See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/356129361 Impact of Trace-Based Mobility Models on the Energy Consumption of DelayTolerant Routing Protocols Conference Paper · November 2021 DOI: 10.1109/ICEEICT53905.2021.9667942 CITATIONS READS 0 24 3 authors: Md. Khalid Mahbub Khan Muhammad Sajjadur Rahim University of Information Technology and Sciences University of Rajshahi 10 PUBLICATIONS 17 CITATIONS 38 PUBLICATIONS 158 CITATIONS SEE PROFILE A. Z. M. Touhidul Islam University of Rajshahi 42 PUBLICATIONS 139 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: MANETs View project Antenna Design View project All content following this page was uploaded by Muhammad Sajjadur Rahim on 13 January 2022. The user has requested enhancement of the downloaded file. SEE PROFILE 2021 5th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) Military Institute of Science and Technology (MIST), Dhaka-1216, Bangladesh Impact of Trace-Based Mobility Models on the Energy Consumption of Delay-Tolerant Routing Protocols Md. Khalid Mahbub Khan1, Muhammad Sajjadur Rahim2, and Abu Zafor Md. Touhidul Islam3 1, 3 Department of Electrical and Electronic Engineering University of Rajshahi Rajshahi-6205, Bangladesh 2 Department of Information and Communication Engineering University of Rajshahi Rajshahi-6205, Bangladesh 1 khalidmahbub.khan@yahoo.com, 2sajid_ice@ru.ac.bd, and 3touhid.eee@ru.ac.bd Abstract—In Delay-Tolerant Network (DTN), nodes’ energy consumption depends highly on their movement pattern since energy-constrained nodes move in a “Store-Carry-andForward” paradigm for data communication. Energy must be expensed efficiently, selecting a suitable mobility model for successfully delivering messages in DTN. Mobility models are generally two types: one is synthetic mobility, and the other is trace-based mobility. In this research, the impact of tracebased mobility on the energy consumption for delay-tolerant routing protocols is evaluated in terms of the average remaining energy and the number of dead nodes. Here, three trace-based mobility models: MIT Reality, INFOCOM, and Cambridge Imotes are considered. Shortest Path Map-Based Movement from synthetic mobility is also investigated in this research for better analysis. These mobility models are implemented for five delay-tolerant routing protocols: Epidemic, Spray and Wait, PRoPHET, MaxProp, and RAPID and simulated in the Opportunistic Network Environment (ONE) simulator using a similar simulation environment. Simulations are performed by varying message generation intervals, message Time-To-Live (TTL), and buffer size, respectively, while others remain fixed. From the outcomes of simulations, we have finally found that INFOCOM trace on MaxProp protocol has the minimum value of average remaining energy, while Spray and Wait protocol with MIT Reality has the maximum value of average remaining energy. Apart from this, Shortest Path Map-Based Movement for MaxProp protocol measures the highest number of dead nodes, and Spray and Wait protocol in Cambridge Imotes computes the least number of dead nodes. Keywords—delay-tolerant network, routing, energy consumption, trace-based mobility, synthetic mobility, opportunistic network environment simulator I. INTRODUCTION AND RELATED WORKS In recent decades, data communication in infrastructureless networking schemes has gained much attention from researchers as a considerable number of portable mobile devices with communication capabilities are available and being used by people. So, the improvement of Mobile Adhoc Network (MANET) has come forward. In MANET, the existence of a contemporary link between source and destination nodes is essential in order to route a packet in a precise path. Packets will be dropped if the destination node XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE is unreachable. So, data routing protocols for MANET are designed considering these issues. Nevertheless, in practical cases, there exist many challenging environments where nodes experience unpredictable movement, sporadic duration of contacts to each other, longer message delivery delay, frequent link disruption, etc. MANET protocols fail to route data efficiently in these cases as the dedicated endto-end path is unavailable, and the topology of such networks becomes dynamic in nature. Delay-Tolerant Network (DTN) has replaced MANET in these hostile environments by allowing intermittent connectivity among nodes. In DTN, nodes store data and carry it for a provisional time to forward until the availability of their connectedness with the destination. This opportunistic behavior of nodes in this networking architecture improves the success rate of data delivery in a wide range of hostile applications, viz., communication in rural areas, postdisaster areas, etc. [1]. An essential concern behind the routing protocols of DTN architecture is the pattern of nodes‟ movement. It is closely related to contact patterns and duration among the nodes. Several physical factors (viz., total area, street map, speed limit, etc.) and social factors (viz., active and free times, friend‟s mobility, etc.) impact on the nodes‟ movement pattern. However, there are two methods to model the contact pattern: to use a synthetic model or to use real traces from the real scenarios. Performance measuring parameters