1 Introduction

In the last decade, a plethora of research papers have been published in the field of multi-hop wireless networks viz., mobile ad hoc networks (MANETs) and alike, with a primary objective to study the various aspects of these networks like application oriented development, complexity reduction, realistic simulation models, development of real network testbeds, mobility modeling, trace collection, link existence modeling, routing, reliability, clustering and information diffusion, transmission range and many more. Despite, being in so much of demand, ad hoc networks pose a great challenge to the researchers for their performance evaluation. The primary reasons behind such a challenging situation could be attributed to the harsh and disruptive environment of applications, mobility of nodes, interference and energy constraints.

In general, MANETs are self-configuring, dynamically changing, multi-hop, and infrastructure-less networks, wherein each node can act as a host as well as a relay by passing the data packets to the nodes in its vicinity. In the beginning, MANETs found applications mainly in defense, disaster recovery and distributed collaborative computing; but, in recent years, due to advancements in wireless technology, ad hoc networks can easily be spotted in many public and office environments, where they usually compliment the pre-existing infrastructure-based communication networks. Besides, recently several “MANET-born” communication network paradigms, namely, mesh, sensor, opportunistic mobile networks (OMNs), delay tolerant networks (DTNs), vehicular ad hoc networks (VANETs), flying ad hoc networks (FANETs) and people-centric networking, have also become very successful and widespread [1].

In MANETs, dynamically changing network topology leads to the formation of new links and/or breaking of existing ones. This phenomenon also changes the data routes among the communicating nodes. The situation becomes even more dynamic in highly mobile networks like FANETs, VANETs and DTNs/OMNs, as these networks either have frequently changing end-to-end connectivity or depend on store and forward mechanism to transfer data packets. Hence, with the increasing dependency of many applications on wireless modes, there seems to be an ever growing demand for more reliable wireless networks. Looking at the versatility and utility of these networks, there has also been tremendous efforts to resolve the issues such as mobility patterns, energy management, self-configuration, routing, reliability, quality of service provisioning, scalability, link reliability models, and cost-effectiveness etc., that affect the design, deployment, and performance of an ad hoc network. But, many other aspects of such dynamic and complex networks continue to pose challenging situations during their implementation and performance evaluation.

The Literature indicates that almost every study on MANETs is carried out with the help of either analytical modeling, using simulators/emulators or by real world experiments (Fig. 1). Each of these approach has its advantages and limitations as well. Through this survey, our main aim is to summarize the current state-of-the-art developments in the area of wireless ad hoc networks, and highlight the changing trends and challenges involved in the design and implementation of these ad hoc networks.

Fig. 1
figure 1

Performance evaluation tools for MANETs

Full size image

Consequently, here we congregate bits of work into a well-arranged survey focusing on the comprehensive review of various models available in the literature for studying different aspects of wireless ad hoc networks, accompanied with various tools for studying mobility of human beings and vehicular traffic. Furthermore, we provide details on the Satellite Navigation Systems (SNS) along with their strengths, limitations & applications, and existing data repositories. We provide an insight into the trends of research focus and discuss various simulators, emulators and testbeds along with their specific features. We also collate several worth mentioning real projects (completed or ongoing) throughout the world to address various challenges in their implementation in disruptive environments. Besides, an important aspect of every network, i.e., reliability and various performance metrics have also been outlined along with various challenges for future research. In general, this survey aims to fill the existing gaps in the literature, which originate (in our opinion) due to the ever expanding nature of the domain with the updated information.

The remaining paper is organized as follows: In Sect. 2, we present various techniques for modeling MANETs. In Sect. 3, we discuss the changing research focus from MANETs to MANET-born networks. In Sect. 4, we have covered a number of simulators, emulators and testbeds designed for studying MANET and alike networks. Section 5 presents a number of real world projects initiated throughout the world with a motive to develop new Information and Communication Technology (ICT) based applications. In Sect. 6, we discuss the reliability evaluation related challenges and available solutions in MANETs. Section 7, presents some future prospects of the domain. Finally, some concluding remarks of this review are presented in Sect. 8.

2 Modeling MANETs

A number of models have been proposed in the literature to study/predict performance characteristic(s) of MANETs. This section details some key models as hereunder.

2.1 Mobility Modeling

MANET mobility is one of its key attributes as the complex mobility patterns of nodes require more parameters and assumptions to be included in the constructed model. Moreover, complex mobility also greatly influences the networking protocols, application services, and reliability of the network. Thus, the impact of mobility should be known in advance for a better design and implementation of MANETs. In the following paragraph, we discuss the two major categories of mobility models i.e., synthetic mobility models and trace based mobility models; where, a synthetic mobility model depicts randomly generated movements and creates synthetic traces while a trace-based mobility model is developed by monitoring and extracting the features from the real movement patterns of users carrying mobile nodes.

2.1.1 Synthetic Mobility Modeling

A number of synthetic mobility models have gradually been evolved for various applications [2,3,4,5,6,7]. The most frequently used models to study the impact of the mobility of nodes in MANETs are: Random Way Point Model (RWPM), Random Direction Model (RDM) and Gauss Markovian Model (GMM) [8, 9]. The node mobility of MANET causes the network topology to change with time and therefore a network’s performance must be dynamically readjusted to such changes. Further, it should be borne in mind that it is very difficult to emulate real-life mobility pattern owing to its tremendous complexity, which depends upon several dependent/independent factors such as: (a) the terrain of movement, (b) traffic pattern over the network, (c) human behavior and (d) mission objectives. For a recent review on exhaustive taxonomy of mobility models, one can refer [10].

