dataesr/scientific-paragraphs-categorization huggingface dataset:
We find higher CD nodes in the real-world dataset (NCCU), while the GRM trace coefficients are lower, with a maximum degree at 0. 4. A forwarding strategy strongly dependent on CD as a metric or decision factor might have reduced performance through this dataset. The plot also shows that the overall CD increases with a higher range (30 m), which means more encounters occur in both datasets. Nevertheless, a substantial increase in the contact durations appears only in NCCU (Fig. 2(a) ). In Fig. 3 (a), we analyzed the CD from the content-generation time (8th day) till the last day of simulation (11th day, i. e. , the time-tolive -TTL). As expected, on both datasets, this plot shows lower CD. That is justified by the reduced amount of days analyzed, impacting fewer potential encounters guided by user routines and displacements.
The results from Fig. 3 (a) highlight the need for combining other kinds of metrics than centrality for achieving better performance and timely delivery in an opportunistic forwarding scenario. Finally, in Fig. 3(b) , we analyze another traditional mobility metric, the Radius of Gyration (RG). RG accounts for node displacement within the geographic space of the datasets (i. e. , NCCU and GRM). The results show that due to the restricted area of the datasets, the RG of avg. 80% of the population is low (up to 300 m) in both datasets. This finding brings insight into the possible need to combine other metrics when forwarding the content to a more distant geographical area where the destination node is. The real trace NCCU has users with a slightly higher RG than GRM. In Section 4 we analyze the RG per period in both datasets and extract further insights.
Usually, they choose the time windows from the last 24 h or the last 6 h of mobility  [6, 11]. In our proposal (i. e. , Table  2 ), we divide the day into six different duration periods. This temporal division was adopted to reflect heterogeneous mobility profiles along the day from the users inside a campus (i. e. , from real-world dataset NCCU). Note that depending on the population analyzed, the time windows might change to reflect users' activity and precise research results. The time windows from our proposal are justified by correlating periods with more displacements or longer confinements. For example, during ''EM'' we expect to capture most users' home-work displacement and confinement at work while shorter lunch-time displacements during ''M''. The intention is to increase the accuracy of the strategy's metrics and decision factors, calculating those through a closer look into the human routines throughout the day.
We consider that every  has a set   = ( 1 ,  2 , … ,   ) of locations per period  of the ''learning'' week. Each location  has coordinates (, ) in a given instant, that is  = (, ). √ √ √ √ √ 1 𝑁 𝑁 ∑ 𝑗=1 ( 𝑙 𝑗 -𝑙 𝑐𝑚 ) 2 𝑎𝑛𝑑 𝑙 𝑐𝑚 = 1 𝑁 𝑁 ∑ 𝑗=1 𝑙 𝑗 (1) Following up on our analysis, we plot in Fig. 4  the users' Radius of Gyration for both datasets. As the NCCU dataset features user movements inside a Campus, the RG inside the area is more restricted. The same applies to GRM, which features movements inside a 1500 m x 1500 m area. The RG coefficients would be much higher if we evaluate a dataset with human users in an entire metropolitan area. Higher displacements (e. g. , home to work) will appear in this case. Fig. 3 (b) also reveals that NCCU users are more mobile than GRM users. In Fig. 4 , we analyze users' RG per period on both datasets.
As expected, there is heterogeneity in each period. The RG per period on NCCU Dataset (Fig. 4(a) ) varies more, while on GRM (Fig. 4(b) ), the difference is smoother. In Fig. 4 (b), we see that the GRM dataset lacks different mobility profiles found throughout the day on the real-world dataset. From these plots, we learn when the users are more mobile in each dataset. With that said, our strategy (TOOTS) based on day periods shall perform better on the real-world dataset. Identifying more mobile nodes helped forward decisions on state-of-art strategies, such as in  [11].
The set of users is  , while   is the set of edges (contacts) detected, and  ∈ (1, 2, … , ) and  ≤ 604,800 s (i. e. , the learning time in seconds for week ). An edge  ∈   exists between two network nodes if at an instant , the distance between them is less or equal the maximum communication range, which means, there is a contact. As previously stated, this work uses two communication ranges: 10 m and 30 m. 𝛥 𝐶𝐷 𝑝 (𝑢) = ∑ 𝑑 𝑖=1 𝐶𝐷 𝑖 𝑝 (𝑢) 𝑑 , 𝑤ℎ𝑒𝑟𝑒 𝐶𝐷 𝑝 (𝑢) = ∑ 𝑛 𝑣=1 𝑒 (𝑢,𝑣) 𝑛 -1 (2) In Figs. 5  and 6 , we analyze the CD per period on NCCU and GRM datasets, respectively. These plots complement the previous section's analysis and reinforce our insights related to our temporal division proposal. From what we know about the human perspective, our social behavior (i. e. , contacts) differs depending on aspects such as the hour of the day, routines, or activities.
