Connectivity Models : A New Approach to Modeling Contacts in Opportunistic Networks.∗ Thabotharan Kathiravelu Uppsala University, Box 337, 751 05 Uppsala, Sweden.
[email protected]
Arnold Pears Uppsala University, Box 337, 751 05 Uppsala, Sweden.
[email protected]
Abstract The influence of node mobility in Mobile Ad hoc NETworks (MANETs) is significant and has significant implications for system performance. The difficulty and cost associated with collecting real movement traces has resulted in the use of synthetic mobility models in many simulation studies. Simulations use mobility models to describe how devices move in the geographical (coordinate) space of the simulated deployment environment. Typical mobility models describe the location, speed, direction of movement etc., of mobile entities over time [1]. It has been observed by many researchers that synthetic mobility models fail to capture common features of real world movement of humans and the connectivity patterns they establish [4, 16]. This has prompted the development of a range of more complex mobility models which address deficiencies in earlier models [7, 8, 11, 14]. However, the increase in the fidelity of mobility models comes at a price. There is a corresponding increase in simulation storage and time overheads. This paper argues that for Opportunistic networking simulations high fidelity mobility models are both too costly and inappropriate. We summarize the early proposals in mobility models for MANETs, then look at the relevance of recent advances. We introduce connectivity modThis work has been supported by SIDA - The Swedish International Development Cooperation Agency - Split PhD funds 2003-2007 ∗
Nalin Ranasinghe University of Colombo, 35, Reid Avenue, Colombo 7, Sri Lanka.
[email protected]
els as an alternative approach and explain their potential when simulating the performance of opportunistic network protocols.
1
Introduction
Opportunistic networking is a new paradigm in mobile ad hoc networks. The basic idea behind Opportunistic networking is that, in the absence of a fixed infrastructure for connectivity, data exchanges could take place using the connection opportunities that arise due to impromptu encounters with other devices [3,9,18]. Widespread use of small portable hand held devices with one or more wireless communication interfaces makes them an ideal platform for opportunistic networking applications [12]. Representing the mobility of nodes more accurately has been seen as an important part of modeling mobile ad hoc networking behavior. Mobility models have significant impact on simulation results [5]. For system design and implementation, extracting modeling parameters from reallife measurements is a challenge, but has been seen as a necessary step towards better simulators. Though much of this is still at the level of lab experiments [4,16]. One of the most important issues is the selection of the most appropriate mobility model, one that accurately represents node mobility in the type of networking environment being investigated. In this paper we argue for a paradigm shift from
mobility models to connectivity models especially in simulation studies of opportunistic networks. This paper is organized as follows: In section 2 we provide the background study of modeling mobility and also look at the recent developments in mobility modeling. In section 3 we provide an analysis of many mobility models used in the field and overheads associated with simulating mobility models. Section 4 addresses the issue of connectivity modeling, talks about the shift to connectivity models, and describes the connectivity modeling. In section 5 we present the discussions and our future research directions and address some of the open questions for future research work.
2
Modeling Mobility
The discussion in this section groups mobility models for MANETs following the classification of Camp et al. [2]. They divide mobility models into two classes Entity mobility models and Group mobility models.
2.1
Entity mobility models
Entity mobility models describe the movement patterns of individual mobile nodes independent of other mobile nodes in the environment. The Random walk and the Random way point mobility models are the best known entity models, and have been used in many case studies. The Random walk mobility model [2] node moves from its current location to a new location by randomly choosing a direction and speed to travel. The new speed and direction are chosen from predefined ranges. Random walk nodes move either for a constant amount of time or for a fixed distance, and then choose a new direction and speed for the next movement. In Random way point (RWP) mobility model [2], each node after staying for a period of time at each way point, chooses a new random destination and a speed from the predefined range and then starts traveling to the newly chosen destination. This process is repeated for each node until it leaves the simulation.
In Random direction model, a mobile node selects a random direction to travel and when it reaches the border of the simulation area it pauses there for a predefined amount of time and then chooses another angular direction and then starts traveling in that direction [17].
