A Framework for WSN-based Opportunistic Networks - IEEE Xplore

Proceedings of the 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

A Framework for WSN-based Opportunistic Networks Xiuwen Fu, Wenfeng Li, and Huahong Ming Dept. of Logistics Engineering Wuhan University of Technology 430063, WuHan, P.R. China Email: [email protected]

Giancarlo Fortino Dept. of Informatics, Modelling, Electronics and Systems (DIMES), University of Calabria 87036, Rende (CS), Italy Email: [email protected] traps people in the debris, damages utilities and roads, and causes fires and explosions. Under this situation, the ability to communicate is extremely valuable for sharing vital information (such as the number and locations of survivors, damages and potential hazards) and the demand for time delay is much stricter due to the principle of "golden time relief' [3]. Although general opportunistic networks are able to achieve message delivery in this case, the price of time delay is too expensive for us to afford. This fact makes us research new ways to ponder how to reduce time delay in message delivery while retaining communication ability of opportunistic networks in sparse settings. WSNs consist of small nodes with sensing, computation, and wireless communications capabilities [4]. Just like in MANETs, in WSNs an end-to-end path between the sensor node and the base station is required in all times. Thus, WSNs have a strict requirement of node density, which is hard to satisfy in some harsh environments. But in WSNs, when a node creates a message, it can transmit the message to the nodes it connects with immediately. The good information­ sharing ability in WSNs gives us inspirations to improve the performance of opportunistic networks in terms of message delay. In this paper, we describe a new opportunistic network architecture called WON (WSN-based Opportunistic Network). WON introduces WSNs into the opportunistic networks to achieve fast message delivery in sparse setting. The main idea behind WON is to use the information-sharing ability of WSN to exploit the opportunities of message forwarding. The rest of this paper is structured as follows. Section 2 discusses related work about opportunistic networks. In Section 3, an overview of WON is given. The layered architecture of WON is presented in Section 4. A simulation to evaluate the performance of WON is conducted in Section 5. We discuss some open issues need to be further addressed in Section 6 and the paper is concluded in section 7.

Abstract-How to shorten time delay and enhance delivery

ratio is still an open problem in the study of opportunistic networks. Most proposals are trying to deal with this issue by introducing infrastructures. Although related research has been proven to be useful in improving the routing performance of the network, there is still room for further improvement. In this article,

inspired

by

the

powerful

message

synchronization

capability of wireless sensor networks (WSNs), we propose a new opportunistic network framework called WON that introduces WSNs into opportunistic network. With the support of WSNs, fast message delivery and high success ratio can be achieved. We specifically compare

present

WON

to

the

layered

existing

architecture

architectures

in

of

WON

and

opportunistic

networking. The simulation results concerning delivery delay and success ratio are highly encouraging. Finally, open issues are outlined. Keywords---opportunistic networks; wireless sensor networks; layered architectures; simulation-based performance evaluation.

I.

INTRODUCTION

In the context of Mobile Ad-hoc Networks (MANETs), which in recent years evolved based on social networks, there is always an assumption that nodes are well-connected and capable of organizing themselves arbitrarily most of the time. In addition, an end-to-end path between the source and the destination is always expected to exist in the network. Despite the fact that MANETs present promising results in the network environment with high density (e.g. conference environment), their performance are far from satisfactory in sparse settings where an end-to-end connected path rarely or never exists [1]. For this reason, opportunistic networks as an evolutionary version of MANETs, were introduced. Unlike in MANETs, in opportunistic networks the operating mechanism of message forwarding is to let nodes with messages (to be forwarded) wait for an appropriate forwarding opportunity rather than deliver messages through a pre-computed path. Through store-ferry-forward paradigm, opportunistic networks are capable of transmitting data in sparse settings. Therefore, opportunistic networks have been widely applied in many scenarios, such as wildlife monitoring and Internet of Vehicles [2]. In these scenarios, a certain level of time delay is allowed. But in some cases, the rising of time delay would compromise the existence value of the network. For example, let us imagine the following hypothetical disaster scenario: a severe earthquake has occurred, which collapses buildings,

II.

