Wireless Netw (2016) 22:723–740 DOI 10.1007/s11276-015-0994-0
DirMove: direction of movement based routing in DTN architecture for post-disaster scenario Amit Kumar Gupta1 • Indrajit Bhattacharya1 • Partha Sarathi Banerjee2 Jyotsna Kumar Mandal3 • Animesh Mukherjee4
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Published online: 25 June 2015 Ó Springer Science+Business Media New York 2015
Abstract Network architecture based on opportunistic Delay Tolerant Network (DTN) is best applicable for postdisaster scenarios, where the controlling point of relief work is any fixed point like a local school building or a hospital, whose location is known to everyone. In this work, 4-tier network architecture for post-disaster relief and situation analysis is proposed. The disaster struck area has been divided into clusters known as Shelter Points (SP). The architecture consists of mobile Relief Workers (RW) at tier 1, Throw boxes (TB) at tier 2 placed at fixed locations within SPs. Data Mules (DM) like vehicles, boats, etc. operate at tier 3 that provide inter-SP connectivity. Master Control Station (MCS) is placed at tier 4. The RWs are provided with smart-phones that act as mobile nodes. The mobile nodes collect information from & Amit Kumar Gupta
[email protected] Indrajit Bhattacharya
[email protected] Partha Sarathi Banerjee
[email protected] Jyotsna Kumar Mandal
[email protected] Animesh Mukherjee
[email protected]
the disaster incident area and send that information to the TB of its SP, using DTN as the communication technology. The messages are then forwarded to the MCS via the DMs. Based on this architecture, a novel DTN routing protocol is proposed. The routing strategy works by tracking recent direction of movement of mobile nodes by measuring their consecutive distances from the destination at two different instants. If any node moves away from the destination, then it is very unlikely to carry its messages towards the destination. For a node, the fittest node among all its neighbours is selected as the next hop. The fittest node is selected using parameters like past history of successful delivery and delivery latency, current direction of movement and node’s recent proximity to the destination. Issues related to routing such as fitness of a node for message delivery, buffer management, packet drop and node energy have been considered. The routing protocol has been implemented in the Opportunistic Networks Environment (ONE) simulator with customized mobility models. It is compared with existing standard DTN routing protocols for efficiency. It is found to reduce message delivery latency and improve message delivery ratio by incurring a small overhead . Keywords Opportunistic Network Delay Tolerant Networks Post Disaster Situation Analysis Direction based Data Mule Fitness
1
Department of Computer Application, Kalyani Government Engineering College, Kalyani, West Bengal, India
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Department of Information Technology, Kalyani Government Engineering College, Kalyani, West Bengal, India
1 Introduction
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Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
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Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India
Post disaster scenario presents a very challenging environment to work in. To analyse those situations is even a harder task to accomplish. Till today, in most parts of the world, use of Information and Communication Technology (ICT) in
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post-disaster situations is limited and very difficult to implement. Setting up network architecture in paralysed conditions and then making it to work to collect information from affected areas is a challenging task. Even if some network infrastructure were present, natural disasters even of average impact break them down. So, a temporary network consisting of humans acting as relief workers can be quickly created in those situations. One distinguishing characteristic of a network in post disaster situation is that its nodes, that are humans carrying some devices, are mobile and thus are intermittently connected. At some instant of time, two nodes are closer enough to communicate, and seconds later they might be completely out of range for possible communication. On the other hand, the situation demands that the information gathered by such nodes, like information about immediate requirement of some life saving drug, etc. must be forwarded to a Control Station that would arrange for quick and coordinated intervention. A lot of research work is being done in these areas to develop applications that work on such environments. Some proposals in the field have been made to use Mobile Ad hoc Networks (MANET) [1]. For example, IMPROVISA [2] propose to implement MANET by distributing antennas in the disaster area. Despite being seemingly viable, this may not be feasible in large scale emergencies as it would incur a large set-up delay and does not seem to be scalable to incorporate changes in the network. This is because a network in a post disaster scenario is quite vulnerable to changes [3]. Martin-Campillo et al. [4] have suggested that wireless opportunistic networks formed by rescue workers carrying mobile devices could be used to transmit data created and collected from the disaster affected area to the control station. Opportunistic networks, working on peer-to-peer communication mode suit such scenario appropriately, by virtue of being infrastructure-less, storecarry-forward capable and providing dynamic routes from sender to receiver. In a peer-to-peer opportunistic network, any possible node can opportunistically be used as next hop if it is more likely (fitter) to take the messages closer to the final destination. The Opportunistic Delay Tolerant Network (DTN) is a viable solution for such scenario, as far as the network architecture is concerned. DTN [5] is a networking approach that can be used in situations where end-to-end connectivity cannot be guaranteed, and where networks suffer huge disruptions because of external factors like wireless radio range limitation, sparse and mobile nodes, energy constraints, etc. In this paper, DTN network architecture has been proposed for Post disaster Situation Analysis with Resource Management and the same has been configured to work in an opportunistic peer-to-peer mode of communication. The network architecture has a 4-tier structure comprising of components distributed at four different levels. The disaster struck area is divided into clusters known as Shelter Points (SP). The
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architecture consists of mobile Relief Workers (RW) at tier 1, Throw boxes (TB) at tier 2 placed at fixed locations at the control centre of the relief at each of the various SP areas. Data Mules (DMs) like vehicles, boats, etc. operate at tier 3 that provide inter-SP connectivity. Master Control Station (MCS) is placed at tier 4. Inside each SP area, the control centre of relief, the TB, is taken as the fixed destination that acts as the repository of all information collected from that SP area. The RWs are provided with smart-phones and they act as mobile nodes. The mobile nodes collect information from the disaster incident area and send that information to the TB of its SP, using DTN as the communication technology. The messages are then forwarded to the MCS via DMs. The MCS acts as the repository of the entire information collected from the system for further processing or distribution. Traditional ad hoc routing protocols are based on end-to-end connectivity for their successful transmission. Since DTN lacks end-to-end connectivity, hence it employs different strategies for routing. In the current work, a novel routing protocol is proposed that works by tracking the recent direction of movement of the mobile nodes by measuring their consecutive distances from a fixed destination at different instants. Based on it, the decision of whether to forward the messages or not is taken. If any node moves away from the destination between two consecutive instants, then it is very unlikely to carry its messages towards the destination. For a node, the fittest among all its neighbouring nodes is selected using parameters like past history of successful delivery and delivery latency, current direction of movement and node’s recent proximity to the destination. It is then chosen as the next hop. Issues related to routing such as fitness of a node for message delivery, network energy, buffer management and packet drop have also been considered. The proposed routing protocol has been implemented in the Opportunistic Networks Environment (ONE) simulator [6] and is compared with existing standard DTN routing protocols for efficiency. It is found to reduce message delivery latency and improve message delivery ratio by incurring a small overhead. A number of proposals for routing strategies in the DTN have been reported in the literature. Routing strategies that are closely applicable in post disaster scenario, based on standard performance metrics, are analysed in the current work. Certain parameters, most relevant to a post disaster situation, are reliability of packet delivery and small delivery latency, as information involving human lives is involved. Again, considering the limitations of the smart phones that act as the DTN nodes, efficient energy utilization and buffer-management are of utmost importance. The existing DTN routing strategies [7–12] mainly focus on improving some particular performance parameter such as message delivery probability or buffer management, but they fail to perform on other parameters like message delivery latency, energy efficiency or message drops [13].
