Optimizing RPL Objective Function for Mobile Low-Power Wireless Networks

Transcription

1 Optimizing RPL Objective Function for Mobile Low-Power Wireless Networks Ifeoma Helen Urama, Hossein Fotouhi, Mohammad Mahmoud Abdellatif School of Innovation, Design and Engineering, Mälardalen University, Sweden British University in Egypt, Cairo, Egypt s: Abstract Supporting mobility in wireless sensor networks is one of the major requirements for future Internet of Things (IoT) applications. This work focuses on optimizing the objective function of Routing Protocol for Low-power and Lossy Networks (RPL) in mobile applications. RPL routing is the most common standard routing protocol designed for IoT applications. We optimized RPL objective function by combining several RPL parameters, such as (i) Expected Transmission Count (ETX), (ii) number of hops, and (iii) average Received Signal Strength Indicator (RSSI) as inputs in a fuzzy logic model. These parameters are more influenced in mobile applications. We applied the fuzzy decision to the mrpl (a hand-off enabled RPL mechanism). We fine-tuned the weighting scheme by running extensive simulations to achieve reliable data communication. We found that the fuzzybased hand-off approach provides high reliability by successfully delivering nearly % of data packets, while achieving a very short hand-off delay. Keywords Wireless Sensor Networks, Low-Power Wireless Networks, Mobility Management, RPL, Fuzzy Logic. I. INTRODUCTION The advent of wireless technologies is the key enabler for designing new applications, where devices are expected to have the abilities to sense, process and communicate data to other devices. Conventional sensor network protocols considered static networks, where nodes are fixed. However, in some of the IoT applications, such as clinical health monitoring, physiological sensors attached to the human body are moving [], [2]. Networks with mobile nodes have more network topology changes and link dynamics that require more complicated and intelligent protocols. Future vision of IoT applications and 5G networks are envisioning connecting all devices, and providing more services. An important step toward the IoT connectivity issue is by employing standard IP-based protocols, were trillions of IoT devices would become transparent to other devices without extra effort. In the past, the lack of IP-based framework in sensor networks prevented sensor applications from participating in the Internet. However, the light implementation of IPv6, known as 6LoWPAN [3] has enabled communication over IPv6 for Low-Power Wireless Networks (LPWNs). Typical mobile IPv6 protocols are unable to provide seamless connectivity while things are moving. Mobility management is the most challenging issue in wide area networks (e.g. cellular networks) and wireless local area networks (e.g. WiFi). It becomes even more challenging in LPWNs, where links are more unreliable, and hence, the mobility imposes more uncertainty to the system. Moreover, various LPWNs, such as ZigBee, Bluetooth, and some WiFi technologies are sharing the same frequency band (2.4 GHz) that causes sever interference problem. Considering these challenges, we aim to carefully select input parameters to develop a new optimized fuzzy-based RPL objective function that guarantees reliable routing mechanism in mobile LPWNs. We believe that with a careful selection and combination of routing parameters, it is possible to find a reliable path from a source to the destination. Combining several parameters can capture the intricacies required in obtaining an efficient route in this type of networks. Concisely, the main contributions of this paper are: ) identifying the main parameters affected in mobile IoT applications, 2) defining fuzzy sets by applying fuzzification, rulebased systems, and defuzzification, 3) evaluating the proposed algorithm by extensive simulations. The remainder of this work is organized as follow. Section II gives a background on the RPL routing, and the literature review on some of the well-known RPL objective functions. Section III describes the design and implementation of the fuzzy-based objective function. Section IV presents the performance evaluation of our simulations, and finally, Section V concludes the paper. II. BACKGROUND AND RELATED WORKS Backgound on RPL. RPL is a distance vector routing protocol that supports IEEE physical and data link layers, where nodes are organised in a Destinaton Oreinted Directed Acyclic Graph (DODAG) architecture. To route information, each router in the network identifies a set of parents, where one of the parents is a potential next hop on a path towards the root of the DODAG. Root node in RPL initiates routing table construction, and collects data from routers. To select a preferred parent, two main algorithms are employed: (i) Neighbour Discovery (ND) algorithm [4] for constructing network topology and accessing parent reachability, and (ii) Trickle algorithm for disseminating control messages [5]. Certain information such as neighbor parents, routing table, and routes are generated after network construction. The accuracy of these information can ensure network stability and connectivity. Analyzing, processing, and reporting of packet errors in the network layer are achieved with the help of IPv6 control messages (ICMPv6) that include: (i) DODAG Information Object (DIO), which is used for construction and maintenance of DODAG upward routes that carries Rank, DODAGID, RPLInstance, Version Number and objective function metric; (ii) DODAG Information Solicitation (DIS) is sent by a node, which requests information from neighbouring nodes; (iii) Destination Advertisement Object (DAO), which is used for The rank value defines nodes position with respect to the root node. This value increases in the down direction and decreases in the up direction.

