Context-aware Policies Research Articles

Exploring animal husbandry in smallholder dairy systems in Ethiopia using photovoice

This study uses photovoice to explore smallholder dairy farmers’ husbandry knowledge and practices and document how they address constraints faced in pursuing their livelihood strategy. Currently, there is a paucity of farmer-led research in Ethiopia which captures farmers’ local knowledge and lived experiences.This study was conducted in April and May 2021 in Kaliti, a sub-city of Addis Ababa, and Holeta, located near Addis Ababa, in the Oromia region of Ethiopia. Farmers were selected through purposive and snowball sampling approaches based on their previous participation in a bovine tuberculosis study. Farmers selection was based on their experience in dairy farming and willingness to attend research-related meetings and to engage in photo-taking and subsequent group discussions. Farmers were trained on the use of the digital camera and asked to take pictures of their day-to-day activities, challenges faced in pursuing dairy production and how they overcome these challenges.The pictures taken by farmers indicated their attachment to their cattle, cattle disease symptoms, manure management, pest control practices, cattle housing, feeding practices, milking hygiene and storage. Discussions revealed that husbandry challenges faced stemmed from land-use change, declining farm sizes, poor access to veterinary and animal health services, low milk prices and high cattle feed prices. Farmers explained that they had developed knowledge of cattle nutrition, such as feed ration mixing and ways to deal with manure problems. The results of this study underscore that farmers have a good understanding of husbandry challenges and, additionally, have a wealth of local knowledge which can be leveraged, if captured through participatory and visual research methods, such as photovoice, by policymakers to develop context-aware policies and interventions and recommendations regarding improved practices which are economically viable, and socially and culturally acceptable.

Context-Aware Policy Analysis for Distributed Usage Control

To boost data spaces and benefit from the great opportunities that they present, data sovereignty must be provided by Distributed Usage Control (DUC). Assuming that DUC will be managed by implementing and enforcing policies, notable efforts have already been undertaken in the context of Access Control (AC) regarding policy analysis due to the impact of low-quality policies on security. In this regard, this paper proposes that policy analysis in the DUC context should be understood as an extension of the AC, which is further affected by other challenging features, chief among which are context-aware control and extended control through action requirements. This paper presents a novel Context-Aware Policy Analysis (CAPA) algorithm for detecting inconsistencies and redundancies for DUC policies by supporting a large set of heterogeneous conditions. In this regard, the dependent relationship of conditions is formulated which will lead to more efficient conflict detection. By implementing this concept, a novel tree structure that combines a resource and a policy structure is presented to search for and compare relevant rules from policies. Built on the tree structure and through the formalization of rule conflicts, CAPA is developed and the security and performance it provides is tested in a wind energy use case.

A robust control-theory-based exploration strategy in deep reinforcement learning for virtual network embedding

Network slice management and, more generally, resource orchestration should be fully automated in 6G networks, as envisioned by the ETSI ENI. In this context, artificial intelligence (AI) and context-aware policies are certainly major options to move in this direction and to adapt service delivery to changing user needs, environmental conditions and business objectives. In this paper, we step towards this objective by addressing the problem of optimal placement of dynamic virtual networks through a self-adaptive learning-based strategy. These constantly evolving networks present, however, several challenges, mainly due to their stochastic nature, and the high dimensionality of the state and the action spaces. This curse of dimensionality requires, indeed, a broader exploration, which is not always compatible with a real-time execution in an operational network. Thus, we propose DQMC, a new strategy for virtual network embedding in mobile networks combining a Deep Reinforcement Learning (DRL) strategy, namely a Deep Q-Network (DQN), and Monte Carlo (MC). As learning is costly in time and computing resources, and sensitive to changes in the network, we suggest a control-theory-based techniques to dynamically leverage exploration in DQMC. This leads to fast, efficient, and sober learning compared to a Monte Carlo-based strategy. This also ensures a reliable solution even in the case of a change in the requests’ sizes or a node’s failure, showing promising perspectives for solutions combining control-theory and machine learning.

Blockchain based context-aware CP-ABE schema for Internet of Medical Things security

Nowadays, the number of corona patients is increasing significantly. The relationship between the Internet of Medical Things (IoMT) and the Internet is struggling to keep up with this number of patients. The transmission of Patient Health Records (PHR) to the care of a patient through Internet plays an important role in the remote monitoring and fast detection of new contaminated patient with coronavirus. Moreover, it has generated significant security and privacy concerns for the global health care system due to tampering of control messages. This paper focuses on the application of blockchain and smart contract mechanisms to solve the shortcomings of the current health application and propose a new security schema based on context-aware CP-ABE. The proposed schema includes context-aware policies to achieve a robust authentication of identity and confidentiality of patient's healthcare data. Therefore, the proposed schema shows promising results in enhancing security and minimizing encryption time in Fog cloud environments based on proxy-fog and reinforcement of security policies.