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Our proposed routing approach focuses on multiple parameters such as message delivery probability, average message latency, energy efficiency, buffer management and message drop that are of critical importance for quick, accurate and well-coordinated response from the network. It has been observed that in disaster relief work, different groups of relief providing people, like Relief Workers, Medical Personnel, Police, etc. collaborate with each other to collect information as well as to manage the distribution of relief material to the affected people. This demands for a group based mobility model. Again, mobility of a group is restricted to fixed local sites, like Hospital, Shelter Point, etc. This can be imagined as a cluster of nodes restricted to a particular site. So, the Cluster Mobility Model [14] is used to model the mobility of nodes in such scenario. The mobile nodes inside the cluster move randomly and communicate with each other via multi-hop communication based on peer-to-peer DTN to send their messages to a destination. Vehicles (cars, buses, boats, etc.) are used as Data Mules (DM) which move across the affected area as carrier nodes and provide communication between the clusters. The rest of the paper is organized as follows: In Sect. 2, related work in network architecture and routing strategies for post disaster scenario is presented. Section 3 explains the proposed network architecture, followed by explanation and discussion of the proposed routing strategy in Sect. 4. The focus areas and novelty of our proposed strategy are also mentioned in Sect. 4. In Sect. 5, the steps of evaluation and the results obtained by Simulation in the Opportunistic Networking Environment (ONE) Simulation Environment have been displayed. The paper ends with conclusions drawn from the work, as presented in Sect. 6.
2 Related work Though proposals related to setting up of network architecture for post disaster scenario are very limited, there are some existing works in the field. There are techniques for setting network infrastructure in post-disaster scenario using wireless mesh architectures, like the Serval Project proposed by Stephen [15], the JaldiMAC by Ben-David [16] and the AirJaldi System by Airjaldi [17]. The Serval project provides network infrastructure for direct connections between cell phones through Wi-Fi interfaces and eliminates requirement of a mobile phone operator. The JaldiMAC provides a point-to-multipoint deployment topology that uses ‘‘natural towers’’ such as hills and mountains to provide connectivity even over great distances. The AirJaldi System is based on the concept that each relay is built to reach specific clients; each client is potentially a relay to other clients. These techniques have largely been
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impractical for use in post disaster conditions because these systems incur huge delays to set-up, and their coverage range is too small to be standardised for conditions in large scale post disaster scenario. Yet another proposal by Heimerl and Brewer [18] suggests use of low power GSM as a solution. High costs of the VSAT and the WiMax technologies make them impracticable for use in post-disaster conditions. A very interesting project by Pentland et al. [19], called Daknet proposes to use vehicles such as cars or buses to work as backhaul in the challenged networking environment. This work is closer to real situation and offers a practical solution only if used to limited extent and if it is not completely dependent on such vehicles. The project ENS, proposed by Braunstein et al. [20], is based on wireless hybrid networking which offers a highly reliable, low-cost, and easily scalable solution but lacks problem specific knowledge to be suitable in a post disaster scenario. Communicating and forwarding data in opportunistic networks is also a challenging task to accomplish. This is because opportunistic networks lack continuous connectivity. A path from a source to the destination cannot be ensured at the time of transferring a message. So, normal MANET routing strategies fail to work. Communication is made possible in an opportunistic way, when some potential neighbour can be found to forward the messages. Different strategies depend on various type of information about their environment to take the decision of data-forwarding. Since a Post-Disaster situation is considered, all opportunistic or DTN routing protocols are not suitable. Because a post-disaster situation demands good performance on specific parameters like storage requirement, high message delivery ratio, low delay, minimal packet drop, etc. Existing routing protocols in DTN are not designed to meet all of these requirements. For example, Epidemic suffers from huge packet losses and high buffer requirement, RAPID suffers from huge delays, and so on. Some routing protocols have been selectively chosen for our comparisons that very closely meet post disaster situation requirements. Such protocols [21] are Epidemic, PRoPHET, MaxProp, Spray and Wait, RAPID and Encounter Based Routing. Some are multi-copy forwarding mechanism, while some rely on single-copy transfer mechanisms. These are discussed in Sects. 2.1–2.6. 2.1 Epidemic routing The Epidemic routing was proposed by Vahdat and Becker [7]. It is in fact the first forwarding strategy for DTN architectures where all the messages of a node are copied and transmitted to all other nodes that come within its transmission range while the nodes move into the network. Each message gets assigned a unique message ID. Whenever two nodes meet, a vector containing the indices of all messages in a node is sent to the other node, and vice versa. In this way, each node gets the information about the
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message IDs not available in its own buffer and requests the other node to transfer all of such messages. Eventually, messages reach the final destination through pair-wise message exchanges between nodes. This strategy creates a huge number of copies of each message and thus increases the delivery chance. It provides a good delivery ratio at small delivery delay but at the cost of high buffer occupancy, large number of packet drops and high energy requirements for doing so many transmissions which, in the context of a post-disaster scenario, is highly undesirable. 2.2 PROPHET routing PROPHET routing has been proposed by Lindgren et al. [8]. PROPHET is an acronym for Probabilistic ROuting Protocol using History of Encounters and Transitivity. It gets the credit of being one of the first improving strategies over Epidemic by addressing the huge resource requirement problem of Epidemic. It limits the number of copies of a message in the network. Whenever two nodes meet, they exchange an additional piece of data called delivery predictability information together with the index vector as in Epidemic. This information calculates the probability of the ability of a node A of delivering a message to the destination. Prophet allows a message to be delivered to another node only if the delivery predictability of the destination of the message is higher at the other node. This decision is based completely on the past performances. In the absence of current directional information, such decisions are prone to go haywire, and the message delivery latency increases, proving its inaptness in post-disaster scenario. It also incurs huge buffer occupancy as lack of acknowledgement makes messages stay in the buffer for long. 2.3 MaxProp routing MaxProp routing was proposed by Burgess et al. [9]. This method prioritizes the schedule of packets transmitted to other nodes as well as the schedule of packets to be deleted from the buffer. The packets are ranked based on some criteria to determine the order of packet transmission and packet drop. MaxProp lets each node keep the track of probability of meeting with other nodes. Once a node gets the values of delivery likelihood for all other nodes, it then calculates the cost of possible path of each node to the destination up to n-(pre-set by the protocol) hops long using a formula. After calculating the cost of all possible paths, the one with the lowest cost is selected to be the final path to the destination. This method again does all the calculations on the basis of past performances. Lack of current directional information makes this method prone to wrong decisions, leading to longer message delivery latencies. Also, huge calculations involved and long paths
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of acknowledgement eat energy and bandwidth that make it unsuitable in post-disaster conditions. 2.4 Spray and wait routing The Spray and Wait routing was proposed by Spyropoulos et al. [10], where the overall job is divided into two different phases. In the Spray phase, a limited number of copies (L) of a message are spread over the network by the source and some other nodes which gradually receive a copy of the message. Some important considerations for such decision making are the number of copies of the message that will be spread and the procedure to spread those copies to other nodes in the network. In the Wait phase, the nodes wait after spreading all the copies of the message until the destination is not encountered by a node with a copy of the message in the spraying phase, after which each of these nodes carrying a message copy try to deliver their own copy to destination via direct transmission independently. Because of such strategy, this method suffers very long delivery latency and the long path acknowledgments eat away bandwidth unnecessarily. The requirement of high mobility so that the deepest nodes too reach the destination is not suitable in a DTN for postdisaster scenario. 2.5 RAPID routing Resource Allocation Protocol for Intentional DTN routing (RAPID) was developed by Balasubramanian et al. [11]. It is a flooding based DTN protocol where messages are ordered using utility functions. These utility functions are defined with the intention of maximizing certain specific performance metrics like the delivery delay. The overall method is composed of four steps. In the Initialization step, a metadata is exchanged to estimate packet utilities. Next step is Direct Delivery where those packets are transmitted which are destined for immediate neighbours. In the third step of Replication, packets are replicated on the basis of marginal utility. The last Termination step ends the method on losing contacts or when all packets have been replicated. RAPID manages high delivery ratio but at the cost of using network resources heavily. Also, all performance metrics cannot be used together in the utility function so that RAPID fails to perform on multiple parameters. Absence of knowledge of current movement direction of a node is also a disadvantage for the method. 2.6 Encounter based routing This (EBR) was proposed by Nelson et al. [12] to utilize the mobility property of some networks where the future rate of node encounters can be roughly predicted by past
