EMA-RPL: Energy and mobility aware routing for the Internet of Mobile Things

Future Generation Computer Systems 97 (2019) 247–258

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Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs

EMA-RPL: Energy and mobility aware routing for the Internet of Mobile Things ∗

Maha Bouaziz a , Abderrezak Rachedi b , Abdelfettah Belghith c , , Marion Berbineau a , Saad Al-Ahmadi c a

Université Lille Nord de France, IFSTTAR, COSYS, F-59650 Villeneuve d’Asqc, France Université Paris-Est, LIGM (UMR 8049), ENPC, ESIEE Paris, UPEM F-77454, Marne-la-Vallée, France c College of Computer and Information Sciences, King Saud University, Saudi Arabia b

highlights • • • • • •

An improved mobility compliant RPL routing protocol called EMA-RPL. EMA-RPL integrates an enhanced mobility detection method. EMA-RPL integrates a novel point of attachment. EMA-RPL integrates an efficient replacement strategy. EMA-RPL overcomes and mitigates problems caused by the mobility of nodes. Extensive simulations using Cooja/Contiki showed that EMA-RPL outperforms both the RPL and its mobility aware variant (MRPL) in terms of handover delay, data loss rate, signaling cost and energy consumption.

article

info

Article history: Received 1 February 2018 Received in revised form 12 January 2019 Accepted 22 February 2019 Available online 5 March 2019 Keywords: Internet of Mobile Things Healthcare WSN 6LoWPAN Micro-mobility support protocol RPL Energy consumption

a b s t r a c t Internet of Mobile Things (IoMT) is a new paradigm of the Internet of Things (IoT) where devices are inherently mobile. While mobility enables innovative applications and allows new services, it remains a challenging issue as it causes disconnection of nodes and intermittent connectivity, which negatively impacts the network operation and performance. In addition, energy saving presents a real challenge for networks with limited resources. In this context, a new energy efficient and mobility aware routing protocol called EMA-RPL is proposed based on the well-known Routing Protocol for Low power and Lossy Networks (the RPL standard). Unlike the RPL which is basically designed for static devices, EMA-RPL enables better sustaining of connectivity of mobile nodes and conserving energy. The proposed protocol integrates an enhanced mobility detection method through a continuous control of the distance between the mobile node and its attachment, a novel point of attachment prediction based on the new location of the mobile node, and an efficient replacement strategy preserving the mobile node energy. EMA-RPL overcomes and mitigates problems caused by the mobility of nodes. Simulations using Cooja/Contiki show that EMA-RPL outperforms both the RPL and its mobility aware variant (MRPL) in terms of handover delay, data loss rate, signaling cost and energy consumption. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The Internet of Things (IoT) [1,2] has recently emerged as a promising research trend targeting a great number of applications. It has become a need to keep up with the advanced technologies in communication, which makes it ubiquitous to ∗ Corresponding author. E-mail addresses: [email protected] (M. Bouaziz), [email protected] (A. Rachedi), [email protected] (A. Belghith), [email protected] (M. Berbineau), [email protected] (S. Al-Ahmadi). https://doi.org/10.1016/j.future.2019.02.042 0167-739X/© 2019 Elsevier B.V. All rights reserved.

all smart users and environments. Such a requirement is met thanks to the use of Wireless Sensor Networks (WSNs) [3] based on the 6LoWPAN technology [4,5]. Undoubtedly, the WSN plays an important role in our everyday life through achieving several tasks. Its concept consists in integrating some embedded computing sensors within the Internet infrastructure. The 6LoWPAN standard proved to be useful as it has eased the deployment of IoT applications offering such benefits as scalability, flexibility, end-to-end connectivity among others. The IoT is applied in many application fields [6–8]. In this paper, we focused on the Healthcare domain in order to facilitate some medical tasks and help

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doctors in monitoring their patients’ health regardless of their whereabouts. Hosting Healthcare application requires processing the mobility of the connected devices. Mobility allows many advantages in terms of flexibility, introducing more innovative services and enlarging IoT application domains [9]. However, mobility generates some challenging issues that need to be appropriately solved such as intermittent connectivity and disconnection of mobile nodes (MNs). Indeed, mobility problems arise when an MN moves away from the coverage area of its attachment point. This attachment point becomes no longer available to forward data from/to the MN. The major problem caused by these disconnections concerns data loss and transmission delays [10]. Therefore, it is crucial to deal with mobility through finding an alternate attachment point in a brief and bounded time, in order to overcome the encountered issues and ensure a continuous connectivity and seamless communication within the WSN. The design of a suitable mobility support protocol for WSNs remains a serious and challenging issue due to the limited resources of the sensors, their unpredictable movement and the application requirement in terms of Quality-of-Services (QoS). Several protocols have already been proposed in the literature but without considering constraints such as energy consumption. Besides, they present tacit inefficiencies with regard to handover delay and data loss [11]. Recall that Energy consumption is directly related to the number of exchanged control messages (the signaling cost), and therefore it is crucial to optimize the control process. Moreover, a high network overload and a large handover delay negatively impact the transmission process and cause data losses. This paper focuses on the micro mobility support, which is defined as the mobility of nodes within the same network with unchanged addresses. However, since it may impact the routing paths within the network, supporting this kind of mobility is related to the routing protocol. Two routing schemes are used for 6LoWPAN: (1) The Mesh-under which uses the 6LoWPAN layer to route fragments of IPv6 packets, (2) The Route-over for the multi-hop mesh communication in the network layer to route IPv6 packets, such as the RPL protocol. The first kind uses a low transmission delay, while the second is more efficient. The RPL standard was mainly proposed for low power and lossy networks. RPL currently stands out as the most suitable routing protocol for WSNs based on the 6LoWPAN technology, thanks to its treebased structure adequate for data collection and its ability to use IPv6 base addressing to perform interoperability with other internet devices [12,13]. Nevertheless, RPL remains inefficient and has a rather low reactivity in scenarios with high and frequent mobility [14,15]. In fact, RPL is basically designed for static lossy networks and it is a reactive routing protocol for topology changes. As a result, it fails to avoid node disconnections and data losses. Moreover, Reducing the energy consumption of MNs is not considered as to increase their lifetimes [16]. The contributions of this paper are: - Study the RPL reactivity to nodes mobility and show its issues and limits. - Provide a review on most relevant work related to mobility with the RPL Standard. - Propose a new routing protocol taking into account the mobility of nodes in the healthcare applications. The aim of our proposed EMA-RPL consists in increasing the mobile devices lifetime and establishing a more robust connectivity by introducing a proactive process able to anticipate and predict the nodes movement. To this end, we proposed a novel point of attachment replacement strategy based on

