# Location-Based Lattice Mobility Model for Wireless Sensor Networks
## 2 State of the Art Overview  
There are several WSN applications such as disaster/earthquake recovery, battlefield surveillance, and monitoring bird and animal activities, which require the availability of strong mobility features. Understanding the mobility models in a realistic environment is a challenging task. In the real world, mobility is of paramount significance. Considering the behavior of the real-world entities, mobility models are categorized as synthetic or trace-based [15]. Synthetic models scale the real-world mobile objects. However, these models are unable to show an exact behavior of movable patterns. On the other hand, trace-based models involve a large number of contestants for a longer monitoring period, but the real trajectory movement of mobile nodes is hard to capture. The above is concerning due to the deployment of emerging applications for mobility-aware environments. In particular, when sensor nodes are moving, it is challenging to maintain the QoS provisioning and prolong the network lifetime. Many researchers have focused on deploying the mobile nodes in WSNs to extend the network lifetime and improve the performance. As a result, there are some of the interesting contributions focusing on the mobility available in the literature. However, majority of the existing contributions in mobility models use the single mobile node to gather the information from stationary nodes. Most of the existing multi-node supported mobility models focus on the mobile sensor nodes but lack the mobile-sink support. One of interesting contributions focusing on the Modified De Bruijn graph (MDBG) model is introduced to support mobile sinks [16]. The model helps collect the data from static sensor nodes. This model aims to combine single and multi-hop communication for decreasing the end-to-end delay, but did not focus on the accuracy. Handling this issue, the extended Mobility Markov Chain model is introduced [17] to combine the n preceding visited locations in order to improve the accuracy when determining the next predictable location. However, the model did not control the movement of individual sensor nodes. The authors in [18] introduced a two-tier autoregressive group mobility model to handle the individual sensor nodes' movement by using a first-tier. Furthermore, the group mobility behavior is apprehended in the second tier by using the mobility correlation of nodes. As a result, the group is acknowledged by applying the correlation index test. In addition, the model estimates the group-based mobility where representative mobility-aware node is used to calculate the mobility state of the group members. On the other hand, the two-tier autoregressive group mobility model is unable to accumulate the complete information of the clusters. In [19], a mobility model for self-configuring mobile sensor networks is introduced to calculate the mobility patterns of mobile sensor nodes applying artificial potential functions and cluster formation. The mobility is calculated by applying two components: particle and potential field, which are based on artificial potential functions. This model lacks support for determining the mobility of individual nodes. Thus, the topology-based mobility model is proposed to support main aspects of the random waypoint mobility model and random-way-walk mobility model [20]. The mobility model is incorporated with a lightweight medium access control protocol to achieve the objects of random walking patterns. This model handles each mobile sensor node individually, but increases the number of the control messages. The simple truncated Levy walk mobility model is introduced to capture the tendency of the people that move within predefined area of routine activities [21]. The simple truncated Levy walk mobility model reduced the control messages by using emulation. However, the model is used for mobile ad hoc networks and delay-tolerant networks. In [22], static sink and mobile sink are comprehensively described that collect the data by ending the locality of the sensor nodes.