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data. EBR decides on the next hop based on past encounter values of nodes. EBR prefers exchange of messages with nodes having high encounter rates. These encounter values are completely historical data and don’t consider the current movement pattern of nodes. This renders it inefficient in providing good delivery ratio and delivery latency. Some of the existing DTN architectures and routing strategies have been discussed above. But none of the existing network architectures provide a complete solution to post-disaster requirements which is cost-effective, easily deployable, scalable to be readjusted in various conditions, and robust enough to stand even in odd conditions. The present work proposes a network based on the philosophy of these features. A post disaster environment has challenges and characteristics of its own. It demands for a network model with fixed destination and mobile nodes. A study measuring the performance in terms of message delivery probability, of existing flooding-based routing schemes of DTN, has been made in [21]. In our proposed work, an exhaustive evaluation of existing DTN routing methods is made, based on all performance metrics of importance in post-disaster conditions. The performance of existing routing protocols degrade in such environment as they are general DTN protocols and not configured to specific requirements of a post-disaster situation. A routing strategy (Sect. 4) has been proposed in our work that performs efficiently over such architecture in post-disaster situation and has performance measures better than those of existing routing strategies. Since the destination is fixed at a particular location, the strategy works by tracing the recent direction of movement of the mobile nodes and guide the messages properly towards the destination. In this way, the method helps reduce message delivery latency and increase their delivery probability. To improve performance further, strategies for efficient energy and buffer management have also been proposed.
3 Proposed network architecture for post disaster scenario Based on the findings and requirements suitable in postdisaster relief scenario, network architecture has been proposed under the current research work. A routing algorithm has also been implemented over this network architecture. The architecture is essentially a 4-tier one consisting of components distributed at four different levels. Figure 1 shows the architecture of the proposed model. Section 3.1 discusses the proposed architecture. The intra Shelter-Point communication architecture has been presented in Sect. 3.2 and that of Inter Shelter-Point communication in Sect. 3.3. The communication strategy for venturing nodes has been discussed in Sect. 3.4.
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3.1 Architecture description During a post disaster scenario the network infrastructure and communication links in the disaster affected area are completely broken and paralysed. The objective is to build network architecture and make it workable in an urgent basis. The disaster affected area that needs to be covered is termed as the Activity Area (AA) (Fig. 1). The Relief Workers (RW) that constitute the tier-1 of the architecture are divided into groups and each group is assigned a particular area (called Shelter Point, SP) to work into. For example, RWs 1–10 work under SP1, RWs 11–20 work under SP2, RWs 21–30 work under SP3, and so on. Each SP is having a local Control Station called a Throw Box or TB that forms the tier-2 of our architecture and monitors the work of that SP. The TB collects all messages generated within its SP for further transmission to a Master Control Station (MCS). The TBs of different SPs communicate with each other via data-mules that make up the tier-3 of the architecture. The MCS constitutes the tier-4 of the network architecture and is the only gateway of the AA to the outside world. MCS acts as the repository of the entire information collected from the network for further processing or distribution. The MCS is placed at a suitable location where the connectivity to the outside world can be ensured and the backhaul is permanent. An existing LAN or cellular connection is used as the possible backhaul. The MCS is suitably placed outside the region of the AA for two reasons. Firstly, there exists some possibility of recurrence of the disaster in the same area after some time of its first occurrence, especially in the case of earthquakes, or tsunamis. So, keeping the MCS within the region of AA is unsafe. Secondly, active communication link to connect to the outside world rarely exists in a post disaster AA. 3.2 Network architecture in Intra Shelter-Point communication For intra-SP communication, a peer-to-peer opportunistic Delay Tolerant Network architecture has been proposed. The nodes arbitrarily move inside the SP to collect reliefinformation, or to provide relief work to the victims. The information collected by the nodes is forwarded to its TB. Each node communicates either using Bluetooth or Wi-Fi direct communication mode. Both techniques have merits and demerits. Bluetooth is easier to implement, but provides very low data rate and has very short range of communication. On the other hand, Wi-Fi direct provides good data rate (250 Mbps against 25 Mbps for Bluetooth) followed by a fair range of communication (around 600 feet against 200 feet of Bluetooth) [22], but comes with high energy requirement (more than four times that of Bluetooth), implementation issues like setting up of
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Fig. 1 Network architecture designed for implementation in post disaster relief work. SP shelter point, TB throw-box, MCS master control station, Temp MCS temporary MCS
ty Area Main MCS (connected to outer world) SP2
TB
SP1
TB
Temp MCS
SP3
TB
Mobile Nodes Data Mules
hot-spots to act as Group Owner (GO), etc. Both the communication modes are present at each node. The advantages of each of the communication modes are implemented in the present work in an intelligent way. Nodes are set-up initially with the default Wi-Fi direct mode of communication. In the absence of Wi-Fi direct mode or when node density in the system is very high (refer page 23, Sect. 5), nodes automatically switch to the Bluetooth mode. A dense population of mobile nodes suffers with frequent handover associated with change of GOs in Wi-Fi direct mode of communication. Bluetooth is a better alternative in those situations. This capability also ensures that even if one technology fails, the other is always ready to be used, increasing the robustness and reliability of the network. The RW nodes collect information from the field and destine the messages to their TB. At this moment, they are completely unaware of their neighbouring nodes, the path that will be followed for the message delivery, and not even the surety of its delivery. Till this point, the RW nodes have only created the message and made it ready for transmission. If any other node arrives within its transmission range at any point of time, and this node has a better chance of taking the message closer to the destination (depending on the routing protocol being used), then the message automatically gets transferred to it. The RWs carry on with their assigned relief
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work. The tasks like neighbour finding, message transfer and selection of underlying transmission technology (Wi-Fi direct or Bluetooth) are automatically done without manual intervention. Nodes find their neighbours automatically by scanning their neighbourhood at periodic time-intervals. Whenever neighbours are detected in its transmission range, the node calculates their fitness values and transfers its messages opportunistically to the fittest neighbour. Also, if the node density is higher than a pre-set threshold, then Bluetooth mode of communication is enabled; otherwise nodes communicate using the default Wi-Fi direct mode. This automatic communication hides the complex details of these processes and thus eases the burden of the rescue workers. From one node to other (hop to hop), the message ultimately reaches the destination i.e., the TB. 3.3 Network architecture in Inter Shelter-Point communication The number of SPs to be set up depends on certain factors like the coverage area, type and severity of the disaster, total man-power available, existing infrastructure, etc. The TBs of each SP communicate with each other using suitable carriers (termed as Data Mule or DM) such as buses, ambulances, boats, helicopters, or hi-range Wi-Fi towers. These DMs carry the messages from one TB to the other.