The role of mobility modeling in studying characteristic properties and performance of MANETs appears to be understood by now. A number of simulation tools to mimic the node movement are now available that can generate and analyze the impact of human or vehicle mobility under various traffic scenarios, and have widely been used by researchers, consultants and government agencies for some specific applications. Table 1, summarizes the various mobility simulation tools that have evolved over a period of time and proven to be of great help to researchers, scientists, government agencies or consultants along with various mobility models supported by them. For each tool, a pointer to their availability information is also provided.

Table 1 Tools for mobility modeling
Full size table

2.1.2 Trace Based Mobility Modeling

Although many synthetic mobility models have been proposed and utilized by researchers, yet, most of them fail to reflect the real movement of nodes. In order to obtain realistic mobility patterns, researchers are devising new methods to harness data from real scenarios with the help of satellite navigation services (SNS). The data obtained from a real scenario is called a trace. A thorough study of the individual movements can successfully be accomplished with the help of mobility traces. The mobility traces with actual geographical coordinates in terms of latitude, longitude and time-stamps collected with the help of Global Positioning System (GPS) device create more real movement pattern of human beings or vehicles. The Pros and Cons, and challenges of both synthetic and trace based mobility models have been collated in Table 2.

Table 2 Pros and Cons, and open challenges of synthetic and trace based mobility models
Full size table

In the following discussion, we present the SNS operating or under development phase throughout the globe along with their salient features.

  1. 1.

    Satellite navigation systems and their importance in location prediction and trace collection

    The United Nation’s satellite navigation system i.e., Navigation Satellite Timing and Ranging Global Positioning System, commonly known as NAVSTAR GPS or simply GPS was primarily fabricated for assisting defense personnel and military planners in targeting enemy troops, fleet management, positioning, and navigation. Later, this service was freely made available to the civilians throughout the globe for numerous applications including tracking package delivery, mobile commerce, emergency response, exploration, surveying, law enforcement, recreation, wildlife tracking, search and rescue, roadside assistance, stolen vehicle recovery, satellite data processing, and resource management [26]. The importance of GPS became evident very soon and countries like Russia and European Union started developing their own SNS. Today, there are a number of SNS under operation or underway worldwide. These SNS provide either regional positioning or global positioning just like NAVSTAR GPS. Table 3, provides a brief description of such SNS.

    Presently, the United Nation’s GPS is the most famous positioning service worldwide for its ability to provide global, precise and continuous three-dimensional position and velocity information along with Coordinated Universal Time (UTC) to the users with the appropriate receiving equipment [33]. The names of few renowned GPS receiver manufacturers are: Avidyne, CMC Electronics, GARMIN, Honeywell, Rockwell Collins, and Universal Avionics.

    Fundamentally, the GPS satellite constellation consists of at least 24 satellites (plus some spares) arranged in 6 orbital planes with 4 satellites per plane. Moreover, at present GPS has a total of 32 satellites deployed in space. GPS mainly relies on trilateration or triangulation techniques for positioning in three dimensions (latitude, longitude, and elevation) and requires that at least four satellites are above the horizon. The accuracy of the geographical coordinates depends on the number of such satellites and their positions [34].

    Despite, the strengths and large application areas, SNS also suffers from many drawbacks. The major drawback is that they cannot provide localization information in an indoor environment. Their accuracy drops considerably in the outdoor environments having obstructions like tall buildings, under bridges or under trees (refer Fig. 2). Researchers have developed several augmentations (space-based and ground based, respectively) to enhance the accuracy, availability, and integrity of stand-alone GPS [33]. Differential GPS (DGPS) and Assisted GPS (AGPS), respectively, are the two developments which can eliminate most of the errors associated with GPS [35]. Even a better precision can be achieved using Wide-area DGPS (WADGPS).

    With the current state-of-the-art technology, it has become possible to cheaply embed highly accurate and precise GPS modules into mobile devices, thus, opening new vistas for gaining knowledge about mobility pattern of individuals. Predicting the mobility pattern of individuals or vehicles precisely is extremely valuable mainly in locations-based services (LBSs), in light of the fact that useful information can be made available to the subscribers beforehand. This mitigates the system delay, thereby enlarges the interactivity of services. Several applications, for example, location finding, early reminder system, traffic planning, and dead reckoning in GPS, need precise position/co-ordinates. These co-ordinates can satisfactorily help in modeling the mobility of various individuals, groups or vehicles.

  2. 2.

    Data repositories

    Recently, several attempts have been made to study the human & vehicle movements and collecting positioning data or trace using custom tailored GPS devices or the radio technology (Bluetooth or Wi-Fi). Some trace based, accurate and realistic mobility models can be seen in [34, 36,37,38,39,40,41]. Two famous and well-used data-sources for armed conflicts are Armed Conflict Location and Event Data Project (ACLED) [42] and Uppsala Conflict Data Program (UCDP) [43]. These are the most comprehensive public collection of political violence and protest data for developing states. However, as stated in [44], there is still a dearth of mobility trace for disaster and battlefield environments, and the existing traces have a limited accuracy. The work in the direction of studying and analyzing traces has already been started in a big way by some researchers, e.g., through the project Invisible Dynamics, researchers collected the mobility trace using GPS for locating the movements of Yellow Cab vehicles throughout the greater San Francisco Bay area and named the dataset as CabSpotting [45]. Another notable GPS based traces are CenceMe and GeoLife, respectively [34]. Besides, the datasets mainly used by the researchers from the field of social science, involving diffusion of information among a group of people, contact and friendship network among high school students, and hospital ward dynamic contact network etc., can be accessed from Sociopatterns repository [46].

