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Puneet Garg, Ashutosh Dixit, Preeti Sethi, Plácido Rogerio Pinheiro, "Impact of Node Density on the QoS Parameters of Routing Protocols in Opportunistic Networks for Smart Spaces", Mobile Information Systems, vol. 2020, Article ID 8868842, 18 pages, 2020. https://doi.org/10.1155/2020/8868842
Impact of Node Density on the QoS Parameters of Routing Protocols in Opportunistic Networks for Smart Spaces
The need and importance of Smart Spaces have been potentially realized by the researchers due to its applicability in the current lifestyle. Opportunistic network, a successor of mobile ad hoc networks and a budding technology of network, is a best-suited technology for implementing Smart Spaces due to its wide range of applications in real-life scenarios ranging from building smart cities to interplanetary communication. There are numerous routing protocols which are available in opportunistic network, each having their pros and cons; however, no research till the time of listing has been done which can quantitatively demonstrate the maximum performance of these protocols and standardize the comparison of opportunistic routing protocols which has been a major cause of ambiguous performance evaluation studies. The work here presents a categorical view of the opportunistic routing protocol family and thereby compares and contrasts the various simulators suited for their simulation. Thereafter, the most popular protocols (selecting at least one protocol from each category) are compared based on node density on as many as 8 standard performance metrics using ONE simulator to observe their scalability, realism, and comparability. The work concludes by presenting the merits and demerits of each of the protocols discussed as well as specifying the best routing protocol among all the available protocols for Smart Spaces with maximum output. It is believed that the results achieved by the implemented methodology will help future researchers to choose appropriate routing protocol to delve into their research under different scenarios.
In the era of consistently changing environment, communication devices are getting intelligent day by day and delivering rapid and robust connections. New applications are emerging with an advanced approach in wireless networking arena which is attracting new researchers in this field for further efforts. Due to the tremendous research in the wireless section, communication has become promising even in remote regions where previously, constructing a simple communication network was a huge challenge.
Owing to the pervasive applications of networking, existing technologies on wireless networking like vehicular ad hoc wireless network, wireless sensor networks, mobile ad hoc networks are observed to be insufficient in some instances such as interplanetary communication , Smart Spaces , and social networks  to cope up with all the aspects and challenges concerned to the wireless networking, Some detected major difficulties with these technologies are connection failure and links discontinuation which degrades the overall performance of the network. To counter this challenge, researchers worked hard to create a new networking technology that led to the development of opportunistic networks (OppNets). According to Shu et al. , opportunistic networks (OppNets) are one of the categories of delay-tolerant networks , which support data communication through movement in nodes as it does not need any long-lasting links from sender to receiver nodes.
According to Kushwaha and Gupta , opportunistic network sare one of the rising advancements of the network system. In opportunistic networks, nodes can communicate with one another regardless of whether the route between source to destination does not exist at that given moment. Opportunistic networks must be delay-tolerant (i.e., ready to tolerate bigger delays). Delay-tolerant network (DTN) utilizes the idea of “store-carry-forward” of data packets. DTNs can move data or set up a correspondence in a remote area or emergency condition where there is no network set up. DTNs have numerous applications like to provide smooth Internet arrangements in remote areas, in vehicular networks, noise observing, extraordinary terrestrial situations, and so on. It is in this manner promising to recognize viewpoints for reconciliation and integration of opportunistic network systems and advances into delay-tolerant networking.
OppNet is different than mobile ad hoc networks (MANETs) in the aspect of connectivity of participating nodes carrying data [7, 8]. The nodes participating in ad hoc networks for data communication remain connected constantly whether the nodes are in motion or static; on the other hand, nodes get to connect with other nodes in the OppNet when the communication is to be done between the nodes that make it a better approach in real-world applications. Therefore, conventionally defined protocols such as TCP/IP, DSR, AODV, and DSDV fail to function properly in opportunistic networks [9–11].
Rather, OppNet which is a type of delay-tolerant network is considered as the next generation of ad hoc networks which is further derived from standard wireless networks. Figure 1 clearly illustrates the evolution of opportunistic networks originating from the wireless networks domain through step-by-step growth. Every growth in each progressive step indicates the extension of personal communication networks towards solving further real-life problems which were a challenge earlier.