As a typical realistic network trying to achieve message delivery in many extreme settings, the framework of opportunistic networks presents evident differences in terms of various application scenarios. According to whether or not networks involve extra infrastructures, the frameworks of opportunistic networks can be classified into two macro­ categories: (i) non-inJrastructure-based framework and (ii)

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RELATED WORK

infrastructure-based framework. In non-infrastructure-based frameworks, the message routing is only relied on the encounters of mobile message-carriers and most of current routing techniques are designed for this framework, such as Prophet [5]. As non-infrastructure-based frameworks do not require extra hardware support, they can be applied in most of cases directly. However, the performance of non­ infrastructure-based framework is far from satisfactory in terms of delivery delay and success rate. Due to this reason, different forms of infrastructures have been introduced to support message forwarding. In infrastructure-based frameworks, message exchange is not only between nodes, but also occurs between infrastructures and nodes. Obviously, the basic idea behind infrastructure-based framework is to use the improvement of network performance brought by infrastructures to offset the hardware cost raised by the introduced infrastructures. Since the message forwarding in WON needs WSNs to accelerate the message-sharing, it is reasonable for us to categorize WON as infrastructure-based framework. Therefore, we only discuss the related work about infrastructure-based frameworks in this section. The most classical infrastructure-based framework is Infostation [6]. In the Infostation, mobile nodes can connect to the network in the vicinity of base stations, which are geographically distributed throughout the area of network coverage. The target node can access the uploaded data stored in the cloud through communicating with Infostation. Although Infostation have been proved to be high-efficient in the cases like Internet of Vehicles, the rapid deployment of Infostation is an extremely challenging task in some scenarios, which lack of Internet access (e.g., earthquake fields or forest fire fields). Shared Wireless Infostation Model (SWIM) [7] is an improved version of Infostation, which allows both node­ to-base-station and node-to-node communications. This means that a node can deliver the message to the base station directly if within the communication range, otherwise it delivers the message opportunistically to a near node that will eventually forward it to the base station when encountered. Obviously, compared with Infostation, the routing mechanism of SWIM is more flexible. If we observe more deeply, it can be seen that if these base stations fail to access internet, the only role they can play is a fixed information sink, which can only collect messages from visiting nodes and then wait for the target nodes to be within the communication range to forward the stored messages to them. Therefore, it is easy to understand that for most disaster scenarios, the only benefit brought by Infostation and SWIM is adding some fixed nodes to increase the encountering probability of nodes. Aiming to further improve the routing performance of opportunistic networks, mobile infrastructures are introduced. In the mobile infrastructure-based framework, mobile infrastructures can be treated as extra mobile node to relay the message from source node to target node. Like in Ferry [8], the mobile infrastructures are referred as message ferries. Message ferries moves around following a pre-defined and known path. Each node in the network has knowledge of the paths followed by active ferries, and moves to meet ferries when it has data to deliver. The performance of Ferry depends on pre-determined routes of message ferries. But for unknown area, the decision making of routes is difficult. Mules [9] is one kind of

opportunistic network designed for message delivery among isolated sensor networks. The message is delivered from one network to another network via data mules. Since mules can only deliver environmental information collected by sensor networks, but cannot deliver messages created by humans in an opportunistic way, its application is limited. Aiming to expand the functions of opportunistic network, human-centric WSNs (HWSN) [10] is proposed. Unlike in Mule, in HWSN, on the one hand, the humans as mobile entities can forward messages to destinations via encounter with each other, on the other hand, when humans cross the sensor networks deployed in the scenarios, they can play the same role of mules as a message carrier to collect environmental information from sensor networks and finish delivery when they meet target nodelbase station. Although HWSN has been proven to be highly efficient in search and rescue activities, WSNs are only treated as data sources rather than message forwarders in message routing. To exploit WSNs as message forwarder, we therefore propose a novel opportunistic architecture named WON (WSN-based Opportunistic Network). In WON, besides general mobile node-to-mobile node and mobile node-to­ deployed sensor node communications are allowed, message forwarding among deployed sensor nodes is also permitted. Compared with existing opportunistic network frameworks, the most prominent feature of WON is to let WSNs participate in message forwarding that can facilitate the message delivery and thus improve the efficiency and effectiveness of the network. To further clarify about the relative strengths and weaknesses of current opportunistic infrastructure-based frameworks, in Table I a comparison among them is provided with respect to four parameters (latency, costs, scenario flexibility and deployment difficulty). TABLE I.