Fog Node Self-Control Middleware: Enhancing context awareness towards autonomous decision making in Fog Colonies

The concept of Fog Colonies has recently been introduced in Fog Computing, and is rapidly gaining attention. As Fog Colonies are extended, Fog Colony Control mechanisms have gained significant interest. Although, there are numerous solutions efficiently supporting a Context Aware coordinating entity, exploring approaches for decentralized control remains a challenge. Towards this direction, there are efforts promoting the concept of autonomous Fog Nodes, making their own decisions with internalized control without the existence of a coordinating entity. In this paper we propose and implement a Context Aware Self-Control management middleware for Fog Nodes, enabling their Self-Management utilizing contextual information shared between Fog colony members. To that end, we employ Policy Rulebooks to define Context Aware Policies that each Fog Node can enforce. Finally, we evaluated the proposed middleware against a non Context Aware one, under exactly the same conditions, using a real-world smart building use case, focusing on performance issues related to the end-user.

Developing Framework and Strategies for Capacity Building to Apply Evidence-Informed Health Policy-Making in Iran: Mixed Methods Study of SAHSHA Project.

SASHA, which stands for "evidence-informed health policy-making (EIHP)" in Persian, is a national project to draw a roadmap for strengthening EIHP in Iran. As a part of SASHA, this research aimed to develop evidence-based and context-aware policy options for increasing the capacity of decision-makers to apply EIHP in Iran. This was a qualitative study, which was informed by a literature review of pull efforts' capacity building programs. Based on the review, we developed policy options and validated them through an expert panel that involved twelve experts. Data were analyzed using a content analysis method. We extracted data from 11 articles. The objectives of capacity building programs were: single-skill development, personal/professional development, and organizational development. According to these objectives, the contents and training methods of the programs vary. Capacity building programs have shown positive impacts on individual knowledge/attitudes to use EIHP. However, the impacts of programs at the organizational or the health system level remain under-researched. We followed several threads from the literature review through to the expert panel that included training the management team, instead of training managers, training for problem-solving skills, and designing tailored programs. Barriers of capacity building for EIHP regard the context of the health system (weak accountability and the widespread conflict of interest) and healthcare organizational structures (decision support systems, knowledge management infrastructures, and lack of management team). Experts suggested interventions on the barriers, particularly on resolving the conflict of interests before launching new programs. A proposed framework to increase the capacity of health policy-makers incorporates strategies at three levels: capacity building program, organizational structure, and health system context. To prepare the context of Iranian healthcare organizations for capacity building programs, the conflict of interests needs to be resolved, decision-makers should be made more accountable, and healthcare organizations need to provide more knowledge management infrastructures and decision support systems.

Hierarchical Control of Microgrid Using IoT and Machine Learning Based Islanding Detection

Due to the increase in penetration of renewable energy sources, the control technique plays a vital role to determine the performance of Microgrid (MG). Recently, the Internet of Things (IoT) and cloud computing has gained significance in solving various industrial problems. Robust and scalable Information Communication Technology (ICT) infrastructure is critical for efficient control of MG. IoT Devices with efficient measurement and control capability can play a key role in the MG environment. In this paper three layers hierarchical control of inverter based MG was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme. MG was operated in both island and grid connected mode. In the Primary layer, a voltage frequency (V-F) droop control with virtual impedance control was applied to avoid the disturbances in island mode. Moreover, Active Reactive (P-Q) power control was used for grid connected mode. In the secondary layer voltage and frequency deviations were removed by using the decentralized averaging based method. Voltage and frequency from each distributed generator (DG) were communicated by using a lightweight IoT-based protocol through an edge device (ED). Context-aware policy (CAP) was adopted in ED to optimize traffic flow over a communication network (CN) by comparing the difference in the present and previous data values. In the tertiary layer, a cloud-based ML model was developed using an artificial neural network (ANN) for islanding detection. ANN model was trained by data produced by simulating islanding scenarios in Matlab. Phasor measurement unit (PMU) data was communicated to the cloud for island prediction. The Proposed scheme was implemented on a modified IEEE-13 bus system with four inverter-based distributed generators (DGs) in Matlab, and Microsoft cloud services were used. The successful implementation of MG hierarchical control using an IoT feedback network with less data traffic along with cloud-based islanding detection using machine learning are the main contributions in this work. The whole system achieves stability within 2 seconds of islanding according to IEEE 1547 standards.

