Traditional information management approaches in distributed systems are most often unsuitable for modern Internet of Things (IoT) environments due to the huge amount and the extreme dynamism of the entities involved. Smart objects (enhanced devices or IoT services belonging to a smart system) interact and maintain relations which need of effective and efficient handling mechanisms better to meet users and services requirements. This article proposes an agent-based algorithm for achieving a distributed resources organization in an IoT environment. Software agents, representing each a single smart object, execute a set of local computations simultaneously, enabling an organized structure to emerge at global level. A natural language processing approach, able to capture the semantic context and represent objects with high-dimensional vectors, was exploited to map the IoT objects, so enabling advanced agents’ operations. The emerging organized virtual structure, i.e., a similarity-based overlay network of agents, allows building informed resource selection/discovery services, therefore more efficient, in a highly dynamic and, basically, unstructured environment. Preliminary results confirm the validity of the approach.

Agents-Based Algorithm for a Distributed Information System in Internet of Things

The Internet of Things (IoT) connects heterogeneous sensors with dynamic networks to monitor smart communication and collect real-time data. Such systems are well adapted to satisfy the needs of smart cities and facilitate remote locations. Many cloud-based solutions for effective routing along with scalable data storage have been presented for constraint IoT systems. However, because of the unpredictable nature of mobile networks and communication links, most of the solutions may not be suitable for realistic applications and usually result in path failure with increasing resource utilization. Hence, data forwarding is only reliable and valuable if the proposed algorithms are trust aware with low overheads and consume balanced energy among nodes. Therefore, this article proposed a fault-tolerant supervised routing (Trust-FTSR) model for trust management in the IoT network, to improve trustworthiness and collaborative communication in smart cities. Each node evaluates the behavior of its neighbors and establishes a direct trust for a reliable and optimized network structure. In addition, using a supervised machine-learning technique, a fault-tolerant relaying system is provided without imposing additional overheads. Moreover, it removes the additional load in determining the optimal decision and training the IoT system to balance the network cost. In the end, a secure algorithm is proposed to ensure the privacy and authentication of the relaying system in the presence of critical attacks with secured keys. The proposed model is tested and its performance has significant improvement as compared to existing work. 

Trust Management With Fault-Tolerant Supervised Routing for Smart Cities Using Internet of Things

Abstract Identifying user requirements and preferences on the basis of the current context, is one of main challenges of the Internet of Things (IoT) paradigm. Users, services and applications interact maintaining, often unreliable, relations which need of smart management systems to satisfy their demands. Traditional information handling approaches in distributed systems are most often unsuitable for modern Smart Environments due to the huge amount and the extreme dynamism of the entities involved. This paper proposes NARIoT platform that allows building recommendation systems in IoT environment. The approach relies on vector representations of IoT resources obtained by using of a word embedding tool, the Doc2Vec neural model, which, starting from text documents describing the resources, provides real-valued vectors mapping them. The vectors are handled through intelligent agents, which self-organize themselves creating an ordered virtual structure, so enabling informed mechanisms of information filtering. In particular, an ordered overlay network emerges from the autonomous, parallel and decentralized work of intelligent agents, thus enabling efficient recommendation operations. The experimental validation confirms the effectiveness of the approach and provides very encouraging results. 

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Recommendation platform in Internet of Things leveraging on a self-organizing multiagent approach

The Internet of Things (IoT) connects heterogeneous sensors with dynamic networks to monitor smart communication and collect real-time data. Such systems are well adapted to satisfy the needs of smart cities and facilitate remote locations. Many cloud-based solutions for effective routing along with scalable data storage have been presented for constraint IoT systems. However, because of the unpredictable nature of mobile networks and communication links, most of the solutions may not be suitable for realistic applications and usually result in path failure with increasing resource utilization. Hence, data forwarding is only reliable and valuable if the proposed algorithms are trust aware with low overheads and consume balanced energy among nodes. Therefore, this article proposed a fault-tolerant supervised routing (Trust-FTSR) model for trust management in the IoT network, to improve trustworthiness and collaborative communication in smart cities. Each node evaluates the behavior of its neighbors and establishes a direct trust for a reliable and optimized network structure. In addition, using a supervised machine-learning technique, a fault-tolerant relaying system is provided without imposing additional overheads. Moreover, it removes the additional load in determining the optimal decision and training the IoT system to balance the network cost. In the end, a secure algorithm is proposed to ensure the privacy and authentication of the relaying system in the presence of critical attacks with secured keys. The proposed model is tested and its performance has significant improvement as compared to existing work.

LOADng-AT: a novel practical implementation of hybrid AHP-TOPSIS algorithm in reactive routing protocol for intelligent IoT-based networks

Arbitrary point-to-point (P2P) routing is becoming a necessity in a multitude of Internet-of-Things (IoT) applications, including building automation, smart cities, and smart manufacturing. For this reason, new P2P routing protocols such as lightweight on-demand ad hoc distance-vector routing protocol next generation (LOADng) and ad hoc on-demand distance vector routing-based RPL protocol (AODV-RPL) are being standardized. Both protocols are inspired by AODV, and hence, they might suffer from broadcast storms caused by flooding route requests (RREQs) and related issues. Thus, while AODV-RPL takes advantage of RPL mechanisms, LOADng uses blind flooding to forward RREQs. However, both lack effective techniques for avoiding unnecessary RREQ transmissions when routes are found. In this article, we first deploy stopping Trickle timers to ensure scalable, efficient, and reliable dissemination of RREQs. Second, we devise and present new techniques to suppress unnecessary RREQs with optimizations for radio duty-cycled networks. Finally, the two mechanisms are combined in a third proposal for better efficiency. These mechanisms provide individually and collectively great enhancements to P2P routing protocols, such as AODV-RPL and LOADng, while staying backward compatible with their base specifications. The performance of the proposed mechanisms when applied to LOADng has been validated using both extensive time-accurate simulations and large-scale public testbeds. The obtained results have shown the effectiveness of the proposed mechanisms with around 50% less overhead and savings in energy consumption, along with a 20% gain in route discovery ratio at the expense of an increase in discovery delays.

Hichem Bessas

Papers

Efficient and Stateless P2P Routing Mechanisms for the Internet of Things