## 4 In-Network Computing via ICN
### Reactive Executor Selection Strategies
#### 4.2.1 Distributed Approaches
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Approach** & **Domain** & **Main Features** \\ \hline
[65] & Distributed, Reactive & IoT & Three resolution strategies (EdgeFox, FaX, (FoP)aX) for stationary and mobile networks with focus on reducing redundant computations. \\ \hline
[60] & Distributed, Reactive & IoT & Identifies in-network executor as the closest node to the data source; minimizes data retrieval latency but may overburden nodes with limited resources. \\ \hline
[4] & Distributed, Reactive & IoT & Selects best on-path executor by calculating execution cost considering data retrieval time and processing latency. \\ \hline
[66] & Distributed, Reactive & Personal Services & Broadcasts over the wireless channel enhanced Interest packets with task attributes; providers in the neighborhood compute weighted cost metrics; random deferral time ensures the best provider responds first. \\ \hline
[61] & Centralized, Reactive & IoT & Executor selection managed by a delegated node; polls edge nodes for availability, computation time, and cost; selects the node with the best offer within a decision interval. \\ \hline
[31] & Centralized, Reactive & 5G services & SDN controller orchestrates executor selection based on global network knowledge; minimizes service provisioning time by evaluating the overall service provisioning latency. \\ \hline
[48] & Distributed, Proactive & IoT & Periodic resource discovery mechanism where nodes share utilization information with neighbors. Update intervals range from 15 to 60 s, with messages flooded within a hop-count scope. \\ \hline
[67] & Distributed, Proactive & IoT & Introduces resource breadcrumbs for proactive resource discovery, allowing edge nodes to distribute resource availability information. Updates occur only when significant resource changes happen, reducing overhead. \\ \hline
[62] & Distributed, Proactive & 5G services & Nodes proactively advertise locally available function name prefixes via routing protocols, enabling Interests to be forwarded directly to appropriate executors. Decisions of what functions host are based on service popularity and requirements. \\ \hline
[68] & Distributed, Proactive & 5G services & Leverages periodic and event-triggered updates on function availability and resource utilization thus nodes group into synchronization clusters. Executor selection leverages neighborhood knowledge before forwarding requests outside the group. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Executor selection strategies. selected node may have limited computational resources, potentially leading to slower processing times. To overcome this issue, a strategy to select the best on-path executor is presented in [4]. There, nodes along the path to the data producer(s) locally calculate the cost of executing the computation, which accounts for both the latency of retrieving the data and the local processing latency. A new header field is introduced in the Interest packet to carry the current lowest execution cost. Consequently, on-path nodes update this field only if they determine that their execution cost is lower, indicating they are the fastest option. A cost-based strategy for the selection of the best executor is also defined in [66], when considering a distributed wireless environment where mobile nodes establish opportunistic contacts. To locate a computing node in the neighborhood, a client broadcasts an enhanced Interest packet (elnt-REQ), which includes attributes of the task to execute, such as the size of the content, task preferences, and constraints like maximum tolerable delay and energy consumption. Potential providers that receive this request first verify if they can satisfy the requirements of the service. If eligible, they compute a weighted cost metric based on the estimated task response time and energy expenditure required to process the task.