According to Nayyar et al. , the primary aim of developing opportunistic networks is to handle critical situations with effective manner such as disaster handling, war-field communications , satellite communications, flying warplanes/drones networking, underwater sensor networks , and forest surveillance. OppNets are highly useful where communication encounters high delays, no reliable connectivity, and high error rates. Nodes participating in OppNet are equipped with several attributes such as short communication range, high dynamic mobility, and low density. OppNet is designed specifically to connect almost every device which is capable of being connected through any wireless medium such as Bluetooth and Wi-Fi, etc. thereby making it a perfect choice for network designers all over the world. A general scenario of the opportunistic network connection is depicted in Figure 2.
1.1. Advantages and Disadvantages of Opportunistic Networks
According to Nayyar et al. , opportunistic networks are considered a strong option among the available networking technologies due to the following advantages:(1)OppNets can tolerate high delays if the destination or another intermediate node is not responding due to any reason during data communication(2)OppNet may allow data transfer with asymmetric data rates(3)OppNets can prevent data loss due to connection failure as it follows the store-carry-forward approach(4)OppNets can manage data communication even in continuous ups and downs with network state as it is specifically designed for operating under the situation of intermittent connection
Despite all the abovementioned advantages, there are certain challenges  in OppNet communication that needs to be dealt with. The various shortcomings of OppNet are as follows:(1)OppNet requires high buffer space as it stores the data to be forwarded which further increases its operational cost.(2)Due to intermittent connectivity, a node communicating in OppNet requires a large amount of energy as it may wait for a long time for forwarding the data it holds.(3)OppNet faces a challenge of security also like MANET because like MANET, nodes participating in OppNet forward the data towards destination via intermediate nodes. These intermediate nodes may be malicious sometime. Therefore, choosing a secure route between two communicating nodes is a challenge.
It is believed that the above-discussed challenges will soon be resolved by upcoming researchers through their continuous efforts to make OppNet better than its current version.
1.2. Role of Opportunistic Networks in Smart Spaces
In the era of Digital Connectivity, a large amount of population is equipped with smartphones that connect a person to the digital world via the Internet [16, 17]. Besides connecting the Internet, a smartphone comes with a different mode of connectivity with other devices such as Bluetooth and Wi-Fi . According to Samaniego et al. , “Smart Spaces are common spaces that have capabilities to get data from the environment and apply knowledge to fulfill requirements of mobility, distribution, and context awareness of its inhabitants.” Smart Space is nothing but a virtual world full of information as per the interest of member nodes [2, 20]. According to Ismagiloiva et al. , the concept of Smart Spaces complements IoT technology specifically for designing smart cities.
These different modes of smartphone enable its user to make his/her private network as per the requirements as well as preferences of connected persons. These small and customizable networks are termed as Smart Spaces in the real world [22, 23]. The connected devices in such Smart Spaces are known as nodes in the networking terminology [2, 24]. For its smooth connectivity, opportunistic network is the best suited due to its inherent traits. The nodes in opportunistic networks use Wi-Fi or Bluetooth for interconnectivity and primarily initiate functioning with a single node known as seed OppNet and expand further by implementing it among more member nodes that facilitate data forwarding in the network [25, 26].
Routing in OppNets relies upon contact opportunity between the nodes which is required due to their versatile nature. The most huge technique used in OppNet for routing movement is the store-carry-forward technique, where a message can be forwarded among intermediary nodes, and accordingly, the message is passed on to the destination node. The store-carry- forward technique is seen as a capable technique to ensure message delivery to destination nodes where message delivery may bear high delays. Thus, OppNets are a subclass of DTN where nodes must be outfitted with high buffer space to store messages for a strange timeframe to evade packet dropping.
Short-distance communication feature enabled node may help OppNet to gain large improvements and numerous scopes covering almost every industry such as information attacking, energy utilization, communication engineering, and information gathering. However, maintaining a stable network topology in OppNet is a cumbersome task; also, predicting the network topology is very difficult due to the frequent mobility among nodes and large communication range.
This paper initially presents the introduction of opportunistic networks followed by its role in building Smart Spaces and applications of OppNets which are presented in Section 2. Section 3 highlights various routing protocols associated with this class of networks. Section 4 elaborates numerous research simulators available in OppNets followed by the enlightenment of Java-based simulator ONE (Opportunistic Network Environment). Section 5 presents the analysis of the total nine standard routing protocols over standard QoS parameters. The paper concludes by submitting future work in this area.
