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A Wireless Sensors Network (WSN) is an ad-hoc network populated by small hand-held commodity devices, running on batteries called stations or sensors. Often used in hostiles and sometimes unreachable environments, stations are subject to energetic constraints which can significantly decrease the network life time. Permutation routing problem is mainly found in the literature of WSN. This problem occurs when some stations have items that belong either or not to them. The goal is to send each item to its receiver. To solve this problem, several works are presented in the literature. In this paper, we present a new permutation routing protocol for multi-hop wireless sensors network that, compared to recent work in the field is more efficient in terms of conservation of sensors' energy, which results in a longer life time of the network. Also, contrary to some other routing protocols which assume that the memory of the sensors is infinite, we show that the memory size of the sensors is limited, which in our opinion is more realistic.

A Wireless Sensor Network (WSN) is an Ad-hoc network populated by small hand-held commodity devices, running on batteries called stations or sensors. These sensors are deployed in an area called a perception zone in order to study one or more phenomena. Generally, the perception zone is an area where access is almost impossible for humans. Given the absence of a previously defined infrastructure, deployed sensors need to organize themselves to ensure not only their connectivity but also effective management of their residual energy and the security of data that transit to them. The residual energy management is very important since we know that communications over long distances are always very energy-consuming for sensors, which most often do not have a secondary source of energy. Multi-hop communication is generally used to connect sensors in order to ensure efficient use of their energy. This being the case, multi-hop communication can be established by partitioning the network into clusters. Subsequently, network security management is also important because most WSN must circulate confidential information. In order to avoid malicious intrusions, all operations involved must be done in a secure manner. In this paper, we propose a secure clustering protocol which connects all the sensors of the network.

The knowledge of link quality in IoT networks will allow a more accurate selection of wireless links to build the routes used by data gathering. Therefore, the number of retransmissions on these links is decreased, leading to a shorter end-to-end latency, a better end-to-end reliability and a larger network lifetime. In this paper, we propose to predict link quality by means of machine learning techniques applied on two metrics: RSSI and PDR. The accuracy obtained by Logistic Regression, Linear Support Vector Machine, Support Vector Machine and Random Forest classifier is obtained on the traces of a real IoT network deployed at Grenoble. Index TermsMachine learning, link quality estimation, wireless network, TSCH

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