## 6 CMR-WSN Mobility Management Utilizing Fuzzy Logic
In [63], the authors proposed an effective mechanism to guarantee the performance of handoff, including a mobility-aware scheme, temporary connection, and quick registration. The main contribution of this paper is that the proposed mechanism is implemented not only in the proposed testbed but also in a real industrial environment. The results indicated that the proposed mechanism not only improved the accuracy of handoff triggering but also solved the problem of the ping-pong effect during handoff. The authors of [64] presented a fuzzy rule-based simulation system for WSN to predict the node's mobility and energy. Mobility prediction provides a stable path for a network and energy prediction increases the lifetime of the network. Network simulator NS3 is used for simulation results. In this paper, the handoff decision for heterogeneous networks was identified as a fuzzy multiple attribute decision-making problem, and fuzzy logic was applied to deal with the imprecise information. In [65], a handover algorithm was proposed to support vertical handovers between heterogeneous networks. This was achieved by incorporating the mobile IP principles in combination with fuzzy logic concepts utilizing different handover parameters. Furthermore, in [66], the authors deal with a vertical handover decision algorithm based on the fuzzy control theory. The algorithm takes into consideration the factors of power level, cost, and bandwidth in order to decide about the vertical handover. In [67], the authors proposed and implemented a fuzzy-based handover system (FBHS), where they showed that the proposed system had good behavior for handover enforcement, but in some cases, it could not avoid the ping-pong effect. In [68], the authors proposed a fuzzy logic mobility controller (FLMC) in order to support critical applications such as personnel safety and healthcare monitoring. The main idea was to use a combination of two existing metrics that are locally available at each node with the purpose of predicting end-to-end losses and supporting the triggering of the handoff procedure. FLMC was evaluated in a real environment and, more specifically, in a refinery area. As mentioned previously, FLMC operation relies on two locally available metrics, the RSSI and the link loss. As a first step, several experiments were run in the refinery area in order to investigate the behavior of the two metrics and extract information about the relationship of RSSI, link loss, and end-to-end packet loss. Figures 15 and 16 show the relationship between the aforementioned metrics, and the FLMC controller with the inputs and outputs, respectively. More specifically, on the basis of Figure 15, we can conclude the following:  
1. For link loss above 15% and RSSI less than -78 dBm, the end-to-end packet loss was significant and clearly not acceptable. 2. For RSSI greater than -60 dBm and the link loss up to 40%, the end-to-end packet loss was acceptable. This happened because the mobile was not able to retransmit the lost packets  
In the proposed fuzzy controller, as shown in Figure 16, the quantities were considered at the discrete instant kT:  
1. T denotes the sampling period. RSSI (kT) is the received signal strength taken every kT. * LL (kT) denotes the rate of link loss measured at kT. * Pd (kT) is the calculated decision probability. This probability is taken into account to trigger the handoff procedure. * SGi1,2 (kT) is the input scaling gains. * PThreshold is a predefined value that is compared to the Pd(kT) and denotes if the specific Pd(kT) will trigger the handoff or not. In order to further improve the performance of the FLMC solution, the authors implemented an adaptive thresholding solution [69] with the main target to reduce the power consumption and to increase the on-time triggering. Furthermore, in [70], the authors extend their solution to support adaptive fuzzy logic operation to produce a successful handoff.