    In fact, contributions from the research community, involving painstaking efforts to collect mobility traces has given birth to the repositories like CRAWDAD [47], Sociopatterns [46], UMass Trace [48], Network Repository [49], Foundations of Data and Visual Analytics (FODAVA) [50], Stanford Network Analysis Platform (SNAP) [51], UC Irvine Machine Learning Repository [52] and MobiLib [53]. Here, a user can access archived wireless data traces, contribute his/her own wireless trace data or participate in the development of better tools for collecting, anonymizing, and analyzing data. It is evident that CRAWDAD stands out the most favorite repository among the researchers with a credentials of 120 datasets, 22 tools, 10661 users from 120 countries, and 2453 research papers by the researchers using its datasets or citing CRAWDAD in their research work [47].

    Recently, Baudic et al. [54], presented a praiseworthy review on the details of trace collection and processing. Besides, Aschenbruck et al. [44], presented a detailed insight into various trace sources, deploying scenario, device utilized, and various types of dependencies. The authors in [44] also surveyed, comprehensively, the mobility traces collected from the real world projects like: Haggle project, Dartmouth Campus, and ZebraNet etc.

    The above datasets are mainly devoted to large, GPS based, collections of traces and related projects. Such datasets fail to provide an appropriate accuracy in indoor environments, which require localization information obtained with the help of sensors or cameras deployed in users’ environment. In order to study and characterize the complex indoor propagation environments, several datasets have been collected by the researchers and efficient software tools have also been developed & available in the market. For instance, the dataset for indoor user movement prediction from radio signal strength (RSS) [55] represents a real-life benchmark in the area of ambient assisted living applications as described in [56]. Besides, CellTrace [57] and WinProp [58], are tools that address Radio Frequency (RF) planning, design, modeling, analysis and optimization of wireless communication systems within buildings, tunnels, stadiums and in urban environments.

Table 3 List of SNS operational/underway throughout the world
Full size table
Fig. 2
figure 2

Strengths, limitations, applications and techniques for improving accuracy, associated with a satellite navigation service

Full size image

2.2 Connectivity Modeling

2.2.1 Percolation and Diffusion Theory Based Model

The percolation theory and probabilistic epidemic algorithms have been used in the literature [59, 60] to handle the phase transition phenomena in MANETs, which results in an abrupt change in the behavior of the network leading to the appearance of some new property in the system. In fact, by percolation and diffusion theories it is possible to solve two dependent aspects: (1) a good diffusion of information in the network (needed for routing, broadcast and communication) and (2) its connectivity (needed to reach each node) [61]. Recent research from the field of percolation and diffusion theory shows that both may be complementary to each other in the context of MANET [61, 62]. In [63] a percolation theory based framework was proposed to calculate the network reliability.

2.3 Graph Theory Based Modeling

2.3.1 Connected Dominating Sets Based Model

A Dominating Set (DS) of nodes is a subset of network nodes such that a node outside this set is directly connected to at least one member of the DS. A Connected Dominating Sets (CDS) is defined as a set of nodes in a network such that each node is either in the set or adjacent to a node in the set. CDS is another approach of modelling a dynamic network that stems from the field of graph theory and finds its application for routing data among nodes in ad hoc networks [64,65,66,67].

2.3.2 Regular Lattice Graph Model

A d-lattice is a labeled, unweighted, undirected, and simple graph that is similar to a Euclidean cubic lattice of dimension d, such that any vertex v is joined to its lattice neighbors, \(u_i\) and \(w_i\), as specified by:

$$\begin{aligned} u_i= [(v - i^{d^{\prime }}) + n] (mod \ n) \end{aligned}$$
(1)
$$\begin{aligned} w_i= (v + i^{d^{\prime }}) (mod \ n) \end{aligned}$$
(2)

where n is the number of vertices in the regular lattice, k denotes the number of neighbors to a vertex, \(1\le i \le k/2\), \(1\le d^{\prime }\le d\), and it is generally assumed that \(k \ge 2d\). Hence, 1-lattice with k = 2 is a ring, a 2-lattice with \(k = 4\) is a two dimensional square grid, and so on [68]. See Fig. 3 for examples of lattice graphs for \(k = 2\) and 4.

Fig. 3
figure 3

Examples of lattice graphs: a 1-lattice for \(k = 2\), b 2-lattice for \(k = 4\)

Full size image

In wireless ad hoc networks, nodes use radio communications to form links with other nodes. In this model, a probability of connectivity between adjacent nodes is defined as p whereas non-adjacent nodes are indirectly connected via intermediate nodes. As the radio signal power decays with the increasing distance between nodes, the probability of existence of a link varies as a function of the distance between the nodes. So, it is evident that the lattice model can suitably represent an ad hoc network as both networks share the notion that the distance between nodes influences the probability of existence of a link [69].

2.3.3 Random Geometric Graph Model

A complete graph on n vertices consists of \(\frac{n(n-1)}{2}\) edges; however, a random graph involving n vertices constitutes \(p\frac{n(n-1)}{2}\) edges, where, p is the probability of a link existence between a specified pair of nodes. The most fundamental assumption in random graph is that the existence or non-existence of an edge between two nodes is entirely independent of the existence or non-existence of any other edge in the network. This may not be true in the case of MANETs, as the active nodes which are within the coverage area of each others are certainly connected. In other words, in MANETs, a node pair having lesser Euclidean distance between them in comparison to their transmission range will have high probability of link availability in comparison to a node pair having a larger distance between them. Therefore, the random graphs of Erdős and Rényi become trivial for studying MANETs [69].