the Received Signal Strength Indicator (RSSI), yet a particular attention is paid to decreasing the energy consumed by the signaling traffic. - Investigate the EMA-RPL performance in comparison to the most relevant previous work, using extensive simulations under the cooja/ContikiOS. The remainder of this paper is organized as follows: In Section 2, we present a brief overview of the RPL protocol, its issues and limits when introducing mobility of nodes. Section 3 discusses some of the relevant work proposed to improve the RPL standard under mobility. The proposed EMA-RPL protocol is detailed in Section 4 before investigating its performance in Section 5. Finally, Section 6 concludes the paper and suggests some further perspectives. 2. RPL overview, issues and limits under mobility RPL is a routing protocol developed by the IETF Working Group (ROLL) for Low and Lossy Networks. It was essentially created for static Networks with a very low reactivity to mobility. 2.1. RPL overview In the RPL Standard specification, forwarding data follows a hierarchical topology based on the concept of the Directed Acyclic Graph (DAG). Data are transferred from nodes up to the root node (edge router) through default routes. This upwarding is elaborated during the graph construction called DODAG, where the default route is defined through selecting the preferred parent of each node in the route. This selection is fulfilled using a given objective function and metrics received from DIO messages in order to find the optimal path. Furthermore, this graph defines the reverse routes for the downward and point-to-point forwarding, relying on the DAO messages and according to either the storing or the non-storing mode. The RPL Standard is a reactive protocol for recovering issues and changes. As such, it is unable to adequately deal with some mobility problems. For example, a node disconnection from its preferred parent results in a loss of data packets before being able to find and then connect to another preferred parent. This tacitly affects the network performance. Our main objective in this paper is to propose a micro mobility support to predict node disconnections and therefore quickly recovering lost links and establishing new routes, hence avoiding data loss, yet allowing a seamless and continuous availability of the nodes. 2.2. Mobility impact with the RPL standard An inconsistency results from a node failure or a physical movement causing a change in the topology. Following the RPL Standard specification, when this problem occurs, the node becomes detached from all its connected nodes (parents and children) and loses its data forwarding ability. Consequently, it has to update its knowledge about neighbors and routing paths (list of vicinities, list of parents, preferred parent and rank). In order to face this problem, the RPL may react through a self healing strategy [12], which consists in dynamically updating the routing decisions and coping with the network change. This strategy is ensured by a Local repair phase eventually followed by another Global repair phase as follows: once an inconsistency problem is detected, the RPL focuses on triggering a local repair phase in order to quickly avoid packet drops. Thus, it tries to seek an alternate path, even if it is not the optimal, according to the topology structure made by several parents and siblings. This concept helps to dynamically discover routing paths between nodes. Besides, a local repair may be performed by the reception

M. Bouaziz, A. Rachedi, A. Belghith et al. / Future Generation Computer Systems 97 (2019) 247–258

Fig. 1. Route change caused by the node mobility.

of a DIO message, which is sent following the trickle timer. The reception of this message helps the MN to reconnect and update the routing path information. Then, in the case when the local repair fails to recover this issue, a global repair is executed to resolve problems caused by the topology change, which is ensured by the reconstruction of a new DoDAG version initiated by the DoDAG root. Before going any further, let us present the main changes that takes place at the MN and its neighbors when a node mobility is performed causing a topology change as illustrated in Fig. 1. 2.2.1. The mobile node When the MN moves away from its current location, it results in an interruption in its links with its parents and children. The MN becomes disconnected from the DoDAG tree. To deal with this issue, the MN tries to create new attachments through new links with other nodes. Therein, two behaviors may be distinguished. The first one is carried out when the MN receives a DIO message. In this case, it updates its parameters (vicinity list, parents list, preferred parent, rank, routing table) and creates the upward path, according to the received message. Then, it sends a DAO message toward its parent for the reverse path creation (downward path); and broadcasts the DIO message after updating its information, trying to find new children and a sub-tree. The other case appears in the absence of the DIO message. Thereby, the MN anticipates by the diffusion of solicitation messages called DIS in order to request to join the DoDAG tree. 2.2.2. The neighbors In order to keep connectivity between mobile nodes and their neighbors, each node periodically broadcasts DIO messages to its children following the trickle timer, and receives in return DAO messages. Thus, the movement of a node away from its neighbors causes the loss of these messages and an interruption of the routing paths through the MN in consequence. To deal with this issue, a node which receives neither DAO nor DIO messages from the MN, deduces that it has left its range. Hence, it deletes the entry to the MN in the routing table and drops it from the list of neighbors and the list of parents [17,18]. Next, it waits to receive control messages from other nodes to update its information and ensure its connectivity for the downward and the upward traffics. 2.3. Issues and limits of the mobility related RPL The RPL routing protocol is inefficient in coping with the dynamics of the network and nodes mobility. As such, it cannot provide suitable performances for time constrained applications such as Healthcare applications [19]. The RPL cannot avoid data losses and MN disconnections. In fact, it has a low reactivity to node mobility as it is designed essentially for static networks. Let us first emphasized the fact that the applied mobility detection method depends on the reception/absence of DIO messages. This concept causes a detection delay, essentially a long

