Managing the data generated by Internet of Things (IoT) sensors and actuators is one of the biggest challenges faced when deploying an IoT system. Traditional cloud-based IoT systems are challenged by the large scale, heterogeneity, and high latency witnessed in some cloud ecosystems. One solution is to decentralize applications, management, and data analytics into the network itself using a distributed and federated compute model. This approach has become known as fog computing. This document presents the conceptual model of fog and mist computing and how they relate to cloud-based computing models for IoT. This document further characterizes important properties and aspects of fog computing, including service models, deployment strategies, and provides a baseline of what fog computing is, and how it may be used.

Security diagnostics expose vulnerabilities and privacy threats that exist in commercial Intelligent Virtual Assistants (IVA) – diagnostics offer the possibility of securer IVA ecosystems.

Numerous schemes have been proposed for secure routing and Intrusion Detection for ad hoc networks. Yet, little work exists in actually implementing such schemes on small handheld devices. In this paper, we present a proof-of-concept implementation of a secure routing protocol based on AODV over IPv6, further reinforced by a routing protocol independent Intrusion Detection System (IDS) for ad hoc networks. Security features in the routing protocol include mechanisms for non-repudiation and authentication, without relying on the availability of a Certificate Authority (CA) or a Key Distribution Center (KDC). We present the design and implementation details of our system, the practical considerations involved, and how these mechanisms can be used to detect and thwart malicious attacks. We discuss several scenarios where the secure routing and intrusion detection mechanisms isolate and deny network resources to nodes deemed malicious. We also discuss shortcomings in our approach, and conclude with lessons learned and ideas for future work.

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