COMPARISON AMONG INFRASTRUCTURE-BASED FRAMEWORKS Performance metrics

Infrastructurebased

Latency

frameworks

Infostation SWIM Ferry Mule HWSN

Low Low Medium Medium Medium

III.

Infrastructure cost High High Medium Low Medium

OVERVIEW OF

Scenario flexibility Low Medium Medium Medium High

Deployment diffiCUlty High High Medium Low Low

WON

WON is an opportunistic network framework that involves WSNs. It uses the data-sharing capability of WSNs to achieve data delivery from source node/network to destination nodelbase station. WON consists of two types of nodes: mobile node and deployed sensor node. In most of application scenarios, we can define a mobile node as a human holding sensor device/s with short-range communication capability. Thus, mobile node is allowed to sense the environment and exchange information with peers/sensor nodes via a communication module. As for deployed sensor nodes, just like general sensor nodes that are usually included in WSNs, they are able to collect environmental information and organize themselves into a sensor network with their neighbors automatically. Besides communication with their peers within the same network, deployed sensor nodes can also communicate with mobile nodes when within their communication range. In order to better illustrate the operational process of WON, we show an entire process of a

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message copies all over the sensor network. At this moment, all of the nodes in the sensor network have the message copies. When mobile node 5 passes close to sensor node 4, node 4 transfers the message copy to node 5 actively. After that, when node 5 encounters with node D, message is transferred from node 5 to node D. Now, the entire process of message delivery is finished. It is not difficult to find that the most significant advantage of WON is data synchronization capabilities of WSN. Unlike Infostation or SWIM that data synchronization only occur when access point (AP) can access Internet, WON can achieve the same effect through deployment of low-cost sensor nodes. Figure 1 only presents the process of message delivery from mobile node to mobile node. In fact, besides the general paradigm of node-to-node communication, message delivery from network to base station can also be accomplished within WON. Here, we consider the procedures of message forwarding depicted by figure l(c) and led) as a complete process of message delivery. The responsibility of WSN is to monitor environment and send the data to node D. However, there is no effective link between the sensor network and node D. When node 5 passes by any sensor node in WSN, node 5 gains the monitoring data of the entire network. Node D can get environmental data from node 5 when they meet each other. From this perspective, the functions of Ferry, Mule and HWSN can also be covered by WON.

simple message delivery within the WON framework in Figure l. We can see there are four mobile nodes (S, 1, 5, D) and three deployed sensor nodes (2, 3, 4) that participate in this message delivery. Different mobile nodes belong to various "shadow zone". Shadow zone can be assigned various meanings depending on different application scenarios. For disaster scenarios, we can consider the shadow zone as the geographical contexts created by obstacles. Thus, the nodes within the same shadow zone have high probability to encounter with each other. Conversely, nodes that belong to different shadow zones are very unlikely to meet each other. For social scenarios, shadow zone can be understood as community. Obviously, in real life, humans who live in the same community are more likely to meet each other. Therefore, as for the nodes at the same shadow zone, they can encounter with each other more frequently. Based on this assumption, if we only counts on the opportunistic encounters between mobile nodes to finish message delivery from node S (Source) to node D (Destination), the routing performance would be far from satisfactory in terms of time delay and delivery rate. Therefore, we introduce WSNs (consisting of nodes 2, 3 and 4) into the network. Node S firstly sends the message to node 1 within the same shadow zone when they meet each other. And then, node 1 shares the message with node 3 when they encounter. After receiving the message, node 3 spreads the ----------------------

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IV.