2 constructing downward routes; and (iv) Destination Advertisement Object-Acknowledgement (DAO-Ack), which is sent by a node in acknowledgement of DAO. RPL objective functions. Every RPLInstance operates with an objective function (OF). The OF contains routing parameters to optimize the network performance. Parameters like remaining energy level, local traffic, end-to-end delay, ETX and local traffic have been previously used in several routing protocols, including RPL. Two objective functions of Objective Function Zero (OF) [6] and Minimum Rank Hysteresis Objective Function (MRHOF) [7] were employed in RPL. The IETF standard has no preference on the choice of objective function, and left it as an open research area for further investigation. OF is a commonly used OF in the network layer by many application protocols, designed for interoperability with other network technologies [8]. It relies on hop count (HPC) to optimize the network by selecting the neighbour with minimum rank (shortest path toward destination) to be the preferred parent. RPL OF ignores some parameters, such as network load, link properties, and energy level of the selected node. Thus, it is possible that some nodes in the network have depleted their battery power due to frequent use of a particular path. Moreover, a node with minimum rank (HPC) can be also assigned to be the preferred parent without considering its link quality. Note that poor link quality leads to high packet losses. MRHOF is an OF that is based on successful transmission over wireless links. It is the default objective function implemented in Contiki operating system 2. By default, MRHOF uses ETX as its input parameter [6]. ETX relies on packet delivery ratio by calculating the probability of a successful data packet transmission towards the destination, and also the probability of successful reception of the intended acknowledgement by the sender [9]. Mobility management techniques. Several techniques have been employed in LPWNs to tackle link dynamics, inconsistency issues and physical mobility. Localization and handoff mechanisms are the most common techniques used for mobility management. Hand-off is a process of switching from one parent to another parent. Hand-off process has been extensively used to manage mobility in cellular and WiFi based networks [], []. However, it is difficult to directly import solutions from cellular and WiFi technologies into LPWNs without considering network limitations. It means that conventional hand-off processes are relied on extensive packet exchanges between nodes that implies using a separate radio to transmit control messages. Previous work on hand-off mechanism in [2] was able to successfully perform handoff operation in LPWNs. By proper tuning of various relevant network parameters, hand-off algorithm enables efficient and reliable data delivery. mrpl [3] presents a hard hand-off process for LPWNs within RPL routing by employing the DIS and DIO control messages. In this process, mobile node disconnects from the current parent upon observing link quality degradation (RSSI less than a threshold), and starts a hand-off process by polling neighbor parents to send their availability. Then by collecting replies and measuring their link quality, mobile node selects the best parent, and starts transmitting data packets to the new parent. In this work, we devise a hand-off algorithm that considers a set of input parameters for a fuzzy logic model by 2 Contiki is an open-source multitasking operating system designed for the IoT applications. It focuses on tiny low-cost, low-power networked embedded microcontrollers. Currently, it is the state-of-the-art open source operating system developed for tiny networked embedded systems. benefiting from the hand-off algorithm proposed in [2]. The input metrics are selected from both link and network layer since the cause of packet losses in LPWN is mainly due to the dynamics in these layers. III. FUZZY-BASED MOBILITY OBJECTIVE FUNCTION In this section, we describe our proposed fuzzy-based mobility OF (FMOF) in three subsections - namely, the fuzzy input parameters, the fuzzy logic process, and the fuzzy-based hand-off process. A. Fuzzy input parameters For the input parameters, we considered various network and hardware related issues, like mobility and memory footprint. So, we deem it necessary to include those network and hardware paremeters