Efficient key distribution and management mechanisms as well as lightweight ciphers are the main pillar for establishing secure wireless sensor networks (WSN). Several symmetric based key distribution protocols are already proposed, but most of them are not scalable, yet vulnerable to a small number of compromised nodes. In this paper, we propose an efficient and scalable key management and distribution framework, named KMMR, for large scale WSNs. The KMMR contributions are three fold. First, it performs lightweight local processes orchestrated into upward and downward tiers.

Internet of Mobile Things (IoMT) is a new paradigm of the Internet of Things (IoT) where devices such as sensors, robots, unmanned aerial vehicles (UAV) and cars, are inherently mobile. While mobility enables innovative applications and allows new services, it remains a challenging issue as it causes disconnection of nodes and intermittent connectivity, which negatively impact the network performance; namely data loss, large handover delay and application functionality failures.

VANET nodes are characterized by their high mobility and they exhibit different mobility patterns. Therefore, VANET clustering schemes should take into consideration the mobility parameters among neighboring nodes to produce relatively stable clustering structure. This paper proposes a novel cluster head selection Fuzzy Logic-based k-hop distributed clustering scheme for VANETs. This scheme considers the safe inter-distance between vehicles as one of important metrics for cluster head selection.

Self-organization and localization capabilities are among the most important requirements in wireless sensor networks (WSNs). While existing localization approaches mainly focus on enhancing the accuracy and minimizing the position error, particular attention has recently been given to reducing the localization algorithm implementation cost. This work re-explores the theory of wireless sensor network localization problem (SNLP), which is then re-considered as a multi-objective optimization problem (MOO-SNLP), maximizing the localization accuracy while minimizing the localization cost.