Internet of Conscious Things: Ontology-Based Social Capabilities for Smart Objects
<p>Semantic Web of Things architecture for SIoT.</p> "> Figure 2
<p>Social IoT framework and interaction model.</p> "> Figure 3
<p>Reference ontology-based data modeling.</p> "> Figure 4
<p>Distributed service/resource discovery.</p> "> Figure 5
<p>Sample network with loosely connected nodes.</p> "> Figure 6
<p>Social smart mobility scenario.</p> "> Figure 7
<p>Electric taxi profile semantic description.</p> "> Figure 8
<p>Semantic annotations of taxi request and friends’ services.</p> "> Figure 9
<p>Semantic description of selected service.</p> "> Figure 10
<p>Test results for small-size networks. Legend denotes values of parameters for each configuration (<generation algorithm>_<number of nodes>_<request rate>).</p> "> Figure 11
<p>Test results for medium-size networks. Legend denotes values of parameters for each configuration (<generation algorithm>_<number of nodes>_<request rate>).</p> "> Figure 12
<p>Test results for large-size networks. Legend denotes values of parameters for each configuration (<generation algorithm>_<number of nodes>_<request rate>).</p> "> Figure 13
<p>Comparison of dynamic (this paper) vs. static [<a href="#B9-futureinternet-16-00327" class="html-bibr">9</a>] relationship management.</p> ">
Abstract
:1. Introduction
- Dynamic interactions: smart objects are designed to interact not only based on predefined technical protocols but also through social relationships, akin to human social networks. Relationships mirror human social connections, such as co-location, co-work, friendship, and ownership, and facilitate more intuitive interactions independent from user engagement and from human Social Networking Services (SNSs);
- Context-aware collaboration: IoT devices act as autonomous agents building networks of social relationships and exploiting them to share information and services, and perform tasks collaboratively [5]. The social layer adds context and relational data to the interactions, enabling devices to make more informed and context-aware decisions;
- Resource optimization: by leveraging social relationships, smart devices can optimize the allocation and negotiate access to shared resources, like network bandwidth or energy power, based on their social standing or priority within the network.
- A novel framework combining a SWoT-based distributed collaborative service discovery protocol with a SIoT dynamic relationship and trust management model. This work extends the early proposal in [9] defining a completely renewed approach for social interactions among smart objects; to the best of our knowledge, existing works have focused on either one or the other aspect, but not on both together or on their mutual influence, incurring in functional limitations with respect to the proposed approach, as discussed in Section 3;
- a decentralized collaborative service discovery protocol [9] based on Linked Data Notifications (LDN) over LDP-CoAP [10], which combines the Linked Data Platform (LDP) [11] for semantically annotated resource management and the Constrained Application Protocol (CoAP) [12] for Machine-to-Machine (M2M) interactions; this explicit and standard-based formalization of interaction primitives improves on existing research proposals for SIoT architectures based on custom interaction models;
- A completely novel dynamic model for SIoT relationship management, measuring trust with a reputation system based on the usefulness of service suggestions in the decentralized collaborative social discovery protocol with additional features to prevent device isolation due to a “cold start” situation (i.e., when a device joins a new network where it has no friends) or to network partitioning after a link removal; this zero-configuration approach is an improvement with reference to most existing SIoT proposals;
- A case study on a Plug-in Electric Vehicle (PEV) charging service marketplace, to illustrate the features and benefits of the proposed approach for Mobility-as-a-Service (MaaS) in a smart city scenario;
- An experimental evaluation assessing the efficiency and effectiveness of both the dynamic relationship management and the collaborative service discovery in device networks of various sizes and configurations.
2. Fundamentals of the Semantic Web of Things
- Pervasive sensing and IoT identification technologies exploited at the field layer, interconnecting embedded micro-devices dipped in the environment. Interaction among social devices is performed by means of CoAP protocol ensuring lightweight communication and information dissemination;
- Data acquired by sensing devices exposed at the upper layers in a uniform fashion, according to LDP and LDN guidelines described in Section 4.1. Data are annotated with respect to well-known ontologies and taxonomies, providing a shared understanding of domain knowledge. By integrating these semantic structures, SIoT devices can communicate more effectively, using a common language to describe their functions, states, and interactions;
- u-KB modeling in a Web Ontology Language (OWL) 2 [15] subset corresponding to the Attributive Language with unqualified Number restrictions ) DL. It provides moderate expressiveness while granting polynomial complexity to both standard and non-standard inference tasks;
- Dynamic service/resource discovery and composition processes, detailed in Section 4.2, exploiting semantic matchmaking between ontology-based annotations describing requests and available resources.