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trickle interval. Because of this delay, RPL cannot quickly react to recover this issue, which affects the MN connectivity and causes data loss. Thereby, this method is not suitable for real time applications, which is a basic requirement in our current study. Reducing the trickle timer interval would necessarily increase the signaling overhead which in turn would amount to more collisions, higher power consumption and increased data loss. For these reasons, it is crucial to propose another method to deal with mobility using a reduced number of control messages. Moreover, the support of mobility based on a reactive concept cannot avoid problems caused by the movement of nodes, because the reaction to a repair is only triggered upon the reception of a new DIO message from another neighbor [20]. This concept causes a large handover delay followed by a disconnection time of the MN and a data loss. It is then crucial to introduce a proactive process based on the prediction of a new attachment. Furthermore and as noted previously, a local repair is performed to recover mobility issues. However, a further global repair is needed in some cases. The latter reconstructs a new DoDAG tree version, which requires a great signaling overhead and causes a large handover delay followed by a disconnection time and data losses. Most importantly, the MN is fully involved in dealing with mobility, and therefore, it uses a large amount of signaling and depletes its energy. MN’s resources are then quickly reduced which tacitly leads to rapid failures. Instead, in this paper, we shall alleviate the involvement of the MN by delegating the mobility management and its signaling/energy costs to static nodes (SNs). This will spare the MN’s resources and lower the signaling overhead. Last but not least, the RPL standard does not take into account the provision of robust paths, as MNs may be used in the routing path causing frequent path disconnections. 3. Related work Some protocols were proposed to integrate the support of mobility into RPL. In [21], the authors proposed Mobility Enhanced RPL (MERPL). To increase routes stability, ME-RPL proposes to avoid the selection of a MN as the preferred parent (PP). To avoid the MN disconnection, ME-RPL uses a periodic sending of DAG Information Solicitation (DIS) messages, to request sending DAG Information Option (DIO) messages used to reconnect upon any connection loss. This solution outperforms RPL in terms of route stability. Nevertheless, the authors did not consider some important parameters such as handover delay, signaling cost, and energy consumption. In the context of Vehicular Ad hoc Networks (Vanets), authors in [22] studied RPL performances with some adaptations to this domain. First, they proposed disabling the DIO trickle timer, because it was not suitable for frequent topology changes. Second, they suggested improving the reactivity by immediately assessing the link quality and updating the routing graph. Third, they proposed carrying the parent’s ID in the DIO message in order to detect and avoid the loop problem. Similarly, the authors in [23] proposed a GI-RPL protocol as an extension of RPL for Vanets. GIRPL is based on node localization to deal with frequent changing topologies. Localization is represented by the distance from the sink and the direction of the vehicle. Besides, this solution is based on an adaptive DIO period instead of the trickle timer to attain better performances. This provided an improvement in terms of packet delivery ratio and packet delay. However, they did not consider the energy constraint as it is not paramount in the case of Vanets. In [24], the authors proposed MoMoRo to support mobility with the RPL standard. MoMoRo uses a mobility detection method

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based on the number of retransmissions. Its process consists in collecting information from neighbors when a node mobility is detected. Then, it uses a fuzzy estimator to estimate the links quality and selects a robust link for the new MN attachment. Nonetheless, this protocol is a reactive solution which needs a larger handover delay and a higher signaling cost. In [25,26], the authors proposed a new protocol called Co-RPL. The scheme is based on the Corona mechanism to detect moving nodes and localize them relatively to the DAG root. However, this solution falls short in solving problems caused by mobility such as long handover delays and paths disconnection times. In [27], the authors proposed a new protocol called PL-Probe based on the link quality monitoring for the mobility detection. The PL-Probe concept helps in improving the packet delivery rate and the signaling cost. However, the handover delay is not evaluated. In [28], the authors also proposed a protocol based on the link quality for the mobility detection. However, the concept of this solution consists in evaluation the traffic sent from the root to the mobile node, not as habitual protocols in literature. This solution contribute to provide a continuous connectivity with the mobile node, reduce the packet loss rate and the signaling cost. In [29], the authors proposed the MRPL protocol to deal with mobility based on a proactive process. The concept of this protocol is based on RSSI values computed through periodic reception of signaling messages by the mobile node. In addition, the mobile node is the responsible to detect its mobility and to trigger and manage the reaction to avoid its disconnection. Although this proposition presented a viable contribution and succeeded in improving some of the necessary performances, further enhancements are still needed. For example, the authors did not take into account the energy consumption issue. The MN remains deeply involved in the mobility support and consequently sees its energy depleting rather quickly. In addition, the periodic sending of control messages makes the solution rather costly in terms of both signaling overhead and energy consumption. The huge signaling cost between the MN and its PP negatively impacts the data transmission and results in data losses. Furthermore, the disconnection time problem still persists because the MN is forbidden to send packets during the handover process as if it were a disconnected node. Based on the same link quality strategy, we propose in this paper EMA-RPL that instead, delegates the mobility handling process to static nodes, and consequently alleviates the MN from such a burden. As a result, we lower the signaling overhead and the energy consumption while providing a seamless connectivity with a reduced overhead delay. We shall compare the performance of our proposed EMA-RPL to that of the MRPL. 4. EMA-RPL: Energy and mobility aware routing protocol The proposed protocol is called EMA-RPL. It is designed for a real time Internet of Things (IoT) application where the data is periodically transmitted by the mobile nodes such as the case of monitoring a mobile patient in a healthcare application. The objective of this application type is to allow doctors to remotely monitor patients regardless of their whereabouts. The suggested system architecture was inspired from the Wireless Body Area Network (WBAN) architecture. Some wireless sensors of this network are attached to the human body, either on the skin as a part of the clothes or even implanted into the body. These sensors are responsible for sensing and collecting Physiological Health Information (PHI) of the patient such as blood pressure, heart beat, body temperature, electrocardiogram (ECG) and electro encephalogram (EEG) signals, etc. These collected PHI are then securely and periodically transferred to a central platform, which