2.2
Group mobility models
Group mobility models describe the movement of mobile nodes that depend on the movement of other mobile nodes in the group. In nomadic community mobility model [2] each node uses an Entity mobility model, e.g RWP, to roam around a given reference point. When the reference point changes all nodes in the group travel to the new area defined by the reference point and then begin roaming around the new reference point and preset parameters define how far an entity may roam from the reference point. The Reference Point Group Mobility model [6] represents the random motion of a group of mobile nodes as well as the random motion of each individual node within each group. Group movement is based upon the the path travelled by a logical center of the group and it completely characterizes the movement of its corresponding group of nodes, including their direction and speed. Individual nodes randomly move about their own pre-defined reference points.
2.3
More focus on obstacles
Analysis and systematic studies of the stochastic properties of earlier models have identified drawbacks such as unrealistic behavior, memoryless movement, decay in the speed, congested node mobility in the center of the simulation area, unrealistic environment etc., [1,2]. Many new mobility models have been proposed that mimic real world environments and related movement patterns. Jardosh et al., [8] create a mobility model by including obstacles in the simulation area. Paths are found for nodes to move towards their destinations, through the modeled obstacles such as buildings, exits and entry points, or by avoiding obstacles. Dijkstra’s shortest path algorithm is
used to establish paths between any two points and nodes move on these established paths to reach their destinations. MobiREAL [11] considers obstacles along the path of movement of nodes, and in addition pays attention to the behavioral changes of the nodes due to the surroundings, information obtained from network system, time etc. It adopts a rule based model to describe the movement patterns of mobile nodes. First mobile nodes are categorized in to multiple groups depending on their movement pattern and then, for each group of mobile nodes, a rule-based description is specified where dynamic and realistic behavior of nodes is specified.
2.4
Applying social network theory based aspects
Mobility models based on social network theory [13, 14] describe group mobility which considers the social relationships that human beings establish among them selves depending on their attraction towards groups of people as the basis for the derivation of the mobility model and considers each individual node’s mobility as a correlated movement with the members of the group to which it belongs. During simulation runs of this model individual node locations are updated based on the randomly chosen speed values and this results in the change of group locations. A good feature of this group mobility model is that, it allows individual nodes to leave their groups and join other groups and even leave the simulation area independently.
2.5
Models generated from user traces
There are significant efforts by some researchers to create mobility models from real world traces. This includes the process of collecting user access traces from networks and then analyze and extract parameters that represent the mobility of nodes and then verify that the developed mobility model resembles the collected traces. Proposed by Hsu et al. [7], Weighted way point (WWP) mobility model recognizes that people do
not choose their way-points randomly. It models this behavior by defining the popular locations in the simulation field and their related popularity weight values according to the probability of choosing these locations as the next destination. In this Markov-based model, the selection of a new destination is determined based on the current location and the value of the current time. Kim et al., [10], have proposed another idea of generating a mobility model from user traces. They describe a method for extracting user’s mobility tracks from recorded Wi-Fi traces. They further have validated their method by performance comparison between trace locations and the locations determined by users carrying both GPS and 802.11 devices. The authors have examined the tracks in the traces and were able to extract information on mobility such as speed, pause times, destination transition probabilities, and way points between destinations. This information then assists in forming an empirical model that can be used to generate synthetic tracks [10]. It is obvious to see that the research community is working hard towards modeling mobility more accurately. Considering all aspects of the environment and then trying to model node mobility accordingly remains a challenging problem. In the following sections we will talk more about the complexity issues involved in some of the well known mobility models.
