Search for: All records

The safety of distracted pedestrians presents a significant public health challenge in the United States and worldwide. An estimated 6,704 American pedestrians died and over 200,000 pedestrians were injured in traffic crashes in 2018, according to the Centers for Disease Control and Prevention (CDC). This number is increasing annually and many researchers posit that distraction by smartphones is a primary reason for the increasing number of pedestrian injuries and deaths. One strategy to prevent pedestrian injuries and death is to use intrusive interruptions that warn distracted pedestrians directly on their smartphones. To this end, we developed StreetBit, a Bluetooth beacon-based mobile application that alerts distracted pedestrians with a visual and/or audio interruption when they are distracted by their smartphones and are approaching a potentially-dangerous traffic intersection. In this paper, we present the background, architecture, and operations of the StreetBit Application. 

The Internet of Things (IoT) devices exchange certificates and authorization tokens over the IEEE 802.15.4 radio medium that supports a Maximum Transmission Unit (MTU) of 127 bytes. However, these credentials are significantly larger than the MTU and are therefore sent in a large number of fragments. As IoT devices are resource-constrained and battery-powered, there are considerable computations and communication overheads for fragment processing both on sender and receiver devices, which limit their ability to serve real-time requests. Moreover, the fragment processing operations increase energy consumption by CPUs and radio-transceivers, which results in shorter battery life. In this article, we propose CATComp -a compression-aware authorization protocol for Constrained Application Protocol (CoAP) and Datagram Transport Layer Security (DTLS) that enables IoT devices to exchange smallsized certificates and capability tokens over the IEEE 802.15.4 media. CATComp introduces additional messages in the CoAP and DTLS handshakes that allow communicating devices to negotiate a compression method, which devices use to reduce the credentials’ sizes before sending them over an IEEE 802.15.4 link. The decrease in the size of the security materials minimizes the total number of packet fragments, communication overheads for fragment delivery, fragment processing delays, and energy consumption. As such, devices can respond to requests faster and have longer battery life. We implement a prototype of CATComp on Contiki-enabled RE-Mote IoT devices and provide a performance analysis of CATComp. The experimental results show that communication latency and energy consumption are reduced when CATComp is integrated with CoAP and DTLS. 

In recent years, the addition of billions of Internet of Thing (IoT) device spawned a massive demand for computing service near the edge of the network. Due to latency, limited mobility, and location awareness, cloud computing is not capable enough to serve these devices. As a result, the focus is shifting more towards distributed platform service to put ample computing power near the edge of the networks. Thus, paradigms such as Fog and Edge computing are gaining attention from researchers as well as business stakeholders. Fog computing is a new computing paradigm, which places computing nodes in between the Cloud and the end user to reduce latency and increase availability. As an emerging technology, Fog computing also brings newer security challenges for the stakeholders to solve. Before designing the security models for Fog computing, it is better to understand the existing threats to Fog computing. In this regard, a thorough threat model can significantly help to identify these threats. Threat modeling is a sophisticated engineering process by which a computer-based system is analyzed to discover security flaws. In this paper, we applied two popular security threat modeling processes - CIAA and STRIDE - to identify and analyze attackers, their capabilities and motivations, and a list of potential threats in the context of Fog computing. We posit that such a systematic and thorough discussion of a threat model for Fog computing will help security researchers and professionals to design secure and reliable Fog computing systems.