of network protocols, such as successful data forwarding rate, delay, overhead, etc. are highly influenced by either these synthetic movement models or the real-based movement. A synthetic model is a statistical model which creates a pattern in accordance with specific rules. These are mainly used to formulate a category of users or applications. Real traces are accumulated from real-life based applications. Real traces are based on contact traces and GPS traces. Contact-based traces provide a complete contact list where two or more nodes are participating in contact. The beginning time and the finish time of that contact are also appended there. On the other hand, in GPS traces, the node‟s location is provided for a particular period of time. Some limitations are experienced in real traces in spite of their attractive feature of exhibiting the real behavior of nodes in a specific scenario. Traces would not be effective for the new scenarios if it is not included in the collection of them. In addition, it is not possible to enhance or extend the traces within time as the size of them is fixed. Again, they are not able to support any changes regarding the perspective of users. For example, it is not considered in traces if users move away from any troubling and serious scenarios. To utilize real-based traces in simulations is not cost-effective since the external files are required to read the next point related information. On the contrary, more flexibility has been offered in synthetic models. They can afford adjustable parameters and support new scenarios. If any characteristic of the network, such as the number of nodes, is increased, synthetic models are suitable to work on that where the traces are incapable [2, 3]. However, a challenging issue of DTN architecture is that how efficiently the nodes utilize energy. Nodes are energyrestricted since most of them are driven by lithium-ion batteries. The energy quantities of nodes tend to decrease with time because of their activities such as transmitting and receiving messages, searching for neighbors, etc. Furthermore, nodes‟ movement pattern highly influences the energy consumption of nodes. So, optimization of energy consumption is essential for delay-tolerant routing protocols since these protocols are implemented into a network, adopting a specific trace-based or synthetic model for nodes‟ movement patterns. For the energy-efficient operation of routing protocols, the amount of energy expenditure taken place within adopted trace-based or synthetic models needs to be a minimum. Many pieces of research have been performed in energy consumption aspects considering trace-based or synthetic mobility models. In [4], the authors proposed “EnergyAware Bubble Rap” (EA-Bubble Rap), an energy-efficient version of a well-known social-based DTN protocol: Bubble Rap, and compared it with other two existing DTN protocols (Epidemic and Bubble Rap). Only two traces (viz., UPB 2012 and Cambridge Imotes) were used to show energy consumption among simulated protocols for different maximum energy values. An analysis of the performance of simulated protocols was also given there in terms of delivery cost, average hop count, hit rate, average latency, etc. Other existing DTN routing protocols, traces, and synthetic models remained unconsidered. Energy efficiency for different synthetic mobility models except for any real traces were analyzed in [5]. In some researches [6, 7], the authors were mainly focused on novel routing solutions. They were motivated to design new routing schemes with respect to existing DTN routing techniques along with the demonstration of their comparative performance analysis. In such cases, Shortest Path Map-Based (SPMB) model, external movement-based systems, and Working Day Movement were utilized to determine the pattern of nodes‟ mobility depending on the network‟s applications. Nevertheless, the impact of well-known real traces and other synthetic mobility models were absent there. On the other hand, evaluations of energy efficiency among different types of conventional DTN routing techniques got the main concern in [810]. Comparative performance analysis of several protocols also took place in the above-mentioned studies considering Shortest Path Map-Based Movement model for the nodes‟ movement. Real traces from real scenarios were unaddressed into those researches. The energy consumption of the nodes highly depends on their movement patterns which come from real cases. So, this fact motivated us to investigate the impact of real trace-based mobility on the energy consumption of nodes for the DTN routing protocols. We have simulated five DTN protocols: Epidemic [11], Spray and Wait [12], PRoPHET [13], MaxProp [14], and RAPID [15] considering individually three real traces for nodes‟ movement: MIT Reality, INFOCOM, and Cambridge Imotes; Shortest Path Map-Based Model from the synthetic models is also included here for the ease of comparison. Our simulation tool is the Opportunistic Network Environment (ONE) simulator [16]. We have evaluated the energy consumption of nodes for simulated protocol with respect to the average remaining energy and number of dead nodes. The rest of the paper is arranged as follows: Section II provides a brief review of the investigated mobility models (real trace-based and