# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 1 Introduction  
Rapid advancements in communication technologies are creating opportunities for various application domains due to their potential for handling massive internet traffic, richer mobility, better security, and seamless mobility [1, 2]. Such potential of these technologies supports the integration of next-generation 6G-enabled IoT mobility [2, 3, 4, 5]. As a result, various domains are motivated to embrace modern communication technologies by integrating modern mobility protocols in large-scale IoT [2, 6]. Examples in such domains include information access using smart devices, processing of transactions, support for modern technologies, and mobility services. Integration of such solutions in modern mobility solutions requires optimized AI-based models, adaptive decision-based algorithms, and secure communication [7]. An effective modern solution requires the integration of 6G-enabled IoT mobility management protocol such as PMIPv6 [8] in a modern IoT environment [9, 10]. PMIPv6 performs its mobility using Mobile Access Gateway (MAG) and Local Mobility Anchor (LMA) [11]. However, various problems are associated with basic PMIPv6 such as the handover delay during the handover, packet loss due to the absence of buffering mechanism,support for network mobility, additional signaling, and load on certain entities due to its involvement in overall mobility [11; 12]. Furthermore, among the solutions provided for solving the aforementioned issues, predictive PMIPv6 extension protocols received much attention from experts [13; 14]. One of the major reasons for its adoption is the prediction of handover occurrence in advance and making necessary arrangements before such an event [1]. Such extensions may use the Mobile Node (MN), LMA, or MAG for performing such prediction [11; 12]. In the context of PMIPv6 protocol, MN is a device that moves among networks in addition to maintaining its IP address. To predict the handover moment in advance, various solutions use Received Signal Strength (RSS) [15; 16]. In the following method, the RSS is used to predict the handover moment. After the timer has been set, RSS is continuously checked, and a condition is set to activate the event when RSS matches a certain threshold value. If the condition is met, it is presumed that MN is about to roam to another MAG's domain, and the relevant handover arrangements are undertaken to minimize changeover latency [16]. However, one of the major problems with such RSS-based schemes is that RSS is prone to environmental conditions and may affect the RSS measure. In such a case, the scheme may initiate the handover process even when the MN is not properly located for handover [15]. Therefore, the overall performance of the RSS-based schemes is dependent on the accuracy of the RSS measure. If the measure is accurate, these schemes will provide optimal performance. On the other hand, errors in RSS measures may lead to decreased performance of the scheme. Such an issue may lead to longer handover latency, addition signaling, and unnecessary buffering [15; 16; 17]. For solving such issues, experts use the location of the resources to enhance the decision regarding the prediction of the handover moment in addition to the identification of target MAG [18; 19; 20]. Location is integrated with other measures for improving handover prediction [21]. For supporting mobility management and enhancing the communication services in 6G-enabled IoT, the proposed scheme combines the location parameters of resources with RSS measures in an intelligent manner to improve handover moment prediction, and resource efficiency by avoiding additional signaling. Furthermore, the network resources' information is utilized more effectively for buffering efficiency too. For measuring the efficiency of different aspects, equations are derived in the context of existing schemes. The proposed scheme is compared with available schemes using the proposed equations.