1-INTRODUCTION:

Wireless sensor network (WSNs) [1] [2] are promising unprecedented levels of access to information about the physical world, in real time. A wireless sensor network (WSNs) consists of spatially distributed autonomous sensors (in fig 1) to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. Wireless Sensor Networks offer unprecedented capabilities for integrating sensing with computing and communication as well as for distributed sensing, coordination and control.

Fig 1: Wireless Sensor Networks and Sensor Node Components.

Many areas of human activity are starting to see the benefits of utilizing sensor networks. Some of the real deployments include UC Berkley’s Smart Dust, MIT’s μ-Adaptive Multi-domain Power aware Sensors and UCLA’s Wireless Integrated Sensor Networks. In almost all such cases, sensor networks are statically deployed. In static networks, the mobility of sensors, users and the monitored phenomenon is totally ignored. The next evolutionary step for sensor networks is to handle mobility in all its forms. One motivating example could be a network of environmental monitoring sensors, mounted on vehicles used to monitor current pollution levels in a city. In this example, the sensors are moving, the sensed phenomenon is moving and users of the network moves as well [3] [4].

WSNs have been a subject of intense research for about a decade; yet, most research activities to date focus on sensor nodes typically deployed in static, pre-determined locations with sensor readings taken at regular intervals and forwarded through multiple hops to remote static sinks where storage and analysis take place. The next step for sensor networks is to handle mobility in all possible forms. In this context, the mobility of sinks, mobility of sensors and actuators as well as the mobility of applications (software) could be considered.

As presented in [5], there are already several WSN test beds, which use dedicated WSN gateway(s) to transfer the sensor measurements to the remote server locations for further analysis and processing. However, static deployment of dedicated sensor nodes and gateways is not always an optimal solution, resulting in many initiatives to use advanced mobile terminals equipped with different embedded sensors and communication interfaces as opportunistic sensors nodes and/or gateways [6].

A. WHY MOBILE SENSOR NETWORKS?

· In the past, WSNs are deployed by static nodes to continuously collect information from the environment. Today, by introducing the concept of mobility to WSNs, we can further improve the network capability on many aspects, for example, automatic WSN deployment, flexible topology adjustment, and rapid reaction to events.

· Recently, there has been a strong desire to de-ploy sensors mounted on mobile platforms such as mobile robots. Such mobile sensor networks are extremely valuable in situations where traditional deployment mechanisms fail or are not suitable.

B. MOBILE SENSOR NETWORKS:

A collection of sensing devices that moves in space over time. In WSNs mobility can appear in three main forms.

Node Mobility: Node mobility implies that the network has to reorganize itself frequently, i.e., the logical topology of the network will change if just one of its members changes its logical link due to a location change.

Sink mobility:

Refers to mobile information sinks, which can be considered as a special case of node mobility.

Event Mobility:

This is a quite uncommon form of mobility. Event mobility refers to applications where event detection is required, particularly in tracking applications.

C.MOBILITY IN SENSOR NETWORKS:

A number of approaches exploiting mobility for data collection in WSNs have been proposed in recent years. These approaches can be categorized with respect to the properties of sink mobility as well as the wireless communication methods for data transfer [21].

Mobile base station (MBS):

Based solutions:

An MBS is a mobile sink that changes its position during operation time. Data generated by sensors are relayed to MBS without long term buffering.

Mobile data collector (MDC)

Based solutions:

An MDC is a mobile sink that visits sensors. Data are buffered at source sensors until the MDC visits the sensors and downloads the information over a single-hop wireless transmission.

Rendezvous-based solutions:

Rendezvous based solutions are hybrid solutions where sensor data is sent to rendezvous points close to the path of mobile devices. Data are buffered at rendezvous points until they are downloaded by mobile devices.

