Congestion control and reliable data delivery are two primary functions of the transport layer in wired and wireless networks. Wireless sensor networks (WSNs) are a special category of wireless ad hoc networks with unique characteristics and important limitations. Limitations concern their resources, such as energy, memory, and computational power, as well as their applications. Due to these limitations and characteristics, the Transmission Control Protocol (TCP), the legacy protocol that implements congestion control and reliable transmission in the Internet, cannot apply to WSNs in its traditional form. To deal with this unavailability of a standard solution, many efforts are taking place in this area. In this paper, we review, classify, and compare algorithms, protocols, and mechanisms that deal directly with congestion control and avoidance in WSNs.

Over the last few years, the analysis and modeling of networks as well as the analysis and modeling of networked dynamical systems, has attracted considerable interdisciplinary interest, especially using the complex systems theory. These efforts are driven by the fact that systems, as diverse as genetic networks or the Internet can be effectively described as complex networks. Contrary, despite the unprecedented evolution of technology, basic issues and fundamental principles related to the structural and evolutionary properties of communication networks still remain largely unaddressed. The situation is even more complicated when we attempt to model the mobile communication networks and especially the 5th generation (5G) and eventually the forthcoming 6th generation (6G). In this work, we attempt to review basic models of complex networks from a communication networks perspective, focusing on their structural and evolutionary properties. Based on this review we aim to reveal the models of complex networks, that may apply when modeling the 5G and 6G mobile communication networks. Furthermore, we expect to encourage the collaboration between complex systems and networking theorists toward meeting the challenging demands of 5G networks and beyond.

Attacks in Wireless Sensor Networks (WSNs) aim in limiting or even eliminating the ability of the network to perform its expected function. WSNs are networks with limited resources and often deployed in uncontrollable environments that an intruder can easily access. WSN attacks target specific network layer's vulnerabilities but normally affect other layers as well. Local sensor activity at multiple sensor network layers should be monitored and evaluated to detect possible malicious intervention. In this work we propose a general methodology of an anomaly-based Intrusion Detection System (IDS), named mIDS, that uses the Binary Logistic Regression (BLR) statistical tool to classify local sensor activity to either benign or malicious. We evaluate the proposed system using routing layer attacks and we show that mIDS is able to detect malicious activity within the range of 88%-100%.