Research into ambient assisted living (AAL) strives to ease the daily lives of people with disabilities or chronic medical conditions. AAL systems typically consist of multitudes of sensors and embedded devices, generating large amounts of medical and ambient data. However, these biomedical sensors lack the processing power to perform key monitoring and data-aggregation tasks, necessitating data transmission and computation at central locations. The focus here is on the development of a scalable and context-aware framework and easing the flow between data collection and data processing. The resource-constrained nature of typical wearable body sensors is factored into our proposed model, with cloud computing features utilized to provide a real-time assisted-living service. With the myriad of distributed AAL systems at play, each with unique requirements and eccentricities, the challenge lies in the need to service these disparate systems with a middleware layer that is both coherent and flexible. There is significant complexity in the management of sensor data and the derivation of contextual information, as well as in the monitoring of user activities and in locating appropriate situational services. The proposed CoCaMAAL model seeks to address such issues and implement a service-oriented architecture (SOA) for unified context generation. This is done by efficiently aggregating raw sensor data and the timely selection of appropriate services using a context management system (CMS). With a unified model that includes patients, devices, and computational servers in a single virtual community, AAL services are enhanced. We have prototyped the proposed model and implemented some case studies to demonstrate its effectiveness.

CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living

The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.

https://www.mdpi.com/2076-3417/10/3/813/pdf

A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0

A historical probability based noise generation strategy for privacy protection in cloud computing

The Internet of Things (IoT) is a network of objects; it is enabled by the Internet technologies The IoT always collects sensitive data, but inadequate protection may lead to serious user privacy leakage Thus, privacy protection functions are important to the IoT Our research aims to provide better privacy protections to IoTs Firstly, user controlled privacy preserved access control protocol is proposed Secondly, context aware k-anonymity privacy policy and filter are designed Thirdly, privacy protection mechanisms are investigated, and the privacy protection mechanisms are improved based on the investigation results Therefore, users can control which of their personal data is being collected and accessed, who is collecting and accessing such data, and when these are happening

User interactive Internet of things privacy preserved access control

Cloud computing promises an open environment where customers can deploy IT services in pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness, various malicious service providers can exist. Such service providers may record service requests from a customer and then collectively deduce the customer private information. Therefore, customers need to take certain actions to protect their privacy. Obfuscation with noise injection, that mixes noise service requests with real customer service requests so that service providers will be confused about which requests are real ones, is an effective approach in this regard if those request occurrence probabilities are about the same. However, current obfuscation with noise injection uses random noise requests. Due to the randomness it needs a large number of noise requests to hide the real ones so that all of their occurrence probabilities are about the same, i.e. service providers would be confused. In pay-as-you-go cloud environment, a noise request will cost the same as a real request. Hence, with the same level of confusion, i.e. customer privacy protection, the number of noise requests should be kept as few as possible. Therefore in this paper we develop a novel historical probability based noise generation strategy. Our strategy generates noise requests based on their historical occurrence probability so that all requests including noise and real ones can reach about the same occurrence probability, and then service providers would not be able to distinguish in between. Our strategy can significantly reduce the number of noise requests over the random strategy, by more than 90% as demonstrated by simulation evaluation.

/pdf/an-association-probability-based-noise-generation-strategy-t9hihlkeeh.pdf

An association probability based noise generation strategy for privacy protection in cloud computing

In the cloud computing era, service provider cloud and context service cloud store all your personal context data. This is a positive aspect for value-added context-aware service cloud as it makes that context information collection are easier than was the case previously. However, this computing environment does add a series of threats in relation to privacy protection. Whoever receives the context information is able to deduce the status of the owners and, generally owners are not happy to share this information. In this paper, we propose a privacy preserved framework which can be utilized by value-added context-aware service cloud. Context data and related services access privileges are determined by context-aware role-based access control (CRAC) extended from role-based access control (RAC). Privacy preserved context service protocol (PPCS) is designed to protect user privacy from exposed context information. Additionally, user network and information diffusion is combined to evaluate the privacy protection effect.

Privacy of Value-Added Context-Aware Service Cloud

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Privacy of Value-Added Context-Aware Service Cloud