A User-Oriented Approach and Tool for Security and Privacy Protection on the Web

We introduce a novel approach to protecting the privacy of web users. We propose to monitor the behaviors of JavaScript code within a web origin based on the source of the code, i.e., code origin, to detect and prevent malicious actions that would compromise users’ privacy. Our code-origin policy enforcement approach not only advances the conventional same-origin policy standard but also goes beyond the “all-or-nothing” contemporary ad-blockers and tracker-blockers. In particular, our monitoring mechanism does not rely on browsers’ network request interception and blocking as in existing blockers. In contrast, we monitor the code that reads or sends user data sent out of the browser to enforce fine-grained and context-aware policies based on the origin of the code. We implement a proof-of-concept prototype and perform practical evaluations to demonstrate the effectiveness of our approach. Our experimental results evidence that the proposed method can detect and prevent data leakage channels not captured by the leading tools such as Ghostery and uBlock Origin. We show that our prototype is compatible with major browsers and popular real-world websites with promising runtime performance. Although implemented as a browser extension, our approach is browser-agnostic and can be integrated into the core of a browser as it is based on standard JavaScript.

Cloud Based IoT Solution for Fault Detection and Localization in Power Distribution Systems

Power restoring time in power distribution systems (PDS) can be minimized by using efficient fault localization techniques. This paper proposes a novel, robust and scalable cloud based internet of things (IoT) solution for identification and localization of faults in PDS. For this purpose, a new algorithm is developed that can detect single and multiple simultaneous faults in the presence of single and multiple device or sensor failures. The algorithm has utilized a zone based approach that divides a PDS into different zones. A current sensing device (CSD) was deployed at the boundary of a zone. The function of CSD is to provide time synchronized current measurements and communicate with a cloud server through an edge device (ED). Another contribution of this research work is the unique implementation of context aware policy (CAP) in ED. Due to CAP, only those measurements are transmitted to cloud server that differ from the previously transmitted measurements. The cloud server performed calculations at regular intervals to detect faults in PDS. A relational database model was utilized to log various fault events that occur in PDS. An IEEE 37 node test feeder was selected as PDS to observe the performance of our solution. Two test cases were designed to simulate individual and multiple simultaneous faults in PDS. A third test case was implemented to demonstrate the robustness and scalability of proposed solution to detect multiple simultaneous faults in PDS when single and multiple sensor failures were encountered. It was observed that the new algorithm successfully localized the faults for all the three cases. Consequently, significant reductions were noticed in the amount of data that was sent to the cloud server. In the end, a comparison study of a proposed solution was performed with existing methods to further highlight the benefits of our technique.

SeCoMan: A Semantic-Aware Policy Framework for Developing Privacy-Preserving and Context-Aware Smart Applications

This paper is intended to provide a solution for developing context-aware smart applications preserving the users' privacy in the Internet of Things (IoT). In this sense, we present a framework called Semantic Web-based Context Management (SeCoMan) aimed at offering a set of predefined queries to provide applications with information about indoor location of users and objects, as well as context-aware services. SeCoMan uses a semantic-oriented IoT vision where semantic technologies play a key role. In fact, SeCoMan uses Semantic Web for modeling description of things, reasoning over data to infer new knowledge, and defining context-aware policies. SeCoMan also defines a layered architecture, including functions related to the management of the users' privacy in a manner that accommodate IoT requirements, in addition to not affecting system performance nor introducing excessive overheads. A thorough discussion on other related works, together with some experiments to measure the throughput and scalability, confirm that SeCoMan is a solution that improves the most relevant proposals existing so far.

Dependency Injection with Static Analysis and Context-Aware Policy.