LAYERED ARClllTECTURE OF

data from mobile nodes also includes the description of the environment created by the human senses. Here, to exemplify this, we still consider the disaster scenario with deployment of WON: rescuers can evaluate the disaster level (e.g. the size of affected crowd and area) and input these data into the handheld terminal by voice or text; when the mobile node encounters other nodes, it will complete message delivery automatically. The MA C layer is the most evident feature that distinct WON from general opportunistic networks. The responsibility of MAC layer is to create the network topology and maintain it well operated. It is worth noting that, in general opportunistic networks, since message delivery between nodes is created by opportunistic encounters, there is no stable topology existing in the network. Thus, there is not MAC layer in the architecture of opportunistic networks. But as far as WON is concerned, since WSN is introduced into WON, we do need MAC layer to manage the topology of WSN. Just like general MAC layer in WSN, the specific responsibilities of MAC layer include channel access policies, scheduling, buffer management and

WON

Compared with traditional patterns of opportunistic networks, WON is able to complete message delivery within relatively short delay while maintaining reasonably high success rate. To achieve this, WSNs are introduced into WON to make network have message synchronization capability. Thus, the architecture of WON is different from that of general opportunistic networks. Figure 2 shows the layered architecture of WON that includes five layers: Sensing, MAC, Network, Relay and Application. The Sensing layer is responsible for capturing information from the operating field, which will be then used to support the decision-making and coordination activities. For a deployed sensor node, its function is to collect environmental information (e.g. humidity, temperature or smoke). For a mobile node, in most cases, it refers to humans that also have the ability to sense the environment. Thus, besides environmental data collected by physical sensors, the sensing

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error control. To run MAC layer, we still require a MAC protocol to consider energy efficiency, reliability, low access delay and high throughput as major priorities to accommodate with sensors limited resources and to avoid redundant power consumption. The Network layer is responsible for building temporary connection when two nodes encounter each other. It's easy to understand that compared with the MAC layer responsible for maintaining static topology of the WSN, the Network layer is to manage the construction of dynamic links when opportunistic encounters occurs. Since WON is composed of deployed sensor nodes and mobile nodes, the responsibilities of the Network layer should cover temporary communication of deployed sensor nodes-to-mobile nodes and mobile nodes-to­ mobile nodes. Such specific responsibilities of network layer includes neighbor discovering, access control and identity authentication. To support the Network layer, the nodes usually should be equipped with BluetoothlZigBee interface. Assume that one mobile node with Bluetooth cruises over one unexplored area and the Bluetooth module is keeping scanning. When a strange Bluetooth channel is found, the opportunistic link is built through assess control. After construction of link, the identity information is exchanged to authenticate each other's identity. When nodes' identity of nodes is confirmed, then we move to next layer (i.e. Relay layer). The Relay layer is responsible for making message­ forwarding decision. In this layer, we need to specify how to deliver messages. Due to this reason, the core of Relay layer is the message forwarding protocol that is encapsulated into the layer. The fundamental role of message forwarding protocol is to decide the next hop of message forwarding. Since we have two types of nodes (i.e. mobile and deployed sensor nodes) in WON, we need to design three kinds of relay protocols in WO : mobile nodes-to-mobile nodes (or mobile-to-mobile), mobile nodes-to-deployed sensor nodes (or mobile-to-deployed) and deployed sensor nodes-to-deployed sensor nodes (or deployed-to-deployed). For mobile-to-mobile message forwarding, many forwarding protocols have been presented like Prophet and Epidemic [11]. As for deployed-to-deployed message forwarding, many kinds of WSN routing protocols can be applied. The selection of forwarding protocols in WSNs should suit the network topology created in the MAC layer. For example, if we aim to exploit LEACH [12] to deliver messages, the topology of WSN should be designed to be cluster­ structured in the MAC layer. For mobile-to-deployed message forwarding, there are currently no references protocols to be used. The basic mobile-to-deployed forwarding mechanism has to carry on forwarding behavior when one of two sides has messages. Since the Relay layer is key to determine the performance of WON, we need to design a cost-effective communication mechanism that considers both energy consumption and time delay. The purpose of the Application layer is to provide a directly exploitable high-level service to the end-users. According to different task scenarios, the design of the Application layer is totally different. For example, in disaster scenarios, the rescuers put more emphasis on the monitoring of disaster zone and on rescuing message delivery in areas with poor communication support. In Internet of Vehicles, the focus is

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A.

