Priscila Solis

Architecture for Privacy in Cloud of Things.

A large number of devices are connected to the internet through the Internet of Things (IoT) paradigm, resulting in a huge amount of produced data. Cloud computing is a computing paradigm currently adopted to process, store and provide access control to these data. This integration is called Cloud of Things CoT and is useful in personal networks, like residential automation and health care, since it facilitates the access to the information. Although this integration brings benefits to the users, it introduces many security challenges since the information leaves the user control and is stored at the cloud providers. Particularly interesting, in order for these technologies to be adopted, it is important to provide protocols and mechanisms to preserve the users privacy when storing their data in the cloud. In this context, this paper proposes an architecture for privacy in Cloud of Things, which allows the users to fully control the access to the data generated by the devices of their IoT networks and stored in the cloud. The proposed architecture enables a fine grained control over data, since the privacy protocols and controls are executed at the IoT devices instead of at the network border by a gateway, which also could represent a single point of failure or a component that could impair the security properties of the system once it is compromised by a successful attack.

Providing privacy on the tuple space model

Conceptually, tuple spaces are shared memory objects that provide operations to store and retrieve ordered sets of data, called tuples. Tuples stored in a tuple space are accessed by the contents of their fields, working as an associative memory. Although there are some proposals for secure tuple spaces, accessing tuples through field contents makes these systems susceptible to attacks that could impair user and data privacy, since servers must access tuple data. In order to deal with these limitations and provide privacy in the tuple space model, this paper proposes some extensions to DepSpace, a tuple space system that implements dependability and security properties through a set of mechanisms that are not enough to ensure privacy. The resulting system provides privacy and, at the same time, allows tuple selection/matches similar to the traditional insecure model, i.e., it does not constraint the matching possibilities. The main problem to be addressed is that servers must select tuples based on their contents without knowing them. The proposed solution uses robust cryptographic schemes, as order-preserving encryption and homomorphic encryption, to provide this functionality without revealing the tuple contents. An analysis concerning security aspects of DepSpace is presented, as well as the benefits of the proposed solutions. A set of experiments, executed with an implementation of the proposed protocols, shows the feasibility of the proposed solutions and shed some light on both the behavior of the system and the costs to provide privacy in the tuple spaces model.

An algorithm based on response time and traffic demands to scale containers on a Cloud Computing system

This paper proposes a cloud computing architecture based in containers and on an algorithm that intends to achieve an efficient allocation of processing resources to comply with response time requirements in a Web system. The algorithm is based on the characterization of web requests and on a PID (Proportional - Integral- Derivative) controller. The proposal was evaluated with a real time series obtained from an operational massive web system in a controlled infrastructure. The results show that the proposal achieves the expected response times allocating a lower number of containers than other related proposals.

Risk Perception of Migrating Legacy Systems to the Cloud.

With the utilization of Cloud Computing as the way to provide Information Technology services, the organizations can migrate legacy systems to the cloud in order to reach several benefits. There are in the literature several proposals to model the critical elements of migration and many have been validated by specific case studies. Also several reference models were defined on top of these proposals and were created with the intention to consolidate different researches, trying to expand their applicability. Based on the above, this paper selects a reference model for migrating legacy systems to the cloud and proposes a method for calculating a score of perceived risk associated with each legacy system migration. The paper presents a proof of concept on government domain to show the method’s applicability.

Performance and Cost Analysis Between On-Demand and Preemptive Virtual Machines

A few years ago, Amazon Web Services introduced spot instances, transient servers that can be contracted at a significant discount over regular price, but whose availability depends on cloud provider criteria and the instance can be revoked at any time. Google Cloud Platform offers preemptive instances, transient servers that have similar behavior and discount level to spot instances. Both providers advertise that their transient servers have the same performance level as servers contracted on-demand. Even with the possibility of revocation at the provider’s discretion, some applications can benefit from the low prices charged by these servers. But the measured performance of both models, transient and on-demand, must be similar, and the applications must survive occasional or mass server revoking. This work compares the performance and costs of transient and on-demand servers from both providers. Results show there is no significant difference in performance measured, but there is real cost advantage using transient servers. On Amazon Web Services a MapReduce cluster composed of transient servers achieved a 68% discount when compared to the same cluster based on on-demand servers. On Google Cloud Platform, the discount achieved was 26% but it can be bigger when the clusters are larger.