The dependency injection design pattern improves the configurability, testability, and maintainability of object-oriented applications by decoupling components from both the concrete implementations of their dependencies and the strategy employed to select those implementations. In recent years, a number of libraries have emerged that provide automated support for constructing and connecting dependency-injected objects. Our experience developing systems with these tools has led us to identify two shortcomings of existing dependency injection solutions: the mechanisms for specifying component implementations often make it difficult to write and configure systems of arbitrarily-composable components, and the toolkit implementations often provide limited capabilities for inspection and static analysis of the object graphs of dependency-injected systems. We present Grapht, an new dependency injection container for Java that addresses these issues by providing context-aware policy, allowing component implementation decisions to depend on where in the object graph a component is required, and using a design that allows for static analysis of configured object graphs. To achieve its objectives, Grapht is built on a mathematical representation of dependency injection and object graphs that facilitates static analysis and straightforward implementation, and forms a basis for further consideration of the capabilities of dependency injection. The mathematical representation includes context-aware policy that we show to be strictly more expressive than the qualified dependencies used in many current toolkits. We demonstrate the utility of our approach with a case study showing how Grapht has aided in the development of the LensKit recommender systems toolkit.

Dependency Injection with Static Analysis and Context-Aware Policy.

Dependency Injection with Static Analysis and Context-Aware Policy.

SBA-XACML: Set-based approach providing efficient policy decision process for accessing Web services

Policy-based computing is taking an increasing role in providing real-time decisions and governing the systematic interaction among distributed Web services. XACML (eXtensible Access Control Markup Language) has been known as the de facto standard widely used by many vendors for specifying access and context-aware policies. Accordingly, the size and complexity of XACML policies are significantly growing to cope with the evolution of web-based applications and services. This growth raised many concerns related to the efficiency of real-time decision process (i.e. policy evaluation) and the correctness of complex policies. This paper is addressing these concerns through the elaboration of SBA-XACML, a novel Set-Based Algebra (i.e. SBA) scheme that provides efficient evaluation of XACML policies. Our approach constitutes of elaborating (1) a set-based language that covers all the XACML components and establish an intermediate layer to which policies are automatically converted, and (2) a semantics-based policy evaluation that provides better performance compared to the industrial standard Sun Policy Decision Point (PDP) and its corresponding ameliorations. Experiments have been conducted on real-life and synthetic XACML policies in order to demonstrate the efficiency, relevance and scalability of our proposition. The experimental results explore that SBA-XACML evaluation of large and small sizes policies offers better performance than the current approaches, by a factor ranging between 2.4 and 15 times faster depending on policy size.

Providing Optical Network as a Service with Policy-based Transport SDN

This paper presents a novel policy-based mechanism to provide context-aware network-wide policies to Software Defined Networking (SDN) applications, implemented with a policy flow based on property graph models. The proposal has been validated in a transport SDN controller, supporting optical network virtualization via slicing of physical resources such as nodes, links and wavelengths, through use case testbed demonstrations of policy enforcement for SDN applications, including optical equalization and virtual optical network control. Additionally, the policy engine incorporates a simulation-assisted pre-setting mechanism for local policy decisions in case of problems in communication with the controller.

OJADEAC: An Ontology Based Access Control Model for JADE Platform

Java Agent Development Framework (JADE) is a software framework to make easy the development of Multi-Agent applications in compliance with the Foundation for Intelligent Physical Agents (FIPA) specifications. JADE propose new infrastructure solutions to support the development of useful and convenient distributed applications. Security is one of the most important issues in implementing and deploying such applications. JADE-S security add-ons are one of the most popular security solutions in JADE platform. It provides several security services including authentication, authorization, signature and encryption services. Authorization service will give authorities to perform an action based on a set of permission objects attached to every authenticated user. This service has several drawbacks when implemented in a scalable distributed context aware applications. In this paper, an ontology-based access control model called (OJADEAC) is proposed to be applied in JADE platform by combining Semantic Web technologies with context-aware policy mechanism to overcome the shortcoming of this service. The access control model is represented by a semantic ontology, and a set of two level semantic rules representing platform and application specific policy rules. OJADEAC model is distributed, intelligent, dynamic, context-aware and use reasoning engine to infer access decisions based on ontology knowledge.

The cross layer RMPA handover: a reliable mobility pattern aware handover strategy for broadband wireless communication in a high-speed railway domain