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One of the TBs is strategically selected as a temporary MCS that initially collects the information from other TBs. This temporary MCS then communicates to the main MCS via DMs. The MCS gives appropriate instructions for relief and resource management as and when required. The MCS is also responsible for communicating the events and activities to the outer world. In this way, the inter-SP communication is done for information exchange. 3.4 Communication strategy for venturing nodes There are some Relief Worker (RW) nodes that work in the affected area but have no particular SP to work in or no TB assigned to report their findings to. This is particularly true in the initial stages of searching and tracing affected people when those RW nodes spread out in remote locations of the AA and collect their findings. Since they have no pre-assigned TB allotted, there exists a challenge for their communication. Such RW nodes are made smart enough to selflocate a TB and destine their messages to it. The information about the locations of each TB is provided to each such venturing RW node. This is done in the initial setup before the RW nodes set to work. Since there are only a few TBs (5 or 10 at the most for large scale disasters), letting each RW node store location information about each TB is not burdening. These locations are defined in terms of Latitude and Longitude dimensions, as obtained from a Global Positioning System (GPS) (Alternative efficient solutions to localised GPS calculations in real scenario has been included in our future research work, where we are trying to use various sensors of the Smartphone to track their changing locations in the absence of GPS). The nodes too are aware of their current location. Whenever a node has some message to deliver, it calculates the distances from each TB using the well-known ‘Haversine’ formula (1). This formula is used to calculate the great-circle distance between two points i.e., the shortest distance over the earth’s surface. To explain the technique, consider the details of the locations of the node and the TB. (lat1,lon1): comprising Latitude and Longitude of the RW node (First location) (lat2,lon2): comprising Latitude and Longitude of the TB (Second Location). Calculate, dlon ¼ lon2 lon1 dlat ¼ lat2 lat1 a ¼ ðsinðdlat=2ÞÞ2 þ cosðlat1Þ cosðlat2Þ ðsinðdlon=2ÞÞ2 pffiffiffi c ¼ 2 a sinð aÞ d ¼ Rc
ð1Þ
where, dlon is the difference between the longitudes and dlat is the difference between the latitudes of the two locations, R is the earth’s radius, a and c are variables to hold intermediate results and d is the distance between the two locations. The angular measurements are in radians. On calculating the distances from all the TBs, the RW node selects the TB with the shortest distance, and destines its messages to that TB. This message is delivered to the TB via hop-to-hop transmission opportunistically through other nodes venturing into its neighbourhood. TB then processes the messages accordingly. The network architecture discussed for a post disaster situation can build a communication process among the nodes with their neighbours. The routing strategy for the proposed model is discussed in Sect. 4. The strategy is appropriately designed to suit the proposed network architecture.
4 Direction of movement based routing strategy in the proposed network architecture It has been pointed out earlier that in post-disaster situations, DTN based opportunistic routing protocols are well suited. But not all DTN routing protocols are suitable for post-disaster relief and resource management. Majority of existing protocols such as Prophet, MaxProp, EBR routing protocols are based on probabilistic calculations from the past. Relying only on such probabilities for taking decisions in disaster situations involving human life renders those protocols unsuitable for practical use. Other DTN routing protocols like Epidemic, Spray and Wait, RAPID, etc. use no information about the current movement pattern of the nodes. Thus they fail to perform efficiently on important parameters in a post disaster scenario. The architecture inside an SP has a fixed TB and nodes move randomly. The routing approach presented here uses a parameter that measures the difference of consecutive distances of a mobile node from the TB at two different instants. If the value of the parameter is negative, it suggests that the second distance is larger, and hence the node is moving farther away from the TB, and vice versa. The direction of the node movement is taken into account to decide on the future path to be followed to the destination. The existing strategies that are based on probabilistic calculations over the past history may assign a higher probability to a node moving away from the TB because of lack of directional information. Our proposed strategy avoids such undesirable situation, thus increasing the message delivery probability and reducing the average message delivery latency to a great extent.
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4.1 DTN Routing in Intra Shelter-Point communication Communication inside each SP is based on a peer-to-peer mode of communication in a DTN. The communication pattern inside each SP is similar and works on architecture with fixed destination and mobile nodes. Let us consider one SP where some nodes are present. They are assigned with one TB to report their messages. All the RW nodes continuously search for information while carrying out their usual tasks. Whenever they find some information to be transferred, they create messages and destine it to the TB. At the same time, the RW nodes also act as intermediate hops to carry already generated messages from other nodes towards the TB. The communication takes place on peer-to-peer mode utilizing dual communication modes of the devices, as discussed previously in Sect. 3.2. Each node creates new messages as well as accepts incoming messages from neighbouring nodes. The combined storage requirement of all such messages is calculated and checked whether it reaches a pre-defined memory threshold. If yes, then the node searches for its neighbouring nodes and transmits its messages to the fittest neighbour detected within its range. The fittest neighbour is found using the strategy described later in Sect. 4.1.1. To have a good response and performance in situations where the message creation rate is slow, or the node does not get many incoming messages, the messages are transmitted after expiration of a pre-defined threshold time limit also. The main purpose of maintaining a threshold for both storage and time is to conserve the energy of the nodes. In the absence of such thresholds, the nodes always keep on searching for neighbouring nodes, and in the process spend a large amount of energy. Battery power being limited for mobile devices, this strategy helps prevent the nodes from dying for want of energy. This energy efficient model is implemented and integrated in our program as a simple model, where each node maintains a threshold for both storage and scan time. Using these values, nodes prevent themselves from always searching for neighbouring nodes, and in the process save their precious energy. The performance improvement of our proposed DTN routing protocol named DirMove (Direction of Movement based routing in DTN), as presented in Sect. 5, has been obtained using a 500 KB buffer threshold and 10 Seconds time threshold. This simple method has not been used for comparison with energy efficient models of DTN routing. Energy efficient routing is out of scope of the current work. This is a probable future work. The procedure is described in Fig. 2 with the steps chalked-out in a Flowchart.