Fig. 5  certifies that and also reveals how raising the contact range impacts differently in each time window. For example, in the ''N'' period, the impact is smaller, as, in this same time window, the users are more confined. Nevertheless, in the ''M'' period, if we increase the contact range (i. e. , 30 m), the CD coefficient also raises, where 80% of the users inside the campus have at least 0. 7 CD. Nevertheless, in the ''M'' period, if we increase the contact range (i. e. , 30 m), the CD coefficient increases, where 80% of the users inside the campus have at least 0. 7 CD. In Figs. 6(a ) and 6(b), the same phenomenon seen in the RG analysis occurs: GRM dataset users show more similar behavior independent of the day period. Furthermore, in this dataset, increasing the contact range to 30 m still keeps the nodes with much lower CD than NCCU.
Sojourn Time We define the Sojourn Time (ST)  [31]  as the quantity of time that a user stays inside a network cell's limits (given its coverage). In TOOTS, we use the ST when the strategy aims to identify nodes that stay more time in the same cell as a user interested in a content (i. e. , he is the destination). The original cell sizes from the mobile operators are enormous compared to both traces' areas. Therefore, we analyzed the geographical space and divided it into nine cells (''A'' to ''I''). Applying the same method as with previous metrics, we calculate the ST of  (user) in each cell  ∈ ( 1 ,  2 , … ,   ) per period  ∈ (, , , , , ) in a week  and apply this knowledge in the week  + 1. Therefore, in the Eq. (  3 ),     () is the average ST in In Fig. 7 , we plot a heatmap with the average minutes per day and per period for both datasets.
We see that NCCU (Fig. 7(a) ) has some cells with a meager average ST per day while GRM (a more dense dataset) shows less ST heterogeneity. The heatmap also helps to identify where users spend more time during the ''N'' period in NCCU Campus. This information and other techniques make it possible to determine if a given user stays on the Campus overnight. In Fig. 7 (b), we see that the GRM users are more distributed within the geographical space. On the other hand, as they are less mobile, they tend to visit a more limited number of cells.
Depending on the coordinates system used in the dataset, the geodesic distance formula might change. 𝛥 𝑀𝑃 𝑐 𝑝 (𝑢) = ∑ 𝑑 𝑖=1 𝑀𝑃 𝑐 𝑝 (𝑢) 𝑖 𝑑 , 𝑤ℎ𝑒𝑟𝑒 𝑀𝑃 𝑐 𝑝 (𝑢) = |𝑚𝑖𝑛 𝛥 𝑐 𝑠 (𝑢)| (4) As to illustrate MP's findings, Fig. 8  plots the MP on the cell ''B'' for both datasets. Cell ''B'' was chosen as it features more user activity (according to the ST analysis). Fig. 8 (a) shows a high percentage of users inside or very close to this cell, and as the previous analysis, more heterogeneity per period of the day. The GRM analysis (Fig. 8(b) ) repeats previous plots' behavior, showing smooth differences per period of the day.
Motivation: Overhead and latency evaluation Epidemic forwarding is a well-known state-of-art algorithm. In opportunistic networks, it works by always trying to forward content to an encountered node that does not have it. This algorithm shows a higher delivery ratio, smaller delivery latency, but a heavy network overhead. In Fig. 9 , we evaluate the average delivery latency of the Epidemic forwarding algorithm with a consumer set size equal to 1% of the number of nodes from each dataset. This scenario with a limited size consumer-set is challenging. The results can show lower bound performance depending on how a strategy works and the dataset's characteristics (e. g. , wireless encounters between nodes). In the experiments, the content generation occurred once an hour, during 24 h, with random source and destination nodes.
