A Survey of Mwsn

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A SURVEY OF MOBILITY MODELS IN WIRELESS SENSOR NETWORKS

Abstract : A mobile wireless sensor network (MWSN) is a small-profile wireless sensing devicesthat are able to control their own movement. Although it has been shown that mobility alleviatesseveral issues relating to sensor network coverage and connectivity, many challenges remain.

Among these, the need for position estimation is perhaps the most important. Thus, it is essentialto study and analyze various mobility models and their effect on MWSN protocols. In this paper,we examine different mobility models proposed in the recent research literature. Beside thecommonly used Random Waypoint model and its variants, we also discuss various models thatexhibit the characteristics of temporal dependency, spatial dependency and geographicconstraint and We conclude with a description of real-world mobile sensor applications thatrequire position estimation.

1. INTRODUCTION:Wireless sensor network (WSN) applications typically involve the observation of some

physical phenomenon through sampling of the environment. Mobile wireless sensor networks(MWSNs) are a particular class of WSN in which mobility plays a key role in the execution of theapplication. In recent years, mobility has become an important area of research for the WSNcommunity. Although WSN deployments were never envisioned to be fully static, mobility wasinitially regarded as having several challenges that needed to be overcome, includingconnectivity, coverage, and energy consumption, among others. However, recent studies havebeen showing mobility in a more favorable light. Rather than complicating these issues, it hasbeen demonstrated that the introduction of mobile entities can resolve some of these problems.In addition, mobility enables sensor nodes to target and track moving phenomena such aschemical clouds, vehicles, and packages.

One of the most significant challenges for MWSNs is the need for localization. In order tounderstand sensor data in a spatial context, or for proper navigation throughout a sensingregion, sensor position must be known. Because sensor nodes may be deployed dynamically(i.e., dropped from an aircraft), or may change position during run-time (i.e., when attached to ashipping container), there may be no way of knowing the location of each node at any giventime. For static WSNs, this is not as much of a problem because once node positions have beendetermined, they are unlikely to change. On the other hand, mobile sensors must frequentlyestimate their position, which takes time and energy, and consumes other resources needed bythe sensing application. Furthermore, localization schemes that provide high-accuracypositioning information in WSNs cannot be employed by mobile sensors, because they typicallyrequire centralized processing, take too long to run, or make assumptions about the environmentor network topology that do not apply to dynamic networks.


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communication range, and higher bandwidth. Furthermore, the overlay network density may be suchthat all nodes are always connected, or the network can become disjoint. When the latter is the case,mobile entities can position themselves in order to re-establishconnectivity, ensuring network packets reach their intended destination. The NavMote system takes thisapproach. The 2-tier architecture is pictured in Figure 2b.

In the three-tier architecture, a set of stationary sensor nodes pass data to a set of mobiledevices, which then forward that data to a set of access points. This heterogeneous network is designedto cover wide areas and be compatible with several applications simultaneously. For example, considera sensor network application that monitors a parking garage for parking space availability. The sensornetwork (first tier) broadcasts availability updates to compatible mobile devices (second tier), such ascell phones or PDAs, that are passing by. In turn, the cell phones forward this availability data to accesspoints (third tier), such as cell towers, and the data are uploaded into a centralized database server.Users wishing to locate an available parking space can then access the database. The 3-tierarchitecture is pictured in Figure 2c. At the node level, mobile wireless sensors can be categorizedbased on their role within the network:

Mobile Embedded Sensor. Mobile embedded nodes do not control their own movement; rather, theirmotion is directed by some external force, such as when tethered to an animal or attached to a shippingcontainer.

Mobile Actuated Sensor. Sensor nodes can also have locomotion capability. With this type ofcontrolled mobility, the deployment specification can be more exact, coverage can be maximized, andspecific phenomena can be targeted and followed.

DataMule. Oftentimes, the sensors need not be mobile, but they may require a mobile device to collecttheir data and deliver it to a base station. These types of mobile entities are referred to as data mules . is generally assumed that data mules can recharge their power source automatically.

Access Point. In sparse networks, or when a node drops off the network, mobile nodes can position

themselves to maintain network connectivity. In this case, they behave as network access points.

Advantage of Adding mobility to WSN:Sensor network deployments are often determined by the application. Nodes can be placed in a grid,randomly, surrounding an object of interest, or in countless other arrangements. In many situations, anoptimal deployment is unknown until the sensor nodes start collecting and processing data. Fordeployments in remote or wide areas, rearranging node positions is generally infeasible. However, whennodes are mobile, redeployment is possible. In fact, it has been shown that the integration of mobileentities into WSNs improves coverage, and hence, utility of the sensor network deployment. Thisenables more versatile sensing applications as well.