2. Applications of Opportunistic Networks
Opportunistic networks have become ubiquitous nowadays. It has numerous applications in real-life scenarios covering almost all levels of modern communication requirements. Figure 3 demonstrates OppNet applications in the real world.
Some of the popular ones are described as follows:(a)LASSO: Saloni et al.  developed LASSO, a general-purpose smartphone-based application that uses the opportunistic networking feature using Bluetooth or smartphone for group monitoring. It has proved to be highly advantageous for the interconnectivity of a group of some persons roaming in a smart city and monitoring their locations to track if someone got missed. Its unique feature of decentralization device-to-device mode of operation makes it able to be used in any mobile scenario. Also, it does not need any pre-existence of any communication infrastructure. LASSO has performed well on small-scale implementation (i.e., 250 persons over a small geographic area) and it is being underdevelopment for further enhancements.(b)Shared wireless infostation model (SWIM): Small and Haas  proposed an infostation concept with the integration of opportunistic networking. It was experimented to observe the whale species by tying sensors on whale’s back, thereby making them radiotagged whales. All sensor nodes are connected via opportunistic networking and data are forwarded in the same fashion as in OppNet and finally delivers to the infostation. This application has proved to be excellent to monitor whales’ life closely.(c)Underwater communication networks: Detweiller et al.  experimented with a communication setup consisting of mobile sensor nodes with acrylic closure and other communications support hardware to establish underwater communication network. It is a quiet application of opportunistic networks as it can tolerate delay and respond accordingly to commensurate the real-life challenges in a typical sea environment. An experimental study proved it successful along with TDMA protocols with depths less than 100 meters for comprehension and demonstrating coral reefs. It can likewise support more prominent depths by supplanting acrylic enclosure with a glass/titanium enclosure.(d)ZebraNet: ZebraNet  is an OppNet-based project implemented by Princeton University under Mpala Research to track and monitor wild creatures in the forest of Kenya with the help of powerful sensors tied at animals’ neck. Every sensor being used in it is enabled with wireless transceiver, CPU, and GPS. All sensors fitted on animal bodies interchange their sensed information in OppNet fashion and finally deliver to the desired station. It is focused to develop for monitoring the movement and speed of wild creatures in forests.(e)Composable Distributed Mobile Applications: Papadaki et al.  presented a system design that permits application developers to consider the future environment as a generic execution that opportunistically distributes and executes automatically the components of their applications. The concept of permitting mobile clients to utilize the resources present in the environment with the help of location-aware services relates this application to opportunistic networking and opportunistic computing. The primary aim of this system design is to hold a vision to a futuristic environment where clients do not require to search and use services already existing in the environment, rather, to use the environment to implement their custom applications. It has experimented successfully with the help of a prototype evaluation.(f)Saratoga: Wood et al.  presented Saratoga which is a light-weight protocol based on opportunistic networks. It was developed by Surrey Satellite Technology Limited (SSTL) for file transfers of data recorded in image format by IP-based Disaster Monitoring Constellation (DMC) satellites revolving around earth from low orbit. Saratoga follows opportunistic routing as it only forwards the data packet when link connectivity is available which guarantees that the maximum possible data are transferred to the node during a 12-minute pass over a satellite ground station. Saratoga is fully operational for many years.(g)Underwater acoustic communication: underwater communication networks have been the prime area of research in recent years due to its various applications such as oil spills detection, disaster detection and avoidance, sea exploration, and detection of submarines. Menon and Prathap discussed  numerous opportunistic routing protocols developed especially for underwater acoustic communication. Two major categories of such protocols are pressure-based protocols and location-based protocols. Rahman et al.  proposed a routing algorithm named TORA (totally opportunistic routing algorithm) with a focus to overcome issues about underwater acoustic communication such as void nodes, horizontal transmission, high end-to-end delay, low throughput, and high battery drain. According to extensive simulation studies, TORA has been proved a better option over the existing algorithm up to a considerable extent.
These are some of the major applications opportunistic networks possess. But its scope has not been limited to the mentioned applications; rather, it has vast scope in airborne networks , space operations , backend support in smart cities [36, 37], and many other domains that are not discussed in this paper.
3. Routing Protocols in OppNets
Opportunistic networks contain a huge number of routing protocols. These protocols came into existence as a result of the rigorous efforts of several researchers done in the domain of opportunistic networking [3, 38–40].