can be visualized by doctors in order to monitor patients and perform early and or continuous diagnosis. The objective of this proposed protocol is to reduce the MN energy consumption while providing a seamless connectivity, avoiding data losses and reducing the handover delay. The hard deployment of MNs necessitates conserving their energy for a longer lifetime which in turn maintains the proper functioning of the healthcare application. EMA-RPL is an enhancement of the RPL routing protocol for Lossy Networks to support node micro mobility [30]. The founding idea consists firstly in introducing a proactive process to recover issues caused by nodes mobility, and secondly reducing the MN involvement as well as the overall signaling overhead. 4.1. RPL protocol enhancement Some improvements need to be integrated into the RPL standard to support mobility and achieve the required performance. Firstly, the MN should join the DODAG tree by selecting a preferred parent (PP) based on a predefined objective function. Then, during the movement, the nearest SN is considered to select a new PP through a proactive process. This process consists in introducing a movement detection step, then, a prediction step of a new attachment before losing its connectivity. This proactive process helps to avoid or reduce disconnection time and data loss during the handover process. Moreover, the MN is considered as a leaf node that can be excluded from the route path to avoid route interruption. As such, it is no longer necessary for the MN to periodically send DIO messages. This helps ensuring routes stability and saves the MN’s scarce resources essentially in terms of energy. Furthermore, EMA-RPL is a network-based protocol where the MN is not involved in the mobility support in order to keep and preserve its resources. The idea consists in distributing the energy consumption between different SNs. Therefore, the MN should be connected to an SN called ‘‘Associated Node’’ (AN), which refers to its PP in RPL. This node is responsible for: (1) Transferring data to/from the MN. (2) Detecting the MN movement away from its range area. (3) Predicting and finding another AN for the MN. This node does not only help the MN to save its energy, but it also allows reducing the signaling between the MN and its AN, hence allowing more data packets to be transferred. In addition, it is crucial for the MN to only disconnect from its current AN after achieving a connection with the predicted AN to ensure a continuous connectivity. Next and in order to ensure the EMA-RPL functionalities, some fields are added in the ICMPv6 control messages (DIS, DIO) such as the RSSI carrying the average RSSI value and a flag to distinguish between the different used types of messages. Finally to update the backward route, the MN is required to send back DAO messages immediately upon receiving a new request to change its attachment, instead of just being scheduled as defined by the RPL standard. To this end, two DAO types are needed. The first is sent to the predicted AN in order to add the new route availability for the MN, whereas the second ‘‘no-path’’ is sent to the previous AN in order to delete the previous path through the previous attachment. This immediate update helps to avoid disconnection and data losses. 4.2. EMA-RPL protocol description When an MN moves away from the range of its attachment point, EMA-RPL follows certain steps to deal with this change as illustrated on Fig. 2. The EMA-RPL process is ensured owing to the existing ICMPv6 control messages used by the RPL routing protocol enriched with the new modifications.

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Algorithm 2 Prediction phase // In the MN side if MN receives DIS (flag = 1) then MN Broadcast DIS (flag = 2) if RSSI ≤ threshold − x) then MN stop sending data EndIf EndIf Fig. 2. The different steps of the EMA-RPL protocol.

4.2.1. Detection and transmission phase The detection process is ensured by measuring the link quality during data transmissions. The AN periodically computes the average RSSI value based on the data received from the MN. Upon detecting that the RSSI has degraded to a predefined threshold, the AN deduces that the MN has moved away from its coverage area. Mobility is then detected and the next phase is triggered. During the handover process, the current AN keeps connection with the MN and continues to receive data packets. Besides, it does not stop checking the RSSI value, in order to notify the MN to stop sending data in case of the RSSI reaches (threshold-x) for a predetermined value x. Algorithm 1 Transmission phase and mobility detection while Expire (TX_Period) do MN send data packet to AN AN compute RSSI if RSSI ≤ threshold then AN broadcast DIS (flag = 1, RSSI , MN_ID) START (DIOS Input timer) Endif EndWhile In EMA-RPL, the AN is the responsible for the detection of node mobility through the MN data transmission and there is no dependence to the trickle timer that leads to delaying the detection. Unlike MRPL, the MN does not need to receive signaling messages to detect mobility during the transmission phase. This allows two benefits: The MN is able to save energy as well as to reduce the number of exchanged signaling messages with its AN, thus avoiding any overload that may result in data packets loss. This process is presented by Algorithm 1. 4.2.2. Reaction and prediction phase After mobility detection, the proposed protocol focuses on predicting a new AN based on the RSSI parameter. This phase is performed by the current AN in order to preserve the MN’s resources. Besides, it is performed before the MN disconnection in order to quickly recover the handover process and provide a continuous MN connectivity. This process is presented by Algorithm 2. As illustrated on Fig. 3, when the AN detects the signal degradation with the MN, it immediately broadcasts DIS messages (flag = 1) carrying the computed RSSI and MN-ID fields. Then, it triggers a timer (DIOS Input) to wait to receive Unicast DIO messages (flag = 2) from other SNs that detect the MN. These DIS messages are received by the MN and its neighboring SNs. Upon the reception of this DIS, the MN checks the received RSSI: - RSSI ≤ threshold: The MN broadcasts DIS messages (flag = 2) three times through a timer (DIS timer), in order to be detected by the SNs, and continues sending data packets. - RSSI ≤ threshold − x: The MN stops sending data packets to avoid their loss as it is supposed to be at the border of the coverage area of its current AN.

