In order to realize an intelligent Internet of Things (IoT) system, various resource-constrained IoT devices today play an important role in data transmission and processing. However, the novel data protection requirements appear since constrained resources like computation power or energy of IoT devices cannot support classic data protection methods like encryption algorithms. To tackle the above issues, we propose a lightweight blockchain-based trust management (BBTM) scheme in Resource-constrained Intelligent IoT Systems. The BBTM initially establishes a genesis block at the onset of network operation. Messages transmitted between nodes are recorded as transactions, forming blocks that are linked to the blockchain. As the network operates, nodes’ behaviors are monitored, and their trust values are updated in real-time. When two nodes encounter each other, a credibility formula-based method is used to determine trustworthiness based on historical behaviors. Nodes that pass the trust verification utilize asymmetric encryption for message transmission. The primary aim of this scheme is to develop a lightweight, blockchain-based secure communication module for Intelligent IoT Systems. It encrypts information using asymmetric cryptography and manages trust during node interactions to ensure security and privacy by blocking malicious or selfish nodes. Compared with the existing solutions, the simulation results show that this scheme can effectively block the attacks from malicious nodes without overly consuming network resources. At the same time, trust verification and encrypted information transmission are effectively managed in resource-constrained intelligent IoT systems.

This paper introduces a novel, Simple-based Dynamic Decentralized Community Detection Algorithm (S-DCDA) for Socially Aware Networks. This algorithm aims to address the resource-intensive nature, instabilities and inaccuracies of traditional distributed community detection algorithms. The dynamics of decentralization is evident in the threefold nature of the algorithm: (i) each node of the community is the core of the entire network or community for a certain period of time dependent on their need, (ii) nodes are not centralized around themselves, requiring the consent of the other node to join a community, and (iii) Communities start from a single node to form an initial scale community, the number of nodes and the relationship among them are constantly changing. The algorithm requires low processor performance and memory capacity size of each node, to a certain extent, effectively improve the accuracy and stability of community detection and maintenance. Experimental results demonstrate that in comparison to classical and classical-based improved community detection algorithms, S-DCDA yields superior detection results.

Vehicular Ad Hoc Networks (VANETs) face challenges due to high vehicle mobility, which leads to frequent changes in network topology and unstable links. To tackle this, we propose a novel routing strategy called MPFD, which enhances message delivery rates and reduces End-to-End (E2E) delay. MPFD operates in two phases. The first phase, the message management phase, prioritizes messages based on urgency and controls message propagation by considering hop count (H) and Time-To-Live (TTL), ensuring only critical messages are forwarded. The second phase, the relay node selection phase, uses free nodes to identify the best relay for forwarding messages. Candidate nodes are evaluated based on community association, movement direction, and proximity to the destination. Simulations show that the MPFD strategy significantly improves message delivery speed, reduces overhead and E2E delay, and enhances the overall message delivery ratio in VANETs.