Employing data communication networks for managing safer evacuation during earthquake disaster

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Abstract

Humans are the most significant resources in the universe. Subsequently, people life is urgently desired to be protected against the natural disasters such as earthquakes in which modern technologies can play an essential role. This paper presents a study of modeling road network traffic (RNT) using data communication networks (DCNs) with the focus on earthquake disaster aiming to achieve safer evacuation. Five DCN models are proposed to represent the traffic analogy of the corresponding RNT cases, namely, speed-flow relationships, left turn effect, four-way cross section control, roundabout cross control, and cracked roundabout. Using the road network (RN) mapping parameters to data network (DN) equivalents in these five models, the DN solutions can be used to solve RN problems. The simulation results indicate a close traffic performance analogy in both RNT and data DCNs. The proposed mapping is verified using two statistic tests called t-test and analysis of variance (ANOVA). Consequently, DCNs can be exploited in managing a safer evacuation during natural disasters.

Introduction

According to the current world needs to improve the road network traffic (RNT) that tremendously affects our life, there are several solutions which depend on various communication systems such as mobile communication, radar, and satellite communication systems as presented in [1], [2], [3]. Other studies also highlight the role of modern communication systems for disaster management such as mobile radio system and localization that are introduced in [4], [5], [6]. With the same vision, Ali et al. [7] exploited the clustering of a cellular network based on device-to-device communication in order to prolong the nodes lifetime during a natural disaster. The optimal distribution for the rescue teams of the disaster victims was further studied in [8]. The regulations between the different organizations and governments for the involved agencies in disaster relief were also discussed in [9]. Unlike the efforts exerted to treat the natural disaster, another point of view considered the disaster as a man-made cause which was studied in [10].

No doubt that, in case of earthquake disaster or during road traffic crisis, a fast and reliable solution is needed to ensure safe evacuation from the afflicted areas as studied in [11], [12], [13]. Therefore, there should be a solution map to guarantee smooth traffic motion at all times. The main motivation of this paper is that most of the previous efforts focus on providing communication-based solutions without integrating the road network (RN) behavior within their suggested models. As this paper proposes data network (DN) equivalent models of different RN cases, more accurate performance results can be obtained by a single integrated model of both the RN and the associated communication solutions using DN performance evaluation tools. Furthermore, by mapping the RN parameters to DN equivalents, the DN solutions can be used to solve RN problems. For example, DN congestion control can be resolved by rerouting the traffic of the congested node which can be used in RN as well. Moreover, DN delay reduction by using multiple processors, which may be implemented in different ways, can be used in the RN toll station to determine the appropriate number of required booth. Another big advantage is integrating both RNT and data communication network (DCN) in a single performance evaluation model; which leads to more accurate results and the ability of investigating the effect of the related parameters changes on the overall system performance. In addition, the performance analogy between the DN and RN can be exploited to strongly assist in disaster evacuation by providing a quick updated evacuation plan based on the occurred damage.

Since the traditional solutions do not solve the day-to-day road traffic efficiently, this paper suggests modeling RNT using the DCN traffic, which have several modeling theories that achieve powerful results in different network conditions. This modeling allows the use of DN solutions for such high data volume to achieve high-performance outcomes and reduce congestion in the RN domain. Recently, there was a high-developing rate of DN techniques and protocols as described in [14], [15]. The development was extended to such source of Big Data and its security [16], [17].

Big Data can claim an important role in that integrated model. Big Data size in such of this platform is expected to be in the order of several Terabytes with formats including but not limited to database records, videos, and still photos. Those different formats can represent input and output traffic of internet-of-things (IoT) network elements. Both of Big Data and IoT systems are two of several hot extension areas that can be built on the paper ideas.

In [18], a paradigm of IoT system has been proposed as an integrator of wireless/wired technologies that are connected using the Internet to enhance emergency response operations. The motivation of using such system is the different disasters such as fire, floods, earthquakes, hurricanes, Tsunamis, that are all in need to a comprehensive collaboration from multiple administrations. Therefore, IoT system can introduce an intelligent and integrated solution that is capable of mitigating the natural disasters risks. More particularly, IoT can deal with technologies involved into different environments such as RFID, WSNs, ad-hoc networks, mobile communication technologies and service oriented architecture, data fusion and information query technologies [18], [19], [20].