A Random Geometric Graph (RGG) is a graph drawn by placing a set of n vertices independently and uniformly on the unit square \([0, 1]^2\) in a random fashion, and by connecting two vertices if and only if their Euclidean distance is at most the given radius r [70]. The deterministic counterpart of RGG is called Unit Disk Graph (UDG). A graph G is a UDG for a radial distance r, if its vertices can be put in one-to-one correspondence with the centers of circles of radius r in a plane such that two vertices in G are connected by an edge if and only if their corresponding circles intersect. Hence, RGGs which resolve the above stated problem associated with the random graphs have found wide application for modeling link formation/disappearance in MANETs. The application of RGG for MANET modelling can be seen in [8, 71, 72].

2.3.4 Evolving Graph/Time Varying Graph/Temporal Graph Model

The notion of evolving graphs basically consists of formalizing a time domain in graphs. The addition of information about times of interactions in a graph/network, make predictions and mechanistic understanding more accurate. These evolving networks are known with many different names in the literature, i.e., temporal graphs, evolving graphs, time-varying graphs (TVGs), time-aggregated graphs (TAGs), time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, spatio-temporal networks and so on [73].

Formally, a TVG could be seen as a network \(G = (V_G, E_G)\), where each node and link has a presence schedule defined for it. Corresponding to each link \(e \in E_G\) (node \(v \in V_G\)) the link existence schedule (node existence schedule) indicates the time instants at which a link (a node) is present, and possibly other parameters they take during each interval [74]. Here, one may assume the presence schedules as a sorted list of intervals. The definition of the TVG model can now be stated as:

Definition

Let there be a graph G(V, E) and an ordered sequence of its subgraphs, \(S_G = G_1(V_1, E_1), G_2(V_2, E_2), \cdots \,G_i(V_i, E_i), \cdots \, G_\tau (V_\tau , E_\tau )\), \(\forall \,V_i \in V\) and \(E_i \in E\), such that \(\cup _{i=1}^\tau G_i(V_i, E_i) = G(V, E)\) and each \(G_i(V_i, E_i)\) is the subgraph in place during \([\,t_{i-1}, t_i\,)\). Let \(S_T = t_0\), \(t_1\), \(t_2\), \(\cdots\), \(t_\tau\) be an ordered sequence of time instants. Then, the system \(G = (G(V, E), S_G, S_T)\) is called an evolving graph.

The graph G(V, E) is called an underlying graph of G. This static graph should be seen as a sort of footprint of G, which flattens the time dimension and indicates only the pairs of nodes that have relations at some time in \(S_T\) [75]. With the help of the edge and node existence schedules, changes in topology of an evolving graph can be studied [76].

To better understand the concept of TVGs see Figs. 4 and 5; where, Fig. 4a–d, represent the time-evolving topologies of the network during four time slots. Figure 5a represents an underlying graph for an evolving graph of five nodes at \(t = 0\). Figure 5b shows TAG at \(t = 1, 2, 3\) and 4, with link number 2, 7 and 10 not appearing in any time interval. Figure 6 represents a time line showing contacts between nodes for a TVG at different time intervals. In the following discussion, we present the paradigm shift of network topology modeling towards TVGs.

  • Paradigm Shift towards TVGs

    Earlier, performance analysis of dynamic networks has been carried out by neglecting the time instants of interaction between mobile nodes. This style of analysis produced aggregated graph, which could only depict nodes and links (along with their weights at most), but failed to represent the time at which links were formed or broken. A new combinatorial model for graph modeling and analysis, namely, evolving graph/TVG (discussed above) is becoming popular amongst researchers these days, because of its power to incorporate time domain feature in the form of contact sequences. Although in TVG the assumption of connectivity does not necessarily hold at a given instant of time, i.e., the network could even be disconnected at every time instant, communication routes generally become available over time and space, enabling broadcasting and routing by means of a store-carry-forward like mechanism [75]. The systems which can be suitably represented as TVG appear to be omnipresent now-a-days. The flow of information via e-mail messages, cellular phone calls, and online networking are few examples that have lately pulled in much consideration. As, TVGs reflect the real environments perfectly in which DTNs operate, therefore, they may be used to model DTNs for evaluating network reliability measures, packet delivery ratio (PDR), average number of link failures during the routing process, routing requests ratio, average end-to-end (E2E) delay and route lifetime. Recently, this model has been utilized in the evaluation of most reliable journey in VANETs [77]. MANETs have also been modelled using this modeling technique [78]. Other dynamic systems that can be modeled as TVGs are human proximity networks, animal proximity networks, human communication, collaboration networks, citation networks, economic networks, brain networks, travel and transportation networks, distributed computing, biological networks, and OMNs [79]. Interested readers are encouraged to explore some of the recent research to get a deeper insight in to the domain of TVGs [73, 75, 79,80,81,82,83,84,85,86,87,88,89,90,91,92].

At this juncture, it is very evident that there is a paradigm shift of research from static graph representation towards the TVGs because of their power to consider the effects of time domain on system topology. However, till date there are not so many methods available to model and analyze the networks varying with time. The primary reasons behind this deficiency are: (a) the field of temporal graphs is relatively young, and (b) it is more difficult to develop methods of performance analysis in dynamic networks, in comparison to the methods available for static networks.

