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Internet of Things (IoT), as an emerging technology, describes a smart world that enables objects to interact with each other and with end-users through developed communication platforms such as the Internet. The limited communication range of things allows packets to be transmitted between objects that are not in each other's range through intermediate objects, which highlights the necessity of routing in this technology. Therefore, the Quality of Service parameters of IoT entirely depend on the cooperation of things in the routing process. Several factors such as energy depletion, conquering objects by an enemy, and resource constraints lead to selfish behavior that may cause network collapse. In this paper, a new watchdog-based identification method is presented to diagnosis selfish objects in the Internet of Things, in which the selected watchdogs are responsible for evaluating the activity of other objects. In the proposed method to achieve a uniform distribution of watchdog objects in the network, the IoT objects are partitioned into disjoint cells using the Voronoi diagram, and a watchdog is selected for each cell. In this way, while the supervision overhead is distributed, the watchdog objects can identify the selfish things through examining the remaining energy and cooperation degree of other objects. The conducted simulations in the MATLAB tool have validated the effectiveness of the proposed method in terms of detection rate, false-positive rate, throughput, end-to-end delay, and energy consumption. [ABSTRACT FROM AUTHOR]

Generally, the ad hoc networks work correctly only if all nodes cooperate in the routing and forwarding of the packets. However, in some cases, selfish nodes are hesitated to share the resources with their neighbors and attempt to preserve their own assets, since splitting nodes into two distinct groups including absolute cooperator and absolute selfish and avoiding the selfish nodes group in network activities will be degraded network quality of service. In this paper, we propose a novel fuzzy-based selfish node detection approach for ad hoc networks that operates based on the social network principles. In the social-based proposed approach, the nodes' status will be determined by three variables, i.e., hop count (H.C.), residual energy (Re-En.), and cooperation history (Co-h.), through fuzzy interface process to prevent the isolating of the likely selfish nodes from the network and maintain more active node in the network as possible. The MobEmu tool is used to evaluate the effectiveness of the proposed approach in terms of the hit rate, delivery latency, delivery cost, and average hop count. The simulation results reveal that the proposed approach has a significant improvement in contrast to counterparts using UPB 2011 and UPB 2012 traces. [ABSTRACT FROM AUTHOR]