## 2 Related Work  
IoT integrates several technologies for gathering data in the intercommunication world. Latency-sensitive applications need complicated processing such as that of time series analysis. However, IoT devices enable limited computing and energy resources to store large amounts of data and cannot perform complex task processing. The work proposed by [19] addresses the resource allocation and routing for IoT tasks that require efficient assignment in multicloud environments. The authors propose an energy-efficient, congestion-aware resource allocation and routing protocol (ECRR) for IoT networks based on hybrid optimization techniques. [PERSON] et al. [20] argue that task offloading leads to extra communication latency and energy cost. The work evaluated the offloading by finding an optimal offloading scheme that maximizes the system and seeks a balance between throughput and fairness. Although MEC servers have been allowing intensive task computing in heterogeneous clouds, the data transmission over the Internet incurs high levels of access delay and jitter according to [PERSON] et al. [21]. This work minimizes MEC energy consumption and satisfies task processing delay requirements. The solution uses dynamic programming to minimize energy consumption by allocating bandwidth and computational resources to mobile devices. The state of the art also shows that energy consumption decrease and response latency mitigation to applications in IoT environments are questions that are difficult to solve since the first edge computing architectures [22]. However, the strategic use of CC as a single alternative to task processing can add high latency due to the distance of IoT devices [23]. The proposals [11; 24; 25] use CC as an option for task execution. Other approaches use fog computing to allow local processing in IoT devices such as the works [14; 26; 27] or without applying the CC [28]. The MEC architecture is assessed in works of [4; 11; 13; 25]. In contrast, TEMS is a three-layer architecture that combines MEC and CC added to local IoT computation. This approach also provides a cost model, with the energy and run time evaluations on the fly, including the data transmission costs. As for the parameters used in third-party cost models, the energy consumption of task processing is used by all works mentioned. However, the energy consumption for data transmissions is shown in the works [4; 13; 14]. The processing time of tasks is evaluated in major of the works, except to [26; 27] and the spent time on data transmissions is limited to studies of [4; 11; 13; 14; 25]. \begin{table}
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\begin{tabular}{c} **Source** \\ **Source**  
5G networks introduce designs and adaptable support to new applications. However, it requires latency management, high energy efficiency, and long-range communication support for IoT-based applications [31]. [PERSON] et al. [30] propose to add a physical layer to the burst data transmission management with the insertion of a zero-energy symbol for the wireless transmission when there is the send of discontinuous data (silence periods). As a result, the system saves energy consumption from IoT devices. In this approach, the silence produces a fine-grain granularity to energy management. The algorithms optimize power consumption between processing and energy spent on transmission. On the other hand, the energy consumption of the equipment in the idle state is measured exclusively in [26; 27]. Models that include the battery level of the IoT devices are only [4; 13; 14]. Our proposed model uses the DVFS technique similar to [16; 17] to allow both the dynamic minimization of energy and execution time during task processing. [PERSON] et al. [29] propose an energy-maximizing solution to prolong the aliveness of the wireless sensor networks. The proposal uses a prolong stable election protocol (P-SEP) in a fog infrastructure to decrease energy consumption.