Fig. 4
figure 4

a Snapshot for a TVG of 5 nodes at \(t = 1\). Clearly, there is no direct path between (1,5) at this time interval, b snapshot for a TVG of 5 nodes at \(t = 2\). Clearly, there is no direct path between (1,5) at this time interval, c snapshot for a TVG of 5 nodes at \(t = 3\). Clearly, there is no direct path between (1,5) at this time interval, d snapshot for a TVG of 5 nodes at \(t = 4\). There is a direct path between (1,5) at this time interval

Full size image
Fig. 5
figure 5

a Complete graph on 5 nodes (\(K_5\)) or underlying graph for a TVG of 5 nodes at \(t = 0\), b time aggregated graph (TAG) of 5 nodes at t = 1, 2, 3 & 4, with link no. 2, 7 & 10 not appearing in any time interval

Full size image
Fig. 6
figure 6

Time line representing contacts between nodes for a TVG of 5 nodes at different time intervals

Full size image

2.4 Radio Propagation Based Modeling

2.4.1 Lognormal Geometric Random Graph Model

The log-normal radio model assumes that the logarithmic value of the received signal power at distance r is normally distributed with standard deviation \(\sigma\) around the logarithm of the area mean power. The magnitude of the standard deviation indicates the severity of signal fluctuations caused by irregularities in the surroundings of the receiving and transmitting antennas. Hekmat and Miegham [93, 94] studied connectivity in MANETs by modeling the network as an undirected RGG. For finding the probability of existence of a link between nodes, they used a radio model based on the log-normal shadowing that takes into account random statistical variations of the radio signal power around its mean power value. These variations are unavoidably caused by the obstructions and irregularities in the surroundings of the transmitting and the receiving antennas, and have two distinct effects on the network. Firstly, they reduce the amount of correlation between links, thereby, causing the RGGs to behave like a random graph with uncorrelated links. Secondly, these variations increase the probability of existence of long links, which in turn, enhances the probability of connectivity for the network.

2.4.2 Propagation-Based Link Reliability Model

As stated earlier, in RGG the mobile nodes in the network can communicate with each other if the Euclidean distance between them is less than or equal to the transmission range of the mobile nodes. Hence, link reliability is theoretically considered as unity under the above criterion. However, even up to the transmission range of the mobile nodes the signal strength deteriorates due to a variety of reasons such as noise, fading effect or interference etc., thereby, decreasing the probability of successful communication. Hence, in propagation-based link reliability model, the link reliability is modelled with a combination of free-space propagation model (FS) and two-ray ground propagation model (TRG) to incorporate the variation in radio signal due to variation in terrain, frequency of operation, speed of mobile nodes, obstacles and other technical factors. Based on this combination, FS-TRG propagation model for modelling link reliability of a MANET as a function of distance has been proposed in [95], and was later utilized in [96] for evaluation of mobile ad hoc network reliability. However, the application of two-ray model in ad hoc networks seems implausible as here both transmitter and receiver are of comparable heights and the inter separation distance between transmitter and receiver is quite less.

2.4.3 Wireless World Initiative New Radio Model

In [97, 98], authors utilized Wireless World Initiative New Radio (WINNER) model for urban scenarios with low antenna heights to study the energy consumption in cellular and device-to-device communication based opportunistic multi-hop networks. They modeled the transmission energy consumption required for a successful communication between two nodes, also included WINNER propagation losses, under assumption that the transmission power, \(P_T\) is necessary to guarantee the required signal power level, \(P_R\) at the receiver, and estimated the transmission power under LOS conditions as:

$$\begin{aligned} \begin{aligned} P_T^{LOS}(d)={\left\{ \begin{array}{ll}\ \frac{P_R\times 10^{4.1}\times (f/5)^2}{G_T\times G_R}\times d^{2.7}, \ if \ d \ < \ d_{bp} \\ \\ \frac{P_R\times 10^{4.1}\times (f/5)^2}{G_T\times G_R \times d_{bp}^{1.73}}\times d^{4}, \ if \ d \ \ge \ d_{bp} \end{array}\right. } \end{aligned} \end{aligned}$$
(3)

where f represents the carrier frequency (in Gigahertz), d represents the separation distance between the transmitter and receiver, \(h_T\) and \(h_R\) are the transmitter and receiver antenna heights (in meters), and \(\lambda\) is the carrier wavelength (in meters), \(G_T\) and \(G_R\) represent the transmitter and receiver antenna gain (here equal to 1), and \(d_{bp}\) is calculated as \(4 \times (h_T-1)\times (h_R-1)/\lambda\).

3 Leap from MANETs to MANET-Born Networks

In the past, it was thought that wireless multi-hop networks would find application mainly in combat fields or during some natural calamity for restoring communications and in providing help in search and rescue operations. However, as the novel technologies (such as Bluetooth and Wi-Fi) have been evolving to their maturity, the scenario of ad hoc networking has also dramatically changed [99]. The ad hoc networks are not just a part of theoretical or simulation studies now, but are expected to play a significant role in various applications in near future in conjunction with already existing infrastructure based communication systems.