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4.1.1 Calculation of Fitness Value of the nodes This procedure is used by a node to do smart calculations about fitness of a neighbouring node to carry forward its messages towards the destination. These calculations are based on tracking the recent direction of movement of the neighbouring candidate node along with some information of its recent past transmission. The steps to calculate the fitness values of neighbouring nodes of a node are presented. Consider a scenario consisting of some arbitrarily moving nodes, where a node A has accumulated some messages and has detected some nodes in its vicinity. Node A wants to send its messages to one of its neighbours. Each node maintains a historical table consisting of fields as shown in Table 1. The From field in Table 1 is the name of the node from which it has taken messages in the past and has delivered to the To node. Success or Failure is a binary field storing a value ‘1’ for successful delivery of the message from the From field to the To field, and ‘0’ for a failed delivery. The Delivery Latency (in sec) field stores the amount of time
Fig. 2 Energy efficient message transmission strategy at each communicating node
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taken to transfer the message from the From node to the To node. The next field Distance from Dest. at delivery is distance from the destination of the From node when it transferred the message to the current node. The reduced distance is the measure of the distance the current node has taken the message closer to the destination. The next field is the Reliability Factor (RF), calculated from the previous fields using formula 2. RF ¼ 0:5 a þ 0:2 ð1=bÞ þ 0:3 ðd=cÞ
ð2Þ
where a, b, c and d are the marked fields from Historical Table 1. The weights of the 4 parameters involved in the calculation of RF are calculated empirically. We had set different values to these weights and obtained results for delivery probability ratio and delivery latency in one simulation parameter setup. We have obtained best results in the simulation assigning those weights in Eq. 2. This can be observed from Fig. 3. A DTN is capable of tolerating some amount of delay. So, the parameter delay has been assigned smaller weight. Looking into formula (2) we find that since successful delivery of a message has a major role in deciding the past performance of a node, so this field (a) is given a larger weight of 0.5. That means if the node has failed in the past to transfer a message to any next node in most of the cases, then it is treated as unreliable. Again, since smaller delivery latencies are preferred, so this factor (b) is inversed in the formula and multiplied with a weight of 0.2 to have one-fifth participation in the formula. Finally, the fraction of the distance the message has been carried towards the destination in the past by the node is calculated as the third factor (d/c) in formula (2). This fraction can be negative for those cases where the node has taken the message even farther from where it was (see Table 2). This fraction is multiplied with the remaining weight of 0.3 to calculate the RF. This RF value gives a measure of how reliable the current node had been in the past to transfer a message from the From to the To node. Appropriate Weights have been given to the fields so as to arrive at a formula to calculate the Reliability Factor giving due consideration to all fields. An example of such a table kept at a node B for past encounters with four nodes is shown in Table 2. The RF for all the nodes are found and based on those values, certain Weights are assigned to each RF. The Weight ranges from values 0 to 0.4 and is assigned in an ascending
order of the goodness of the RF value. Smaller RF values get smaller Weight, and larger RF values get better Weights. That is, If ðRF [ ¼ 0:8Þ; wt RF ¼ 0:4; Else if ðRF [ ¼ 0:6Þ; wt RF ¼ 0:3; Else if ðRF [ ¼ 0:4Þ; wt RF ¼ 0:2; Else if ðRF [ ¼ 0:2Þ\ wt RF0:1; Else wt RF ¼ 0; where wt_RF is the associated weight of RF. Initially, the RF for all nodes is set to 0.5, with corresponding weight of 0.2 (see above). This choice is to initialize the system parameters. Whenever the nodes begin to calculate RFs of other nodes, they update the old RF value with the average of the old RF and the newly calculated RF, i.e., RF updated ¼ ðOld RF þ Calculated RFÞ=2: The RF for each node is updated after each contact so that they always carry the latest information about their neighbouring nodes. In this way, the Historical table at each node is populated. At the same time, each node also maintains a Distance Table to calculate the Directional Factor (DF) for its current direction of movement. For calculating the DF, each node continuously calculates its distance [using the Haversine formula (1)] from the destination (whose fixed location is known to each node present in the area) at two consecutive time instances, say at a gap of t time units. [Each node in our scenario is a Smartphone carried by a Relief Worker (RW). The RWs of each relief team are bound to a local control station that we call as a Throw Box (TB). This TB is placed at a fixed location, and is actually the repository of all messages that the RWs collect and send from their SPs. The smart phones are fed with this fixed location before they are set to work. This is the reason we claim that each node knows beforehand the location of the message destination (TB)]. The difference of those two distances is calculated and based on this difference the DF of the node is evaluated using formula (3). * 1; if ðdifference [ 0Þ DF ¼ 0; if ðdifference ¼ 0Þ
ð3Þ
1; if ðdifference\0Þ The DF value signifies the current direction of movement of the node. A value ‘1’ of the DF means that the node is
Table 1 Structure of the historical table at each node From
To
Success or failure (a)
Delivery latency (in s) (b)
Distance from dest. at delivery (in Km.) (c)
Reduced distance (d)
Reliability Factor (RF)
Weights
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Fig. 3 Graphs showing optimal results at different weight sets for a message delivery probability and b message delivery latency
Table 2 Example historical table at a particular node B (‘-’ value in the To field indicates packet is dropped) From
To
Success or failure
Delivery latency (in sec)
Distance from dest. at delivery
Reduced distance
Reliability Factor (RF)
Weights
0
Historical table at B to calculate Reliability Factor A
C
1
7
110
-190
0.01038961
X
Y
1
2
800
70
0.62625
0.3
C
D
1
5
750
100
0.58
0.2
P
–
0
2
880
90
0.130681818
0
N.B.: A negative value in the reduced distance field means the To node has taken the message even farther from the destination
moving towards the destination, and so is more preferable over other values of DF. If DF is 0, then it has not moved over the past two time instants, while a ‘-1’value means that the DF is moving away from the destination, in the opposite direction, and hence is very unlikely to reach the destination in the recent future. While maintaining the DF value, the nodes also calculate weights to be assigned to those DF values. These weights range from 0.6 to 0. The weight is calculated by measuring the proximity of the current node from the destination. If the destination is within the direct reach of the current node, then it is given the highest weight so that it surely gets selected for delivering the messages. For other cases, the weight values are distributed based on the distance of the current node from the destination so that nodes nearer to the destination get higher preference over distant nodes. That is,
Table 3 Distance table at a particular node B to calculate Directional Factor Location1 Lat1 20.973547
Lon1 80.437482
Location2 after time
10
Lat2
Lon2
20.994321
80.542821
dlat
0.044695602
dlat
0.0443329
dlon a
0.120569411 0.003606394
dlon a
0.1187302 0.0035039
c Distance, d Difference Weight
0.120178825 765.6592939 10.96672145
c Distance, d DF
0.1184575 754.69257 1
0.21
If ðcurrent dist from destination\ ¼ node transmit rangeÞ; wt DF ¼ 0:6; Else wt DF ¼ 0:6 ðMax dist ðcurrent dist from destination node transmit rangeÞÞ=Max dist;
where Max_dist is the maximum distance in the entire affected area that any node has to cover in the worst case, and wt_DF is the weight associated with DF. An example of a Distance Table is shown in Table 3.