Consequently, IoT systems have intervened the industry and marketing world from several point of views and have taken a significant priority from multinational companies [21]. IoT has a four-layered architecture that can be listed as sensing layer, networking layer, service layer, and interface layer. First, the sensing layer is in charge of the well-known hardware (e.g., sensors, actuators, RFID, etc.). Second, the networking layer supports the protocols needed for data transfer in both wired and wireless networks. Third, the service layer, is called service manager representing the player that fulfills the requested service by the user. Finally, the interface layer provides the interface between the user and the desired application [19]. Moreover, the IoT system can be extended to the structural health monitoring to pinpoint the real statistics of building health to demonstrate the accuracy of using such system. The performance evaluation of Big Data and/IoT systems is outside the scope of this paper.

This paper proposes mapping between the RN and DN parameters based on their operation and influence. To the best of our knowledge, this mapping methodology has not been presented in the literature. The mapping is addressed and evaluated over several models of which five of them are provided, namely, speed-flow relationships, left turn effect, four-way cross section control, roundabout cross control, and cracked roundabout. As this paper proposes DN equivalent models to different real statistical RN cases, more accurate performance results can be obtained by a single integrated model of both the RN and the associated communication solutions using DN performance evaluation tools. This integrated model can assist as a real time overall system emulation to catch up the available dynamic system changes during the natural disasters due to consequent failure in the RN and/or communication sites.

The first model presents the analogy achieved between the relationship of speed vs. flow in RNT compared to the corresponding DCN data transit speed in terms of distance/queuing delay vs. throughput. The second model discusses the relationship of vehicle capacity vs. the percentage of vehicles that turn left in RNT as compared to the throughput vs. the percentage of packets directed to a destination emulating the left direction in an equivalent DCN. In the third model, vehicles total delay vs. the total entry volume of the vehicles in a four way cross section of RNT is presented by an analogy with packets delay vs. entry volume of the packets in an equivalent DCN model. The fourth model shows the analogy between the vehicles total delay vs. vehicles total entry volume in a roundabout of RNT and packets total delay vs. packets total entry volume in an equivalent DCN. Finally, the fifth model compares the packets total delay vs. throughput in an equivalent DCN to a roundabout cross section of RNT throughout two scenarios. That latest scenario is studied without and with road crack to represent normal and earthquake disaster cases, respectively. To sum up, theoretical expectations are validated by simulating selected mapped DN entities, and the results are compared to those of the road traffic analysis to prove the mapping concept. The verification of this mapping is based on two statistical tests, namely, t-test and analysis of variance (ANOVA), which are explicitly discussed later.

The paper is organized as follows: Section 2 addresses the related work of using the integrated systems based on IoT aiming to mitigate the natural disaster. Section 3 introduces the analogy between the data network traffic and road network traffic. The proposed mapping models are presented in Section 4. The obtained simulation results and mapping verification are represented and discussed in Section 5. Finally, the paper is concluded in Section 6.

Section snippets

Related work

There have been several academic and industrial efforts and plans to limit the natural disasters harmful effects with more focus recently on IoT capabilities. In [22], Noel et al. present a comprehensive survey for the structural health monitoring (SHM) system designed to monitor a critical infrastructure (e.g., bridges, high-rise buildings, and stadiums) using wireless sensor networks (WSNs). SHM is capable to observe the natural catastrophic events such as an earthquake that could require a

Data network to road network analogy

This section presents a study to show the analogy between DN and RN traffic behavior throughout mapping tables. These tables are categorized based on network parameters that illustrate similarity in traffic attitude. Last column includes the discrepancy of the items that do not achieve full analogy. The common items in those tables representing the five models are verified using OPNET simulation models as proof of concept, which are presented in Section 5.

It is clearly known that the DN is a

Mapping models

This section provides five of the main common real RN statistical cases as mentioned in [59], [65], [66], which represent the RN side performance in the paper, and proposes the equivalent DN models to represent their analogy based on the mapping tables and two verification hypotheses, t-test and ANOVA. The mapping of those five case studies provides the details of the RN and DN parameters. More particularly, the proposed studies illustrate the analogy between RNT and DCN throughout five models,

Mapping performance evaluation

This section presents the modeling analogy simulation results of DN and RN. Those results represent the common cases of our research efforts to study the proposed mapping. OPNET Modeler is used to simulate the DN models corresponding to the main common real RN statistical cases. Table 6 lists the significant OPNET simulation parameters. Then, the mapping verification results based on t-test and ANOVA hypotheses are presented to prove the analogy of the proposed first four models. However, the

Conclusion

This paper proposes mapping between the RN traffic and DN traffic, and then analyses some of the mapping table entities that achieve explicit analogy in the functionality and traffic behavior. Our model results, which represent DN behavior, are compared with existing road traffic analysis. This comparison confirms that the DN model results show analogy to those corresponding RN ones through the mapping tables. In addition, t-test and ANOVA statistical tests are utilized to verify the mapping

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