empty. Thus, they always have to look for random transmission times to send their packets. Since many ANs may accomplish their transmissions simultaneously, the number of successfully transmissions decreases as the number of transmission requests increases (which depends on the number of MNs). Note that the results of Fig. 11(a) are similar to the those presented in [[6]] (see Fig. 6). This is because both strategies use random transmission times in the COM SINK 1 phase. Furthermore, although the phase is divided into slots according to the hierarchical level, a collision

Please cite this article in press as: Robles JJ, et al. Using the node’s experience from previous transmissions to improve the communication in an indoor localization system. Int J Electron Commun (AEÜ) (2014), http://dx.doi.org/10.1016/j.aeue.2014.05.016

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Fig. 19. Number of MAC errors using the experience-based strategy after a certain number of periods (scenario with 25 ANs).

Please cite this article in press as: Robles JJ, et al. Using the node’s experience from previous transmissions to improve the communication in an indoor localization system. Int J Electron Commun (AEÜ) (2014), http://dx.doi.org/10.1016/j.aeue.2014.05.016

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can occur between the transmitting ANs and the ACKs generated by the receivers. The self-organized strategy does not outperform the original strategy of HCP in the first period (See Figs. 11(a) and 6). Here, both strategies try to find random times to transmit data. After 10 periods, Fig. 11(b) shows the effect of finding and keeping successful transmission times. Ergo, the ANs gradually learn and organize themselves, improving communication. Fig. 11(c) illustrates the case after 80 periods, where the average number of received packets gets closer to the ideal amount of received packets. We also investigated the average number of transmitted frames and MAC errors per period in four ANs at different hierarchical levels (AN 1-4 from Fig. 8). Figs. 12(a)–(c) show the results for periods 1, 10 and 80, respectively. The results illustrated in Fig. 12(a) are similar to those in Fig. 7. The ANs registered several errors due to CAF and NO ACK. Consequently, if we consider the ratio between the number of transmitted packets and the errors, we can observe that this ratio decreases as the number of hops between the AN and the coordinator increases. Observe that in AN 4 (4 hops to the coordinator) there are more MAC errors than transmitted frames. In the periods 10 and 80, each AN can use its time list to decide the moment to transmit its packets. As expected, the MAC errors decrease considerably as showed in Fig. 12(b) and (c). The improvement in the number of received packets over 80 periods is depicted in Fig. 13. Here, there are always 8 MNs operating in mode 3. In the ideal case, the computer should receive 80 packets. The results show a significant improvement just in the first three periods (approx. 42%). Then, the performance slightly improves in the next periods. Finally, a saturation effect can be detected after 40 periods.

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In the scenario with 10 ANs, all nodes are in each others’ transmission range, and therefore the hidden terminal problem [11] does not occur. Our proposal was also simulated in a scenario with 25 ANs and 5–40 MNs. In this scenario, the traffic in the network increases considerably in comparison with the scenario with ten nodes. However, the COM SINK phase for transmitting all those packets was kept to one second. With a data rate of 250 kbps, the duration of a transmission with ACK takes about 2 ms. When there are 40 MNs in the network, it is possible that more than 500 transmissions (depending on the distribution of MNs) are necessary in order to ensure that the coordinator receives 240 packets. Fig. 14 presents the network topology for this case. This network was deployed in an area of 70 m × 70 m in a grid configuration as Fig. 15 shows. The transmission range of the nodes is 26 m. In each simulation, a two-dimensional random deployment was used to place a certain number of MNs (working in mode 3) in the scenario. We executed 80 consecutive HCP periods for each simulation. More than 250 runs were performed to calculate confidence intervals with a 95% confidence level. Fig. 16 presents the performance of the strategy used in Section 2.3 during COM SINK 1 phase. Again, the ideal curve depicts the case of a network without communication errors and the simulation curve presents the number of packets received by the central computer. Fig. 17 shows the average number of transmitted frames and MAC errors per period in four ANs at different hierarchical levels (ANs 8,12 16 and 20). This figure proves that the network suffers a packet overload when there are more than 20 MNs. In general, after 20 MNs the number of CAF errors increases, exceeding the number of successfully transmitted packets. This situation shows that it is difficult for ANs to find a time when the channel is free. The next figures refer to the performance of our self-organized strategy after 1, 10 and 60 periods. Specifically, Fig. 18(a)