// In the SNs side if SN receives DIS(flag = 1) then START (DIS Delay timer) SN computes RSSIs through received DIS(flag = 2) EndIf if EXPIRE (DIS Delay timer) then SN computes RSSI_Av erage SN send DIO (flag = 2, RSSI_Av erage) to AN EndIf // In the AN side AN receives DIOs (flag = 2) if EXPIRE (DIOS Input timer) then AN predicts New_AN with the highest RSSI_Av erage EndIf

Then regarding the SNs, when they receive DIS (flag = 1) from the AN, they begin triggering a timer (DIS delay) to detect the MN according to the received ID. After expiration of this timer, each SN computes the average RSSI through the received burst DIS (flag = 2) from the MN; and subsequently sends a unicast DIO (flag = 2) carrying the computed RSSI to the current AN (the one, which sends the notification to detect the corresponding MN). The current AN also receives DIS (flag = 2) from the MN, computes the average RSSI, and includes it for the best RSSI selection. The transmission of DIO by the static nodes would result in a DIO storm and consequently creates a lot of collisions. It should be noted here that transmissions of DIO are subject to a randomization period; a static node selects a random back off delay before attempting to transmit its DIO. At the end of this phase, when the (DIOS Input) timer expires, the current AN compares all the collected RSSI values in order to select the highest value that refers to the nearest SN to the MN. The selected SN represents the predicted AN, which may remain the same as the current AN.

4.2.3. Notification phase After the new attachment prediction, the current AN triggers the notification phase in order to perform the required update of the path routes. In case the AN remains unchanged, this node just notifies the MN about the process completion. Otherwise the predicted AN is a new node that gets informed by the current AN. Receiving this notification, the predicted AN sends a unicast DIO message (flag = 1) to the MN to request attachment. The MN starts by updating its parameters (rank, PP, default route to the root node), then it sends back two immediate DAO messages to the new predicted AN and the previous AN respectively to update its backward path route. This concept allows the MN to disconnect from its previous AN only after establishing a new attachment with the predicted AN, which helps to avoid disconnection and data losses.

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Fig. 3. Timing diagram of the EMA-RPL protocol.

4.2.4. Exception cases During the transmission phase and in case of a detection failure in which the current AN misses receiving data packets from the MN during a predefined timer, the ‘‘NO Data receiving process’’ is triggered. During the handover process and in case of a prediction failure or a signaling message loss, the ‘‘MN involvement process’’ is triggered to recover an eventual disconnection. No Data Receiving Process: The current AN begins by broadcasting a trickle DIO (flag = 0), in order to check whether the MN is still within its range, and reconnects with it. If it does not receive back a DAO message, it deduces that it is not within its range. Hence, it notifies its neighbors (by broadcasting DIS messages carried the MN-ID), in order to request them to broadcast a trickle DIO and try to connect the MN again. This process is presented by Algorithm 3. Algorithm 3 No Data receiving process by the current AN if PP receives data packet from MN then START (check_no_data timer) Endif if EXPIRE (check_no_data timer) && TX_phase then AN broadcast DIO (flag = 0) if AN does not receive DAO then AN broadcast DIS to neighbors Endif Endif Mobile Node Involvement: When the prediction process fails, the MN is required to be involved to support mobility and find its new attachment. This failure may be caused by the loss of some signaling messages, or the inability to detect the MN when it moves. As such, in the beginning of each handover process, when the MN receives a DIS message (flag = 1) from its AN, a timer is triggered. Then, the MN will be involved in case the timer expires without achieving a new attachment. The MN broadcasts DIS messages (flag = 0) to request receiving trickle DIO messages (flag = 0) from the SNs in its range. Trickle DIO helps the MN to be attached with the sender of the received DIO. This process is required to be repeated until the reattachment of the MN and its return to the transmission phase. The MN involvement concept is presented by Algorithm 4.

Algorithm 4 MN involvement process if MN receives DIS (flag = 1) then START (check_attach timer) Endif if EXPIRE (check_attach timer) && discovery_phase then while MN does not receive DIO (flag = 0) do MN broadcast DIS (flag = 0) EndWhile Endif

4.3. ICMPv6 control messages change To ensure that the EMA-RPL protocol deals with micro and node mobility, some fields have to be introduced in the ICMPv6 control messages defined by the RPL standard. In the DIO message, the added fields are: - Flag (2 bit): It is equal to ‘‘0’’ when it is a message of the periodic trickle timer. It is set to ‘‘1’’, when it is used by the predicted AN to notify the MN about change and to process the new attachment. But, it is set to ‘‘2’’, when it is used by the static ANs in the vicinity to carry the computed RSSI value. - RSSI (7 bits): It is used in the discovery phase to carry the RSSI value computed between the MN and the SN in the vicinity. Regarding the DIS message, the added fields are: - Flag (2 bit): It is equal to ‘‘0’’ when it is a simple solicitation message. It is set to ‘‘1’’, when it is used by the current AN to broadcast a notification about the mobility detection. However, it is set to ‘‘2’’, when it is used by the MN to burst DIS used for the prediction phase. It is also set to ‘‘3’’ to notify the predicted AN. - RSSI (7 bits): It is used in the detection phase to carry the RSSI value computed between the MN and its current AN. - MN_ID: It carries the MN identifier used in the prediction phase for its identification upon its detection by its neighboring SNs.

