A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like COUNT, SUM, and AVERAGE. Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.info:eu-repo/semantics/publishedVersio

Robust and efficient membership management in large-scale dynamic networks

Epidemic protocols are a bio-inspired communication and computation paradigm for large-scale networked systems based on randomised communication. These protocols rely on a membership service to build decentralised and random overlay topologies. In large-scale, dynamic network environments, node churn and failures may have a detrimental effect on the structure of the overlay topologies with negative impact on the efficiency and the accuracy of applications. Most importantly, there exists the risk of a permanent loss of global connectivity that would prevent the correct convergence of applications. This work investigates to what extent a dynamic network environment may negatively affect the performance of Epidemic membership protocols. A novel Enhanced Expander Membership Protocol (EMP+) based on the expansion properties of graphs is presented. The proposed protocol is evaluated against other membership protocols and the comparative analysis shows that EMP+ can support faster application convergence and is the first membership protocol to provide robustness against global network connectivity problems

Web3Recommend: Decentralised recommendations with trust and relevance

Web3Recommend is a decentralized Social Recommender System implementation that enables Web3 Platforms on Android to generate recommendations that balance trust and relevance. Generating recommendations in decentralized networks is a non-trivial problem because these networks lack a global perspective due to the absence of a central authority. Further, decentralized networks are prone to Sybil Attacks in which a single malicious user can generate multiple fake or Sybil identities. Web3Recommend relies on a novel graph-based content recommendation design inspired by GraphJet, a recommendation system used in Twitter enhanced with MeritRank, a decentralized reputation scheme that provides Sybil-resistance to the system. By adding MeritRank's decay parameters to the vanilla Social Recommender Systems' personalized SALSA graph algorithm, we can provide theoretical guarantees against Sybil Attacks in the generated recommendations. Similar to GraphJet, we focus on generating real-time recommendations by only acting on recent interactions in the social network, allowing us to cater temporally contextual recommendations while keeping a tight bound on the memory usage in resource-constrained devices, allowing for a seamless user experience. As a proof-of-concept, we integrate our system with MusicDAO, an open-source Web3 music-sharing platform, to generate personalized, real-time recommendations. Thus, we provide the first Sybil-resistant Social Recommender System, allowing real-time recommendations beyond classic user-based collaborative filtering. The system is also rigorously tested with extensive unit and integration tests. Further, our experiments demonstrate the trust-relevance balance of recommendations against multiple adversarial strategies in a test network generated using data from real music platforms

Supporting cooperation and coordination in open multi-agent systems

Cooperation and coordination between agents are fundamental processes for increasing aggregate and individual benefit in open Multi-Agent Systems (MAS). The increased ubiquity, size, and complexity of open MAS in the modern world has prompted significant research interest in the mechanisms that underlie cooperative and coordinated behaviour. In open MAS, in which agents join and leave freely, we can assume the following properties: (i) there are no centralised authorities, (ii) agent authority is uniform, (iii) agents may be heterogeneously owned and designed, and may consequently have con icting intentions and inconsistent capabilities, and (iv) agents are constrained in interactions by a complex connecting network topology. Developing mechanisms to support cooperative and coordinated behaviour that remain effective under these assumptions remains an open research problem. Two of the major mechanisms by which cooperative and coordinated behaviour can be achieved are (i) trust and reputation, and (ii) norms and conventions. Trust and reputation, which support cooperative and coordinated behaviour through notions of reciprocity, are effective in protecting agents from malicious or selfish individuals, but their capabilities can be affected by a lack of information about potential partners and the impact of the underlying network structure. Regarding conventions and norms, there are still a wide variety of open research problems, including: (i) manipulating which convention or norm a population adopts, (ii) how to exploit knowledge of the underlying network structure to improve mechanism efficacy, and (iii) how conventions might be manipulated in the middle and latter stages of their lifecycle, when they have become established and stable. In this thesis, we address these issues and propose a number of techniques and theoretical advancements that help ensure the robustness and efficiency of these mechanisms in the context of open MAS, and demonstrate new techniques for manipulating convention emergence in large, distributed populations. Specfically, we (i) show that gossiping of reputation information can mitigate the detrimental effects of incomplete information on trust and reputation and reduce the impact of network structure, (ii) propose a new model of conventions that accounts for limitations in existing theories, (iii) show how to manipulate convention emergence using small groups of agents inserted by interested parties, (iv) demonstrate how to learn which locations in a network have the greatest capacity to in uence which convention a population adopts, and (v) show how conventions can be manipulated in the middle and latter stages of the convention lifecycle