illustrates the number of packets received at the coordinator for the first period. This figure shows that the self-organized strategy performance is worse than that obtained when the original strategy, described in Section 2.3, is used (Fig. 16). In order to identify the reason for this behavior, we performed an additional simulation changing the size of the slots and the configurations. We concluded that the main reason for the poor performance of the self-organized strategy in the first period compared with the original one is due to the used MAC configuration. In the original strategy, the MAC was configured by default (MacMaxCSMABackoffs = 4 and macMaxFrameRetries = 3). In this case, when a packet is sent from the upper layer to the MAC layer for its transmission, the MAC generates a random time and then transmits the packet. If the ACK is not received, the MAC retransmits the same packet. That means, that the difference between the moment when the packet is sent from the upper layer and this real transmission time is variable. In the self-organized strategy, due to the use of the time list, the upper layer has to control when a packet is transmitted. Therefore, we disabled the random backoff periods and MAC retranmissions. As a conclusion, it is more effective to send data in the original strategy than the self-organized one in the first period, because of the high number of attempts allowed in the original strategy. The improvement over the time achieved by the self-organized strategy can be observed in Fig. 18(b) and (c). Note that the selforganized strategy achieves a better performance, especially when there are less than 20 MNs. Above 20 MNs, when the network begins to become saturated, it is more difficult for the strategy to find the required amount of timestamps for transmitting all the packets. This situation is illustrated by the amount of CAF errors show in Fig. 19(b) and (c). However, in such saturated scenarios, our self-organized strategy based on the experience achieves, after 60 periods, a better performance than the original strategy.

5. Conclusions This paper deals with the improvement of the communication between wireless ANs in localization systems. A self-organized strategy based on the experience in previous transmissions was proposed. Here, the ANs register the moment, when they were able to transmit packets successfully in the previous periods. These times are saved in a time list and used for future transmissions. Additionally, a slot allocation approach is presented for a tree topology to avoid communication problems between parent and child nodes. For this case, we defined two different kinds of slots. In the first kind, the ANs from odd hierarchical levels can transmit their packets, while the other ANs of the network have to listen during this slot. Then, the process is inverted in the next slot, and those ANs, which were in reception mode in the last slot, can transmit their packets and the remaining AN located in odd hierarchical levels have to hear. This communication strategy was implemented in the COM SINK 1 phase of the HCP protocol were tested in two scenarios. Our simulation results showed that our strategy improved the original approach of HCP for sending packets to the central computer, which used random transmissions times. Specifically, we observed an improvement of approximately 70% (after 80 periods) in the average number of received packets at the central computer for the reference scenario of ten nodes. The MAC errors at the ANs were also reduced. Additionally, the results observed in the scenario with 25 ANs showed that our self-organized strategy performs better than the original strategy, even in networks with a high traffic load.

Please cite this article in press as: Robles JJ, et al. Using the node’s experience from previous transmissions to improve the communication in an indoor localization system. Int J Electron Commun (AEÜ) (2014), http://dx.doi.org/10.1016/j.aeue.2014.05.016

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Acknowledgment The authors would like to thank Jorge Pérez Hidalgo for his feedbacks and suggestions. The support received from the Gesellschaft von Freunden und Förderern der TU Dresden and from the “División de investigación de la sede Bogotá” (DIB) of the Universidad Nacional de Colombia is also gratefully acknowledged.

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Please cite this article in press as: Robles JJ, et al. Using the node’s experience from previous transmissions to improve the communication in an indoor localization system. Int J Electron Commun (AEÜ) (2014), http://dx.doi.org/10.1016/j.aeue.2014.05.016

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