## 4 System Components
### Computing System
#### 4.2.1 Self-Organized Decision Making
\begin{table}
\begin{tabular}{c c c} \hline
**Ref.** & **Performed Function** & **Technique Deployed** \\ \hline
[66] & Deployed to perform distributed cooperative search in Multi-UAV system & Implements the distributed decision making based on receding horizon techniques \\ \hline
[67] & Resolve slot access problem for neighbor UAV swarm network cooperation & Implements a collision discovery method to ensure slot access is not delayed by topology information exchanges \\ \hline
[68] & Implements self-organized collision avoidance in autonomous UAVs & Algorithm Compute’s safe reaction distance on which UAVs begin collision avoidance movement \\ \hline
[69] & Tackles sensing coverage constraints in multi-UAV swarm operations & Reciprocal decision-based approach performed between neighbor UAVs to reduce trajectory oscillations \\ \hline
[70] & Provides formation control for UAV and Mobile Robots communication networks & Distributed asymmetric control to implement formation control based on ’mergeable nervous systems’ approach \\ \hline
[71] & Plays the role of implementing distributed control laws in swarm indexing and position-free density control & Pseudo-localization method used to localize agents to a new coordination frame with distributed control policies for desired coverage extensions \\ \hline
[72] & Solves limitations in transmission extensions in UAV wireless networks & Deploys relay selection techniques to improve coverage transmission constraints \\ \hline
[73] & Provides solution for slot access transmission problem in UAV Swarms & Makes use of game model as collision detection method to derive feedback in a common control channel for access slots \\ \hline
[74] & Performs flock distribution for UAVs to fly and coordinate as a unit & UAVs coordinates without a leader and on constant basis broadcast and as well receive movement information to share their common goal \\ \hline
[75] & Deployed to perform formation control in multi-UAV system & Formulates formation solution based on circle and arbitrary polygon formations techniques \\ \hline \end{tabular}
\end{table} 
Table 2: Self-Organizing distributed algorithms. There are several techniques proposed in the literature aiming to provide various levels of synchronization functions in distributed systems. Mention can be made to decentralized techniques that exploit the resources of visible light communication to facilitate effective video streaming synchronization in coordinated digital cameras [76]. In what concerns systems that require a tight clock synchronization, an adaptive synchronization method has been proposed based on time slotted channel hopping technique to empower devices needing to synchronize the ability to track and predict its own clock trickling relative to neighbors [77]. Other approaches related to clock synchronization for the coordination of multi-robot models have been exploited, namely the theory of swamalators in space and time to form a coupling block for swarms [78]. Synchronization allows self-organized systems to be managed and coordinated aiming to response swiftly to internal and environmental influences on the system with regards to failing nodes, resource variability and other factors contributing towards the overall system objective. This demand is termed system adaptability, which mainly contribute for making drone swarms robust. Adaptability models are required to be instantiated at runtime to allow an effective system control while responding to varying mission demands, and topology changes due to mobility of drones [17]. The self-adaptation property of a swarm system relies on an efficient propagation of information within the swarms. This requires an efficient set of network functions capable of maintaining the global knowledge of the network while localizing the interactions between drones to ensure that they do not rely on global knowledge to operate. Such networking functions (e.g., routing) should support system scalability, allowing for the expansion of the network without hampering performance [17].