Most of the current generation sensor nodes of mobile wireless sensor network (MWSN) are designed to have heterogeneous mobility to adapt itself in the applied environment. Energy optimization in MWSN with heterogeneous mobility is very challenging task. In this paper, a heterogeneous game theoretical clustering algorithm called mobile clustering game theory-1 (MCGT-1) is proposed for energy optimization in a heterogeneous mobile sensor environment. Energy optimization is achieved through energy-efficient cluster head election and multipath routing in the network. A heterogeneous clustering game is modelled with varying attributes and located an asymmetric equilibrium condition for a symmetric game with mixed strategies. The real-time parameters, namely, predicted remaining energy, distance between a base station and nodes, distance between nodes, and mobility speed, were used to calculate the probability to elect the cluster head (CH). The efficient multipath routing is achieved through prior energy prediction strategy. It has mitigated the generation of "hot spots," reducing its delay and improving the overall residual energy of the network. Simulation results showed that the average lifetime of MCGT-1 has increased by 6.33 %, 13.1% and 14.2% and the PDR has improved by 4.8%,11.8%, and 17.2% than MCGT, LEACH-ME and LEACH-M respectively. The hot spot delay is reduced to 0.063025 seconds, improving the efficiency of the network. K E Y W O R D Sclustering, mobility, predicted remaining energy, hot spot problem it is essential to use the battery power in an optimal way. Secondly, the battery energy gets depleted rapidly during active communication if it is not properly monitored and controlled. Therefore, a better energy optimization in a sensor network is inevitable, and hence, a clustering architecture is proposed.Many protocols for mobile sensing environment works with constant mobile speed or with homogeneous mobility. 4 In the existing protocols, the node speed and energy dissipation remain constant irrespective of the distance moved. These phenomena lead to waste of the attained bandwidth and timeslot and affect the overall network performance, lifetime, and quality of service (QoS). In practice, it is not possible to attain the constant mobility speed throughout thedeployment and in real-time, the nodes may move with different speeds either arbitrarily or naturally called-heterogeneous mobility. 4 According to Osborne, 5 a game is defined as a method of competitive play by-law. Game theory (GT) is a mathematical branch that deals with competitive situations while analyzing the strategies of every player and produce an outcome based on actions of other participants. In recent years, GT is applied in clustering of sensor network. Since then, all the algorithms are proposed for a static environment where the nodes are fixed without frequent clustering. The game of these algorithms is not defined from the view of sensors and its topology. Further, these algorithms do not provide a solution for hot spot problem i...

As the state-of-the-art technology in the areas of IoT, battlefield surveillance, medical, military applications, and so on, the Mobile Wireless Sensor Network (MWSN) has attracted the attention of vendors and academics in recent years.In MWSN, energy optimization is the major issue because of its dynamicity property. The energy optimization can be achieved by avoiding node compromise attacks and ensuring secured data transmission in the network. In this paper, an algorithm SDCH-M (Secured Data for Mobile Clustering Hierarchy) is proposed to ensure secure data delivery in a double cluster head hybrid topology, which optimizes the network energy consumption. Data redundancy is reduced by estimating spatial-temporal correlation factors, and the variantround voting mechanism is used to verify the accuracy of delivered data and to identify compromised nodes through voting by the base station. Results from the simulation show that the proposed SDCH-M algorithm optimizes energy than M-LEACH and C-LEACH algorithm. The average delivery ratio of SDCH-M (DCH), SDCH-M (SCH), E 2 RP, M-LEACH, and C-LEACH are 0.9175, 0.9037, 0.828, 0.799, and 0.681, respectively. The average delay of M-LEACH, C-LEACH, E 2 RP, SDCH-M (DCH), and SDCH-M (SCH) algorithms is 6.851, 8.655, 6.3, 5.585, and 5.97 s, respectively.