SIMULATION ANALYSIS V. Simulation Settings

In order to show the potential benefits of using WON to improve the network performance, in this section we conduct extensive simulations based on Matlab to evaluate the performance of WON and compare it with the currently representative frameworks of opportunistic network (i.e. non­ infrastructure, Infostation and Ferry). To simplify the simulation without loss of generality, in all selected frameworks mobile nodes are equipped with two interfaces (i.e. ZigBee and Bluetooth). Bluetooth interface is used for communication between mobile nodes and for mobile node-to­ infrastructure communication. ZigBee interface is set for peer communication inside WSN, which can only be used in WON. The communication protocols for mobile node-to-mobile node and mobile node-to-infrastructure are configured as Epidemic protocol. The basic idea of Epidemic protocols derives from flooding protocols, in which the node with message will send the message copy to an encounter when it finds that an encounter doesn't have the message. For routing inside WSN, once the node receives a new copy that it never meets before, it will spread the copies to the other nodes to which it connects immediately. In WON, we assume that the deployed sensor node can only communicate with its neighbors within its communication range. In Infostation, we only consider the general scenario where base stations lack of Internet access and can only be treated as data-relay stations. In Ferry, the routes of ferries have already been pre-determined during the initialization stage. Since the simulation area is square, ferries will follow a square route whose central point is also the center of simulation area. The size of square route that each ferry follow is randomly generated but cannot be larger than the size of the simulation area. Other default settings of simulation are listed in Tables II and a snapshot of simulation at a certain moment is shown in Figure 3. TABLE IT

SIMULATION SETTINGS

Parameter

Default value

Simulation area

1000 X 1000 m 5000s Random Way Point (RWP) 5m1s 10 100s Imsg/5s unlimited 200m 10m

Simulation time Mobility model Moving speed(mlS) Number of mobile nodes Time to live (TTL) Message generation rate Storage capacity Communication radius of Zigbee Communication radius of Bluetooth

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B.

whilst Ferry and Infostation performances are hardly satisfactory, i.e. less than 70%. If we look deeper, it is easy to observe that the lifting effect of adding infrastructures in WON is more obvious with increasing number of infrastructures. This is because the more deployed sensor nodes the WON has, the higher probability a newly-incoming sensor node has to connect with other peers so becoming a member of the sensor network.

Simulation Metrics

We define a set of metrics that we use in evaluating routing protocols in opportunistic networks: 1) Message delivery ratio (or Success Rate), the ratio of the number of messages delivered out of the number of total messages generated. 2) Delay, the duration between a message generation time and the delivery time of the generated message. C. Simulation results and analysis

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Fig. 5. Delivery delay of the four frameworks

Regarding the message delivery ratio in Figure 4, the performance of all network frameworks is significantly different. As for non-infratructure, the success rate is only less than 25%. Most of messages in non-infrastructure fail to reach destination due to TTL expiration. As far as infrastructure­ based frameworks is concerned, the success rate of all three frameworks (i.e. Infostation, Ferry and WON) is improved a lot by increasing the number of infrastructures. At the early stage (i.e. number of infrastructures less than 6), Ferry performs best due to the mobility of ferries that makes mobile nodes have higher probability to encounter infrastructures than static infrastractures (such as sensor nodes and base stations). Besides that, the performance of Infostation and WON is almost similar in the early stage. This is because the number of deployed sensor nodes is too small to self-organize into a sensor network, thus miling sensor nodes play the similar role of "ferries" in Ferry. But the situation is changed when the amount of infrastructrues exceeds 6. Starting from this point, WON outperforms Ferry in terms of success rate. The success ratio of WON reaches almost 100% when there are 10 infrastructures (i.e. deployed sensor nodes) in the network,

Figure 5 shows the delay of all the frameworks. Firstly, we need to point out that during the statistical process, we treat the time delay of failure message (i.e. the message fail to arrive at destination) as the length of TTL. As a consequence, the upper limit of the delay in all selected frameworks is 100s. Wthout the help of infrastructures, the delivery delay of the network is far from satisfactory, i.e. more than 80s. By contrast, the time delay of infrastructures-based frameworks is reduced dramatically due to the involving of infrastructures. The delay performances of Infostation, Ferry and WON do not have too much difference when the network do not have yet too many infrastructures. Conversely, when the number of infrastructures reaches a certain amount, the difference of delay performance becomes more apparent. When the number of deployed sensor nodes reach 10, the time delay of WON is less than 20s. In comparison, the delay of Infostation and Ferry is still above 30s, in the presence of the same amount of infrastructures. From the above simulation, it is easy to fmd that the routing performance of WON in terms of success rate and delay is much better than current prevailing opportunistic frameworks, especially when the number of infrastructures achieve a certain amount. Besides the highlighted advantages, there are still

347

some challenges we need to deal with. In the next section, we discuss several open challenges that will be addressed as part of the future work. VI. CHALLENGES A.

deployed sensor nodes, only a small portion of nodes at working model is able to guarantee the coverage of the entire area. Thus, developing a density-aware scheduling algorithm can be seen as part of the future work that is worth exploring. VII.