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5.2. Signaling cost

Fig. 4. Simulated scenarios: (a) (b) (c) and (d). Table 1 Simulation parameters. Data rate

30 packet/s

Traffic type Speed Transmission range Mobility model Simulation time Threshold RSSI DIS timer DIS delay DIO input timer

CBR 2 m/s 50 m Random walk model 900 s −90 dBm 20 ms 60 ms 180 ms

5. Performance evaluation Extensive simulations are conducted to properly evaluate the performances of the proposed EMA-RPL protocol and compare them with those provided by the RPL standard, as well as those provided by the MRPL protocol [29]. Simulations are repeated sufficiently to reach a 95% confidence interval. 5.1. Simulation setup We used the Contiki IPv6/6loWPAN platform and the widely used open source implementation of RPL called ContikiRPL [31, 32]. The simulation tool used is Cooja which is a discrete event simulator in the Contiki OS. In order to compare the proposed EMA-MRPL protocol performances with those of MRPL, we implemented both of them based on the ContikiRPL. We introduced three distinct entities: MN, SN and ROOT nodes. The scenarios are illustrated in Fig. 4. An MN is used to generate and periodically send data packets towards the root node through SNs. In scenarios (a), (c) and (d), the MN moves horizontally from one corner to another, whereas in scenario (b), the MN moves within the network according to the trajectory traced in dashed red. The simulation parameters are given in Table 1. We focused on four performance metrics: signaling cost, energy consumption, handover delay and packet delivery ratio.

The signaling cost is defined as the total number of the bytes exchanged in signaling messages to support the mobility (DIS, DIO and DAO ICMPv6 messages). The main aim of EMA-RPL is to get a seamless continuous connectivity along with an MN reduced energy consumption. To this end, it is crucial to reduce the signaling cost. The reduction of the number of exchanged messages between the MN and its AN alleviates the network and consequently allows the transfer of more data packets. Fig. 5 presents the signaling cost for each node in scenarios (a), (b), (c) and (d) while distinguishing between the incoming and outgoing messages (Traffic In/Out). As observed on this figure, the signaling cost for all node types is significantly reduced by EMA-RPL compared to that necessitated by MRPL. The RPL protocol evidently has the lowest signaling cost as no extra signaling is used. Furthermore, it can be noticed that the signaling cost is very high for the MN with the MRPL protocol compared to that of the other kinds of nodes. EMA-RPL reduces the signaling cost by 23 and 27 times for scenarios (a) and (b) respectively, and by around 8 times for both scenarios (c) and (d). Recall that MRPL is a host-based protocol where the MN is very involved to support mobility. In addition, the mobility detection process in MRPL is based on periodic control messages. In contrast, EMA-RPL is a network-based protocol where the AN is the responsible for performing the mobility management process. The signaling cost is then shared among different SNs as the AN is changed along the MN movement. Moreover, EMA-RPL uses the DIS messages instead of the DIO, the former have a much smaller size. 5.3. Energy consumption The energy consumption has an important impact on the nodes lifetime. It is directly related to the number of transmitted/received messages, the processing time, and the overhearing at the idle state (LPM). EMA-RPL focuses on preserving energy of the MN, as we have noted previously. The cost of receiving signaling messages is significantly reduced for the MN, and consequently the consumed energy is reduced with EMA-RPL compared to both RPL and MRPL. Fig. 6 portrays the energy consumed after the delivery of 2500 packets. We observe that the consumed energy is on the average of 1 mW for EMA-RPL compared to an average of 13 mW for MRPL and 7 mW for RPL. As for the other kinds of nodes, EMA-RPL succeeded to reduce their consumed energy compared to the MRPL protocol. However and as illustrated on the figure, the energy consumption of some SNs with the RPL standard is the least compared to the MRPL and the EMA-RPL protocols. Recall that SNs with RPL do not contribute to cope with mobility. As such, they do preserve their energy but at the cost of not providing a continuous connectivity along with a longer delivery time for data packets. For example, in scenario (b), RPL needs around 900 s to deliver 2500 data packets compared to only 241 s and 362 s with EMA-RPL and MRPL respectively. Moreover, the nodes nearest to the RN consume more energy than other nodes: about 19.25 mW and 19.07 mW for SN9 and SN12, compared to 4.06 mW and 6.67 mW for SN2 and SN3 with EMA-RPL in scenario (b). Nodes nearest to the RN are more involved in the routes paths. These are the nodes contributing the most to transfer the data packets to the RN. EMA-RPL stands out to be the most energy efficient while at the same time helps the MN to preserve its energy and consequently keeping it alive the longest for the proper functioning of the Healthcare application.

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Fig. 5. The signaling cost for each node in scenarios (a) (b) (c) and (d).

Fig. 6. Consumed energy of each node after 2500 delivered packets in scenarios (a) (b) (c) and (d).

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Fig. 7. Handover delay in scenarios (a) (b) (c) and (d).

5.4. Handover delay The Handover delay represents the necessary delay to reestablish a new connectivity. It is the delay elapsing between the sending of the last packet in the previous attachment and the sending of the first packet using the new attachment. As such, an MN that does not send data is considered as a disconnected node even if it is connected. Both of the MRPL and EMA-RPL protocols are based on a proactive process to deal with mobility. They succeed in reducing the handover delay compared to the RPL standard. However, MRPL cannot avoid the disconnection of the MN. As illustrated on Fig. 7, the handover delay respectively for scenarios (a) and (b) is 156.25 ms and 60.65 ms for our proposed protocol compared to 1, 639.06 ms and 399.6 ms for MRPL, and 45, 737 s and 56, 640 s for RPL. This represents a huge performance gain attained by our EMA-RPL. Similar results are observed for scenarios (c) and (d). On the other hand, EMA-RPL has the nice property to provide a continuous connectivity. In contrast, the MRPL protocol requires the MN to stop sending data during the handover process as soon as it detects an unreliable link. However, the EMA-RPL protocol allows the MN to continue sending data during the handover process. It stops sending data only when it receives a notification carrying an RSSI value less or equal to −93 dBm (threshold-x). In fact, during the simulation, we observed very few handovers where the MN stopped sending data packets. 5.5. Packet delivery ratio The Packet Delivery Ratio (PDR) represents the ratio of the total number of delivered packets to the total number of sent packets. As illustrated on Fig. 8, the EMA-RPL PDR is very much improved. EMA-RPL achieves a PDR of nearly 100% for all scenarios, while RPL and MRPL achieve a PDR of 27% and 70% respectively. The EMA-RPL remarkable results are attained by providing a continuous connection as well as by reducing the signaling overhead between the MN and its AN. 5.6. Scalability In the context of this paper, scalability is defined as the capability of the mobility management protocol to handle a growing number of mobile nodes. Indeed, using more than one mobile node increases the signaling overhead which may overload the shared links. To the best of our knowledge, virtually all previous work restrain their investigation to just one mobile node. The challenge here concerns the EMA-RPL reaction to adequately

