an observation transmitted to the FC, the estimator can still read a measurement resulting from an event set defined by the triggering mechanism as secondary information. Including this secondary information in the interim of a non-event, iterations lead to a strategy that updates the state estimation in a hybrid fashion, simultaneously employing the measurements as set and point values. As a result of this hybrid scenario, the posterior distribution becomes non-Gaussian. One primary strategy to overcome this problem is to employ stochastic triggering [17] for transmitting observations, which results in a Gaussian approximation [18] of the posterior distribution. (ii) Second, EBE strategies include a binary decision method on whether or not the sensor communicates its raw measurement during an epoch (defined as the time frame between consecutive sensor activations and based on the EBE strategy to decide when to transmit the observation, i.e., event epochs or idle epochs) since transferring raw data during all the event occurrences would be inefficient. As a result, the sensor communicates its raw measurement during an event epoch while retaining its local measurement during an idle epoch [13]. To address the aforementioned challenge [PERSON] et al. [14; 19] introduced a different approach, known as the ternary event-triggering (TET) approach. Likewise, instead of a stochastic triggering technique, a deterministic triggering mechanism via thresholding, referred to as send on delta (SOD) [20; 21], has been utilized to convert the non-Gaussian posterior distribution to its Gaussian equivalent. The key rationale behind this choice is to provide a more accurate estimation of the event-based posterior distribution than a Gaussian estimation provided by a stochastic triggering [22]. As a result, the fact that a definitive triggering method (e.g., SOD) is dependent on a predetermined threshold becomes a critical issue. This research aims to develop a novel multi-objective framework, called ternary event triggering swarm optimization (TETSO), to improve the TET mechanism's threshold values. The objectives of this paper improve the state of the art by the following: (1) Automating the process for selecting threshold values versus with human intervention; (2) Finding the optimized threshold values for the TET mechanism; (3) Reducing the communication overhead; (4) Improving the energy efficiency for both the sensor side and the remote estimator, and (5) Requiring less maintenance and, as a consequence, a decrease in the overall cost.  
The significant contribution of this new approach is the lowering of the communication overhead by optimizing threshold levels for the TET mechanism. To achieve this goal, a multi-objective approach is developed to increase the transmission rate of quantized observations while simultaneously sending minimal periodic data during idle epochs, while avoiding the transmission of redundant information, which, in turn, results in preserving limited power supplies.  
The main contributions of the proposed work can be summarized as follows:  
* An automated and efficient thresholding event triggering swarm optimization (TETSO) technique is proposed.
* Multi-objective PSO is utilized to improve boundary selection of the TET mechanism.
* The efficiency of the proposed TETSO method is confirmed with simulation results.  
The rest of this work is structured as follows: Section 2 presents the background information and the problem formulation. The suggested TETSO architecture is introduced in Section 3. Simulation results and their interpretation with regards to performance are given in Section 4. Finally, we conclude this paper in Section 5.