Enhancing the handover process in broadband wireless communication deployment has traditionally motivated many research initiatives. In a high-speed railway domain, the challenge is even greater. Owing to the long distances covered, the mobile node gets involved in a compulsory sequence of handover processes. Consequently, poor performance during the execution of these handover processes significantly degrades the global end-to-end performance. This article proposes a new handover strategy for the railway domain: the RMPA handover, a Reliable Mobility Pattern Aware IEEE 802.16 handover strategy “customized” for a high-speed mobility scenario. The stringent high mobility feature is balanced with three other positive features in a high-speed context: mobility pattern awareness, different sources for location discovery techniques, and a previously known traffic data profile. To the best of the authors’ knowledge, there is no IEEE 802.16 handover scheme that simultaneously covers the optimization of the handover process itself and the efficient timing of the handover process. Our strategy covers both areas of research while providing a cost-effective and standards-based solution. To schedule the handover process efficiently, the RMPA strategy makes use of a context aware handover policy; that is, a handover policy based on the mobile node mobility pattern, the time required to perform the handover, the neighboring network conditions, the data traffic profile, the received power signal, and current location and speed information of the train. Our proposal merges all these variables in a cross layer interaction in the handover policy engine. It also enhances the handover process itself by establishing the values for the set of handover configuration parameters and mechanisms of the handover process. RMPA is a cost-effective strategy because compatibility with standards-based equipment is guaranteed. The major contributions of the RMPA handover are in areas that have been left open to the handover designer’s discretion. Our simulation analysis validates the RMPA handover decision rules and design choices. Our results supporting a high-demand video application in the uplink stream show a significant improvement in the end-to-end quality of service parameters, including end-to-end delay (22%) and jitter (80%), when compared with a policy based on signal-to-noise-ratio information.

A synergy between context-aware policies and AOP to achieve highly adaptable Web services

Modern service-based systems are frequently required to be highly adaptable in order to cope with rapid changes and evolution of business goals, requirements, as well as physical context in a dynamic business environment. Unfortunately, adaptive systems are still difficult to build due to their high complexity. In this paper, we propose a new approach for developing highly adaptable Web services based on a synergy between context-aware Web service policies and Aspect-Oriented Programming. This synergy is achieved through the elaboration of an innovative extension of the Web Service Policy Language (WSPL), which allows for context specification at both policy and rule levels. In addition, we provide a tool for the development of aspect-oriented policies, including an option to translate WSPL policies into aspect-oriented policies. These policies can be automatically woven into composite Web services (e.g., a BPEL process). The elaborated synergy between context, policies, and aspects allows service providers to increase the level of adaptability of Web services at different layers of applications.

A Conceptual Framework and Principles for Trusted Pervasive Health

BackgroundUbiquitous computing technology, sensor networks, wireless communication and the latest developments of the Internet have enabled the rise of a new concept—pervasive health—which takes place in an open, unsecure, and highly dynamic environment (ie, in the information space). To be successful, pervasive health requires implementable principles for privacy and trustworthiness.ObjectiveThis research has two interconnected objectives. The first is to define pervasive health as a system and to understand its trust and privacy challenges. The second goal is to build a conceptual model for pervasive health and use it to develop principles and polices which can make pervasive health trustworthy.MethodsIn this study, a five-step system analysis method is used. Pervasive health is defined using a metaphor of digital bubbles. A conceptual framework model focused on trustworthiness and privacy is then developed for pervasive health. On that model, principles and rules for trusted information management in pervasive health are defined.ResultsIn the first phase of this study, a new definition of pervasive health was created. Using this model, differences between pervasive health and health care are stated. Reviewed publications demonstrate that the widely used principles of predefined and static trust cannot guarantee trustworthiness and privacy in pervasive health. Instead, such an environment requires personal dynamic and context-aware policies, awareness, and transparency. A conceptual framework model focused on information processing in pervasive health is developed. Using features of pervasive health and relations from the framework model, new principles for trusted pervasive health have been developed. The principles propose that personal health data should be under control of the data subject. The person shall have the right to verify the level of trust of any system which collects or processes his or her health information. Principles require that any stakeholder or system collecting or processing health data must support transparency and shall publish its trust and privacy attributes and even its domain specific policies.ConclusionsThe developed principles enable trustworthiness and guarantee privacy in pervasive health. The implementation of principles requires new infrastructural services such as trust verification and policy conflict resolution. After implementation, the accuracy and usability of principles should be analyzed.

Dynamic Privacy in Public Surveillance

In implementing privacy protection in surveillance systems, designers must maximize privacy while retaining the system's purpose. One way to achieve this is to combine data-hiding techniques with context-aware policies governing access to securely collected and stored data.

The Use of Context-Aware Policies and Ontologies to Facilitate Business-Aware Network Management

The purpose of autonomic networking is to manage the business and technical complexity of networked components and systems. However, existing network management data has no link to business concepts. This makes it very difficult to ensure that services offered by the network are meeting business objectives. This paper describes a novel context-aware policy model that uses a combination of modeled and ontological data to determine the current context, which policies are applicable to that context, and what services and resources should be offered to which users and applications.