Fig. 8. Packet Delivery Ratio in scenarios (a) (b) (c) and (d).

manage the mobility of different nodes, as well as to provide adequate performances under high workloads. To evaluate the scalability of the EMA-RPL protocol, we consider an extended scenario with three mobile nodes. As illustrated in Fig. 9, these mobile nodes circled in green move through many static nodes (circled in yellow) following different trajectories (shown using dashed lines) and sharing the same associated node and links. As in the previous one mobile node scenarios, these mobile nodes periodically generate and send data towards the root node through static nodes. The simulation of this multiple mobile node scenario with the MRPL fails to provide tangible results. The MRPL could not maintain the connectivity of the mobile nodes. This is essentially due to the high periodic signaling between each mobile node and its associate node. The extremely high signaling causes links overload and the loss of the majority of the necessary messages to establish the attachment with a new associate node. In contrast, simulation results of the EMA-RPL provided adequate performances. As illustrated in Fig. 10, the overall average handover delay is of 195.82 ms. This average handover delay, though larger than that of the one mobile node scenario, represents an acceptable delay. In addition, mobile nodes kept their connectivity during virtually all performed handovers. Keeping a low handover delay contributes in maintaining a seamless connectivity of mobile nodes, which in turn allows a higher data transfer through the associate nodes. However, the packet delivery ratio is reduced compared to a scenario with only one MN. As shown in Fig. 11, the overall average PDR is reduced to 73.27%. Nonetheless, this reduction is not related to the disconnection of mobile nodes but relates to the capacities allowed by nodes to receive packets. Using more static nodes

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Fig. 9. Extended scenario to three mobile nodes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 10. Handover delay in the extended scenario with three mobile nodes.

Fig. 11. Packet delivery ratio in the extended scenario with three mobile nodes.

may fulfill the required capacities to receive all messages and consequently limits the data loss. Recall here that we used the same parameters given by Table 1; in particular a data rate of 30 packets per second. Therefore reducing this data rate lowers the traffic load and would tacitly result in higher PDRs. Simulation results showed that the EMA-RPL protocol successfully maintained the connectivity of all mobile nodes, as well as it uses a reduced signaling overhead and conserved energy.

Fig. 12. Signaling cost in the extended scenario with three mobile nodes.

Fig. 13. Power consumption after 2500 delivered packets in the extended scenario with three mobile nodes.

As illustrated in Fig. 12, the signaling overhead used to manage mobility is significantly well suited. The overall average signaling input at the mobile nodes is 12,601 bytes which is slightly higher than that of the scenario with one mobile node 10,234 bytes. The overall average traffic output by the mobile nodes is around 10,000 bytes on the average, which is similar to that of the one mobile scenario. Comparable results are also found for the other type of nodes, with a slight increase cost for static nodes as they now manage the signaling from three mobile nodes. However, there is a cost increase for the nodes close to the root node (refers to SN Set1 that consists of SN11 and SN14), since they are involved in the mobility management as well as the data transfer from other nodes. SN Set2 have a higher signaling than SN Set3. Note that Sn Set2 refers to the nodes involved in the mobility management and the routing updates within the network. In contrast, SN Set3 refers to the leaf nodes that are only involved in the mobility management. The power consumption is portrayed on Fig. 13. Mobile nodes consume around 1.45 mW on the average compared to 1.14 mW when there is only one mobile node. Comparable results are found for the other nodes with a slight increase as they are now managing more than one mobile node. It should be noted here that MRPL in this scenario employs a huge signaling overhead (i.e, a very large number of control messages). We noted too many collisions and consequently the loss of most of the control messages. As such, the handover processes failed and the mobile node finishes by getting disconnected.