According to Juyal et al. , numerous protocols can be categorized into various classes, viz., flooding-based routing protocols (e.g., Epidemic routing protocol and Spray-and-Wait routing protocol), forwarding-based routing protocol (e.g., Direct Delivery routing protocol and First Contact routing protocol), probability-based routing protocols (e.g., PRoPHET and MaxProp), knowledge-based routing protocols (e.g., Epidemic Oracle routing protocol), social relationship-based routing protocols (e.g., FRESH routing protocol), and off-course hybrid routing protocols (e.g., RAPID protocol). The work presents the exhaustive survey of all these protocols in each category of routing protocols in opportunistic networks.
The taxonomy of routing protocols is depicted in Figure 4.
where ⟶ expected time of xj to reach node z and ⟶ the number of times each of the j nodes, respectively, required to contact the destination to deliver I directly.
It has been found through simulation that RAPID performs better than MaxProp, Spray-and-Wait, PRoPHET on the ground of average delay, packet delivery ratio, and overall efficiency in opportunistic networks.
4. Simulation Trends in Opportunistic Networks
Various researchers developed numerous simulators and made them available for simulation purposes. Some of the popular simulation tools are as shown in Table 1.
In addition to the abovementioned simulators, various custom-built simulators are also being an option for pursuing research in opportunistic networks. These simulators help to share original coding work for its reuse in the future. Few examples of such simulators are MONICA , E-ONE , and UDTNSim .
Kuppusamy et al.  surveyed the simulation trend followed by researchers focusing on opportunistic networks. The results reveal that there has been a substantial increase in the use of ONE simulator during the current decade. Figure 5 presents the contribution of different available simulators towards OppNet research.
Also, Kuppusamy et al.  have brought in to light the fact that the foremost reason for selecting ONE simulator as a major tool is that it is capable of supporting the maximum number of participating nodes during simulation among all discussed simulators (except custom-based simulators). However, it has some limitations regarding the underlay layers such as the MAC sublayer, but that can be ignored for the research work of this paper. Further, it is also found as the most accurate simulator which allows the researcher to get results with the maximum number of QoS parameters among all its counterparts.
Based on the abovementioned details, it may be stated that ONE (Opportunistic Network Environment) simulator is the most widely used simulator among researchers. Therefore, this paper uses ONE simulator for the implementation of the mentioned routing protocols aiming to cover a large group of researchers engaged in the opportunistic network research domain.
4.1. ONE Simulator
ONE (Opportunistic Network Environment) is a Java-based discrete event simulator whose main functions are node movement modeling, routing, message-handling, and internode contacts, Result collection and analysis are achieved through visualization and other postprocessing tools.
The results which are generated as a result of the simulation are generally logs of events that are further processed by external tools such as Graphviz for plotting graphs. Figure 6 illustrates the simulation environment of ONE simulator.
The popularity of ONE simulator is because it provides various tools to generate difficult mobility scenarios that are closer to real-life situations than any other available simulator in current time. Some of its features such as GPS Map data and Working Day Movement Model make it a better option to reality.
However, still, ONE simulator has some challenges; for example, the message generation process may perform better if group relationship and context information be added. Also, it must be mentioned here that several research groups are putting their efforts into enhancing supporting features in ONE simulator. Maybe, a newer version of ONE simulator will be added with some better features.
5. Performance Evaluation of Routing Protocols for Smart Spaces
Rigorous review uncovers the fact that though numerous routing protocols have been proposed by various eminent researchers, yet none of them has quantitatively evaluated them. The authors in this work showed that there is an urgent need to do the same to determine which protocol is best suited in a given environment. Keeping this in mind, the authors have meticulously compared the numerous protocols of opportunistic networks.