- Concept Contraction: if a request and a supplied resource are not compatible with each other, Contraction determines which part of is conflicting with . By giving up only conflicting requirements G (for Give up) in , an expression K (for Keep) remains, which is a contracted version of the original request. The solution G to Contraction represents “why” and are not compatible;
- Concept Abduction: if request and resource are compatible, but does not satisfy completely, Abduction determines what should be hypothesized in in order to obtain a full match, i.e., to make the subsumption relation true. The solution H (for Hypothesis) to Abduction can be interpreted as what is requested in and not specified in ;
- Concept Covering: in advanced distributed systems it is often useful to aggregate multiple low-complexity resources = {, , … , } in order to satisfy an articulate request . The (Abduction-based) Covering service finds a pair where includes concepts compatible with whose conjunction covers as much as possible; H is the residual part of possibly not covered by concepts in .
3. Related Work
4. Social Awareness in the Semantic Web of Things
4.1. LDP-CoAP Social Framework
- Friend, a bidirectional relationship where nodes and can exchange both information and services. In particular, a device sends a friendship request; since the receiver accepts it, they become able to (i) read and write on each other’s wall, acting as an LDN receiver and an LDN sender (to write) or consumer (to read), respectively; (ii) request the friend’s service descriptions; (iii) activate or deactivate the friend’s services.
- Follower: a unidirectional relationship where a node is interested only in receiving the updates published by on its wall. In other words, if sends a follower request to , becomes an LDN consumer for .
- Creation date (dcterms:created);
- Sender device (swst:postedBy);
- Content of the post (sioc:about), as Internationalized Resource Identifier (IRI) of the individual representing the received OWL annotation;
- Like value (swst:likeValue).
- Creation date;
- Sender device;
- Tagged (i.e., activated) services, selected through the covering process described in Section 4.2 (sioc:topic);
- Content of the comment, representing the part of the original post not covered by tagged services provided by the friend device.
4.2. Cooperative Service/Resource Discovery
- The request is fully covered or the covering procedure reaches a minimum threshold of like (i.e., satisfaction) value. When a device reaches this limit value, the covering procedure can be stopped, and there is no need to forward the potentially uncovered part of the request to other friends. In this way, each device can prevent the network from being flooded with unnecessary communication which can degrade performance and lead to delays or lost messages;
- A maximum distance from the request source is reached in the social graph. It represents the maximum number of hops a message can make from its original sender during the discovery process. In particular, a direct friend has a depth of 1, whereas a friend of a friend has a depth of 2. This parameter ensures that only nodes within a relevant proximity receive the message, improving the overall efficiency of the network by focusing communication on the most pertinent areas. Basically, the proposed protocol exploits a classic expanding ring search, with the peculiarity that it is not based on topological distance, but on social distance.
Algorithm 1: Procedure |
|
Algorithm 2: Procedure |
|
- The natural hierarchic structure of social network items (comment ∈ post ∈ wall) is neatly mapped to LDP containers and resources, where each item can be uniquely addressed and explored;
- The LDN notification framework accommodates the needs of social network applications; at a lower level, the proposed machine-to-machine SIoT approach implements LDN with the CoAP Observe pattern;
- Service discovery is collaborative and recursive, exploiting service descriptions shared between friends and the possibility to write posts and comments on each other’s wall. Semantic matchmaking underpins logic-based service relevance ranking and request covering;
- Becoming a follower of a device D grants automatic registration as observer for every post published on D’s wall from that moment on, through the CoAP Observe pattern;
- A discovery session produces, for each participating device D, exactly one wall post, with a number of comments equal to the devices D has directly or indirectly involved in the matchmaking process. As described in the next subsection, devices can exploit them as a log to adapt their relationships dynamically, calculating metrics of usefulness for each friend.
4.3. Managing Dynamic Social Relationships
- : number of comments created by D;
- : number of useful services provided by D, i.e., D’s services tagged as result of Concept Covering;
- w (between 0 and 1): used to weigh the contribution of , considering a device as direct provider of useful services, and , representing the ability of D to contribute to service discovery by acting as a “bridge” toward other useful devices;
- T: timestamp at which all functions are evaluated;
- : timestamp at which a comment/post was created;
- : decay rate coefficient, weighing the past history of a device: the higher the value, the lower the relevance of older contributions.
- is the number of D’s services, detected as incompatible with reference to the requests received in the period T;
- is the timestamp at which the covering process was performed and the incompatibility was identified;
- is the penalty score measured by Concept Contraction.