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6. Conclusion and perspectives We proposed a new routing protocol called EMA-RPL to deal with the micro mobility in the Internet of Mobile Things. EMARPL provides an adequate platform for real time applications. It provides a continuous seamless connectivity and keeps the mobile nodes reachable regardless of their whereabouts. EMA-RPL is a proactive protocol capable of predicting the new attachments before disconnection. The prediction process is based on the Received Signal Strength Information (RSSI) and extended ICMPv6 messages. We integrated the EMA-RPL as well as the MRPL into the ContikiRPL. Extensive simulation results showed that EMA-RPL is an efficient mobility support for the RPL protocol. It provides a seamless connectivity, yet it reduces the required signaling cost which in turn saves energy and permits the delivery of much more data packets. Comparisons with the seminal RPL as well as with the enhanced MRPL showed that EMA-RPL outperforms both. Our proposed EMA-RPL stands out by reducing the MNs involvement to save their energy, distributing the signaling overhead among different static and associated nodes, and sustaining the transfer of much more data packets. Simulation scenarios with several mobile nodes testify the scalability of EMQ-RPL whereas MRPL stands short to deliver any adequate results. Further enhancements especially to the prediction process are being investigated. The use of RSSI may be replaced by a more elaborate technique as RSSI remains inefficient in an indoor environment or in the presence of obstacles. Acknowledgment The authors extend their appreciation to the Deanship of Scientific Research at King Saud University, Saudi Arabia for funding this work through research group no RGP-1439-023. References [1] A.J. Jara, S. Varakliotis, A.F. Gmez Skarmeta, P.T. Kirstein, Extending the internet of things to the future internet through IPv6 support, J. Mob. Inf. Syst. 10 (1) (2014) 3–17. [2] P. Lopez, D. Fernndez, A.J. Jara, A.F. Skarmeta, Survey of internet of things technologies for clinical environments, in: International Conference on Advanced Information Networking and Applications Workshops, 2013, pp. 1349–1354. [3] J. Yick, B. Mukherjee, D. Ghosal, Wireless sensor network survey, Int. J. Comput. Telecommun. Netw. 52 (12) (2008) 2292–2330. [4] Z. Shelby, C. Bormann, 6LoWPAN: The Wireless Embedded Internet, Internet Engineering Task Force, 2009, 6LoWPAN-WG. [5] Md.S. Hossen, A.F.M.S. Kabir, R.H. Khan, A. Azfar, Interconnection between 802.15.4 devices and IPv6: Implications and existing approaches, Int. J. Comput. Sci. Issues (IJCSI) 7 (1) (2010). [6] S. Prasanna, S. Rao, An overview of wireless sensor networks applications and security, Int. J. Soft Comput. Eng. (IJSCE) (ISSN: 2231-2307) 2 (2) (2012). [7] J.A. Stankovic, A.D. Wood, T. He, Realistic applications for wireless sensor networks, Springer Theor. Comput. Sci. (2011) 835–863. [8] D. Wang, Z. Tao, J. Zhang, A. Abouzeid, RPL based routing for advanced metering infrastructure in smart grid, in: IEEE International Conference on Communications, ICC, May 2010, pp. 1–6. [9] L.M.L. Oliveira, A.F. de Sousa, J.P.C. Rodrigues, Routing and mobility approaches in IPv6 over LoWPAN mesh networks, Wiley Int. J. Commun. Syst. (IJCS) 24 (11) (2011) 1445–1466. [10] L. Bartolozzi, F. Chiti, R. Fantacci, T. Pecorella, F. Sgri lli, Supporting monitoring applications with mobile wireless sensor networks: The eN Route forwarding approach, in: IEEE International Conference on Communications, Ottawa, Canada, 2012, pp. 5403–5407.

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Dr. Maha Bouaziz is currently occupying a Postdoctoral position at the French Institute for Science and Technology of Transport, Development and Networks (IFSTTAR), France. She received her Ph.D. degree in computer science in 2017 from the National School of Computer Sciences (ENSI) in Tunisia, in collaboration with the University of Marne-la-Vallée, Paris, France. She received her Master of Science and her Main Engineering Diploma both in computer science from the National School of Engineers of Sfax (ENIS) in Tunisia. Her current research focuses on wireless networking, mobility management for Wireless Sensor Networks based on the 6LoWPAN technology, and intelligent transportation systems.

Dr. Abderrezak Rachedi (S’05, M’08, SM’15) received the Engineering degree in computer science from the University of Technology and Science Houari Boumedienne, Algiers, Algeria, in 2002, the M.S. degree in computer science from the University of Savoie, Chambéry, France, in 2003, the Ph.D. degree in computer science from the University of Avignon, Avignon, France, in 2008, and the H.D.R. degree in computer science from Paris-Est University, Champs-sur-Marne, France, in 2015. He has been a member of the Gaspard Monge Computer Science Laboratory since 2008. He is currently an Associate Professor (maître de conferences HDR) with the University Paris-Est Marne-la-Valleée (UPEM), France. His research interests include the field of wireless networking, Internet of Things, network performance analysis, and evaluation. His research efforts have culminated in more than 95 refereed journal, conference, and book publications in a wide variety of prestigious international conferences and journals, including the IEEE Transactions on Vehicular Technology, Elsevier Ad Hoc Networks, the IEEE ICC, and the IEEE Globecom. He serves on the Editorial Board of the IEEE ACCESS Journal, Security & Privacy (SPY) journal, Wireless Communications and Mobile Computing (John Wiley) journal, and the International Journal of Communication Systems (John Wiley).

Abdelfettah Belghith received his Master of Science and his Ph.D. degrees in computer science from the University of California at Los Angeles (UCLA) respectively in 1982 and 1987. He is since 1992 a full Professor at the National School of Computer Sciences (ENSI), University of Manouba, Tunisia. He is currently on a sabbatical leave at King Saud University, Saudi Arabia. His research interests include computer networks, wireless networks, multimedia Internet, mobile computing, distributed algorithms, systems and information security, simulation and performance evaluation. He runs several research projects in cooperation with other universities, research laboratories and research institutions. He is the Past chair of the IEEE Tunisia section, the chair of the IEEE ComSoc and VTS Tunisia Chapters, and the Director of the HANA Research Laboratory (www.hanalab.org) at the National School of Computer Sciences. He published more than 350 research papers in international journals and conference proceedings. Dr. Marion Berbineau received the Engineer degree from Polytech Institute of Lille (France) and the Ph.D. degree from the University of Lille (France) both in electrical engineering, respectively in 1986 and 1989. She is currently a Research Director at IFSTTAR (Institute for Science and Technology of Transport, Development and Networks), France. She is expert in the fields of radio wave propagation in transport environments (particularly in tunnels and high speed lines), channel characterization and modeling, MIMO, 4G, 5G, wireless systems for railways. She coordinates Railway research at IFSTTAR. She is expert at the French National Council for the railway system. Dr. Saad Al Ahmadi received his Ph.D. (2014) in computer science from King Saud University, Riyadh, Kingdom of Saudi Arabia. He is currently Assistant Professor at the Computer Science Department, College of Computer and Information Sciences, King Saud University. His current research includes Wireless Sensor Networks, Information Centric Networking, Internet of Things, Cybersecurity, and Machine Learning. He is the author of several papers in highly impacted journals and peer reviewed international conferences.