Secure service proxy : a CoAP(s) intermediary for a securer and smarter web of things

As the IoT continues to grow over the coming years, resource-constrained devices and networks will see an increase in traffic as everything is connected in an open Web of Things. The performance- and function-enhancing features are difficult to provide in resource-constrained environments, but will gain importance if the WoT is to be scaled up successfully. For example, scalable open standards-based authentication and authorization will be important to manage access to the limited resources of constrained devices and networks. Additionally, features such as caching and virtualization may help further reduce the load on these constrained systems. This work presents the Secure Service Proxy (SSP): a constrained-network edge proxy with the goal of improving the performance and functionality of constrained RESTful environments. Our evaluations show that the proposed design reaches its goal by reducing the load on constrained devices while implementing a wide range of features as different adapters. Specifically, the results show that the SSP leads to significant savings in processing, network traffic, network delay and packet loss rates for constrained devices. As a result, the SSP helps to guarantee the proper operation of constrained networks as these networks form an ever-expanding Web of Things

Coordination Models for Internet of Things

In constrained environments, there is a variety of devices like sensors and actuators with limited computation power or energy that form an Internet of Things (IoT) system. When processing complex tasks is required, those devices send the data to the cloud and obtain the result later. However, the IoT system could process complex task if more devices work together, sharing computational resources and cooperating. This cooperation can be achieved using a coordination model that distributes the load among the different devices based on a set of parameters, laws and defined entities. This research implements and evaluates a data-oriented coordination model with three variations for Internet of Things (IoT). It also presents, implements and evaluates a new process-oriented coordination model that can make constrained environments much more effective and allow the processing of more complex tasks closer to the network. The development of all the coordination models was focused on using the system’s computational resources effectively. As IoT is a heterogeneous field, devices with more power can process more complex tasks, creating an uneven but adequate load distribution. Various experiments were conducted to evaluate the performance of each model using one and two workers. The results showed that every coordination model works effectively when distributing the load among more workers. For the process-oriented model, implementing some CoAP features allowed the system to perform better when repetitive tasks are required

Efficient Proxying of CoAP Observe with Quality of Service Support

The Observing Resources extension of the Constrained Application Protocol (CoAP) is expected to play a major role in future Internet of Things (IoT) applications, since it enables asynchronous monitoring of IoT resources in an efficient manner. As the number of IoT applications will grow, solutions are needed to manage multiple observe relationships for the same resource, especially if hosted by a constrained IoT device. The problem is further exacerbated since multiple observers may have heterogeneous Quality of Service (QoS) requirements on the minimum and maximum frequency with which to receive notifications, while the resource-constrained server may only be able to provide notifications with one fixed period for all. In this work, we propose an efficient solution to support multiple CoAP observers with QoS requirements. In particular, we formulate the problem of finding the notification period which minimizes the amount of messages sent by the server, so as to reduce energy consumption of IoT devices and congestion in constrained networks, while ensuring all observers' requirements are met. We then propose an algorithm, named Observation Period Selection (OPS), which is proved to find the optimal solution to this problem. OPS is also evaluated experimentally to analyze its effectiveness in some simple scenarios