Wireless Sensor Data Transport, Aggregation and Security

abstract: Wireless sensor networks (WSN) and the communication and the security therein have been gaining further prominence in the tech-industry recently, with the emergence of the so called Internet of Things (IoT). The steps from acquiring data and making a reactive decision base on the acquired sensor measurements are complex and requires careful execution of several steps. In many of these steps there are still technological gaps to fill that are due to the fact that several primitives that are desirable in a sensor network environment are bolt on the networks as application layer functionalities, rather than built in them. For several important functionalities that are at the core of IoT architectures we have developed a solution that is analyzed and discussed in the following chapters. The chain of steps from the acquisition of sensor samples until these samples reach a control center or the cloud where the data analytics are performed, starts with the acquisition of the sensor measurements at the correct time and, importantly, synchronously among all sensors deployed. This synchronization has to be network wide, including both the wired core network as well as the wireless edge devices. This thesis studies a decentralized and lightweight solution to synchronize and schedule IoT devices over wireless and wired networks adaptively, with very simple local signaling. Furthermore, measurement results have to be transported and aggregated over the same interface, requiring clever coordination among all nodes, as network resources are shared, keeping scalability and fail-safe operation in mind. Furthermore ensuring the integrity of measurements is a complicated task. On the one hand Cryptography can shield the network from outside attackers and therefore is the first step to take, but due to the volume of sensors must rely on an automated key distribution mechanism. On the other hand cryptography does not protect against exposed keys or inside attackers. One however can exploit statistical properties to detect and identify nodes that send false information and exclude these attacker nodes from the network to avoid data manipulation. Furthermore, if data is supplied by a third party, one can apply automated trust metric for each individual data source to define which data to accept and consider for mentioned statistical tests in the first place. Monitoring the cyber and physical activities of an IoT infrastructure in concert is another topic that is investigated in this thesis.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Data Centric Peer-to-Peer Communication in Power Grids

We study the use of peer-to-peer based declarative data management to enable efficient monitoring and control of power transmission and distribution networks. We propose methods and an architecture for data centric communication in power networks; a proof-of-concept decentralized communication infrastructure is presented that uses and advances state of the art peer-to-peer and distributed data management protocols to provide real time access to network state information. We propose methods for adaptive network reconfiguration and self-repair mechanisms to handle fault situations. To efficiently handle complex queries, we present a centralized metadata index, and propose a query language and execution method that allows us to handle high volume data streams in-network

Prediction and explanation on adolescent aggression: a study protocol

Introduction: Adolescent aggression is an important public health concern with escalating prevalence of juvenile cases and violence among these age groups including robbery, homicide, and gang fights. The objectives of this study protocol are to determine the biopsychosocial predictors and explore the contextual factors of adolescent aggression among secondary school students in Hulu Langat. Methods: Explanatory mixed method study design will be used, consist of quantitative cross-sectional study followed by basic qualitative study. Proportionate population sampling among Form 4 secondary school students from selected public secondary schools in Hulu Langat will be executed. Questionnaires will be distributed to 481 students on aggression as the dependent variable, and several independent variables: demographic (ethnicity, family income), biological (sex, head injury, nutritional deficiency, breakfast skipping), psychological (attitude and normative beliefs, personality trait, emotional intelligence), and social factors (family environment, single parent status, domestic violence, peer deviant affiliation, alcohol, smoking, substance abuse). Subsequently, participants with moderate to high aggression scores will be further explored on the contextual factors of adolescent aggression by in-depth interview. Multiple linear regression will be executed using SPSS to determine significant predictors whereas thematic analysis will be applied for qualitative data analysis on the context of adolescent aggression. Both findings will be further integrated and discussed to give comprehensive description on the phenomena. Conclusion: Better knowledge and understanding on adolescent aggression may generate new framework to drive more effective preventive strategies and unravel adolescent aggressive related Public Health problems