synthetic). Section III delivers a short description of the selected delay-tolerant routing protocols. Simulation environment-related discussions are given in Section IV, and an analysis of research outcomes is provided in Section V. Finally, Section VI concludes the research and provides a direction on the future endeavor of this research. II. INVESTIGATED MOBILITY MODELS We have included three real trace-based mobility models in our research: MIT Reality, INFOCOM, and Cambridge Imotes. We also added Shortest Path Map-Based Movement, which is representative of the synthetic mobility models, for a better understanding of the results. This section briefly explains the above-mentioned mobility models. MIT Reality is a campus-based trace in which 97 Nokia 6600 phones were deployed with Bluetooth connectivity. Here, the information of contact was collected from coursewise faculties and students of MIT in the 2004-2005 academic year. The information regarding data transmission among the participants, their location, and their adjacencies was included in that contact information. The duration of this data set is 246 days [17]. INFOCOM trace is also known as Haggle 3 project trace. It is based on an IEEE authorized conference named by the same name held in 2005. In this case, Intel motes (Imotes) devices were distributed among 41 participant volunteers through the Bluetooth interface. Later in 2006, the organizer of the same conference involved 98 participants, which is known as INFOCOM 6 data set. The total duration of both INFOCOM 5 and INFOCOM 6 is 3 days. Since we have included only INFOCOM 5 in our study, we recognize INFOCOM 5 as INFOCOM trace [2]. Another campus-oriented trace is Cambridge Imotes. Here, Imotes were provided to 36 participants around the campus area, University of Cambridge. These devices are deployed in the most popular places on the campus for 10 days. Bluetooth technology is used as the interface in this case. This trace is also known as Haggle 4 project trace [2]. Shortest Path Map-Based Movement is a synthetic mobility model which is more realistic than other MapBased Movement models. In this model, nodes move randomly through the paths that are well-defined from fixed map data. Nodes choose random points on the map from their current positions. Then they try to find out and follow the shortest path onto those random points from their present locations according to Dijkstra‟s algorithm. The destined random points are selected randomly, otherwise from a list known as Point of Interest (POI). This list is chosen from the real world popular destined places such as tourist-attraction places, more popular shops or restaurants, etc. [16, 18]. III. SELECTED DELAY-TOLERANT ROUTING PROTOCOLS This section briefly describes the examined delaytolerant routing protocols in this research: Epidemic, Spray and Wait, PRoPHET, MaxProp, and RAPID. Unbounded replication of messages is the main idea behind the Epidemic protocol. In this strategy, the message containing node continually replicates the message and forwards the replica of messages to those recently encountered nodes with it that have no copy. In such a way, finally, the message would reach the destined target node. But the same message would be flooded in the entire network. Successful message transmission is possible in this approach without considering network resources, viz., bandwidth, delay, overhead, buffer size, etc. For this reason, this approach is not an ideal solution for routing data in the DTN environment [11]. The limited replication-based concept is implemented in the Spray and Wait technique instead of uncontrolled replication in Epidemic. Here, only a maximum allowable number of message copies are forwarded to the same number of relays. This maximum allowable number is denoted as L. The value of L depends on several factors, viz., the density of nodes in the network required for message transmission, etc. This forwarding approach consists of two phases, one is the Spray phase, and the other is the Wait phase [12]. Spray: In the „Spray‟ phase, a source node forwards L copies of a message to the L distinct relay nodes. Wait: L relay nodes wait with carried message copy for the future encounter with the destination node to route their message copy to the destination. So, this phase is known as „Wait‟. The source node maintains a set of probabilities to forward a message in Probabilistic Routing Protocol using the History of Encounters and Transitivity (PRoPHET) approach. Instead of multiple message-copy transmission, this technique considers the likelihood regarding actual real- world encounters of nodes. When meetings occur with the source, messages are forwarded based on probability from the source. The encountered node which has a higher probability of delivery would receive the message soon [13]. MaxProp forwarding strategy assigns and prioritizes definite schedules to route data packets. The sender maintains a ranked list of packets that decides which packet would be transmitted or dropped at the earliest basis. This list relies on the cost determined from the sender toward all destinations and reflects the probability of message delivery. MaxProp strategy stabilizes the preference of packets based on minimal hop counting and avoids numerous copies acceptance of the same message [14]. Resource Allocation Protocol for Intentional DTN (RAPID) is a routing protocol for DTN where resource allocation problem is the primary concern and attempting to improve the forwarding criterion such as average latency, maximum delay, etc. Here, the DTN model is seemed to be a utility-controlled scheme where a utility function is managed. A utility value is referred by that utility function for every data packet on the basis of the routingrelated criterion. serves as the expected contribution of packet i against the recognized routing criterion. Within the utility, the packets that resulted in maximum increases would be replicated first by RAPID protocol, and this protocol is intended to replicate all the messages in this way if the network resources (viz., bandwidth. etc.) permit [15]. IV. SIMULATIONS In this research, we have investigated the effects of nodes‟ real trace-based mobility models on the energy consumption of delay-tolerant routing protocols. To do so, we have simulated five delay-tolerant routing protocols: Epidemic, Spray and Wait, PRoPHET, MaxProp, and RAPID using three trace-based mobility models: MIT Reality, INFOCOM, Cambridge Imotes, and also included Shortest Path Map-Based Movement as a synthetic mobility model. Simulations have been performed in Java-based Opportunistic Network Environment (ONE) simulator, whose one of the notable features is to support different mobility models including real traces. An energy module [18] is configured within ONE simulator for conducting simulations since the evaluation of the energy consumption of nodes is the main focus. Table I and Table II show the settings of the necessary simulation parameters and the information regarding the investigated trace-based mobility models, respectively. Here, the entire simulation area is 4.5 km × 3.4 km. A single group of pedestrians carrying portable devices is considered as the mobile nodes since the real traces are campus or conference venue oriented, and the speed of mobile nodes is 0.5-1.5 m/s. But the number of mobile nodes in the group is fixed according to the settings of the investigated traces and synthetic mobility model. Here, the Bluetooth interface is used for data communication among the mobile nodes. To rationalize the simulations and to evaluate the energy consumption for the selected delay-tolerant protocols using the considered real traces, we have normalized the simulation duration to 3 days for all real traces and Shortest Path Map-Based Movement. Simulations are performed on the map of Helsinki city. TABLE I. NECESSARY SIMULATION PARAMETERS Parameter Simulation area Simulation time Routing protocols Number of message replica (for Spray and Wait) Seconds in time unit (for PRoPHET) Utility algorithm (for RAPID) Mobility models Interface type Transmission speed Transmission range TTL Message generation intervals Buffer size (MB) Message size (MB) TABLE II. Value 4.5 km × 3.4 km 3 days Epidemic, Spray and Wait, PRoPHET, MaxProp, and RAPID 10 30 Average delay MIT Reality, INFOCOM, Cambridge Imotes, and Shortest Path Map-Based Movement Bluetooth 2 Mbps 10 m 6 h, 12 h, 1 d, 2 d, and 3 d 515 s, 1525 s, 2535 s, 3545 s, and 4555 s 5, 10, 15, 20, 25 1 INFORMATION RELATED TO MOBILITY MODELS Mobility Type Duration Trace/Campus Trace/Conference Number of nodes 97 41 MIT Reality INFOCOM (Actually INFOCOM 5, also known as Haggle 3) Cambridge Imotes (Haggle 4) Shortest Path Map-Based Trace/Campus 36 10 d Synthetic mobility model 100 3d 246 d 3d Table III provides the necessary parameters for the energy settings of the simulations. We have conducted simulations and evaluated the outcomes by varying message generation interval, TTL and buffer size, respectively, for the following metrics: average remaining energy of nodes and number of dead nodes. TABLE III. Fig. 1. Average remaining energy vs. message generation intervals. PARAMETERS RELATED TO ENERGY MODULE Parameter Initial energy of each node Scan energy Transmit energy Receive energy Energy recharge interval Threshold value of energy for each node V. A. Varying Message Generation Intervals Figs. 1 and 2 demonstrate how node‟s remaining energy and the number of dead nodes for the investigated mobility models (MIT Reality, INFOCOM, Cambridge Imotes, and Shortest Path Map-Based) on simulated delay-tolerant routing protocols (Epidemic, Spray and Wait, PRoPHET, MAxProp, and RAPID) are affected with the variation of message generation interval (from 515 s to 4555 s). In this case, TTL and buffer size are fixed at 1 d and 5 MB, respectively. In Fig. 1, MIT Reality experiences higher average remaining energy for all simulated protocols with the variation of message generation interval while INFOCOM exhibits the least average remaining energy for that case. Moreover, MIT Reality for both Spray and Wait and RAPID protocol can be regarded as the highest energyefficient because of their maximum value of average remaining energy. But between these two, Spray and Wait protocol has a slightly higher value for average remaining energy. So, this protocol with MIT Reality is the most energy-efficient. The minimum message replication strategy of Spray and Wait within a limited area like campus reduces additional energy expenditure. In opposition, MaxProp protocol for INFOCOM consumes the most nodes‟ energy and is