Enhancing Identifier/Locator Splitting Through Social Internet of Things

Atzori, Luigi; Campolo, Claudia; Da, Bin; Girau, Roberto; Morabito, Giacomo; Quattropani, Salvatore

doi:10.1109/jiot.2018.2877756

Cited by 16 publications

(9 citation statements)

References 26 publications

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“…Table II lists some of the common computing tasks in SIoT and the literature of social-aware solutions. Trust management in SIoT [44], [45], [46], [47], [48], [49], [16], [50] Establishing trust between SIoT devices for social clustering and social community detection Social features extractions and classification [39], [38], [51], [52] Assigning social properties such as personality traits, mood, emotions and interest to SIoT users Social relationships management in SIoT [53], [54], [55], [56], [57], [58], [59] Maintaining the user-user, user-device and devicedevice social relationship history and logging the relationship properties and preferences Social-aware recommendation system in SIoT [43], [60], [61], [62], [63], [64] Services recommendation and content customization based on the social properties of SIoT entities Security and privacy in SIoT [65], [66], [67], [68], [69] Securing and preserving the privacy of social data and social context…”

Section: A Asi In Computingmentioning

confidence: 99%

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

Dhelim

Ning

Farha

et al. 2021

IEEE Internet Things J.

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With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deeplearning techniques.

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Section: A Asi In Computingmentioning

confidence: 99%

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

Dhelim

Ning

Farha

et al. 2021

IEEE Internet Things J.

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“…Atzori et al [16] proposed a new storage format for the locator based on the splitting of the address in the Internet of things. Here, the new storage format is used to define the social relationship between the devices.…”

Section: Related Workmentioning

confidence: 99%

Trust-Based and Optimized RPL Routing in Social Internet of Things Network

Selvaraj¹,

Thangarajan²,

Saravanan

2021

Lecture Notes on Data Engineering and Communications Technologies

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Internet of things plays a major role in all the applications that are utilized for automating the environment. In this, the searching of devices in the larger network is a tedious process. Further, it became easier by defining the social relationship among the devices in the network. Social Internet of things is the combination of real-world network and multiple devices in the Internet of things. In social Internet of things, the user can access information globally at any place based on its ID. But, the accessing of this ID in the large database is difficult. Several works were carried out to locate the ID of the devices in the social Internet of things [SIoT]. Community and effective search-based algorithm is one of the methods for effective localization of devices in the social Internet of things as the conventional method for device localization. The Routing protocol for Low power and Lossy networks (RPL) able to increase lifetime of network with high packet loss. In existing, the localization and routing is performed separately. This problem is overcome with the proposed trust-based community nodes and optimized RPL in social Internet of things network. Here, the community is formed with the combination of high trust and low trust nodes of similar taste. These trusted node-based community formation helps to locate the trusty nodes in the transmission process. The dragon fly optimization is used to improve the RPL performance by selecting trusty nodes as next hop in the routing process. Due to the combination of trust community and optimized RPL, the lifetime and success rate of the data transmission is increased by 90% as compared to the existing preference-based device localization and traditional RPL protocol. The whole process is realized with the help of MATLAB R2018a in windows 10 environment. Therefore, the proposed trust community and optimized RPL is best

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“…• Transactional Object Relationship (TOR): established between devices that interact with each other frequently [15].…”

Section: Social Internet Of Thingsmentioning

confidence: 99%

SIoT for cognitive logistics: Leveraging the social graph of digital twins for effective operations on real-time events

Cimperman¹,

Dimitriou²,

Kalaboukas³

et al. 2021

ITU-J FET

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Over the years, with the migration of organizations towards the concepts of logistics 4.0, a paradigm shift was necessary to guarantee logistics efficiency. The challenge is to dynamically cope in real time with vast number of shipments and destinations, which need to be realigned both with a determined lead time and with a finite of available resources. Although a number of standards have already been adopted for the management of transport and logistics operations, taking advantage, for instance, of Decision Support Systems and Geographic Information Systems, new models are required for achieving effective handling of the dynamic logistics environment that is shaped today. In this paper, an integrated logistics framework addressing the previous challenges is presented, for the first time, as a result of the activities of the H2020 COG-LO project. This novel approach exploits Social Internet of Things (SIoT) and the digital twins technique to realize the concept of the Cognitive Logistics Object (CLO). A CLO is defined as an entity that is augmented with cognitive capabilities, it is autonomous, and bears social-like capabilities, which enable the formulation of ad hoc communities for negotiating optimal solutions in logistics operations.

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Enhancing Identifier/Locator Splitting Through Social Internet of Things

Cited by 16 publications

References 26 publications

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

Trust-Based and Optimized RPL Routing in Social Internet of Things Network

SIoT for cognitive logistics: Leveraging the social graph of digital twins for effective operations on real-time events

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