In this section, nine different routing protocols are compared based on standard Quality-of-Service (QoS) parameters by varying the number of participating nodes. It is believed that this simulation comparison will describe the performance behavior of different protocols on the ground of node density [26, 38]. The main purpose of choosing node density as a primary factor is that it correlates to the real-life scenario of Smart Spaces very closely, for example, if we consider mobile handset device as participant node connected to OppNet via Bluetooth/Wi-Fi, then it would be around 50–60 nodes per square km in case of a park in opportunistic networks, but it can increase up to 500 or more in the situation of a conference hall. If we talk about interplanetary communication, then the number of participant nodes would be at most 1 or even less than 1 per square km. The ratio of the number of nodes per square km becomes 100–150 when a normal highway situation is considered. It may be increased up to 200–250 when a busy pedestrian path is taken as an example. There are many other real-life situations as well where node density differs as per the environment which directly affects the overall performance of Smart Spaces. Therefore, this paper aims to find the suitability of the protocol being used in different scenarios. The simulation comparison will result in the performance of different protocols in different node densities; it will help upcoming researchers to choose routing protocol accordingly for establishing different Smart Spaces.
5.1. Common Parameters Used in Each Case
Besides the variation in node density with a different routing protocol, several other parameters are kept constant to analyze the performance change only due to the change in several participating nodes. The details of these parameters are listed in Table 2.
5.2. Quality-of-Service (QoS) Parameters Used
The comparison needs some standard parameters, so that the performance comparison could explain which is better and which is worse [60–64]. Table 3 explains the various standard parameters that have been taken to decide the behavior of routing protocols for a different number of participating nodes.
5.3. Analysis of Performance Evaluation
Total nine protocols (Epidemic protocol, PRoPHET protocol, Spray-and-Wait protocol, Epidemic Oracle protocol, MaxProp protocol, Direct Delivery protocol, First Contact protocol, RAPID protocol, and FRESH protocol) among the six different categories of routing protocols of opportunistic networks (flooding-based routing protocols, forwarding-based routing protocols, probability-based routing protocols, knowledge-based routing protocols, social relationship-based routing protocols, and hybrid routing protocols) over eight different QoS parameters (number of participating nodes, message delivered, message dropped, delivery probability, overhead ratio, average latency, average hop count, and average buffer time) have been thoroughly explored in this paper. Besides it, some protocols are observed superior in particular cases of node density concerning mentioned QoS parameters. A comprehensive evaluation of the suitability of routing protocols over node density has been prepared in Table 4 based on our simulation study.
Furthermore, below are some findings taken after the simulation study of mentioned protocols in a different environment about node density over discussed QoS parameters:
It has been experimentally observed that two of the routing protocols (Spray-and-Wait and FRESH protocols) are found finest in every instance of node density. However, these routing protocols are leading as well as lagging to each other based on individual QoS parameters. The comparative performances of the two finest emerged protocols have been presented along with QoS metrics in Table 5.
From the observations made from Table 5, it can be summarized that Spray-and-Wait and FRESH protocols are exhibiting their best performance on different QoS metrics. However, due to the absence of common QoS parameters, it is hard to declare one protocol to be best among the discussed protocols on the mentioned criteria, but, for making a viable conclusion, one protocol must be declared as the best one.
To cope up with this problem, the importance of individual QoS metric must be evaluated based on its suitability for the best performance under Smart Space Environment. Literature survey [65, 66] reveals the fact that certain QoS metrics such as message dropped, average latency, and average buffer time are of significant importance for accurate and fast delivery with least additional storage requirement other than data packet to be transmitted which is an ideal condition for receiving best results in Smart Space Environment. In light of this fact, it can be stated that FRESH protocol is optimally suited to Smart Space Environment.
6. Conclusion and Future Scope
This paper in depth explores various routing protocols of opportunistic networks which can be used for establishing Smart Spaces. It also discusses in detail various simulation trends prevailing in the arena of opportunistic networks. The protocols are thereafter compared based on node density to determine the best-suited protocol for building Smart Spaces in the given simulation environment. The paper concludes with the fact that Spray-and-Wait outperforms the FRESH protocol by giving better results on the 5 standard QoS parameters. However, with the eye and muscle of Smart Space Environment, the paper also highlights the fact that certain QoS metrics such as message dropped, average latency, and average buffer Time are of significant importance for getting the best outcome in Smart Space Environment. Therefore, FRESH protocol must be considered as the best routing protocol suited for Smart Spaces Environment.
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this article.
The simulation work of this paper was carried out in the Research Lab of J. C. Bose University of Science and Technology YMCA, Faridabad, Haryana, India. The authors are thankful to J. C. Bose University of Science and Technology YMCA, Faridabad, Haryana, India for permitting them to use the Lab Facilities. The authors are also thankful to the National Council for Scientific and Technological Development (CNPq) for the support received via grant no. 305805/2017-7.
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