Each content has a 72 h time-to-live. The experiments were repeated 30 times through the Opportunistic Network Environment (ONE) Simulator  [32]. The confidence intervals are presented in Fig. 9. Although commonly evaluated by previous work, we show and discuss the Epidemic forwarding performance within the NCCU and GRM datasets, with varying contact ranges (10 m and 30 m). When the contact range is 30 m, the Epidemic Forwarding average delivery latency is 2. 6 and 3. 6 h on NCCU and GRM datasets. In both datasets, the algorithm delivery rate performance was 100%, but the average network infection (overhead) was 98. 44% and 99. 62% on NCCU and GRM. With that said, most nodes received the content and became forwarders. For reaching superior performance in terms of delivery rate and delivery latency, Epidemic forwarding flooded the network with many copies of the same message.
In the experiments with a 10 m range, the overhead was quite similar on NCCU (98. 47%) but smaller on GRM (86. 50%). Still, the average delivery rate on GRM was 97%, meaning that with the restricted contact range on this dataset, even the Epidemic Forwarding could not deliver all the contents in the time-to-live. In Fig. 9 , we see that the contact range restriction causes more proportional delay on GRM than on NCCU (on GRM, there is a 7-fold increase). That happens due to a characteristic shown in the previous section. GRM has nodes with shallow centrality degrees and shorter contact duration per day. With that said, the epidemic struggles with fewer contacts, so it takes more time to ''flood'' the network and make the content reach the destination. Through the Epidemic Forwarding evaluation, we see that a restricted contact range and the characteristics of a population in terms of their contact can affect the strategy's effectiveness.
The TOOTS proposal In Fig. 10 , we describe TOOTS' evaluation scenario. We simulate a network with mobile nodes and a Mobile Edge Computing (MEC)  [33]  site. The MEC site stores popular content that the nodes commonly request in this context. In this scenario, the strategy's goal is to perform cost-effective content delivery using opportunistic D2D communication among the nodes. In a real scenario, they could receive incentives (e. g. , operator data plan savings, discounts, or access to exclusive applications) for their cooperation. The development and use of incentive mechanisms are out-of-the-scope of this work, so as previous state-ofart work  [10, 11] , we consider the nodes are willing to cooperate. In this scenario, the first task for the content delivery is a dissemination process to inject content on the network based on users' information.
e. ,     ()). After that, all consumers with direct encounters along the selected node  are removed (line 6). With this, we avoid the possibility of selecting another user with a similar coefficient for direct contacts and which covers the same nodes. The Alg. 1 loops (line 3) while the consumer set is not totally covered ( ≠ ∅). We assume all users in the dataset encounter at least one node (i. e. , there are no isolated nodes). Following in Section 6, the TDP is evaluated with varying consumer set sizes (1%, 5%, and 10% of each dataset total nodes).
e. , that does not suffer disconnection caused by a topology change) until an amount of time  necessary to transfer 's full data. As previously stated (Section 4), the geographic space of both datasets was divided into network cells. If  is at the same cell reported by the destination (given by ), the algorithm compares the centrality degree from  and  in. If 's avg. CD is higher (line 7),  is forwarded, as  has a higher forwarding potential inside its cell (i. e. ,  met more nodes). If the previous test is false, the algorithm checks if    () >    () and     () >     (). If true, that means  has less ''popularity'' than  (i. e. , lower avg. CD). On the other hand,  has a higher avg. RG which gives it the potential to do larger displacements inside the cell given by. Furthermore, 's routine makes it stay longer inside  ( has higher avg.
Experimentation results and analysis As previously stated, TOOTS' scenario's goal is cost-effective content delivery through users' opportunistic communication. In the state-ofart of opportunistic forwarding strategies, the cost-effectiveness comes through reducing the content overhead (i. e. , the percentage of network infection) and the delay (herein analyzed as the contents average latency from source to destination) while maximizing the delivery rate. In our evaluations, the contents range from 11-14 MB, an average size for 60 s YouTube 720P HD 30 fps advertising videos. TOOTS uses seven days of user mobility (extracted from NCCU and GRM datasets) during a learning phase. The content generation happens through one day, with one content delivery task starting every hour. Finally, three days refer to the delivery deadline, which is a reasonable time for delay-tolerant content  [16].