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Suppose node A has got messages to forward towards the destination, and while looking for some carrier, it has detected some nodes in its range. A notifies that it wants to deliver some messages by multicasting a ping message to
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them. The other nodes reply back with their Fitness Values (FV), that they calculate within themselves using the DF and RF and their associated weights. The FV is calculated using formula (4). FV ¼ wt DF DF þ wt RF RF
ð4Þ
where wt_DF is the weight associated with DF, and wt_RF is the weight associated with RF. It is observed from formula (4) that the FV is directly proportional to the DF and RF values as well as to their weights. So, better DF and RF values, along with associated weights yield a good FV and make the corresponding node a suitable carrier of messages. On the other hand, the smaller the DF and RF values are (including negative values), the smaller are the associated weights that result in an inferior value to the FV. So, the construction of formula (4) is justifiable to the proposed strategy. The FV helps the sending node to decide the best carrier for its messages. It gives a measure of how fit a node is to carry forward the sender’s messages towards the destination. The more is the FV of a node, the better are its chances to deliver the incoming messages towards the destination. If a node does not get any better FV than its own FV, then it defers transferring its messages and keeps on carrying the messages itself, until it discovers a suitable next carrier for its messages. 4.1.2 Buffer management The buffer management procedure is used to provide a mechanism to efficiently manage the storage of a node and in the way reduce packet drops. The method works by maintaining a buffer threshold value that decides the safekeeping of its messages. A message that lies within the buffer threshold value of a node is guaranteed to be completely safe. A message created by a node or received from another node is treated with equal precedence inside the buffer. The receiver notifies the correct receipt of a message by sending a positive acknowledgement to the sender. So the sending node can delete its copy of the message, thus reducing memory occupancy, and making way for newer messages to proceed further. On the contrary, if any message is stored in the buffer outside the memory threshold, then it will be stored temporarily, and there is no guarantee for its successful delivery. This is notified by sending a negative acknowledgement to the sender. So the sender retains its copy and keeps on trying other alternatives for transmission of its messages. These instant and shallow acknowledgements provide an efficient way to reduce buffer occupancy and allow the network Bandwidth to be used more for transferring messages than acknowledgements. An acknowledgement is sent only to the immediate predecessor, and is not allowed to flow any
further inside the network. A packet is dropped from the memory portion outside the memory threshold area to ensure guaranteed messages are not deleted. This is done in a FIFO order, as earlier messages have been there for some time and there are chances that its sender has found another path for its transmission by that time. This helps in reducing the delivery latency even further. The buffer management and packet drop procedure is depicted in Fig. 4. 4.2 DTN routing in Inter Shelter-Point communication Here we consider transferring information from each TB to the Temporary MCS for further transmission to the highest level, i.e. to the MCS. As it is quite evident from the complete network architecture, the Inter SP Communication follows a fixed communication strategy where the schedule of transmission is periodic (or at-least predictable) and the carriers (DMs) are controlled and managed by humans. Carriers have fixed paths to follow and their source and destination are fixed. Hence, this communication does not require any DTN routing protocol.
5 Evaluation and results A detailed illustration is presented regarding the applicability of the proposed scheme to calculate the fitness values of the nodes to choose the fittest one. Consider four different scenarios, with node connections at four different
Fig. 4 Buffer management and packet drop mechanism
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Fig. 5 An example scenario showing node connections at four different instants. a Node A communicating with nodes B and C. b Node B communicating with nodes A and C. c Node C communicating with nodes A and B. d Node D communicating with nodes A, B and C.
instances, as shown in Fig. 5. In first instance, nodes A, B, and C are connected and node A wants to send messages to the fitter of B and C (Fig. 5a). In second instance, again nodes A, B, and C are connected. But, here node B wants to send messages (Fig. 5b). The third instance connects nodes A, B, and C with C being the sender of the messages (Fig. 5c). Finally, in the fourth instance, nodes A, B, C, and D are connected and node D wants to send messages to the fittest among its neighbouring nodes (Fig. 5d). Because of presence of more than one neighbour, each sender requires the fitness values of its neighbouring nodes. The Historical Tables (similar to Table 2) and the Distance Tables (similar to Table 3) of the nodes at an instant have been generated. The fitness values for a sample run of the strategy (obtained from Reliability and Distance tables) have been calculated and compared. Table 4 shows the fitness values of the neighbouring nodes for nodes A, B, C and D. It can be observed from the table that the fitness values of the neighbouring nodes differ from each other to a great extent. This is because their suitability to carry a message to the destination is different. From the Distance tables, it can also be observed that the closer a node is to the final destination, the greater is its fitness value. The corresponding graph has been plotted for the different Fitness Values for the four different scenarios (Fig. 6). It can be observed from Fig. 6 that the varying Fitness Values give an indication that the nodes with maximum fitness value are the most suitable to carry messages to the
Table 4 Fitness values generated for neighbouring nodes Options Node A
Node B
Node C
Node D
Fitness values For A 0.31
0.21
0.711644444
–
For B
0.708666667
0.29
0.716
–
For C
0.703714286
0.326
0.38
–
For D
0.59
0.31
0.815
0.34
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Fig. 6 Variation in fitness values for available node options for a sender node
final destination. For example, Node A has selected node C as its next hop and rejected node B, as C is already within the range of transmission of the destination. Again, node C has selected node A over node B because A is much closer to the destination and is moving towards the destination. So it has a very high chance of reaching the destination and delivering the messages to it very quickly. The fitness values of Node B are very small for all other nodes because it is far away from the destination. A direct relationship has been established between the fitness value of a node and its chances to efficiently carry the message towards the destination. The proposed method generates realistic results based on current situation of the nodes in the network, and the decision leads to efficient routing of the messages with increased message delivery probability and decreased average message latency. The performance of the proposed routing strategy is evaluated with set up simulating the proposed DTN architecture with fixed destination and mobile nodes (Table 5), using the ONE simulator. We have configured the ONE simulator to include two interfaces for each node. The interfaces have different Transmission Range and
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Table 5 ONE simulation parameters Parameter
Value
Total simulation time
6h
Movement model
Customized map-based and random way point movement with one fixed destination
Routing protocol
MaxProp, epidemic, PROPHET, spray and wait, RAPID, encounter based routing, DirMove
No. of nodes
30, 60, 100
Node buffer size
50 MB (1 GB for the destination)
Interface transmit range
50 m for Wi-Fi direct, 10 m for bluetooth
Interface transmit speed
1 Mbps for Wi-Fi direct, 250 Kbps for bluetooth
Node Movement speed
0.5–1.5 m/s for pedestrians, 9–10 m/s for Police Cars, 11–13 m/s for ambulance
Message creation rate
One message per 25–35 s
Message size
5 KB–2 MB
Different Transmission speed to depict both Wi-Fi direct and Bluetooth. The default interface is Wi-Fi direct for the devices, but when the node density is higher or no Wi-Fi could be found, devices switch to Bluetooth mode. Node density is measured using the following: (Here, Range of Wi-Fi Direct is in meters, and total area to cover is in sq. meters).
street map portion of the Jammu state in India. The state has recently been the victim of a devastating flood. On that Map based mobility model, we have used nodes depicting Relief Workers (RW), Medical Personnel (MP), Police Patrol (PP), and Ambulances (AM). RW nodes are larger in number and spread to the entire affected area on the map. A suitable point (a theatre) has been selected as the Shelter Point (SP). MPs
Node Density ðNDÞ ¼ ððTotal number of nodesÞ ðRange of Wi-Fi DirectÞ 2Þ=ðsqrtðtotal area to coverÞÞ: If ND [ ¼ 1:5; set Bluetooth mode Else if ND \ 1:5; set Wi-Fi mode:
We have made the following settings for the two interfaces: 1.
