## 1 Introduction  
Wireless technology has greatly improved operators' ability to access information conveniently, regardless of location and time [1]. This led to the development of mobile ad hoc networks (MANETs), which enable mobile nodes to collaborate and form temporary networks without centralized control [2, 3]. MANETs are essential in environments lacking fixed infrastructures, such as disaster recovery and city surveillance. Despite challenges like high packet loss and frequent route interruptions due to limited resources and dynamic topologies, MANETs offer a valuable solution [4, 5, 6]. Studies [7, 8] highlight how node mobility impacts network performance and emphasize the need for adaptive, self-configuring protocols. These studies categorize routing protocols as proactive, reactive, or hybrid, each designed to meet specific performance criteria [7; 8; 9; 10]. Proactive protocols, such as OLSR, keep network routes updated continuously but may incur high overhead. Reactive protocols, such as DSR and AODV, dynamically establish routes on demand, resulting in reduced overhead but potentially increased delays. On the other hand, hybrid protocols combine the strengths of both [9; 10]. The rise of smart cities has driven advances in multihop ad hoc wireless networks (MANNs), such as MANETs, wireless sensor networks (WSNs), vehicular ad hoc networks (VANETs), and wireless mesh networks (WMNs) [11]. These technologies support Internet of Things (IoT) applications by connecting devices across diverse networks. In smart environments, integrating IoT with MANETs and WSNs enhances mobility, reduces deployment costs, and improves localized data transmission [12]. Routing remains a critical challenge in MANETs due to frequent topology changes and the distributed nature of the network. Routing protocols, such as OLSR, use multi-point relays (MPRs) to optimize routing and minimize control traffic [13; 14]. However, mobility, energy, and security issues affect node stability, requiring better MPR selection mechanisms [15; 16]. Our multicriteria adaptive MPR selection algorithm addresses these challenges by selecting stable and trusted MPRs based on energy, trust, and mobility, improving network performance as shown in NS3 simulations with Random Waypoint and Manhattan Grid models [17; 18; 19; 20]. The insight behind our work stems from the inherent challenges in MANETs, where frequent topology changes, resource constraints, and security vulnerabilities affect network performance, particularly in routing. Traditional MANET routing protocols, while effective under certain conditions, struggle with maintaining network stability, reachability, and efficiency in highly dynamic environments. The Optimized Link OLSR, specifically its MPR selection mechanism, plays a key role in minimizing control traffic. However, the current MPR selection strategies often fail to consider critical factors such as node mobility, energy levels, and trustworthiness. This leads to frequent MPR modifications, increased network overhead, and degraded network performance in dynamic and resource-constrained environments. The motivation of our work lies in addressing these gaps by introducing a multicriteria-based adaptive MPR selection algorithm. Unlike previous works that focus on single criteria (e.g., mobility or energy), our proposed scheme integrates mobility, energy, and trust metrics to enhance the stability and reliability of selected MPRs. This approach significantly reduces the frequency of MPR re-affiliations, thereby lowering overhead, and improving overall network performance, particularly in high-mobility environments. Compared to existing schemes, our algorithm improves packet delivery ratios (PDR), reduces delay, and lowers packet loss rates, as validated through extensive NS3 simulations using Random Waypoint and Manhattan Grid mobility models. Furthermore, by incorporating trust metrics, our approach enhances network security, leading to more reliable communication, especially in environments prone to malicious activity.