that have strong fluctuation in mobile LPWNs. It is also recommended to employ parameters that have least memory footprint in terms of implementation. Thus, we opted to select the available parameters in Contiki OS to reduce implementation cost. Based on these issues, we selected ETX, average RSSI and HPC parameters, which are described below. ETX is the link loss ratio using the expected number of MAC retransmissions needed to successfully deliver a packet from the sender to the receiver [7]. The lesser the ETX metric for a link, the better is the link. RSSI (Received Signal Strength Indicator) is a hardware-based link layer metric. This is an important parameter in a mobile LPWN environment which measures the received signal power. HPC is a network metric that determines the path with least number of hops to the destination. This parameter is included to resolve the effect that could occur when two or more nodes on different hop level possess the same ETX and RSSI value. B. Fuzzy logic process model Fuzzy logic is well known model for classifying data in linguistic terms. It helps in finding casual dependencies between linguistic variables, which are beyond classical logic of true or false. This is acheived by defining the degree of dependency to either true or false. Therefore, we considered it a good model to be used as it enables the combination of more than one parameter. Stages involved in the fuzzy process model are described below. Fuzzification stage. This involves injecting raw values of ETX,Average RSSI and HPC into the system. The values are then mapped to some predefined linguistic variables (fuzzy set) as illustrated in Figures, 2, and 3. Degree of membership (DoM) is computed using Equation with respect to the input on the x-axis coordinate. 8 < if metric apple LP, metric RP Fuzzy set(metric) = : LP RP if LP < metric < RP, if metric 2. () Where LP refers to the left point of the x-axis and RP refers to the right point of the x-axis ETX metric fuzzification. In contiki, ETX value is obtained from the dio.mc.obj.etx metric object. This value is

3 Degree2of2Membership Small Average Large Path2ETX Degree2of2Membership Near Far Hop2Count 6 7 Very;Far Fig.. Path ETX Value mapping to fuzzy Set. Fig. 3. Hop count value mapping to fuzzy Set. TABLE I. OBTAINING QUALITY OUTPUT MEMBERSHIP VALUE. Fig. 2. Degree2of2Membership Connected Transitioning Disconnected!5!55!6!!65!7!75!8!85!9!95! Average RSSI2(dBm) RSSI value mapping to fuzzy sets. mapped to the fuzzy sets of small, average and large. Depending on the input values, a membership degree in the interval of [, ] is determined as shown in Figure. Average RSSI metric fuzzification. In contiki, the RSSI received packet is obtained using PACKETBUF_ATTR_RSSI variable. Adding an offset of ( 45 dbm) to the obtained RSSI value, the real RSSI value is determined. The offset value is the front end gain, empirically determined by the manufacturers. From previous work in [2] reliable link connection is ensured by a Mobile Node (MN) receiving a reply packet from the serving Access Point (AP). Upon receiving a predefined number of data packets (n) in a given window, the serving AP replies with the average RSSI value of (n) number of packets. The average RSSI value is then mapped to the fuzzy set of Disconnected, Transitioning, and Connected, and the membership degree determined as shown in Figure 2. HPC metric fuzzification. This metric parameter basically measures number of hops a node s data will pass to reach its destination. In Contiki, hop count value is obtained from the rank parameter. This is achieved by dividing the rank of any node in the network by 256, which in RPL is the minimum hop rank increment, the hop count value will be obtained. By default, the root node has a hop count of zero, which it takes when initiating the DODAG formation by broadcasting the DIO message containing the rank value. Maximum rank increment is defined as = 792. Dividing this value by 256 gives 7 as the default maximum hop count value used. Fuzzy inference stage. It involves developing a rule-based system using the three input metrics. Components of the rulebased includes the input metric, Weights (W) applied to the input parameter to control how the rules affect the quality of the output fuzzy set, logical operator, fuzzy set score, antecedent score, quality output fuzzy set, Rule Number (R/N), Fuzzy Set Score (FSS), Antecedent Score (AS) and Output Membership Function (O/P MF) described in details [4]. By logically combining the input metric values in the rule-based, rules which correspond to the received input metric gets fired see Table I. The fired rules produce quality output fuzzy set values. The output is then aggregated and deffuzzified. R/N ARSSI W=.6 FSS ETX W=.3 FSS Hop count W=. FSS AS O/P MF Connected Small Near 2 Connected Small Far Connected Small Very far Connected Avg 2 Near Connected Avg 2 Very far Connected Large 3 Near Transitioning 2 Small Near Connected Large 3 Far Transitioning 2 Small Far Connected Large 3 Very far Transitioning 2 Small Very far Transitioning 2 Avg 2 Near Transitioning 2 Avg 2 Far Transitioning 2 Avg 2 Very far Transitioning 2 Large 3 Near Disconnected 3 Small Near Transitioning 2 Large 3 Far Disconnected 3 Small Far Transitioning 2 Large 3 Very far Disconnected 3 Small Very far Disconnected 3 Avg 2 Near Disconnected 3 Avg 2 Far Disconnected 3 Avg 2 Very far Disconnected 3 Large 3 Near Disconnected 3 Large 3 Far Disconnected 3 Large 3 Very far To tune the system, several rule-based systems were constructed using Mamdani fuzzy inference system [4] with different weights applied to the input parameters to find the best weight combination to ensure reliable network of data. Table I shows a sample of 27 rules generated for the system. A threshold of 3 is applied to indicate the point at which the performance of three input combination becomes unreliable. Defuzzification stage. The centre of gravity defuzzification method is applied to output membership function to determine the final crisp output from aggregated fired rules see Equation 2. Figure 4 shows sample of output membership function of the fired rules. Q = P n i= W i µ(w i ) P n i= µ(w i) C. Fuzzy-based hand-off algorithm Fuzzy-based handoff algorithm performed in this work follows two phases (Discovery and Data Transmission phases). In Discovery phase, MN sends burst of three DIS packets containing RSSI value to neighbour APs. Upon receiving the DIS packets, a reply timer (T R ) is initiated for reception of the DIS, while the APs keep track of number of received packets using a counter (C) variable as shown in the Algorithm. When T R expires, the APs calculates the average RSSI (ARSSI) value. Then, the fuzzy process begins by mapping the hop count, (2)

4 Degree2of2Membership Output2quality (W) TABLE II. SIMULATION PARAMETERS. Radio Medium Unit Disk Graph Medium (UDGM): Distance Loss Positioning Linear positioning for router nodes No of APs 2 No of MN (node in Figure 5) Sink Node (node in Figure 5) MN speed m/sec OFs OF, MRHOF, FMOF Transmission Range 5 meters Interference Range meters Transmit Ratio % Receive Ratio % Simulation Time 9 seconds Packet interval pkt in sec, pkt/sec, and 2 pkt in 2 sec Fig. 4. Defuzzification of aggregated rules. ARSSI and ETX values from the DIO structure to the fuzzy set variables, and compute the Degree of Membership (DoM) to which the input value belongs to the fuzzy sets (i.e., connected, trans, disc, small, avg, large, near, far and very-far) as shown in Table I. For example, reading hop count value of 4, ETX value of 2 and ARSSI value of -75 as illustrated in Figures, 2 and 3. In Algorithm, the DoM functions calculate the degree of membership of various parameters involved in the hand-off process. For instance, the DoM-connected(rssi-average) function computes the degree of membership in the interval of and based on ARSSI reading. Then the fuzzy inference stage initiates to determine the quality of the output membership function (O/PMF) based on the fired rules as shown in Table I. The fired rules are used to form an output fuzzy set. This output fuzzy set is defuzzified using center of gravity method described in [5] to obtain a crisp neighbour quality output value. This value is then embedded in the APs DIO message structure, and then a unicast reply is sent to the MN. Algorithm Algorithm for Discovery Phase : begin 2: if received unicast DIS message then 3: store RSSI and latest counter C value; 4: reset T R with (ws C) T DIS 5: if T R expires then 6: calculate ARSSI 7: DoM-connected(rssi-average) 8: DoM-trans(rssi-average) 9: DoM-disc(rssi-average) : DoM-small(dio.mc.obj.etx) : DoM-avg(dio.mc.obj.etx) 2: DoM-large(dio.mc.obj.etx) 3: DoM-near(dio.rank/256) 4: DoM-far(dio.rank/256) 5: DoM-very-far(dio.rank/256) 6: Fuzzification() 7: Rule Evaluation() 8: Defuzzification() 9: return FMOF neighbour quality output 2: send unicast DIO message with (FMOF neighbour quality output) 2: continue Discovery Phase 22: end if 23: continue the Data Ttansmission Phase 24: end if For the Data Transmission Phase, if MN receives DIO with the fuzzy output values from neighbour APs, it resets the connectivity timer (T conn ), and then checks if the fuzzy output value is below the fuzzy threshold (T f ). If true, the Discovery Phase initiates, otherwise, the Data Transmission Phase continues. If no DIO is received, mobility detection timer (T md ) issues, while the period depends on the data generation rate. This timer resets once the period is elapsed, and no DIO is received. Thus, the MN sends a unicast DIS burst, and continues Data Transmission Phase. Algorithm 2 Algorithm for Data Transmission Phase : begin 2: if received DIO packet then 3: reset T conn 4: if fuzzy output <T f then 5: go to the Discovery Phase 6: else 7: continue Data Transmission Phase 8: end if 9: else if T md expires then : reset T md : unicast burst of DIS 2: go to begin 3: else if T conn expires then 4: go to Discovery Phase 5: end if IV. PERFORMANCE EVALUATION To evaluate the proposed FMOF, first, we define the parameters involved in our simulation (defined in Table II), and simulation setup (shown in Figure 5). Contiki OS version 2.7, and Cooja simulator were used for evaluating our hand-off model. By default, Cooja simulator has no mobility support. We enabled physical mobility by adding a mobility plugin [6] that provides the possibility of defining MN position in different timeslots. Simulation setup. Figure 5 shows our simulation setup, where Node acts as a root node, and router nodes (Nodes 2 to 3) are responsible for forwarding information/packets generated by MN (Node ). The MN collects environmental measurements while moving randomly in the deployed area of 2m 6m. At the start of simulation, the MN stands at the vicinity of router 2 and router 3 for 3 seconds, after this period, it moves up towards the server and back to its original position with a speed of m/s, stopping randomly for few seconds at four different positions. m/s speed is considered for the MN since human walking speed is around.4m/s. Therefore, using m/s speed seems moderate for a sick person, since we are targeting health monitoring applications.

5 Fig Mobilenode 7 4 Simulation set-up. A. Performance metrics Rootnode Routernode To evaluate the performance of FMOF, three metrics are considered, namely: () Packet delivery ratio (PDR), which is the ratio of number of packets received at the sink node to the number of packets sent by the mobile node. (2) Control message overhead that represents the number of non-data packets DIO, DIS, and DAO used by ICMPv6. (3) Average hand-off delay which is the total time spent during the handoff process. For FMOF, this metric is determined by taking the average time spent during the hand-off process, while for OF and MRHOF, it is obtained by the time spent before MN finds a new preferred parent. Results and discussion. To evaluate the proposed FMOF solution, first we present results obtained from different weight combination of the input parameters. Then, we analyse the best combination of parameters, and compare it with the results obtained from MRHOF and OF. We evaluated the performance of different weights for the input parameter to find the best weighting to tune the system. The three performance metrics mentioned above are used to determine the best optimized weights (see Figures 6 to 7). Figure 6(a) shows the PDR of all tested input weight combinations. We observed that giving more weight to either RSSI and ETX link metric parameter improves the reception ratio. However, scenarios with high weights given to hop count, (RSSI.25, ETX.25, HPC.5) shows higher losses in the packet reception, because the forwarding decision is highly influenced by minimum hop count which does not consider link stability. As a result, a lot of retransmissions will be experienced in the network, unlike the ETX with less packet retransmissions that ensures network stability before forwarding packets. With higher weight given to either ETX and/or RSSI, there is high PDR, which reveals that there is better link stability in the network. Additionally, with low data transmission rate ( pkt per 2 sec), there is less congestion in the network, resulting