Dynamic Nature:

The dynamic nature of mobile wireless sensor networks introduces unique challenges in aspects like data management, accuracy and precision, coverage, routing protocols, security, software support. Many of the above mentioned problems related to a static deployment of the sensors are well addressed by the researchers. One of the most important constrains on sensor nodes is the route enabling when the nodes keep moving. It has been reported that the clustering mechanisms and hierarchical routing make huge improvement in sensor networks in terms of energy consumption and efficient data gathering [7] [8]. Such improvement is due to the structure of the network, assumed before the deployment of the sensor nodes. Once the network becomes dynamic we do not have the freedom to pre-assume such structures.

II. CHALLENGES IN MOBILE SENSOR NETWORKS:

Adaptive Localization:

There has been a lot of research in the area of probabilistic localization in robotics off late [9]. Incorporating mobility into sensor networks would need distributed lightweight implementations of such algorithms to implement localization in sensor networks.

Coverage:

Maximizing coverage in sensor networks using static and mobile nodes has received some attention. However, there has not been much work on mobile sensor networks and how they could be used to adapt networks by varying coverage dynamically [10].

Massive Reprogramming:

Massive reprogramming of sensor networks is one of the envisioned problems [11]. It is possible to consider solutions using mobile nodes that travel across the geography of the sensor network, reprogramming parts of it.

Distributed Calibration:

Another hard problem in sensor networks is calibrating the sensors, particularly when the sensors used are cheap and erroneous. We can think of having a calibrated sensor on a mobile node and the mobile node covering the area of sensor node deployment calibrating the nodes in its neighborhood.

Network Repair:

An interesting area of work is that of network repair. As mentioned earlier, it can be imagined that a few mobile nodes can be used to repair static networks by positioning themselves at hotspots or points of disconnection. However, moving the mobile nodes expends energy and there is scope for study of the tradeoff.

III. APPLICATIONS OF MOBILITY

Dynamic Coverage Planning:

There are two methods to plan physical locations of sensor nodes in the network: First approach is traditional static coverage planning and the second one is utilizing dynamic features of a mobile sensor network.

Energy saving:

In sensor networks with high sampling rate and high-bandwidth data communication but limited throughput maximum especially in delay sensitive applications.

Reliable data transfer:

Due to high resource limitations and unpredictable conditions of deployment environment in the sensor networks, reliable data transfer is not guaranteed.

IV.MOBILITY MODELS IN SENSOR NETWORKS

There is much attention currently focused on the development and evaluation of wireless routing protocols for wireless sensor networks. Most of this evaluation has been Performed [12] with the aid of various network simulators (such as ns-2 and others) and synthetic models for mobility and data patterns [13].

There are two types of mobility models (in fig 2):

· Entity/Individual mobility models: Nodes’ movements are independent of each other such as Random Waypoint, Random direction, Random Walk etc.

· Group mobility models: Mobile nodes move dependent of one another like Reference Point Group Mobility model, Column, Nomadic, Pursue, and Exponential Correlated. The pathway, Manhattan, obstacle are under geographical restricted model.

Fig 2: Classification of Mobility Model

Random Waypoint model:

It is a very simple model based on pause time between changing direction/speed. Background a random point in the simulation area with a uniformly distributed speed between [minSpeed, maxSpeed]. After arriving to the destination again waits for the same period of time (pause time) before moving to a new place. There are common problems with simulation studies using Random Waypoint model due to poor choice of velocity distribution, uniform distribution [14]. If minspeed is zero, such velocity distribution leads to a situation where average speed approaches zero and at the stationary state each node stops moving.

Manhattan Grid model:

The Manhattan mobility model [15] uses a grid road topology. This model is mainly proposed for the movement in urban area, where the streets are in an organized manner and the mobile nodes are allowed to move only in horizontal or vertical direction. At each intersection of a horizontal and a vertical street, the mobile node can turn left, right or go straight with certain probability.

Random Direction and walk

Nodes change their speed/direction every time slot. In this model new direction from θ is chosen randomly between (0,2π]. The speed chosen from uniform (or Gaussian) distribution. In this model node reaches boundary it bounces back with (π-θ) [16].

Gauss-Markov model:

In the Gauss-Markov Mobility Model each mobile node is initialized with a speed and direction. By fixed intervals of time movement occurs to updating the speed and direction of each node. To be specific, the value of speed and direction at the nth instance of time is calculated based upon the value of speed and direction at the n - 1st instance and a random variable. In paper [17] authors elaborates the equations for calculating speed and direction in detail.