Recently, several ad hoc networking paradigms like DTNs, OMNs, FANETs, and VANETs (also known as “MANET-born” networks) have evolved from MANETs. These networks have mainly been developed to resolve the issues viz., (a) implementation, integration, and experimentation, (b) simulation credibility, and (c) socio-economic motivation, associated with MANETs via their pragmatic development approach [1]. Although the commonalities do exist in these networks, however, a major difference lies in the deployment environment. For instance, in areas where the user density is high, MANET provides high network reliability; whereas, wherever the user density is low and/or the moving speed of the users is high, DTNs achieve a certain level of network connectivity and the performance of MANET decreases drastically in such a situation [100]. An opportunistic network phenomena is mainly based on store and carry forward mechanism wherein a data packet is saved for future time instants when a communication link can be successfully established; thereby, is accompanied by an additional delay in data delivery. In opportunistic networking, no assumption is made with regard to the existence of a complete path between two nodes wishing to communicate. In other words, both DTNs and OMNs are deployed in applications that can tolerate large delays and present intermittent connectivity; also, they utilize some agent usually called as ferry or data mules for propagating messages from source to destination. Frodigh et al. [99] reviewed and elaborated the concept of networking wireless communication devices and underscored the challenges faced by the researchers while network formation and routing in these networks.

As these new (MANET-born) ad hoc networking paradigms use more realistic and utilitarian development foreplan involving application oriented development, complexity reduction, focussed research, realistic simulation models and development of real network testbeds, they have become very successful lately [1]. With the improvement in wireless networking devices and evolution of various techniques to harness the precise localization information from the satellite navigation systems, the real potential of these MANET-born networks has started to become clearly visible now. The present research trends shows jump from MANETs to MANET-born networks. Although these networks have huge potential to be deployed in diversified applications, that is, in military battlefields, rural communication, e-commerce, collision avoidance, environment and wildlife monitoring and many more, yet, their real implementation involves many grave challenges. In this review, we have listed in Table 4 the application areas and challenges of various multi-hop mobile ad hoc communication paradigms, and presented in Table 5 a comparison of main features of these networks.

Table 4 The application areas and challenges of various multi-hop mobile ad hoc communication paradigms
Full size table
Table 5 The comparison of main features of multi-hop mobile ad hoc communication paradigms
Full size table

Further, in the forthcoming section we discuss some simulators, emulators and testbeds developed for studying such dynamic networks.

4 Simulators, Emulators and Testbeds for Dynamic Networks

Increasing research efforts towards time varying networks/dynamic networks requires new strategies to effectively study the various features associated with these networks. A brief review of the representation styles developed to analyze the time varying networks may be seen in [101,102,103,104,105]. Various network simulation/visualization tools & packages, emulators and testbeds have also been developed for studying the dynamics and complex processes occurring in large networks emanated from different fields of study such as social science, biology, citation networks, World Wide Web (WWW), Internet, MANETs, VANETs, OMNs and DTNs etc. The following sub-sections give an extensive taxonomy of these simulation/visualization tools, emulators and testbeds, respectively.

4.1 Simulation Tools

Simulation is widely used in the domain of wireless networks, where one can find a number of free and commercial simulation platforms. To the best of our knowledge, there is a lack of a detailed taxonomy on these simulation and visualization tools in the literature. We have compiled 22 popular simulation and visualization tools in Table 6 with a brief description of each. Out of these 22 simulators, ns-2 and OMNeT++ have widely been used for studying the performance of various routing protocols in MANETs. Besides, the Pajek is extremely popular among researchers working on large graphs and ONE is a well-known DTN simulator.

Table 6 Simulators for modeling and visualizing dynamic networks
Full size table

4.2 Emulation Tools and Testbeds

A rich literature is present on the topic of emulators and testbeds [139,140,141,142]. Choosing an appropriate emulation/testbed-platform for validation of research outcomes is an endeavor on its own. There is no panacea for all possible application scenarios, but a compromise between characteristics, such as scalability, realism, performance, and cost has to be reached. Table 7, provides a compilation of 14 emulators and testbeds (along with their features) available for experimenting and modeling wireless ad hoc networks. In Table 8, we have specifically collated 12 real prototypes for target tracking and performance evaluation of VANETs, FANETs and DTN/OMNs along with their brief description. However, it seems impossible from the available literature to identify any single ace emulator/testbed.

Table 7 Testbeds and Emulators for modeling and visualizing dynamic networks
Full size table
Table 8 Various testbeds for VANETs, FANETs and DTN/OMNs and their brief description
Full size table

5 Real World Project Implementations

Recently, a large number of projects have emerged from various universities and research organizations for developing DTNs, FANETs and VANETs to study the performance of such ad hoc networks and to ensue new applications. American Red Cross Apps [173], and Google Public Alerts [174] are two such applications which use internet for the crisis information dissemination. Even, social media platform like Twitter has also been shown to opportunistically spread the tweets using the Twimight [175] client for Android phones and the Bluetooth technology. Furthermore, there is an increase in concerted efforts directed towards the development and evaluation of new, efficient routing protocols and mobility models specifically for search and rescue operations in disaster scenarios.

Here, we have collected some real life projects (completed or ongoing) from the literature, executed with a prime objective to study the wireless multi hop networks (MANETs, DTNs, OMNs and VANETs) and to develop new technologies based on the challenges encountered in their implementation. Another objective achieved with these projects is the enhanced confidence that these networks are no underdogs in comparison to infrastructure based networks, and they will help in bringing out new ICT based applications even for those remote areas where conventional infrastructure based network setup is not present or is almost impossible to install.

These projects have helped to: (a) collect real mobility traces of humans and vehicles, (b) develop new ICT based services and (c) provide the internet connectivity and communication facility in the remote areas particularly the rural areas and small towns of developing countries like India, Cambodia and Kenya. We have collated the information for 14 such projects along with their important features and funding sources in Table 9. Interested readers can refer the cited papers and references therein for finding more information related with the considered projects.