Each content has a set of random consumers with varying set sizes (in the percentage of nodes from each dataset). Network content injection happens through the mobile nodes. The first set of users carrying the content is called the Disseminator set. They run an algorithm to make a forwarding decision upon having a contact (i. e. , given by a specific communication range). Every node in the simulation has an 802. 11/11 Mbps network interface. We evaluate two communication ranges: 30 m (like an avg. for WiFi Direct) and 10 m (commonly referred to as the Bluetooth range). These technologies mentioned are just examples of existing D2D options for the scenario. The strategy proposed herein nor its evaluations depend on other particular characteristics from WiFi Direct or Bluetooth. Evaluations with other communication ranges or parameters from different technologies such as BLE  [34] , or Google Nearby  [35]  are possible without limitations from our strategy.
The entire scenario simulations occur through the Opportunistic Network Environment (ONE) Simulator. We used the ONE simulator for several reasons, among those: (i) due to its wide availability of stateof-art opportunistic forwarding algorithms implementations and prior published evaluations within the simulator; (ii) the fact that NCCU and GRM were made available ready to import into ONE; (iii) the highly customizable and suitable specific features of opportunistic networks already implemented and available; (iv) a customizable report module that measures performance parameters commonly considered in opportunistic networks. We imported the NCCU and GRM datasets into ONE as mobility models. Each experiment case runs 30 times, and the confidence intervals appear when necessary.
62% overhead respectively on NCCU and GRM. However, the TDP-enhanced epidemic might decrease delivery latency with a comparable delivery rate. Given the metrics applied in the TDP policy, the TDP-enhanced store-wait-forward can increase the delivery rate while decreasing the delivery latency. Even though the store-wait-forward algorithm's success depends on direct encounters with the consumers, the TDP content dissemination improves it. Still, depending on dataset characteristics and the social bonds between source and destination nodes, the improvements might take time or never happen.
Delivery rate evaluation In Fig. 11 (a), the TDP-enhanced store-wait-forward shows an improved average delivery rate of 82% and 75% respectively on NCCU and GRM, independent of the consumer set size evaluated (1%, 5%, and 10% of each dataset). The explanation for this delivery rate improvement recalls one of TDP insights, which is choosing nodes with direct encounters per period with the consumers. Compared with the traditional store-wait-forward, the TDP-enhanced strategy's average delivery rate with 1% of consumer set size was 18% and 60% higher respectively on NCCU and GRM. In Fig. 11(a) , the TDP-enhanced epidemic forwarding, as expected, reaches a 100% average delivery rate regardless of the consumer set size and dataset. Compared with the traditional Epidemic forwarding algorithm, The TDP-enhanced Epidemic had decreased average hop count, impacting the delivery latency (analysis to follow).
With a more restricted contact range (Fig. 11 (b)), as expected, the performance of the content delivery system decreases on both datasets. Still, when compared with the traditional store-wait-forward, the TDPenhanced has better performance in delivery rate and delivery latency. The plot also shows that the TDP-Enhanced Epidemic reaches a 100% delivery rate in all scenarios on both datasets, which is an improvement compared to the traditional Epidemic. Furthermore, thanks to TDPpolicy, the overhead on GRM with 1% of consumers was 50% smaller. The reason behind this recalls a characteristic of this dataset, which is nodes with fewer encounters (i. e. , lower CD). The plot from Fig. 11(b ) also shows there is no impact caused by the contact range when the percentage of consumers varies up to 10% in those datasets.
Delivery latency evaluation Following in the evaluations, in Fig. 11 (c), we plot the average delivery latency. The TDP-enhanced store-wait-forward, regardless of the consumers' size, takes 13-15 h to deliver the contents on NCCU and 18 h-19 h on GRM. With that said, the TDP policy makes it possible to deliver at least 75% of the delay-tolerant contents in an acceptable time  [16] , regardless of the dataset, and with zero overhead. Compared with the traditional store-wait-forward, the TDP-enhanced strategy's average delivery latency with 1% of consumers was reduced 23% on NCCU and 48% on GRM. As previously stated, thanks to the TDP policy, the number of hops in epidemic forwarding decreased, so the delivery delay (18. 6% and 64. 5% smaller respectively on NCCU and GRM with 1% of consumers).
Still, the networking infection of epidemic forwarding makes this algorithm very costly or unfeasible in real scenarios. Again, with a more restricted contact range (Fig. 11(d)) , there are fewer contacts on both datasets, so the delivery delay increases. Nevertheless, even in this scenario, thanks to TDP policy, most delaytolerant contents still reach their consumers in an acceptable time. As Epidemic forwarding is unfeasible in realistic scenarios, and TDP-Enhanced store-wait-forward showed an average delivery rate of 55%, we look for a forwarding algorithm to increase the delivery rate as lower as possible overhead and delivery latency.