3. RANDOM-BASED MOBILITY MODELS :In random-based mobility models, the mobile nodes move randomly and freely without

restrictions. To be more specific, the destination, speed and direction are all chosen randomlyand independently of other nodes. This kind of model has been used in many simulationstudies. The two variants of the Random Waypoint model, namely the Random Walk model andthe Random Direction model.


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3.1 Random Walk ModelThe Random Walk model was originally proposed to emulate the unpredictable movement of

particles in physics. It is also referred to as the Brownian Motion. Because some mobile nodesare believed to move in an unexpected way, Random Walk mobility model is proposed to mimictheir movement behavior. The Random Walk model has similarities with the Random Waypointmodel because the node movement has strong randomness in both models. We can think theRandom Walk model as the specific Random Waypoint model with zero pause time.

However, in the Random Walk model, the nodes change their speed and direction at eachtime interval. For every new interval t, each node randomly and uniformly chooses its newdirection (t) from (0, 2]. In similar way, the new speed v(t) follows a uniform distribution or aGuassian distribution from [0, Vmax]. Therefore, during time interval t, the node moves with thevelocity vector (v(t)cos(t), v(t)sin(t)). If the node moves according to the above rules andreaches the boundary of simulation field, the leaving node is bounced back to the simulationfield wit h the angle of (t) or (t), respectively. This effect is called border effect.

The Random Walk model is a memoryless mobility process where the information about theprevious status is not used for the future decision. That is to say, the current velocity isindependent with its previous velocity and the future velocity is also independent with its currentvelocity.

3.2 Limitations of the Random Waypoint Model and other Random Models :The Random Waypoint model and its variants are designed to mimic the movement of

mobile nodes in a simplified way. Because of its simplicity of implementation and analysis, theyare widely accepted. However, they may not adequately capture certain mobility characteristicsof some realistic scenarios, including temporal dependency, spatial dependency andgeographic restriction:1. Temporal Dependency of Velocity: In Random Waypoint and other random models, the

velocity of mobile node is a memoryless random process, i.e., the velocity at current epoch isindependent of the previous epoch. Thus, some extreme mobility behavior, such as suddenstop, sudden acceleration and sharp turn, may frequently occur in the trace generated by theRandom Waypoint model. However, in many real life scenarios, the speed of vehicles andpedestrians will accelerate incrementally. In addition, the direction change is also smooth.

2. Spatial Dependency of Velocity : In Random Waypoint and other random models, themobile node is considered as an entity that moves independently of other nodes. However,in some scenarios including battlefield communication and museum touring, the movementpattern of a mobile node may be influenced by certain specific 'leader' node in itsneighborhood. Hence, the mobility of various nodes is indeed correlated.

3. Geographic Restrictions of Movement: In Random Waypoint and other random models,the mobile nodes can move freely within simulation field without any restrictions. However, inmany realistic cases, especially for the applications used in urban areas, the movement of amobile node may be bounded by obstacles, buildings, streets or freeways.

4. MOBILITY MODELS WITH TEMPORAL DEPENDENCYMobility of a node may be constrained and limited by the physical laws of acceleration, velocity and

rate of change of direction. Hence, the current velocity of a mobile node may depend on its previousvelocity. Thus the velocities of single node at differ ent time slots are correlated'. We call this mobilitycharacteristic the Temporal Dependency of velocity.


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However, the memoryless nature of Random Walk model, Random Waypoint model and othervariants render them inadequate to capture this temporal dependency behavior. As a result, variousmobility models considering temporal dependency are proposed. They are Gauss-Markov MobilityModel and Smooth Random Mobility Model.

For the Gauss-Markov model, the velocity of a mobile node at any time slot is a function of itsprevious velocity. We could say that the Gauss-Markov Model is a mobility model with temporaldependency. The degree of temporal dependency is determined by the memory level parameter . Inthe Smooth Random Mobility Model, both the speed and movement direction of nodes are also partlydecided by their previous values. Thus, it is also a mobility model that captures the characteristic oftemporal dependency. The degree of temporal dependency is affected by its acceleration speed a andthe maximum allowed direction change per time slot (t). By adjusting these parameters, we are ableto generate various mobility scenarios with different degrees of temporal dependency.

References

1. Ekici, E., Gu, Y., Bozdag, D.: Mobility-based communication in wireless sensor networks.Communications Magazine, IEEE 44(7), 56 62 (2006)

2. Isaac Amundson and Xenofon D. Koutsoukos.: A Survey on Localization for Mobile WirelessSensor Networks

3. Fan Bai and Ahmed Helmy.: A survey of mobility model in Wireless Ad hoc Networks.

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A Survey of Mwsn