Traffic Engineering based on Shortest Path routing algorithms for FTV (Free-Viewpoint Television) applications

This paper proposes a traffic engineering methodology based on the self-similarity parameter of video traffic as a restriction on an Integer Linear Programming problem (ILP) for the definition of link costs for routing optimization in the OSPF (Open Shortest Path First ) routing protocol. The goal is to optimize video routing considering QoS constraints and to identify the maximum delay and link load parameters for transmission between video sources and middleboxes for FTV (Free Viewpoint Television) applications. The proposal was evaluated in a simulation environment and the results show that the routing optimization procedure based on the self-similar parameter of video traffic for defining link weights, can significantly reduce the delay in the network and allow the identification of more accurate threshold values for link loads and delays for video transmission on the Internet.

A mechanism of auto elasticity based on response times for cloud computer enviroments and autossimilar workload

This paper proposes a cloud computing architecture based on containers and an algorithm for auto elasticity based on the response time requirements in a Web system. The proposed algorithm promotes an efficient allocation of containers to achieve expected response time in processing requests. This proposal was evaluated with a real time series obtained from a web system hosted by Controladoria-Geral da União (CGU). The results show that the proposed algorithm achieves the expected response times allocating a lower number of containers than other related proposals.

Evaluation of TSCH/IEEE 802.15.4e in a Domestic Network Environment

The IEEE 802.15.4e standard was published in 2012 as an amendment to the Medium Access Control (MAC) protocol defined by the IEEE 802.15.4-2011 standard. The purpose of this paper is to evaluate the Timeslotted Channel Hopping (TSCH) mode of IEEE 802.15.4e in the context of IoT (Internet of Things) regarding environment and changes in application requirements. A simulation scenario of a typical domestic sensor network is designed to evaluate the TSCH mode in a dynamic environment with the presence of WiFi devices. Also are explored the upper and lower bounds in performance gain due to self-learning. The relatively recent release of such standard accounts for its lack of support in network simulators and this work implements the TSCH in the well known open-source network simulator ns-3. This work enables the preview analysis of TSCH networks, decreasing necessary resources and therefore facilitating the use of such networks for social goods such as health monitoring, The results clearly show that the presence of WiFi signals greatly degrades the IEEE 802.15.4e network performance, in terms of throughput, delay and energy consumption. When applying self-learning techniques to avoid degraded channels, the network can properly function and achieves better performance. Also, a significant decrease in delay is also achieved when adapting the slotframe size according to the number of active devices.

REA-WSN: Intercluster routing algorithm for energy optimization in wireless sensor networks

In the last years, wireless sensor networks (WSNs) have received an increasing attention from the research community since sensor nodes are generally battery-powered devices and one of their critical aspects is how to optimize energy expenditure of nodes to extend network lifetime. In this paper we propose a method for improving energy efficiency in a WSN with a homogeneous and hierarchical topology. The proposal is based in the integration of proposals funneling-MAC and LEACH-C with clusters and cluster heads routing data to the sink through an approach based on Dikjstras shortest path algorithm. The network is divided into two regions, non-intensity region (NIR) and intensity region (IR). In the NIR the medium access method is CSMA/CA and TDMA is used to optimize the communication between the cluster heads and the nodes within the RI. Between the cluster heads the routing is implemented using an adaptation of Dikjstras shortest path algorithm based on the residual energy in each node. The experimental results show a decrease in the average energy expenditure in the evaluated scenarios and a sharp advantage regarding the number of dead nodes, as well as an increase in the total network lifespan.