Freeway Model:

Model emulates the motion behavior of mobile nodes on a Freeway. It can be very well used in exchanging traffic status or tracking a vehicle on a Freeway [18]. This model makes use of use maps. There are several freeways on the map and each freeway has lanes in both directions. Each mobile node is restricted to its lane on the freeway.

Reference Point Group Mobility model:

The main use of this model is in military battlefield. In paper [19] authors have described Reference Point Group Mobility (RPGM) model nodes are divided into groups and each group has a leader. The leader’s mobility follows random way point the members of the group follow the leaders. Instant of time, every node has a speed and direction that is specified by randomly deviating from that of the group leader. This general description of group mobility can be used to create a variety of models for different kinds of mobility applications such Group tours, conferences, meetings ,Emergency crews, rescue teams, Military divisions/platoons. It is used as generic method for handling group mobility. Hong, Gerla, Pei and Chiang illustrate that the RPGM model is able to represent various mobility scenarios including

i. In-Place Mobility Model:

The entire field is divided into several adjacent regions. Each region is exclusively occupied by a single group. One such example is battlefield communication.

ii. Overlap Mobility Model: Different groups with different tasks travel on the same field in an overlapping manner. Disaster relief is a good example.

ii. Convention Mobility Model: This scenario is to emulate the mobility behavior in the conference. The area is also divided into several regions while some groups are allowed to travel between regions.

Nomadic community model:

The Nomadic Mobility Model is to represent the mobility scenarios where a group of nodes move together. This model could be applied in mobile communication in a conference or military application. The whole group of mobile nodes moves randomly from one location to another.

Pursue model:

The Pursue Mobility Model emulates scenarios where several nodes attempt to capture single mobile node ahead. This mobility model can be used in target tracking and law enforcement. The node being pursued (target node) moves freely according to the Random Waypoint model by directing the velocity towards the position of the targeted node; the pursuer nodes (seeker nodes) try to intercept the target node.

Pathway model:

One simple way to integrate geographic constraints into the mobility model is to restrict the node movement to the pathways in the map. The map is predefined in the simulation field. In paper [19] authors utilize a random graph to model the map of city. This graph can be either randomly generated or carefully defined based on certain map of a real city. The vertices of the graph represent the buildings of the city, and the edges model the streets and freeways between those buildings. Initially, the nodes are placed randomly on the edge. Then for each node a destination is randomly chosen and the node moves towards this destination through the shortest path along the edges.

Obstacle mobility model:

Another geographic constraint playing an important role in mobility modeling includes the obstacles in the simulation field. To avoid the obstacles on the way, the mobile node is required to change its trajectory.

Therefore, obstacles do affect the movement behavior of mobile nodes. Moreover, the obstacles also impact the way radio propagates. For example, for the indoor environment, typically, the radio system could not propagate the signal through obstacles without severe attenuation.

In paper [18] authors have developed three realistic mobility scenarios to depict the movement of mobile users in real life, including

· Conference scenario consisted of 50 people attending a conference. Most of them are static and a small number of people are moving with low mobility.

· Event Coverage scenario where a group of highly mobile people or vehicles are modeled. Those mobile nodes are frequently changing their positions.

· Disaster Relief scenarios where some nodes move very fast and others move very slowly. In paper [16] authors have also investigate the impact of obstacles on mobility modeling in details. After considering the effects of obstacles into the mobility model, both the movement trajectories and the radio propagation of mobile nodes are somehow restricted.

CONCLUSION:

Most of the research related to sensor networks considers the static deployment of sensor nodes. Adding mobility to sensor networks can significantly increase the capability of the sensor network. In this paper we intent to present a review of network dynamics with mobility of the wireless sensor networks that depend on the process of sensor movement.

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Received on 17.02.2013 Accepted on 01.03.2013

Research J. Science and Tech 5(3): July- Sept., 2013 page 295-299