in high PDR in most scenarios, especially for scenarios with weights RSSI.33, ETX.33, HPC.34 and RSSI.25, ETX.5, HPC.25. Values represented in Figure 6(b) show that control message overhead is reduced with lower data transmission rate. High control message overhead in higher data transmission rate is due to the additional control messages generated during the Discovery Phase. More DIS and DIO messages are triggered during the hand-off process since more packets are sent per second, leading to an increase in control message overhead for higher data traffic. Result for different weights is influenced by data transmission interval and also the threshold value in rule base. This has an impact on average hand-off delay value (see Figure 6(c)). In scenarios with weight (RSSI.33, ETX.33, HPC.34), and also with (RSSI.5, ETX.25, HPC.25), hand-off delay is about ms. This shows fast switching during hand-off process from current parent to the preferred parent compared to other weight-send intervals. Having carefully examined various weighting scenarios, we found that (RSSI.33, ETX.33, HPC.34) and (RSSI.5, ETX.25, HPC.25) are the two input parameters weights that best optimize the network in terms of overall performance. The choice of best scenario then depends on the application requirement. Since this work targets health monitoring applications with reliable data, the choice of weight (RSSI.33, ETX.33, HPC.34) is considered. This weight scenario shows that with a send interval of pkts/sec, the chosen weight has PDR of %, 57 messages overhead and 25 ms hand-off delay. This gives the best optimized scenario for the intended application requirement. It is also important to note that even with the hand-off delay of 25 ms, all packets were successfully received at the sink node. Based on this, it can be guaranteed that all packets shall be delivered with reduced control message overhead and hand-off delay. The second stage of the evaluation is to compare FMOF best weight result with MRHOF and OF. This is to determine if proposed FMOF optimizes RPL OF for the mobile environment. Figure 7(a) shows that combining more than one network parameter generally improves PDR. It helps in capturing the dynamics inherent in LPWNs. MRHOF is a combination of two network parameter ETX and hop count while OF is only one parameter represented by a number of hops. FMOF performs best in low traffic ( pkt per 2 sec) scenario, followed by ETX and lastly by OF. In scenarios with higher data traffic ( pkt/sec and 2 pkts/sec), more time is needed to perform the fuzzy process. Hence, input parameters are updated less frequently in the DIO message structure. Its improvement may be considered as a future work. Generally, high data generation rate increases control message overhead as shown in Figure 7(b). The rate of increase in MRHOF and OF is proportional to the number of packet generated per second. In FMOF, more control message overhead is present especially with 2 pkts/sec due to congestion caused by hand-off message triggers generated in the fuzzy based handoff process. Fast parent switching, enabled by the fuzzy based hand-off process with more AP nodes in the transmission range of the MN reduces the average hand-off delay in FMOF. Handoff delay in FMOF is extremely small (in the range of -25 ms) compared to other OFs see Figure 7(c). V. CONCLUSIONS Reliability in LPWNs with mobility support is one of the most challenging issues due to the high dynamics of the channels. In this work, we tackled mobility issue in RPL routing protocol. We considered some of the relevant links and network parameters for mobility management. We devised a fuzzy-based hand-off process for RPL routing protocol in order to obtain network reliability and timeliness. Three routing parameters of ETX, RSSI and hop count were selected due to their impacts on mobile LPWN. These parameters serve as input parameters to the fuzzy logic model. The system was designed and implemented using the parameters to optimize RPL objective function. Extensive simulations were performed using some evaluation metrics. The evaluation metrics were used to test various weighting scenarios. The weights were used in tuning the system so as to identify the best scenario that guarantees reliable delivery of data in health monitoring applications. The best optimized scenario was selected and results were compared with RPL OF and MRHOF.