3
Analysis
All mobility models have a common aim, representing a more realistic and accurate mobility pattern of mobile nodes in simulation environments. Mobility models that consider obstacles: It is obvious to note that many of the mobility models make unrealistic assumptions about obstacles which may be present in any environment for the ease of implementation. It is mostly assumed that there are no obstacle that blocks the random movement of the mobile nodes and how these affect the strength of the signal being transmitted. This results in experimental results that are not trustworthy. Therefore it is necessary to consider obstacles in any mobility model and techniques
that find paths and model the effects of obstacles on the transmitted signal are an essential part of any mobility model for future networking research. Mobility models based on social networking theory: Recent proposals [13, 14] to consider the social relationships among people to design sound mobility models have gained much attention. The idea behind considering social networks is simple. After all, mobile devices are carried by humans and human mobility is mostly influenced by the social relationships amongst them. Authors of [13, 14] claim that their generated traces represent characteristics of real traces, especially in terms of inter-contact times and duration of such contacts. Mobility models extracted from user traces: Models derived from user traces have been shown to represent realistic movement patterns with slight error rates. The most recent work by Kim et al., [10] is based on WiFi traces. Again the question of applicability of traces collected from access points in covering all the areas of possible node mobility and the real connectedness among the nodes exists. There is also a criticism about the type of the scenario from which the traces are collected and how well such scenarios represent all use cases.
Services TCP / IP Connectivity Mobility Figure 1. Layered approach to simulations
Much research in wireless ad hoc networks is to support traditional applications and services that are available to the fixed nodes, in particular end-to-end reliable data delivery. Ultimately to evaluate any proposed protocols etc., we need to do simulation based studies to understand the behavioral changes. Traditional simulations of
MANETs have the layered architecture shown in Figure 1. A conventional simulation uses the node mobility model to determine the pattern of internode wireless connections. In situations where physical node mobility plays no part in the simulation outcomes (e.g we are simulating to determine inter-contact times) mobility models can be replaced by connectivity models, in which the internode connectivity graph perturbations are modeled directly. It is clear to see that the trend in representing node mobility in MANETs is to rely on the synthetic mobility models. These synthetic models have been long criticized for not representing the mobility of nodes realistically. The property of exponential decaying of the inter-contact time distributions of many of the mobility models has also been observed. Mobility models may be a basis for simulations where the nodes move slowly and are normally used to model mostly connected environments where perturbations of the network graph occurs to a fairly limited degree. The overheads of simulating such mobility is lower than in opportunistic networking environments where the disconnectedness among nodes is high, the nodes are sparsely distributed, and the overhead of simulating enormous numbers of such nodes in an intermittently connected environment is significantly greater than for traditional MANETs. Mobility models already suffer from issues such as simplicity in implementation, simulation efficiency, fidelity etc. This analysis leads to the following realization: what mobility models do is to provide a mechanism to generate permutations in the network connection graph. In other investigations of routing protocols for mobile ad-hoc wireless networks by Wibbling et al., [19] the changes in the network graph are simulated directly, rather than indirectly using a mobility model. Why then we use mobility models at all? What impact would directly modeling the connectivity patterns for the network have on simulations? There are several aspects to consider, fidelity, simulation/modeling/storage complexity
and computational overhead.
3.1
Modeling/Storage complexities simulating mobility models
Issues in mobility modeling
in
As the need for a more realistic mobility model increases the need to store more data to represent the node locations, and other metrics proposed by the mobility model need to be stored as the simulation progresses.
3.2
Model RW/RWP WWP Nomadic RPMG Social NWT Obstacles
O(E) O(E)+O(L2 ) O(E) O(E) O(E2 ) O(N * M)
O(E) O(E) O(E) O(E) O(E2 ) O(E logE)
Low Moderate
Low Low Moderate Moderate
the number of obstacles in the simulation area and M is the number of coordinates for each obstacle assumed in the simulation area.
4
Fidelity
Fidelity of a mobility model refers to the dependability of the test results obtained. Most of the mobility models have assumptions that avoid some of the crucial issues such as the fading of the signal as it passes through walls etc., and such assumptions will result in lower fidelity levels. The parameters that are used in the derivation of the above order complexity for the three overhead issues are number of nodes in the simulation area, the spatial representation of each node, the speed of the nodes under consideration, the direction of their movement, number of attractor places in the simulation field etc. The complexity trade off table in Table 1. summarizes the complexity issues of the mobility models we have looked in Section 2. In this table, E is the number of moving entities/nodes in the simulation environment and L represents the possible number of attractor places for selection for the Weighted way point mobility model. For the obstacles model, the storage overhead should consider different factors such as the , and here N is
Fidelity
Table 1. A comparison of modeling complexity
4.1 3.3
Computing Complexity
Complexity
Computational Overhead in simulating mobility models
Computational Overhead in simulating the mobility model refers to the complexity in the calculations of node movements and other related issues as the number of mobile nodes increases. Anybody who wants to model with greater realism should be able to model real dimensions, locations of obstacles etc. Placement of nodes, their movement through the simulation field throughout the simulation time poses extra computational overheads.