Table 9 Few real life projects implementing wireless ad hoc networks (MANETs, DTNs and VANETs)
Full size table

6 Reliability Evaluation

Often, we need measurable quantities to quantitatively assess the degree to which a system is able to provide the required services. One such performance measuring attribute is reliability. For example, the reliability of a MANET can measure how reliably a packet sent by a source node can be received by a destination node. Though, connectivity, routing protocols, mobility models, energy conservation, security issues and various applications etc., of MANETs have been widely studied in the last decade, yet, the reliability evaluation of these dynamic networks is still in its nascent stage.

The reliability evaluation techniques for static infrastructure based networks are well defined and understood. The literature in this domain contain a plethora of algorithms/techniques. Most of these algorithms originate from graph theory and Boolean algebra, such as enumeration, inclusion-exclusion principle based, sum of disjoint products (SDP) based, decomposition/factoring theorem based, transformation methods, Binary Decision Diagram (BDD), direct methods and approximation method etc., [201,202,203,204]. But, this advantage is absent with MANETs and alike because their topology shows dynamicity and environmental conditions show huge variations affecting their transmission range. Hence, for MANET-born networks the existing analytical approaches of reliability analysis are found to be impractical.

The modeling of the erratic topology, node mobility, phase transition, link failure, PDR, routing requests ratio, E2E delay, route lifetime and the reliability analysis are some of the challenges that a MANET and alike networks poses to the reliability research community. Table 10 presents a list of performance measure metrics studied by the researchers for evaluating the performance of wireless ad hoc networks.

Table 10 Metrics/performance parameters for analyzing performance of ad hoc networks
Full size table

Recently, a concerted effort towards evaluating the reliability of MANETs has been undertaken by the researchers. This challenging issue of wireless networks has mainly been addressed using Monte Carlo simulation because it provides a feasible alternative in dynamically changing environment [9] or with the help of ubiquitous analytical approaches of infrastructure based network, like one using SDP [224] to compute the expected hop count and reliability [210]. It is worth mentioning here that, though simulation method is a tool of great significance, especially in the areas where analytical methods are inappropriate and testing over real system is riskier and costly, it has its own limitations. For instance, if the simulation does not reflect an important aspect of reality, it can not give insight into the operating characteristics of the system which the developers are studying. Generalization and lack of rigor can lead to wrong conclusions or inappropriate implementation decisions. Hence, one must be very careful while choosing parameter values that are used as input for simulators [225, 226].

Recently, in [217], authors have utilized logistic regression based modeling followed by simulation in ns-2.35 simulation software to evaluate ad hoc network reliability. Some papers, utilizing the techniques other than simulation for reliability evaluation of MANET are [205, 212, 213, 227], where a new method based on universal generating function for evaluating reliability of MANET is proposed. In [223], authors have utilized factoring method to evaluate reliability of MANET. Rebaiaia and Ait-Kadi [228] have proposed polygon-to-Chain and series-parallel reduction for evaluating reliability of MANETs. In [218], authors modified the reliability evaluation algorithm for computer networks proposed in [229] to evaluate the reliability of MANETs with imperfect nodes. Ahmad and Mishra [230] have used critical node detection based approach for reliability evaluation of large scale MANETs. A stochastic link failure model was used in [231] for reliability evaluation of wireless multi-hop networks. A list of research papers published for the reliability evaluation of MANETs is provided in Table 11, which is by no means exhaustive. More solutions and understanding is expected to arise in the future. It is clearly evident from Table 11 that simulation has mostly been used for reliability evaluation in MANETs.

Table 11 Reliability evaluation of MANETs
Full size table

Apart from the research efforts dedicated towards the issues related with the reliability evaluation of MANETs and alike, several papers concerned with the connectivity issue of these networks have also been published. The famous model viz., TVG, has been utilized to address the issues like mobility pattern, changing topology and connectivity of such networks [74, 235]. In [236], authors have proposed a method to evaluate the MANET connectivity by taking inter-node communication probability and physical connectivity status into consideration. In [237], authors have proposed a probabilistic connectivity matrix to assess the quality of network connectivity for wireless networks, which are inherently unreliable. It was demonstrated that the largest magnitude of eigenvalue of probabilistic connectivity matrix (which is positive) quantifies the quality of network. This work resembles with the recent work of Brooks et al. [238], in which a slightly different probabilistic version of the adjacency matrix is considered, wherein, (i, j)-th entry of the matrix represents the probability of having a direct connection between distinct nodes i and j and entries \(\forall i = j\) are set to zero. Moreover, in [238] authors also presented a range-limited graph model for the ad hoc networks.

7 Future Prospects

In order to facilitate a hassle-free communication within ad hoc networks, a variety of routing protocols have been proposed in the recent past. Almost all MANET routing protocols mainly aim to provide a communication platform that is fault tolerant to frequent path breaks, expends minimum processing power and bandwidth to provide high reliability. Some praiseworthy review papers on routing protocols for MANETs can be seen in [239,240,241]. Although many routing protocols have already been devised and presented in the literature, many new protocols are being proposed almost every day. Hopefully, this field will continue to be favorite and will garner more attention from researchers.

Ad hoc networks are generally energy starved and their network topology and energy consumption are considerably affected by the transmission range of nodes. Hence, in order to reduce energy expenses in data transmission, there must be a trade-off between energy consumption and transmission range to find an optimum balance between them. There are some studies [242, 243], which analyzed the critical transmitting range for connectivity in wireless ad hoc networks. This is another field, which has huge potential of furthering the research.