For Wi-Fi Direct 1. 2.
2.
Transmission Range = 50 m Transmission Speed = 1Mbps
For Bluetooth 1. 2.
Transmission Range = 10 m Transmission Speed = 250 Kbps
The simulations and comparisons have been performed with 3 sets of population data. These are: Set 1: Total 30 nodes: (Ambulance—2, Police Van—3, Medical Team—10, Rescue Team—15) Set 2: Total 60 nodes: (Ambulance—4, Police Van—6, Medical Team—20, Rescue Team—30) Set 3: Total 100 nodes: (Ambulance—6, Police Van— 15, Medical Team—25, Rescue Team—54). Simulations over these three sets of data have been performed on two different mobility models namely a customized Map Based post disaster mobility model [23] and the Random Way Point mobility model [24]. We have created a customized Map-based mobility model on an Open
are made to move in an adjacent smaller area of the SP. PPs patrol on important arterial roads on the map throughout the affected area. AMs move on a road connecting SP to some distant place where helicopters could be landed. Speeds of RW and MP are same. Speeds of PP and AM are greater. Vehicular based mobility model has been used for the movement of the vehicles in the scenario. We have used heterogeneous speeds for different entities in our system. The speed for movement of the pedestrians is 0.5–1.5 m/s. The Police Patrol vehicles move at a speed ranging from 9 to 10 m/s. The Ambulances have been provided with even higher speeds of 11–13 m/s. The path of movement of the vehicles (Police Patrol cars, Ambulances) is pre-set in the map. The vehicles start from a point, select their destination, move with a velocity between their given speed ranges, reach the destination and wait there for some time. From there, they choose their next destination, and follow the above steps for their movement. Similar type of vehicular movement has been employed by Aschenbruck et al. [23]. Other important changes in the parameters of the simulation have been marked in Table 5. As has been used in [12], we have also provided results with the existing Random Waypoint Mobility Model to give variations to our work. This has been
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Based on the parameters described in Table 5, simulations have been performed in the ONE simulation environment. Graphs, as presented in Fig. 7, have been plotted for results obtained in the simulations performed using customized map based mobility with total 30 nodes for four performance metrics. The seven routing protocols
mentioned in Table 5 have been compared. The mean results for each performance parameter have been taken out of ten (10) simulation runs for each set of input data for a length of 6 h. The results are put with a 95 % confidence interval. Figure 7a shows the comparison of the Message Delivery Probabilities of the Routing Protocols. It can be observed that the message delivery probability of the proposed routing strategy (DirMove) is higher than MaxProp, RAPID and others. This is attributed to the fact that MaxProp and other probabilistic approaches base their decisions on past communication patterns and do not consider the current direction of movement of a node. There are situations when a highly probable candidate node comes in contact, but actually is moving in a direction away from the destination. So, the decision to transfer messages to this node proves completely wrong. This is where DirMove works better as it considers the current direction of movement of the nodes as well. Also, placement of the destination node (TB) at strategic locations on the Map based mobility provides good decision making to DirMove, resulting in better performance. Comparison of the Average Message Delivery Latencies of the seven routing protocols, as shown in Fig. 7b, points out the fact that DirMove has the smallest message delivery latency of all other strategies. The novelty of DirMove to focus on the current direction of movement and the proximity to the final destination, at multiple instants of transferring messages, is the reason behind good
Fig. 7 Comparison of DirMove with other DTN routing protocols with respect to various parameter values on customized Map-based Mobility model. a Message delivery probability of the routing
protocols, b Average message delivery latency of the routing protocols, c Overhead ratio of the routing protocols, d Packet drop of the routing protocols
done to provide a standard comparison of proposed work with some of existing DTN routing strategies such as MaxProp, Epidemic, Prophet, Spray and Wait, RAPID and Encounter Based Routing (EBR). The specific simulation parameters that have been used are detailed out in Table 5. The following performance metrics have been considered for making an overall comparison. 1.
2.
3.
4.
Message Delivery Probability (ratio): It is the ratio of the number of correctly delivered messages to that of the total generated messages. Average Message Latency: It is the measure of average time gap between the generation of a message and its successful delivery at the destination. Message Overhead Ratio: This gives a measure of the extra number of message copies being used by the protocol to perform the required task. It is calculated as the ratio of the extra copies created and the actual number of deliveries. Packet drop: It finds the number of packets (messages) dropped, if any.
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performance. If a node is nearer to the destination, it is selected with utmost precedence, and thus results in quicker message delivery. Proximity consideration in DirMove reduces the delivery latency effectively. Epidemic and other flooding based methods lack direction of movement and proximity calculations. They transfer messages to all other nodes arbitrarily. This results in huge buffer overhead, resulting in undelivered packets to be dropped. This in turn increases overall delay. DirMove effectively optimizes both the routing protocols and the buffer management. The Overhead Ratio of DirMove is low (Fig. 7c) because the method employs an efficient buffer management mechanism with instant and shallow acknowledgements to remove unwanted messages early. DirMove ensures that a single copy of the message gets transmitted from the source to the destination in most of the cases, that too with guarantee of its proper safe-keeping by the efficient buffer management and packet drop mechanism. More than one copy exists in the system only in the rare case of a negative acknowledgment from the receiver to the sender that occurs in case buffer threshold is crossed. DirMove provides an efficient buffer management technique that results in minimum packet drop at any time while the communication goes on in the network, and is justified by the results of Fig. 7d.
