In this article, we present a unified perspective on the cognitive internet of things (CIoT). It is noted that within the CIoT design we observe the convergence of energy harvesting, cognitive spectrum access and mobile cloud computing technologies. We unify these distinct technologies into a CIoT architecture which provides a flexible, dynamic, scalable and robust network design road-map for large scale IoT deployment. Since the prime objective of the CIoT network is to ensure connectivity between things, we identify key metrics which characterize the network design space. We revisit the definition of cognition in the context of IoT networks and argue that both the energy efficiency and the spectrum efficiency are key design constraints. To this end, we define a new performance metric called the 'overall link success probability' which encapsulates these constraints. The overall link success probability is characterized by both the self-sustainablitiy of the link through energy harvesting and the availability of spectrum for transmissions. With the help of a reference scenario, we demonstrate that well-known tools from stochastic geometry can be employed to investigate both the node and the network level performance. In particular, the reference scenario considers a large scale deployment of a CIoT network empowered by solar energy harvesting deployed along with the centralized CIoT device coordinators. It is assumed that CIoT network is underlaid with a cellular network, i.e., CIoT nodes share spectrum with mobile users subject to a certain co-existence constraint. Considering the dynamics of both energy harvesting and spectrum sharing, the overall link success probability is then quantified. It is shown that both the self-sustainability of the link, and the availability of transmission opportunites, are coupled through a common parameter, i.e., the node level transmit power. Furthermore, provided the co-existence constraint is satisfied the link level success in the presence of both the inter-network and intra-network interference is an increasing function of the transmit power. We demonstrate that the overall link level success probability can be maximized by employing a certain optimal transmit power. Characterization of such an optimal operational point is presented. Finally, we highlight some of the future directions which can benefit from the analytical framework developed in this paper.

Abstract-In this paper, we develop a comprehensive analytical framework for cellular networks that are enhanced with coordinated device-to-device (D2D) communication, where the D2D devices are equipped with content caching capabilities. The base station (BS) coordinates the D2D communication by establishing a D2D link between the requesting user and the nearest D2D helper within the same cell if the latter contains the requested content, otherwise, the BS serves the user itself. The motivation behind restricting D2D pairs within a macro cell is to make coordinated D2D communication realizable as the BS can keep track of the content of the devices without the increased overhead of inter-BS coordination. This approach is similar to LTE direct, where D2D pairing is managed by the BS. We model the locations of BS and D2D helpers using a homogeneous Poisson point process (HPPP). The distribution of the distance between the tagged user and its neighboring D2D helper within the cell is derived using disk approximation for the Voronoi cell, which is shown to be reasonably accurate. We fully characterize the cellular and D2D coverage and the link spectral efficiency of such a network. Our results reveal that cache enabled D2D communication becomes more effective as the requesting user moves away from the BS and high performance gains can be achieved compared to conventional cellular networks, especially when the popularity distribution is skewed and most popular files are requested.

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Instant messaging applications (apps) have played a vital role in online interaction, especially under COVID-19 lockdown protocols. Apps with security provisions are able to provide confidentiality through end-to-end encryption. Ill-intentioned individuals and groups use these security services to their advantage by using the apps for criminal, illicit, or fraudulent activities. During an investigation, the provision of end-to-end encryption in apps increases the complexity for digital forensics investigators. This study aims to provide a network forensic strategy to identify the potential artifacts from the encrypted network traffic of the prominent social messenger app Signal (on Android version 9). The analysis of the installed app was conducted over fully encrypted network traffic. By adopting the proposed strategy, the forensic investigator can easily detect encrypted traffic activities such as chatting, media messages, audio, and video calls by looking at the payload patterns. Furthermore, a detailed analysis of the trace files can help to create a list of chat servers and IP addresses of involved parties in the events. As a result, the proposed strategy significantly facilitates extraction of the app’s behavior from encrypted network traffic which can then be used as supportive evidence for forensic investigation.

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