An experiment conducted by Milgram [244] verified the small world phenomena on social networks. In [245], the phenomena of small world was proven to be applicable in mobile opportunistic networks as well. Also, it was shown that a destination node could be reached by a small number of relays; hence, opportunistic mobile networks have small diameter. Tang et al. [246] investigated and confirmed the temporal small world behavior in social and biological networks. They inferred that these networks have highly clustered links in time, and nodes with small average temporal distances. A visible impact of this phenomena in dynamic networks can be seen in information diffusion throughout the network or in studying clustering coefficient and calculating other parameters like betweenness centrality and closeness centrality.

Another prospective field is mobility trace collection. Although synthetic mobility models along with Monte Carlo simulation method have been utilized in performance analysis, yet, these models fail to mimic reality. A thorough study of the individual movements can be successfully done with GPS data mining. Many studies are going on in this direction and many more will certainly evolve with time.

The domain of reliability evaluation of dynamic networks is also in its nascent stage as the algorithms and techniques available for static networks cannot be directly applied on these networks.

Although a lot of research work has been undertaken for the performance evaluation of ad hoc networks, but, as the number of users with smart phones are increasing tremendously, new robust applications needs to be designed addressing the various challenges. This demands testing of the applications in reality. Though, many testbeds, simulators and emulators have been built, yet, there is no panacea. Hence, new generic testbeds need to be built which can test different paradigms with great efficiency into the real application environments.

Some other worth mentioning challenges for future research are:

  1. 1.

    Usually researchers have considered that all the devices in the system are homogeneous, that is, manufactured by the same company and have similar bandwidth, energy, processing power, and storage capacity. This assumption reduces the complexity of the simulation. However, in reality heterogeneous devices must be considered for simulation. Hence, the research in this area needs more attention.

  2. 2.

    Secure and robust techniques for transmitting the information over wireless media must be comprehensively taken into account.

  3. 3.

    Various new models, exhibiting different aspects of diverse real scenarios need to be developed.

  4. 4.

    Traces collected using GPS may have missing values or can give biased results; hence, more studies based on traces are necessary to validate any proposed mobility model. In addition, the performance of routing protocols should also be evaluated using real traces for more credible performance evaluation.

  5. 5.

    Flow, capacity and other constraint based performance analysis of the multi-hop ad hoc networking paradigms needs to be explored in disruptive environmental conditions because of the frequently changing end-to-end connectivity and highly fluctuating link capacity.

  6. 6.

    The fusion of routing technologies between MANETs and DTNs has already been demonstrated successfully in [100, 247]. However, the interoperability between other paradigms like FANETs, VANETs, MANETs and DTNs needs attention.

The characteristics of ad hoc networks (or the complete domain of wireless networks) is quite dynamic. The research in this domain has been undertaken from several perspectives in the past, still active at present and shall be continued in near future to devise new performance metrics and evaluation techniques for such dynamic systems. We are hopeful that in the near future many new developments will take place in the above directions.

8 Conclusion

In this paper, we have made an attempt to comprehensively summarize the current state-of-the-art research in the area of wireless ad hoc networks varying from MANETs, DTNs, FANETs, OMNs to VANETs from multiple dimensions. We have provided a comprehensive account of the major advancements in the domain of wireless multi-hop networks by highlighting the main results along with the key research and review papers published recently. For this reason, we have avoided description of the mathematical models, their derivations and formulas involved. We have compiled various discussions with figures and tables.

We are hopeful that this survey will support the novice readers to have a better overall understanding of the domain and assist them to start conducting research quickly. The foremost contributions of this survey are:

First, we reviewed various models available in the literature for studying different aspects of wireless ad hoc networks, accompanied with various tools for studying mobility of human beings and vehicular traffic (see Table 1). We found that, during the last decade plenty of research efforts to mitigate the challenges associated with the modeling have been made; at present, the pool of research in this direction is quite rich and an ample number of research papers have already been published.

Second, we highlighted various data repositories, which may be accessed for downloading and validating various traces. We discussed in detail about the SNS along with their strengths, limitations, and applications. We also tabulated different SNS variants (see Table 3), operating or in progress, across the globe and surveyed their increasing usage in location prediction, trace collection and mobility modeling along with inherent challenges.

Third, we found from the literature that currently the research momentum is shifting from MANETs to MANET-born networks. Besides, we also compared different multi-hop ad hoc network paradigms and listed their challenges in Tables 4 and 5.

Fourth, we discussed various simulators, emulators and testbeds (Tables 6, 7, 8) along with their specific features and identified some extremely popular simulation and visualization tools amongst the researchers. In Table 9, we covered several worth mentioning real world projects ongoing/carried out throughout the world to address various challenges in implementation and networking in disruptive environments.

Fifth, we dealt with an important aspect of every network i.e., reliability and identified various performance measure metrics and tabulated them in Table 10. Further, we collated in Table 11 various research papers published in the literature for the reliability evaluation of MANETs. We also established from Table 11, that the maximum number of publications utilize simulation for the reliability evaluation of MANETs.

At last, we outlined various challenges for future research.

Although we have discussed the various developments in the field of wireless ad hoc networks in terms of: (1) the available techniques for modeling MANETs, (2) trace collected and data repositories and (3) their reliability evaluation, yet the present state-of-the-art with respect to the models, traces and reliability evaluation seem to be at nascent stage and still unable to resolve the various associated issues in their entirety. We are very optimistic that in the forthcoming days/years the multi-hop network paradigms will flourish more and will help in bringing out newer and better ICT based applications. We feel that there will be intermixing of different paradigms. Moreover, the performance evaluation of these networks (including times of contacts) in terms of reliability and Quality-of-Services (QoS) will garner more attention and will be assessed more rigorously.