considered the least energy-efficient. Nodes need more communication of data in a conference type area like INFOCOM, so they consume more energy for the MaxProp protocol. Value 6000 J 0.92 mW/s 0.08 mW/s 0.08 mW/s More than 3 days, we do not like to recharge the batteries within the simulation period 800 J (about 14% of the initial energy) ANALYSIS OF THE SIMULATION RESULTS This section provides a discussion on the outcomes of the simulations. The entire discussion consists of the following three sub-sections. Fig. 2. Number of dead nodes vs. message generation intervals. In Fig. 2, the number of dead nodes is the highest in Shortest Path Map-Based Movement and the lowest in Cambridge Imotes for all examined protocols. Due to the random mobility of nodes for Shortest Path Map-Based movement, the energy of many nodes reaches almost zero in this type of mobility, and MaxProp protocol produces the highest number of dead nodes. Furthermore, Cambridge Imotes for Spray and Wait protocol has a minimum number of dead nodes because a limited number of mobile nodes compared to other mobility models are considered in this trace. As a result, the energy of few nodes goes to zero. B. Varying TTL Average remaining energy and number of dead nodes are plotted against varying message TTL (from 6 h to 3 d) in Figs. 3 and 4. Message generation interval and buffer size remained fixed at 2535 s and 5 MB, respectively. As in Fig. 3, Spray and Wait protocol exhibits the highest average remaining energy, while for MaxProp, it is the lowest with the increasing message TTL. A message with larger TTL may cause more energy expenditure for more replications within the extended message lifetime. Again, MIT Reality has the maximum and INFOCOM has the minimum average remaining energy for all protocols than other mobility models. So, Spray and Wait in MIT Reality and INFOCOM in MaxProp are the most and the worst energy-efficient respectively in this case. Since we have considered 100 nodes for Shortest Path Map-Based Movement and nodes are moving randomly in this mobility, the energy of many nodes reduces to almost zero or goes below the set threshold value of energy (as indicated in Table III). So, in Fig. 4, the number of dead nodes is higher than other traces for this mobility. On the contrary, Cambridge Imotes shows a lesser count of dead nodes since the total number of nodes is a minimum than other mobility models. MaxProp protocol in Shortest Path Map-based Movement has the highest count of dead nodes, whereas it is the minimal for Spray and Wait protocol in Cambridge Imotes. MB) by keeping message generation interval and TTL fixed at 2535 s and 1 d respectively are illustrated in Figs. 5 and 6. Similar to Figs. 1 and 3, Spray and Wait in MIT Reality trace consumes less energy than other simulated. Because of its controlled replication strategy, nodes expense the least amount of energy within MIT reality trace. On the other hand, MaxProp in INFOCOM trace consumes the highest amount of nodes‟ average energy. Following Figs. 2 and 4, Shortest Path Map-Based Movement for MaxProp protocol measures the highest number of dead nodes as the buffer capacity increases. In contrast, Cambridge Imotes trace for Spray and Wait protocol conceived the minimum number of dead nodes when increased buffer capacity. Fig. 5. Average remaining energy vs. buffer size. Fig. 6. Number of dead nodes vs. buffer size. VI. CONCLUSIONS AND FUTURE WORK Fig. 3. Average remaining energy vs. TTL. Fig. 4. Number of dead nodes vs. TTL. C. Varying Buffer Size Nodes‟ average remaining energy and number of dead nodes with the increase of buffer size (from 5 MB to 25 Within this work, we have investigated the energy consumption of four mobility models (including three real trace-based and one synthetic): MIT Reality, INFOCOM, Cambridge Imotes, and Shortest Path Map-Based Movement for five delay-tolerant routing protocols: Epidemic, Spray and Wait, PRoPHET, MaxProp, and RAPID. We have evaluated the energy consumption aspect of the selected DTN routing protocols over the abovementioned mobility models (with an emphasis on real traces) from the perspective of two metrics: average remaining energy and number of dead nodes by considering the variation of message generation interval, TTL and buffer size. We have eventually found from the outcomes of the simulations that MIT Reality trace for Spray and Wait protocol is the most energy-efficient in terms of average remaining energy. In contrast, INFOCOM trace in MaxProp protocol is the least energy-efficient with the increasing message generation interval, TTL, and buffer size. For the number of dead nodes, MaxProp protocol in Shortest Path Map-Based Movement mobility counts the highest than other DTN routing protocols for message generation interval, TTL, and buffer size. Cambridge Imotes for Spray and Wait has the minimum number of dead nodes for such cases. [7] Our future endeavor would be to analyze the performance of these trace-based and synthetic mobility models for DTN routing protocols in terms of delivery ratio, average latency, transmission cost, etc. which would lead us to reach a proper understanding of the impacts of tracebased and synthetic mobility models on the energy efficiency and performance of DTN routing protocols. 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