TOOTS forwarding results and analysis Finally, we present the entire strategy's evaluation regarding delivery rate, delivery latency, and overhead within NCCU and GRM datasets. As with the state-of-art epidemic forwarding evaluation, the scenario has random consumers with a set size equal to 1% of dataset total nodes and varying communication range (10 m and 30 m). We compared TOOTS' performance with Bubble Rap, which was remarked as the most popular social-based forwarding algorithm in delaytolerant networks  [10, 11, 26]. Both forwarding algorithms run in a distributed fashion, taking decisions at the node that holds the content. Bubble Rap works by assigning a global rank and a local rank to each node. The global rank accounts for the node's popularity in the whole network, while the local rank measures the popularity inside a community.
Delivery rate evaluation First, in Fig. 12 (a), TOOTS and Bubble Rap are evaluated in terms of delivery rate through the NCCU dataset with a 30 m communication range. TOOTS reached 100% delivery ratio and was also the fastest strategy, with approx. 90% of the generated contents delivered in up to 9 h. Even in the NCCU dataset with fewer nodes, those with high centrality degrees, Bubble Rap-CDP failed to deliver about 10% of the contents. Adopting a CDP is not feasible in the real world. This strategy originates a ''bottleneck'' tending to train the most ''popular'' users' device resources and creates a point of failure. Despite reaching a 100% delivery ratio, we expect a higher overhead in Bubble Rap-RDP (analysis to follow). Finally, Bubble Rap-TDP also delivered 100% of the contents, but this strategy took slightly more time than TOOTS to deliver all contents in such experiments.
Within the GRM dataset and 30 m communication range (Fig. 12(b )), TOOTS reaches 100% delivery rate, with approx. 97% of the contents in up to 12 h. Bubble Rap-CDP delivery rate is even worst than in NCCU. The explanation relates to the fact that GRM nodes' have a much lower centrality degree. Further, a communication bottleneck arises due to a higher node density, where nodes' interfaces can transmit only one content at once. As in the previous analysis, Bubble Rap-RDP reached a 100% delivery ratio but with an expected higher overhead (analysis to follow). Bubble Rap-TDP delivered approx. 94% of the contents. The explanation relates to a characteristic of GRM: nodes with low centrality degrees, which makes Bubble Rap-TDP need more time to reach 100% delivery rate. From Figs. 12(e) to 12(h), we evaluate how imposing a more restricted contact range (10 m) affects the strategies' performance in terms of delivery rate, delivery latency, and overhead on both datasets.
In Fig. 12 (e), we see that TOOTS is the fastest and the only strategy that reaches 100% delivery rate on the real-world NCCU dataset. Bubble Rap-TDP and Bubble Rap-RDP reach close to 90% delivery rate and require more time, which could be more than acceptable for delaytolerant content. Bubble Rap-CDP fronts an even worst bottleneck. The restricted communication range reduces the number of contacts and also their duration. When the dataset is the GRM (Fig. 12 (f)), we see that the reduced contact range impacts the algorithms' lower performance. The delivery rate ranges from 56% (TOOTS) to 68% (Bubble Rap-RDP). On the other hand, Bubble Rap-TDP reaches a very similar delivery rate (65%), with less average latency. That means, again, thanks to TDP, there was less delivery latency. The reason behind this reduced performance on the GRM is the already shallow nodes of the dataset.
Delivery latency evaluation In Fig. 12(c ), the strategies are evaluated in terms of delivery latency, considering a 30 m communication range. TOOTS had the lower average delivery latency on both datasets, followed by Bubble Rap-RDP and Bubble Rap-TDP, respectively, on NCCU and GRM. We remark that only TOOTS and Bubble Rap-RDP delivered 100% of the contents in up to 72 h. On GRM, Bubble Rap-CDP had a much higher average delivery latency, as this strategy struggles with the bottlenecks on fewer nodes with higher centrality, and by a characteristic from this dataset shown in Section 3: from the content-generation time till their time-to-live (i. e. , end of the simulation), over 90% of the nodes had less than 0. 4 centrality degree, i. e. , low coefficients. Fig. 12 (g) certifies TOOTS as the strategy with the smaller delay on the NCCU dataset when the contact range is restricted (i.