The wireless sensor networks are autonomous Ad hoc Networks that form the backbone of IoT technology. Their vital role is to collect data from their physical environments and transfer these data to the Base Station (BS). This task implies high energy consumption when a suitable data routing policy is not applied. Then, the data collection method determines the efficiency of WSNs. Moreover, the IoT-based applications that require the mobility of the IoT sensor nodes introduce a new challenge regarding network connectivity. The IoT sensor node’s mobility leads to regular changes in the network topology, resulting in high packet loss during communication. The management of network energy use in order to prolong the battery life of IoT sensor nodes is a crucial issue, as IoT sensor nodes possess limited battery capacity. This paper addresses these issues by proposing an efficient Hybrid Biologically-Inspired Optimization Algorithm, named HBIP, for Data Gathering in IoT Sensor Networks. The flagship idea of the HBIP algorithm is to incorporate the swarming stage of the Bacterial Foraging Optimization (BFO) into the Artificial Bee Colony (ABC) Optimization’s exploitation phase. Simulation results show that the HBIP protocol outperforms the LEACH protocol and ABC and BFO metaheuristics regarding network energy consumption, lifetime, and data packets received at the BS. In practice, we demonstrated that the HBIP protocol increases the data collection by $$84.40\%$$ 84.40 % over LEACH, $$19.43\%$$ 19.43 % over BFO, and $$7.26\%$$ 7.26 % over ABC in the network scenario considered. Further on, we extend the study of the HBIP algorithm on Mobile IoT Sensor Networks by proposing a Multi-Objective Optimization-based Data Gathering Protocol (MOO-DGP). The MOO-DGP protocol employs the HBIP algorithm to determine the best solution that balances Velocity, Cluster Stability, Link Quality, and Energy Consumption. An extensive evaluation of the MOO-DGP protocol shows it outperforms the existing Mobility-Based Clustering (MBC) protocol regarding effective data packet delivery rate, average control overhead, network lifespan, and energy consumption.

Future 5G networks, including vehicular networks, will witness the coexistence of different access technologies like IEEE 802.11p and LTE-V. This diversity will encourage vehicles moving through such technologies to select appropriate networks in order to enhance the system performance. However, Handover decision can be critical if it does not take into consideration service specificities especially in V2I (Vehicle- to-Infrastructure) context. Different MADM (Multiple Attribute Decision Making) based algorithms were previously proposed to include various selection criteria in V2I Handovers. They mainly assign various weights to the performance attributes in order to build their Handover decision. In this paper, we show the limitations of the former weighting techniques and propose a new approach to better adapt the weight to the required multimedia service quality. More precisely, we introduce a combination of objective and subjective weighting techniques taking into consideration the required quality of service metrics as well as the vehicle velocity. We especially consider two multimedia services with different quality of service requirements in vehicular communications. We show the effectiveness of our strategy in terms of throughput, packet delay and jitter for each service as compared to former RSS (Received Signal Strength) and MADM based approaches.