To prove our method’s efficiency with respect to varying node densities, we have plotted graphs (Fig. 8) for performance parameters with varying node densities on the map based mobility model. The densities have been taken for 30 nodes, 60 nodes and 100 nodes in the same area of dimension 2500 m 9 3500 m. As can be seen from Fig. 8, DirMove continues to outperform all other DTN routing methods in denser scenarios (for 60 and 100 nodes) as well. DirMove provides highest message delivery probability (Fig. 8a). The increase in the number of forwarding neighbours enables better decision making by DirMove in denser conditions. Map based mobility model ensures better placement of the destination (TB) along with better movements of RW nodes and vehicles over the Activity Area. DirMove uses this pattern to its advantage as it employs an efficient formula based on the current movement direction and proximity to the destination node. DirMove provides minimum message delivery latency (Fig. 8b) in all scenarios. This is because of the use of recent network information like the current direction of movement, and the current proximity to the destination. The number of packet drops for DirMove is also the minimum always (Fig. 8d). This is attributed to the efficient buffer management policy by DirMove, as explained earlier. But, as has been seen in earlier cases, it incurs small overhead
Fig. 8 Comparison of DirMove with other DTN routing protocols with respect to various parameter values on customized Map-based Mobility model with varying node density. a Message delivery probabilty with varying density of nodes, b Message delivery latency
with varying density of nodes, c Message overhead ratio with varying density of nodes, d Number of packet drops with varying density of nodes
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Fig. 9 Comparison of DirMove with other DTN routing protocols with respect to various parameter values on random way point mobility model with varying node density. a Message Delivery Probabilty with varying density of nodes. b Message delivery latency
with varying density of nodes. c Message Overhead Ratio with varying density of nodes. d Number of packet drops with varying density of nodes
(Fig. 8c) for its performance. This is in terms of small number of multiple copies of messages in case of negative acknowledgements and some system and ping messages used. This overhead is lesser than Epidemic, EBR, and some others. To generalize our results further, we have plotted graphs (Fig. 9) for performance parameters with varying node densities on the random way point mobility model, which is the most widely used mobility model in research community. The densities have been taken for 30 nodes, 60 nodes and 100 nodes in an area of dimension 4000 m 9 2000 m. As can be observed from Fig. 9, DirMove performs reasonably well in this scenario also. DirMove achieves a good message delivery probability (Fig. 9a). Only RAPID outperforms DirMove for lower densities. With increase of node density above 75 nodes, DirMove manages to perform best. At low node density, DirMove gets small number of forwarding neighbours. So, the options of choosing best forwarder are less. The intentional utility function mechanism employed by RAPID for DTN routing provides it a good performance in the low node density scenario. RAPID performs well here for message delivery probability, but incurs higher message delivery latency. As node density increases, the possibility of getting more forwarding neighbours also increases. This results in better decisions
taken by DirMove. Hence DirMove over-performs RAPID for high densities. DirMove continues to provide minimum message delivery latency (Fig. 9b) because of its decisions based on recent movement direction, and current proximity to the destination. Possibility of messages going away from the destination is minimized. DirMove achieves minimum packet drop (Fig. 9d) as it uses very efficient buffer management technique, as discussed earlier. As has been observed and discussed in earlier cases, it incurs little overhead (Fig. 9c) for its performance in this case also.
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6 Conclusion The area of Post Disaster Situation Analysis and Resource Management needs more focus because of increased severity and losses accompanied with disasters. We have presented a work that focuses on network setup and routing strategy in a post disaster relief work. We have proposed a robust network architecture that can be set up easily and quickly in those situations. The proposed architecture is scalable and robust in extreme situations. We have also proposed a novel data routing strategy that best applies to our proposed network architecture. Our novelty lies in selection of the most recent information
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about current direction of movement of the nodes to transfer a message. To increase reliability, some historical information of message delivery are also considered while taking decisions. The protocol has been run in the ONE Simulation Environment, and is found to yield a result that is best applicable among the available alternatives and thus has been found to be an efficient strategy in post disaster scenario. The proposed strategy out-performs most of the other strategies in almost all of the important performance parameters for such routing strategies in a customized map-based mobility model depicting a post-disaster condition. It also performs fairly in the standard random way point mobility model.
7 Future work In our current work, we have focussed on a Post Disaster Scenario for developing our routing strategy. We would like to extend the work into those areas also where general Delay Tolerant Networking is suitable. Security being a major concern for such systems, we would, in the future, like to focus on the security issues in DTN routing, like selfish behaviour of any node, malicious nodes such as wormholes and black-holes in a DTN. Also, alternative ways to find localized GPS positions utilizing Smartphone’s sensors will be worked out in the future. This will be used to track a node when it is moving, without using costly GPS. Acknowledgments This research work is an outcome of the Government of India Project titled DiSARM funded by Information Technology Research Academy, Media Lab. Asia, Department of Electronics & Information Technology, Ministry of Communications and Information Technology.
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740 Amit Kumar Gupta is a Senior Research Fellow (SRF) at Kalyani Government Engineering College in West Bengal, India. His areas of interest include Delay Tolerant Networks, Wireless Networks, Sensor Networks, Security in Wireless Networks, Social Networks Data Analysis, and Data Management in wireless transmission. He is pursuing his Ph.D. from the University Of Kalyani, West Bengal. He is currently working under a Project titled DiSARM (Post Disaster Situation Analysis and Resource Management using Delay Tolerant peer-to-peer opportunistic networks) funded by Information Technology Research Academy, Media Lab. Asia, Govt. of India since 2014. He has completed his masters in Computer Applications and in Information Technology from the University of Calcutta. He carries a teaching experience of 4 years of teaching masters students.
Indrajit Bhattacharya is an Assistant Professor at Kalyani Government Engineering College in West Bengal, India. He has completed his Ph.D. from Jadavpur University in West Bengal, India in 2014. He has obtained his masters degree in Computer Science from the University of Calcutta, West Bengal. He has a teaching and research experience of more than 14 years in different institutes of repute. He is the Principal Investigator of the Project titled DiSARM (Post Disaster Situation Analysis and Resource Management using Delay Tolerant peer-to-peer opportunistic networks) at Kalyani Govt. Engg. College, funded by Information Technology Research Academy, Media Lab. Asia, Govt. of India. His research interests include Delay Tolerant Networks, Wireless Networks, Sensor Networks, and Radio Frequency Identification. Partha Sarathi Banerjee is an Assistant Professor at Kalyani Government Engineering College in West Bengal, India. He is pursuing his Ph.D. from Jadavpur University in West Bengal, India. He has completed his master’s degree in Computer Science and Engineering from Jadavpur University, West Bengal. He is working as a CoPI in the Project titled DiSARM (Post Disaster Situation Analysis and Resource Management using Delay Tolerant peer-topeer opportunistic networks) at Kalyani Govt. Engg. College, funded
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Wireless Netw (2016) 22:723–740 by Information Technology Research Academy, Media Lab. Asia, Govt. of India. He has a teaching and research experience of more than 12 years. His research interests include Wireless Networks, Sensor Networks, and Security in Sensor Networks. Jyotsna Kumar Mandal, Ph.D., is a Professor at the Computer Science and Engineering Department of The University of Kalyani, West Bengal, India. He has been an Ex-Dean to the Faculty of Engineering, Technology & Management at the University of Kalyani. He has an active academic carrier with more than 30 years of teaching and research activities in various reputed institutes and Universities. He is active member of more than 10 professional bodies like IEEE, ACM, to name a few. He has frequent involvement with organizing International and National Seminars in different areas of computer science and allied subjects. His fields of interests include Network Security, Steganography, Remote Sensing & GIS Application, Image Processing. He leads various government and non-government projects in his computing laboratory. He has produced Eleven Ph.D. Scholars, and eleven more are in the pipeline. His total number of research publications is more than three hundred and fifty, in addition to publications of five books from LAP Lambert and nine edited volumes of AISC Springer, Germany.
Animesh Mukherjee, Ph.D., is an Assistant Professor in the Computer Science and Engineering Department at the Indian Institute of Technology, Kharagpur, West Bengal, India and a Simons Associate, ICTP, Trieste, Italy. Prior to this, he was working as a post doctoral researcher in the Complex Systems Lagrange Lab, ISI Foundation, Italy. He received his Ph.D. from the Department of Computer Science and Engineering, IIT Kharagpur on selforganization of human speech sound inventories. His main research interests focus around applying complex system approaches (mainly complex networks and agent-based simulations) to different problems in human language evolution and change, web social media, information retrieval and natural language processing. He is involved in various research projects executing at IIT Kharagpur, as well as outside the institute. He has been an active researcher and academician, carrying research and teaching experience of more than 10 years.
