Unmanned Aerial Vehicles (UAVs) bring both potential and difficulties for emergency applications, including packet loss and changes in NETWORK topology. UAVs are also quickly taking up a sizable portion of the airspace, allowing Flying Ad-hoc NETWORKs (FANETs) to conduct effective ad hoc missions. Therefore, building ROUTING protocols for FANETs is difficult due to flight restrictions and changing topology. To solve these problems, a bio-inspired route selection technique is proposed for FANET. A combined trustworthy and bioinspired-based transmission strategy is developed as a result of the growing need for dynamic and adaptable communications in FANETs. The fitness theory is used to assess direct trust and evaluate credibility and activity to estimate indirect trust. In particular, assessing UAV behavior is still a crucial problem in this field. It recommends fuzzy logic, one of the most widely utilized techniques for trusted route computing, for this purpose. Fuzzy logic can manage complicated settings by classifying nodes based on various criteria. This method combines geocaching and unicasting, anticipating the location of intermediate UAVs using 3-D estimates. This method guarantees resilience, dependability, and an extended path lifetime, improving FANET performance noticeably. Two primary features of FANETs that shorten the route lifetime must be accommodated in ROUTING. First, the collaborative nature necessitates communication and coordination between the flying nodes, which uses a lot of energy. Second, the flying nodes' highly dynamic mobility pattern in 3D space may cause link disconnection because of their potential dispersion. Using ant colony optimization, it employs trusted leader drone selection within the cluster and safe ROUTING among leaders. a fuzzy‐based UAV behavior analytics is presented for trust management in FANETs. Compared to existing protocols, the simulated results demonstrate improvements in delay ROUTING overhead in FANET.

Energy constraint in wireless sensor NETWORKs, have been made the packets ROUTING with essential issues. In event based applications, the inherent constraint of these NETWORKs and large packet traffics affects the NETWORKs lifetime. Hence, the energy of sensor nodes near the sink node will be exhusted and the duty of WSNs will be affected with this problem. NETWORK coding scheme with encoding and decoding of data packets, have improved the throughput, reduces the average end-to-end delays and resilient the NETWORKs against the fails. In this paper, we proposes a ROUTING algorithm using NETWORK coding scheme. For selecting appropriate ROUTING paths, the proposed scheme considers the link quality and residual energy of neighbor nodes. Simulation results show that the proposed scheme increases the NETWORK reliability and decreases the energy consumption in comparing with other relate scheme.

Wireless sensor NETWORKs consisting of tens or hundreds of sensors that are based on intended use in a distributed environment and their main purpose is to record information. In numerous applications, sharing information in short period of time is gravely important. Also, sharning information would spend most of system’ s energy and considering the limitaions of energy resource in system, controlling the energy usage in NETWORK is a challenge. So, the most important problems in wireless sensor NETWORK are energy consumption and latency on the NETWORK. To optimize energy consumption and transmission delay, separately and hand-to-hand, meta-heuristic algorithms in wireless sensor NETWORK can be used. Based on results, hand-to-hand optimization of these two parameters can increase longevity and performance of the NETWORK. The results show that by repeating optimization algorithm, suggested Metaheuristic algorithm’ s results are more similar to results of Shortest path algorithm, in comare to results of Particle Swarm Optimization (PSO) algorithm.

Different types of contact, including contact between node pairs, any-contact of nodes, and contacts of the entire NETWORK, are used to characterize social relations in mobile social NETWORKs. Different modes of ROUTING, from the point of view of message delivery semantics, encompass unicasting, multicasting, any-casting, and broadcasting. Studies have shown that using probability distribution functions of contact data, which is mainly assumed to be homogeneous for nodes, improves the performance of these NETWORKs. However, there exists an important challenge in studies on distributions. A lot of works apply the distribution of one type of contact to other types. Hence in ROUTING applications, it causes to use of the distribution of one type of contact for any mode of ROUTING. This study provides a complete solution to model each type of homogeneous contact data distribution and to use them in different modes of ROUTING. We propose a ROUTING algorithm that uses this new model. Results show that our solution improves the average latency of comparing methods Epidemic, TCCB, and DR about 3.5-times, 30%, and 45%, respectively. It achieves a delivery rate of about 5% and 6%, and average latency about 6% and 8% better than that of DR and TCCB, respectively.

One of the important challenges in Wireless Sensor NETWORKs is to proceeds data transmission in a way that tries to increase the life of the NETWORK. One of the main issues is the reduction of latency in the node and energy in the sink nodes. Due to the limited energy of the nodes, data transmission has the largest share in energy consumption, so it is important to design a structure that has the least amount of energy in sending data to the base station. In this paper, we use fuzzy logic and Mamdani method for clustering to solve the challenge and time division multiplexing method to connect the nodes with the header. The proposed clustering is based on the use of the LEACH algorithm, the capability and reliability of which are improved by fuzzy systems, and the particle optimization algorithm is used to optimize the path of the NETWORKs. The simulation results show that energy consumption decreases with increasing number of cycles. For example, energy consumption reached 0. 9 in the 2000 round and 0. 1 in the 5000 round.