The growth of Internet of Things (IoT) faces the growing concern of cyber-attacks toward IoT. Unfortunately, the constrained resources of IoT devices and their networks make many traditional attack detection methods less effective or even not applicable. In this paper, we present a framework for a smart home environment that considers the unique properties of IoT networks. This framework utilizes a two-mode adaptive security model to enable users to balance performance, security, and overhead.
Directed Research Project
The rapidly growing number of large network analysis problems has led to the emergence of many parallel and distributed graph processing systems—one survey in 2014 identified over 80. Since then, the landscape has evolved; some packages have become inactive while more are being developed. Determining the best approach for a given problem is infeasible for most developers.
In both high-performance computing and Internet of Things (IoT) applications, it is important for programmers to utilize the hardware as efficiently as possible in terms of both performance and power usage. Automatic methods of tuning code for such efficiency have been developed and used for many years in HPC, but less so in other areas. In this paper, we apply these methods to the domain of IoT to explore the feasibility of using autotuning in this domain. Our case study examines the application of roadway traffic analysis.
Transactional programming models have been proposed as an important part of the evolution of threaded programming in shared memory environments; however, a critical shortcoming of these models is their inability to function and compose properly in the presence of operations that do not have a transactional nature (e.g., system calls).