Opportunistic routing

In this paper, we select Epidemic protocol as our opportunistic routing to testify the routing performance of WON. Although Epidemic has been proven to maximize the delivery ratio and minimize the delay, the shortcomings such as frequent message exchange and excessive number of message copies flowing over the network brought by Epidemic will negatively affect the lifetime of the network and occupy too much cache space. Therefore, it is necessary for us to design a specialized routing mechanism for WON. For mobile node-to-mobile node routing, since mobile nodes in most of scenarios refer to real human beings, social features (e.g. community) of human beings encourage us to think how to use these knowledge to make better forwarding decisions. Existing routings (e.g. Bubble rap [13]) give us a good example for reference. For mobile node-to-deployed sensor node routing, it is reasonable for us to exploit the routing by considering geographical location preferences. Obviously, for the sensor network that the destination node often crosses through, we can treat the zone covered by this network as "hot zone". When mobile nodes with message copies come by, the probability for node to drop the message copy should be reasonably high. Conversely, for "cold zone" where target node hardly passes by, the probability of dropping messages should be lower. B.

In order to speed-up the message delivery and enhance the success ratio of message forwarding, in this paper we have proposed a novel opportunistic network framework called WON that introduces WSNs into the network. Compared with existing infrastructure-based opportunistic frameworks, the routing performance of WON has a distinct advantage in terms of both success rate and delay. Furthermore, the layered architecture of WON is also presented. Through simulation, the promising routing performance of WON is also validated. Nevertheless, there are still some technical challenges that need to be addressed before a real implementation. How to design a cost-effective routing scheme and cache management strategy, as well as minimizing energy wasting in deployed sensor nodes, is the core activity that we will carry out as future work. REFERENCES [I]

[2]

[3]

[4]

Cache mangement

In most cases, due to hardware limitation, the sensor nodes in WSNs cannot have ample cache space. Therefore, this requires us to find a highly efficient cache management strategy instead of spreading message copies all over the network. Since most of WSNs are dynamic-cluster-structured, it inspires us to develop a distributed way to hand out the copies into each cluster head. By this strategy, each cluster can have all the message copies and each member of the cluster do not have to store every message copies. Therefore, when making rotation decision of cluster head, cache space should be a key factor to be taken into account. C.

CONCLUSIONS

[5]

[6]

[7]

[8]

Energy conservation

For general WSNs, sensor nodes only need to maintain wireless communication with their peers. But in WON, besides peer communication, sensor nodes are also required to communicate with the mobile nodes. Therefore, the sensor nodes are often equipped with two communication interfaces (i.e. ZigBee for communication inside WSN and Bluetooth for sensor node-to-mobile node communication in general). As a consequence, the energy consumption of sensor nodes in WON is much faster than that of the conventional sensor nodes, which will negatively influence the lifetime of the network. Aiming to tackle this issue, it is necessary for us to develop a node scheduling algorithm. The node density in various areas is very different. For areas with heavy density of

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[10] Carreno P,Gutierrez F,Ochoa S F,et al. Using Human-Centric Wireless Sensor Networks to Support Personal Security. Internet and Distributed Computing Systems. Springer Berlin Heidelberg,2013: 51-64. [II] Ramanathan R,Hansen R,Basu P,et al. Prioritized epidemic routing for opportunistic networks[C]//Proceedings of the 1st international MobiSys workshop on Mobile opportunistic networking. ACM,2007: 62-66. [12] Heinzelman W R, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks//System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on. IEEE,2000: 10 pp. vol. 2. [13] Hui P, Crowcroft J,Yoneki E. Bubble rap: Social-based forwarding in delay-tolerant networks. Mobile Computing, IEEE Transactions on, 2011,10(11): 1576-1589.

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