5. Case Study: Social Smart Mobility for Electric Vehicles
6. Experiments
- Medium-size networks exhibit higher variations in the average number of friendships per node (Figure 13a). With 100 nodes, the network is large enough to facilitate multiple connections without becoming overwhelmingly complex. Medium-sized networks often see the formation of dynamic tightly knit communities or clusters, which naturally enhances the number of friendships as nodes within these clusters are more likely to be interconnected. In particular, novel friendships are created for small and medium networks with frequent device requests (i.e., RND_70, BA_70 and DM_70) where several interactions foster more connections and higher levels of friendships. In large fully connected networks, the number of friendships tends to remain stable due to a dynamic equilibrium in the formation and retraction of connections. As the network grows, new relationships are established, but existing ones may also be retracted if they are deemed less useful or redundant;
- With the proposed approach like values in Figure 13b become higher in all configurations, particularly in loosely connected networks with frequently isolated objects (i.e., RND configurations). In such networks, dynamic relationships allow nodes to continuously form and adjust connections based on evolving interactions and needs, and the like values tend to rise more noticeably due to the enhanced potential for discovering novel and useful connections. As nodes interact with a more diverse set of peers, they can uncover and leverage previously inaccessible resources and services. On the contrary, in fully connected networks (i.e., DM configurations), new connections are relatively saturated. Many nodes are already connected and aware of existing services, leaving less room for discovering and activating new functionalities. As a consequence, the number of activated services tends to increase in small and loosely connected networks (cells with light colors in Figure 13c), whereas it even decreases in large and fully connected networks (blue cells). In these cases, the network tends to reach an optimal configuration by activating only the most suitable subset of services, as testified by high like values; this means that network efficiency is maximized and unnecessary complexity is avoided;
- In small and medium networks, the number of forwarded messages remains stable or slightly increases due to the simplicity and directness of social interaction routes (Figure 13d). The limited network size allows for efficient message routing with fewer forwarding steps. In contrast, large networks exhibit an increase in the number of forwarded messages due to their complexity and the need for more indirect communication paths also in case of dynamic and re-configurable relationships. Nevertheless, across all network configurations (Figure 13e), there is a noticeable decrease in the amount of exchanged data related to service discovery. This trend reflects the improvements introduced by the proposed approach in the efficiency of the service discovery process, regardless of the network size or connectivity pattern. However, the decrease is particularly significant in loosely connected networks where the dynamic relationship management model significantly enhances the overall behavior of the social network. Devices are capable of quickly contacting new nodes and active services while minimizing unnecessary data exchanges. As a result, the network operates more efficiently, with improved resource utilization and enhanced communication pathways.
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Features | [22] | [25] | [26] | [32] | [44] | [9] | This Work |
---|---|---|---|---|---|---|---|
Friend/follower relationships | ✗ | ✗ | ✶ | ✗ | ✗ | ✓ | ✓ |
Co-location/co-work relationships | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✶ |
Dynamic relationships | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ |
Context-awareness | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Collaborative service discovery | ✗ | ✗ | ✓ | ✗ | ✗ | ✓ | ✓ |
Autonomous device interactions | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ |
Trust management | ✗ | ✓ | ✗ | ✗ | ✓ | ✗ | ✓ |
Real-time data processing | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ |
Semantic Web integration | ✗ | ✶ | ✓ | ✗ | ✗ | ✓ | ✓ |
Parameter | Value | Description |
---|---|---|
Generation algorithm | RND | Randomly generated nodes with mean number of friends set to 1. Loosely connected network with frequent isolated objects. |
BA | Nodes generated exploiting the preferential attachment rule of the Barabási–Albert model [51]. Each node has 2 friends on average. Hierarchical topology similar to a sensor network with several edge nodes and one or more sinks. | |
DM | Nodes generated using the Dorogovtsev–Mendes algorithm [52]. Strongly connected network with nodes having 4 friends on average. | |
Nodes | 10 | Small-size network. |
100 | Medium-size network. | |
1000 | Large-size network. | |
Request rate | 20 | 20% of nodes identifies a new event and posts a message in each period. Few device requests. |
70 | 70% of nodes identifies a new event and posts a message in each period. Frequent device requests. |
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Ruta, M.; Scioscia, F.; Loseto, G.; Pinto, A.; Fasciano, C.; Capurso, G.; Di Sciascio, E. Internet of Conscious Things: Ontology-Based Social Capabilities for Smart Objects. Future Internet 2024, 16, 327. https://doi.org/10.3390/fi16090327
Ruta M, Scioscia F, Loseto G, Pinto A, Fasciano C, Capurso G, Di Sciascio E. Internet of Conscious Things: Ontology-Based Social Capabilities for Smart Objects. Future Internet. 2024; 16(9):327. https://doi.org/10.3390/fi16090327
Chicago/Turabian StyleRuta, Michele, Floriano Scioscia, Giuseppe Loseto, Agnese Pinto, Corrado Fasciano, Giovanna Capurso, and Eugenio Di Sciascio. 2024. "Internet of Conscious Things: Ontology-Based Social Capabilities for Smart Objects" Future Internet 16, no. 9: 327. https://doi.org/10.3390/fi16090327