6 Packet'Reception''Ratio'(%) Rssi!.6,Etx!.3,Hpc!. Rssi!.33,Etx!.33,Hpc!.34 Rssi!.5,Etx!.25,Hpc!.25 Rssi!.25,Etx!.5,Hpc!.25 Rssi!.25,Etx!.25,Hpc!.5 'pkt/2sec 'pkt/sec 2'pkt/sec Total""Control"Message"Overhead Rssi!.6,Etx!.3,Hpc!. Rssi!.33,Etx!.33,Hpc!.34 Rssi!.5,Etx!.25,Hpc!.25 Rssi!.25,Etx!.5,Hpc!.25 Rssi!.25,Etx!.25,Hpc! "pkt/2sec "pkt/sec 2"pkt/sec Average'Hand7off'Delay'(ms) Rssi!.6,Etx!.3,Hpc!. Rssi!.33,Etx!.33,Hpc!.34 Rssi!.5,Etx!.25,Hpc!.25 Rssi!.25,Etx!.5,Hpc!.25 5 Rssi!.25,Etx!.25,Hpc! 'pkt/2sec 'pkt/sec 2'pkt/sec (a) (b) (c) Fig. 6. Performance metrics with different send interval, (a) packet delivery ratio, (b) total overhead, and (c) average hand-off delay. Packet'Reception'Ratio'(%) MRHOF FMOF OF 5 MRHOF FMOF OF MRHOF FMOF OF 'pkt/2sec 'pkt/sec 2'pkt/sec 'pkt/2sec 'pkt/sec 2'pkt/sec 'pkt/2sec 'pkt/sec 2'pkt/sec (a) (b) (c) Control Message'Overhead Fig. 7. Performance metrics of MRHOF, FMOF and OF in terms of (a) packet delivery ratio, (b) total overhead, and (c) average hand-off delay. Average'Hand7off'Delay'(ms) The evaluation showed that the system is able to deliver data from a MN to the sink through APs in a mobile network. Routes are updated more frequently based on more accurate information, generated by the optimized FMOF. The average hand-off delay observed from several weighting combination of FMOF were small compared to the RPL routing with other objective functions. Performance of the network was enhanced in terms of average hand-off delay, control message overhead and PDR. The FMOF provides high reliability with almost % PDR and good connectivity with 25 ms hand-off delay. For future work, there shall be an implementation of FMOF with real sensor hardware since simulation is unable to create all environmental dynamics. ACKNOWLEDGMENT This work is partially funded by the Swedish Knowledge Foundation (KKS) throughout research profile Embedded Sensor System for Health (ESS-H), the distributed environments E-care@home, and Research Environment for Advancing Low Latency Internet (READY), and the Swedish Foundation for Strategic Research via the project Future Factories in the Cloud (FiC). REFERENCES [5] P. Levis, N. Patel, D. Culler, and S. Shenker, Trickle: A self-regulating algorithm for code propagation and maintenance in wireless sensor networks, in USENIX/ACM, 24. [6] P. Thubert, Objective function zero for the routing protocol for lowpower and lossy networks (RPL), 22. [7] O. Gnawali, The minimum rank with hysteresis objective function, 22. [8] J. Ko, J. Eriksson, N. Tsiftes, S. Dawson-Haggerty, A. Terzis, A. Dunkels, and D. Culler, Contikirpl and tinyrpl: Happy together, in IPSN, 2. [9] D. S. De Couto, D. Aguayo, J. Bicket, and R. Morris, A highthroughput path metric for multi-hop wireless routing, Wireless Networks, vol., no. 4, pp , 25. [] M. Shin, A. Mishra, and W. A. Arbaugh, Improving the latency of 82. hand-offs using neighbor graphs, in MobiSys. ACM, 24, pp [] I. Ramani and S. Savage, SyncScan: practical fast handoff for 82. infrastructure networks, in INFOCOM, vol., 25, pp [2] H. Fotouhi, M. Zúñiga, M. Alves, A. Koubâa, and P. Marrón, smarthop: A reliable handoff mechanism for mobile wireless sensor networks, in EWSN, 22. [3] H. Fotouhi, D. Moreira, and M. Alves, mrpl: Boosting mobility in the Internet of Things, Ad Hoc Networks, vol. 26, pp. 7 35, 25. [4] C. Wang, A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making, 25. [5] O. Gaddour, A. Koubaa, N. Baccour, and M. Abid, Of-fl: Qos-aware fuzzy logic objective function for the rpl routing protocol, in WiOpt. IEEE, 24, pp [6] F. Osterlind, Mobility cooja plugin, 24. [] L. Atzori, A. Iera, and G. Morabito, The internet of things: A survey, Computer networks, vol. 54, no. 5, pp , 2. [2] H. Fotouhi, Reliable Mobility Support in Low-Power Wireless Networks, Ph.D. dissertation, Phd Thesis CISTER-TR-552, CISTER Research Center, 25. [3] G. Mulligan, The 6LoWPAN architecture, in Workshop on Embedded networked sensors. ACM, 27, pp [4] T. Winter, RPL: IPv6 routing protocol for low-power and lossy networks, 22.