# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 1 Introduction
Mobile sensor node distribution and selection of an appropriate mobility model not only improve the overall performance, but also improve the clustering process of a routing protocol [15; 16]. To determine the best mobility model in a specific WSN application is still a complex issue. The mobility model includes movement order for MWSN, location, and acceleration showing maximum and minimum velocity over time. Therefore, it will help to evaluate the performance of MWSN routing protocols. In [17], effect of mobility models on Distance Vector (DV)-hop based localization algorithm is discussed. In [18], the performance of Routing Protocol for Low power and Lossy network (RPL) routing algorithm is analyzed on three different mobility models. There are various mobility models, but, in this study, we considered four mobility models: Reference Point Group Mobility model (RPGM), Random Waypoint Mobility Model (RWP), Gauss-Markov Mobility Model (G-M) and Manhattan Grid Mobility Model (MG) [19]. In the areas such as battlegrounds, disaster relief mission and other hazardous missions, where mobile sensor nodes have got their applications, each member (mobile sensor) node has to follow a group leader to achieve successful completion of the mission [1]. In RWP model [20], the nodes move in random order within the simulation area without following any group leader. In GM model [21], the values of direction and speed of any node at a particular time are updated depending upon immediate previous value. In MH model [22], the simulation area is split into horizontal and vertical path lines. Each node has to change direction (it can turn left, turn right or go straight) at intersection point depending upon probabilistic value. In RPGM mobility model, which is quite similar to clustering process, each group of nodes has a logical center (group leader) along with member nodes [17; 23]. RPGM is used to simulate real time applications such as battle field, border monitoring, and disaster relief mission where mobile sensor nodes need to move in the form of group while following a movement of the group leader [24]. Consequently, RPGM shows higher connectivity than other mobility models [25; 26]. Sensor node connectivity is specified as a ratio of number of sensor nodes that can actively communicate with the base station to the total number of sensor nodes. In MWSN, connectivity and coverage factors are interrelated. An ensured connectivity with a dynamic coverage is required for efficient sensing of any event. Connectivity depends upon several aspects such as sensor node distribution, communication energy, mobility, distance between sensor nodes, signal dissemination medium, signal dissemination loss, etc. [27]. Due to mobility, sensor nodes alter their positions after initial distribution. Sensor node mobility and node failure affect the transmission path, which impacts connectivity in MWSN [28]. Unplanned mobility can create coverage problem [29], whereas planned mobility (mobility models) can be applied to improving connectivity and enhancing lifetime of network [30]. To best of our knowledge [31], only two connectivity based LEACH algorithms have been proposed thus far: LEACH based on Density of node distribution (LEACH-D) [32] and Orphan- LEACH (O-LEACH) [33]. However, both studies are proposed for static sensor nodes. In this paper, a novel connectivity based LEACH-Mobile Energy Efficient and Connected (LEACH-MEEC) algorithm is proposed. The binary disk sensing model is used to calculate neighborhood density. We propose a probabilistic connectivity model to compute connectivity among neighboring nodes. The main contributions of this paper are given as below. * We propose LEACH-MEEC, where the connectivity and remaining energy of mobile sensor nodes are used as metrics for CL selection after the first round and onwards. This proposed metric significantly improves the performance as compared with the existing schemes. * The proposed LEACH-MEEC is analyzed under different mobility models, using eight datasets with two different speed levels.