Storage/ Modeling
Connectivity Models Connectivity Models and Opportunistic Networks
In opportunistic networking environments, the aim is to optimize the connection opportunities and any mobility model that attempts to represent such environment should consider the factors that affect the most important features of real traces: the inter-contact times and the contact duration [3]. Movement of clusters of nodes in extreme environments also pave the way for good opportunistic networking environment and any mobility modeling should represent the cluster of node’s movement in the form of group mobility. A mobility model that captures these desirable properties should be capable of generating synthetic traces that closely match real traces.
4.2
Connectivity models.
We suggest that connectivity models should be based on an analysis of connectivity patterns in real networking environments. Analysis of these traces for their stochastic properties allows
us to derive probability distributions to model inter-node relationships (so called small world properties of social networks) as well as the probability of a relationship resulting in two nodes being connected at a given point in time. Our connectivity model is constructed in two phases: (i) we determine the social connectedness of the node set (ii) once the social relationship network has been determined the frequency of connection events between two related nodes is used to generate a connectivity trace with the desired properties. Initially we define the connectivity model M as a 4-tuple M = (K, PR , PC , N )
al. [16] for an example of such a connection graph. After phase one the connectivity model consists of a set of nodes and a set of edges defining their social relationships. This structure can then be used to obtain a synthetic connectivity trace appropriate for simulating opportunistic network behavior.
4.3
Related Work
Work on formulating temporal connectivity models is emerging. Nykvist et al., [16]. argue for developing temporal connectivity models directly using the underlying properties of real movement traces as an alternative to attempting to parameterize mobility features of the traces in order to generate synthetic mobility models.
where
5 N is the total number of entities in the simulation. K is a set of clusters [K1 ..Kk ], for a model with k clusters. PR (A, B), is the relationship function defining the probability that A and B are socially related. PC (e), is the connectivity function defining the probability that two nodes related by an edge e are currently connected. Here e ∈ E, the set of edges determined by applying the relationship function PR to all pairs of nodes in the model. 4.2.1
Relationship modeling
We define PR , in terms of λ, the intra-cluster relationship distribution and φ, the inter-cluster relationship distribution. Thus ( λ; where A, B ∈ Ki PR (A, B)= φ; where A ∈ Ki , B ∈ Kj and i 6= j PR (A, B) is used to compute a set of edges E which defines a graph representing the social relationships between simulation entities. If the parameters are chosen correctly the graph defined should closely resemble the weighted aggregate connection graph in the real traces from which the model parameters are obtained. See Nykvist et
Discussion & Future Work
Simulations of mobile ad hoc networks make many simplifying assumptions to obtain a setup that is computationally tractable. The movement of the nodes is one aspect in simulations. Understanding the consequences and tradeoffs of mobility is a difficult topic and needs to be explored well. In this paper we have introduced the concept of connectivity models for the simulation studies of MANETS especially in the field of opportunistic networking. Instead of employing some unrealistic mobility models or mobility models that are derived from the user mobility traces, temporal connectivity models that are developed from the stochastic behavior on humans seem to be promising. In future work we intend to develop temporal connectivity models derived from human mobility characteristics, implement and test their suitability in our scenario case studies [9] as in well known test bed environments [15]. In addition, from our analytical study of mobility models and the derivation of the tuple for the connectivity model in this paper, we have come up with some open research questions suitable for further exploration in future: • can entities belong to more than one cluster?
• what about spontaneous encounters with unknown entities, with which we have no preexisting social contacts?
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